pax_global_header00006660000000000000000000000064114031517710014512gustar00rootroot0000000000000052 comment=477839397cf875d2e116d0665122c227ad8bc832 libfann-2.1.0~beta~dfsg/000077500000000000000000000000001140315177100152175ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/AUTHORS000066400000000000000000000002711140315177100162670ustar00rootroot00000000000000Project Administrator: Steffen Nissen (lukesky@diku.dk) - Creator and maintainer of the fann library Contributors: For a list of contributors see http://leenissen.dk/fann/authors.php libfann-2.1.0~beta~dfsg/BorlandC++Builder6.0/000077500000000000000000000000001140315177100205645ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/BorlandC++Builder6.0/all.bpg000066400000000000000000000035771140315177100220420ustar00rootroot00000000000000#------------------------------------------------------------------------------ VERSION = BWS.01 #------------------------------------------------------------------------------ !ifndef ROOT ROOT = $(MAKEDIR)\.. !endif #------------------------------------------------------------------------------ MAKE = $(ROOT)\bin\make.exe -$(MAKEFLAGS) -f$** DCC = $(ROOT)\bin\dcc32.exe $** BRCC = $(ROOT)\bin\brcc32.exe $** #------------------------------------------------------------------------------ PROJECTS = libfann.lib simple_train.exe simple_test.exe xor_train.exe \ xor_test.exe momentums.exe mushroom.exe steepness_train.exe robot.exe \ cascade_train.exe #------------------------------------------------------------------------------ default: $(PROJECTS) #------------------------------------------------------------------------------ libfann.lib: libfann.bpr $(ROOT)\bin\bpr2mak -t$(ROOT)\bin\deflib.bmk $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak simple_train.exe: simple_train.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak simple_test.exe: simple_test.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak xor_train.exe: xor_train.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak xor_test.exe: xor_test.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak cascade_train.exe: cascade_train.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak momentums.exe: momentums.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak mushroom.exe: mushroom.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak robot.exe: robot.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak steepness_train.exe: steepness_train.bpr $(ROOT)\bin\bpr2mak $** $(ROOT)\bin\make -$(MAKEFLAGS) -f$*.mak libfann-2.1.0~beta~dfsg/BorlandC++Builder6.0/cascade_train.bpf000066400000000000000000000001421140315177100240320ustar00rootroot00000000000000This file is used by the project manager only and should be treated like the project file main libfann-2.1.0~beta~dfsg/BorlandC++Builder6.0/cascade_train.bpr000066400000000000000000000202401140315177100240470ustar00rootroot00000000000000 [Version Info] 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èlibfann-2.1.0~beta~dfsg/BorlandC++Builder6.0/libfann.bpf000066400000000000000000000003221140315177100226630ustar00rootroot00000000000000//--------------------------------------------------------------------------- #include #pragma hdrstop #define Library // To add a file to the library use the Project menu 'Add to Project'. libfann-2.1.0~beta~dfsg/BorlandC++Builder6.0/libfann.bpr000066400000000000000000000104621140315177100227050ustar00rootroot00000000000000 [Version Info] IncludeVerInfo=0 AutoIncBuild=0 MajorVer=1 MinorVer=0 Release=0 Build=0 Debug=0 PreRelease=0 Special=0 Private=0 DLL=0 Locale=1030 CodePage=1252 [Version Info Keys] CompanyName= FileDescription= FileVersion=1.0.0.0 InternalName= LegalCopyright= LegalTrademarks= OriginalFilename= ProductName= ProductVersion=1.0.0.0 Comments= [HistoryLists\hlIncludePath] Count=4 Item0=..\src;$(BCB)\include;$(BCB)\include\vcl;..\src\include;C:\Program Files\madCollection\madBasic\BCB6;C:\Program 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Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the library `Frob' (a library for tweaking knobs) written by James Random Hacker. , 1 April 1990 Ty Coon, President of Vice That's all there is to it! libfann-2.1.0~beta~dfsg/ChangeLog000066400000000000000000000131231140315177100167710ustar00rootroot00000000000000libfann (2.1.0) stable; urgency=low * Added fann_create_train_from_callback for creating training data from a callback function * Amended fann_create_train_from_callback for dynamic link library compatibility * Added fann_get_activation_function and fann_get_activation_steepness matching the set functions * Added a set of functions to allow restricted access to neural network layout, connections, and weights. * Added FANN_SIN, FANN_COS, FANN_SIN_SYMMETRIC and FANN_COS_SYMMETRIC activation functions * Changed the create_array functions to indicate that the layers array parameter is const i.e. input only * Added FANN_API calling convention to fann_get_bit_fail and two cascade functions for dll compatibility * Added FANN_EXTERNAL/FANN_API calling convention to previous extension functions for dll compatibility * Removed warning from fann_print_connections with explicit casts * Added fann_set/get_user_data to allow user programs to refer to their own data during callbacks * Added documented C++ wrapper and sample C++ program * Fixed bias count for shortcut networks in fann_get_layer_array and fann_get_bias_array * Minor fixes of FANN_EXTERNAL/FANN_API for windows dll compatibility * More advanced scaling functionality * Added advanced scaling functionality to fann_cpp.h * Additional documentation and fix of FANN_EXTERNAL for create train callback in fann_cpp.h * Fixed problems with FANN_GAUSSIAN and FANN_GAUSSIAN_SYMMETRIC activation functions * Added semi Widrow + Nguyen to initialization of cascade weights * Fixed problem with RPROP, which makes it go suboptimal in the beginning * Changed definition of MSE, so that it calculates a value pr output, and not a sum for all outputs. (MSE = MSE/ann->num_output) * Added more benchmark data * Added possebility to make benchmark as average of several * Changed so that RPROP and Quickprop may not alter a weight to more than 1500 libfann (2.0.0) stable; urgency=low * Added Delphi bindings * Cascade training * Momentum parameter * Steepness and activation function available for single neurons * New documentation * Removed some depricated functions * New set of fann_create functions * Altered the way the callback function works * Training can be stopped by counting the number of datasets that fail instead of just MSE * Fixed a memory leak when copying training data * More activation functions * Enums used instead of integers for several parameters. * Possebility to set default logging facility * All parameters are saved to configuration file and not just the parameters needed to execute the ANN * More readable configuration file * Visual Studio .Net Project Files * Borland C++ Builder 6.0 Project files -- Steffen Nissen Fri, 06 Jan 2006 23:13:30 +0100 libfann (1.2.0) stable; urgency=low * Fixes for better compability with different compilers * Shortcut connections that skip layers * Changed version of save fileformat * fann_print_connections function * More activation functions * Batch training * RPROP training (default) * Quickprop training * fann_get_training_algorithm and fann_set_training_algorithm functions * fann_test_data function * fann_print_parameters function * Renamed some functions and variables to avoid confusion of terms (kept deprecated functions) -- Steffen Nissen Sat, 09 Oct 2004 11:13:22 +0200 libfann (1.1.0) stable; urgency=low * Error checking when allocating memory * Debian package * RPM package * Added pkg-config * Deallocate correctly in case of an error * Renamed fann_get_error to fann_get_MSE (keeping old one for now) * Renamed fann_reset_error to fann_reset_MSE (keeping old one for now) * Rearanged some code for easy access by the newly created fann php module * Defined more activation functions and documented the old (in fann_activation.h) * Customize error output * Added fann_set_error_log, fann_shuffle_train_data, fann_merge_train_data, and fann_duplicate_train_data, fann_init_weights * Added python bindings * Created DocBook XML documentation, including API reference * Rearanged files for easier development * Added MSVC++ project files -- Steffen Nissen Wed, 31 Mar 2004 21:19:32 +0200 libfann (1.0.5) stable; urgency=low * Added a new constructor * Added a callback function for output during training * Changed compile parameters for further performance * Error value not set to zero after training * Thanks to Evan Nemerson for some of these patches -- Steffen Nissen Tue, 06 Jan 2004 20:51:34 +0100 libfann (1.0.4) stable; urgency=high * Bugfix concerning default activation function -- Steffen Nissen Thu, 11 Dec 2003 12:42:41 +0100 libfann (1.0.3) stable; urgency=low * Added FANN_SIGMOID_STEPWISE activation function (faster) * Moved files around and altered some files for easyer benchmarking * Added a benchmark pdf file, benchmarking the new behavior. * Better compability with non gnu compilers (should work off the shelf) -- Steffen Nissen Tue, 09 Dec 2003 23:30:45 +0100 libfann (1.0.2) stable; urgency=low * Added configure script * Moved files around, to make the transition to configure more easy * Removed other versions of documentation than pdf -- Steffen Nissen Thu, 13 Nov 2003 11:06:47 +0100 libfann (1.0.1) stable; urgency=high * Bugfix concerning steepness parameter * Moved some files around * Added the changelog -- Steffen Nissen 3 Nov 2003 libfann (1.0) stable; urgency=high * Initial release of full functioning library -- Steffen Nissen 1 Nov 2003 libfann-2.1.0~beta~dfsg/INSTALL000066400000000000000000000172271140315177100162610ustar00rootroot00000000000000Basic Installation ================== These are generic installation instructions. The `configure' shell script attempts to guess correct values for various system-dependent variables used during compilation. It uses those values to create a `Makefile' in each directory of the package. It may also create one or more `.h' files containing system-dependent definitions. Finally, it creates a shell script `config.status' that you can run in the future to recreate the current configuration, a file `config.cache' that saves the results of its tests to speed up reconfiguring, and a file `config.log' containing compiler output (useful mainly for debugging `configure'). If you need to do unusual things to compile the package, please try to figure out how `configure' could check whether to do them, and mail diffs or instructions to the address given in the `README' so they can be considered for the next release. If at some point `config.cache' contains results you don't want to keep, you may remove or edit it. 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(  &  (  (&  ((&((  &(&(( &&&&&! %&'# &' &&""!  &þïþïõRC:\work\NeuralNetwork\fann-1.2.0\MSVC++\libfann.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\libfann.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\simple_test.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\simple_test.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\simple_train.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\simple_train.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\steepness_train.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\steepness_train.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\xor_test.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\xor_test.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\xor_train.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\MSVC++\xor_train.vcproj|Debug|Win32C:\Program Files\Microsoft Visual Studio .NET 2003\Vc7\vcpackages\prebuilt.ncbc:\work\NeuralNetwork\fann-1.2.0\src\fann.cork\fann-1.2.0\VS.NET2003\libfann.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\simple_test.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\simple_test.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\steepness_train.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\steepness_train.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\xor_test.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\xor_test.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\xor_train.vcproj|Release|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\xor_train.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\simple_train.vcproj|Debug|Win32C:\work\NeuralNetwork\fann-1.2.0\VS.NET2003\simple_train.vcproj|Release|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\libfann.vcproj|Release|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\libfann.vcproj|Debug|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\simple_test.vcproj|Release|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\simple_test.vcproj|Debug|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\simple_train.vcproj|Debug|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\simple_train.vcproj|Release|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\steepness_train.vcproj|Release|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\steepness_train.vcproj|Debug|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\xor_test.vcproj|Release|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\xor_test.vcproj|Debug|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\xor_train.vcproj|Release|Win32C:\Documents and Settings\stn\Desktop\fann\vs.net2003\xor_train.vcproj|Debug|Win32c:\Documents and Settings\stn\Desktop\fann\src\fann.cfann_create_standard_arrayfann_create_standardfann_create_sparse_arrayfann_create_sparsefann_update_stepwisec:\Documents and Settings\stn\Desktop\fann\src\fann_error.cconst enum fann_errno_enum errno_ffann_default_error_logFANN_EXTERNAL FANN_APIc:\Documents and Settings\stn\Desktop\fann\src\fann_io.cfann_scanf{ if(fscanf(conf, "=""\n", ) != 1) { fann_error(NULL, FANN_E_CANT_READ_CONFIG, , configuration_file); fann_destroy(ann); return NULL; } }typenamevalfann_create_from_fd_1_1c:\Documents and Settings\stn\Desktop\fann\src\fann_options.cc:\Documents and Settings\stn\Desktop\fann\src\fann_train.cfann_get_bit_failFANN_EXTERNAL unsigned intfann_layer *layer_beginfann_layer *layer_endenum fann_activationfunc_enum activation_functionfann_set_activation_function_layerint layerunsigned int first_weightunsigned int past_endfann_activationfann_get_layerfann_get_neuron_layerfann_layer *layerint neuronfann_get_neuronunsigned int layerfann_set_activation_steepness_layerfann_type sumfann_set_activation_functionfann_set_activation_steepnessfann_update_MSEfann_neuron *neuronfann_type neuron_diffc:\Documents and Settings\stn\Desktop\fann\src\fann_train_data.cfann_num_input_train_dataconst char *filenamefann_scale_output_train_datafann_type new_minfann_type new_maxfann_scale_input_train_datafann_subset_train_dataunsigned int posunsigned int lengthfann_scale_datafann_type **dataunsigned int num_elemfann_length_train_datafann_num_output_train_datafann_desired_error_reachedfann_scale_train_datac:\Documents and Settings\stn\Desktop\fann\src\include\compat_time.hc:\Documents and Settings\stn\Desktop\fann\src\include\fann.hc:\Documents and Settings\stn\Desktop\fann\src\include\fann_data.hfann_errorfunc_enumfann_stopfunc_enumFANN_STOPFUNC_BITFANN_STOPFUNC_MSEenum fann_errno_enumfann_neuron::first_confann_neuron::last_confann_neuron::sumfann_neuron::activation_steepnessfann_neuron::activation_functionenum fann_activationfunc_enumfann_train_enumfann_activationfunc_enumFANN_SIGMOID_STEPWISEFANN_LINEARFANN_ELLIOTFANN_GAUSSIANFANN_SIGMOID_SYMMETRICFANN_THRESHOLDFANN_ELLIOT_SYMMETRICFANN_GAUSSIAN_STEPWISEFANN_GAUSSIAN_SYMMETRICFANN_THRESHOLD_SYMMETRICFANN_LINEAR_PIECE_SYMMETRICFANN_LINEAR_PIECEFANN_SIGMOID_SYMMETRIC_STEPWISEFANN_SIGMOIDFANN_ACTIVATIONFUNC_NAMESfann::learning_momentumfann::weightsfann::connectionsenum fann_train_enumfann::sigmoid_resultsfann::sigmoid_valuesfann::sigmoid_symmetric_resultsfann::sigmoid_symmetric_valuesfann::num_bit_failfann::bit_fail_limitenum fann_errorfunc_enumfann::train_stop_functionenum fann_stopfunc_enumfann::cascade_output_change_fractionfann::cascade_output_stagnation_epochsfann::cascade_candidate_change_fractionfann::cascade_candidate_stagnation_epochsfann::cascade_best_candidatefann::cascade_candidate_limitfann::cascade_weight_multiplierfann::cascade_max_out_epochsfann::cascade_max_cand_epochsfann::cascade_activation_functionsenum fann_activationfunc_enum *fann::cascade_activation_functions_countfann::cascade_activation_steepnessesfann::cascade_activation_steepnesses_countfann::cascade_num_candidate_groupsfann::cascade_candidate_scoresfann::total_neurons_allocatedfann::total_connections_allocatedfann::rprop_delta_zerofann::prev_weights_deltasFANN_STOPFUNC_NAMESFANN_EXTERNAL intc:\Documents and Settings\stn\Desktop\fann\src\include\fann_internal.h"FANN_FIX_2.0""FANN_FLO_2.0"FANN_GETFANN_EXTERNAL  FANN_API fann_get_(struct fann *ann) { return ann->; }FANN_SETFANN_EXTERNAL void FANN_API fann_set_(struct fann *ann,  value) { ann-> = value; }FANN_GET_SETFANN_GET(, ) FANN_SET(, ){if() { free();  = NULL; }}(((float)())+(((float)()-((float)()))*rand()/(RAND_MAX+1.0f)))fann_train_outputs_epochfann_initialize_candidatesfann_install_candidatefann_train_outputsfann_set_shortcut_connectionsfann_train_candidatesfann_train_candidates_epochc:\Documents and Settings\stn\Desktop\fann\src\include\floatfann.hc:\Documents and Settings\stn\Desktop\fann\examples\simple_test.cc:\Documents and Settings\stn\Desktop\fann\examples\simple_train.cc:\Documents and Settings\stn\Desktop\fann\examples\steepness_train.cc:\Documents and Settings\stn\Desktop\fann\examples\xor_test.cc:\Documents and Settings\stn\Desktop\fann\examples\xor_train.ctest_callbackfann_train_data *trainc:\Documents and Settings\stn\Desktop\fann\src\fann_cascade.c"string.h"fann_add_layerfann_set_cascade_activation_functionsFANN_EXTERNAL voidenum fann_activationfunc_enum *cascade_activation_functionsunsigned int cascade_activation_functions_countfann_reallocate_neuronsunsigned int total_neuronsfann_print_connections_rawfann_get_cascade_num_candidatesfann_add_candidate_neuronfann_update_candidate_weightsfann_set_cascade_activation_steepnessesfann_type *cascade_activation_steepnessesunsigned int cascade_activation_steepnesses_countfann_reallocate_connectionsunsigned int total_connectionsfann_update_candidate_slopesfann_cascadetrain_on_fileunsigned int max_neuronsunsigned int neurons_between_reportsfann_cascadetrain_on_datac:\Documents and Settings\stn\Desktop\fann\src\include\fann_train.hfann_get_train_stop_functionfann_set_callbackfann_callback_type callbackc:\Documents and Settings\stn\Desktop\fann\src\include\fann_activation.hfann_linear_func((((()-()) * (()-()))/(()-())) + ())v1r1v2r2sumfann_stepwise( <  ? ( <  ? ( <  ? ( <  ?  : fann_linear_func(, , , , )) : fann_linear_func(, , ,  , )) : ( <  ? fann_linear_func(,  , ,  , ) : fann_linear_func(,  , ,  , ))) : ( <  ? fann_linear_func(,  , ,  , ) : ))v3v4v5v6r3r4r5r6minmaxfann_linearfann_mult(, )steepnessfann_linear_derive()fann_sigmoid(1.0f/(1.0f + exp(-2.0f *  * )))fann_sigmoid_real(1.0f/(1.0f + exp(-2.0f * )))fann_sigmoid_derive(2.0f *  *  * (1.0f - ))fann_sigmoid_symmetric(2.0f/(1.0f + exp(-2.0f *  * )) - 1.0f)fann_sigmoid_symmetric_real(2.0f/(1.0f + exp(-2.0f * )) - 1.0f)fann_sigmoid_symmetric_derive * (1.0f - (*))fann_gaussian(exp(- *  *  * ))fann_gaussian_real(exp(- * ))fann_gaussian_derive(-2.0f *  *  * )fann_gaussian_symmetric((exp(- *  *  * )*2.0)-1.0)fann_gaussian_symmetric_real((exp(- * )*2.0)-1.0)fann_gaussian_symmetric_derive(-2.0f *  * (+1.0f) * )fann_elliot((( * ) / 2.0f) / (1.0f + fann_abs( * )) + 0.5f)fann_elliot_real((() / 2.0f) / (1.0f + fann_abs()) + 0.5f)fann_elliot_derive( * 1.0f / (2.0f * (1.0f + fann_abs()) * (1.0f + fann_abs())))fann_elliot_symmetric(( * ) / (1.0f + fann_abs( * )))fann_elliot_symmetric_real(() / (1.0f + fann_abs()))fann_elliot_symmetric_derive( * 1.0f / ((1.0f + fann_abs()) * (1.0f + fann_abs())))fann_activation_switchswitch() { case FANN_LINEAR:  = (fann_type); break; case FANN_LINEAR_PIECE:  = (fann_type)(( < 0) ? 0 : ( > 1) ? 1 : ); break; case FANN_LINEAR_PIECE_SYMMETRIC:  = (fann_type)(( < -1) ? -1 : ( > 1) ? 1 : ); break; case FANN_SIGMOID:  = (fann_type)fann_sigmoid_real(); break; case FANN_SIGMOID_SYMMETRIC:  = (fann_type)fann_sigmoid_symmetric_real(); break; case FANN_SIGMOID_SYMMETRIC_STEPWISE:  = (fann_type)fann_stepwise(-2.64665293693542480469e+00, -1.47221934795379638672e+00, -5.49306154251098632812e-01, 5.49306154251098632812e-01, 1.47221934795379638672e+00, 2.64665293693542480469e+00, -9.90000009536743164062e-01, -8.99999976158142089844e-01, -5.00000000000000000000e-01, 5.00000000000000000000e-01, 8.99999976158142089844e-01, 9.90000009536743164062e-01, -1, 1, ); break; case FANN_SIGMOID_STEPWISE:  = (fann_type)fann_stepwise(-2.64665246009826660156e+00, -1.47221946716308593750e+00, -5.49306154251098632812e-01, 5.49306154251098632812e-01, 1.47221934795379638672e+00, 2.64665293693542480469e+00, 4.99999988824129104614e-03, 5.00000007450580596924e-02, 2.50000000000000000000e-01, 7.50000000000000000000e-01, 9.49999988079071044922e-01, 9.95000004768371582031e-01, 0, 1, ); break; case FANN_THRESHOLD:  = (fann_type)(( < 0) ? 0 : 1); break; case FANN_THRESHOLD_SYMMETRIC:  = (fann_type)(( < 0) ? -1 : 1); break; case FANN_GAUSSIAN:  = (fann_type)fann_gaussian_real(); break; case FANN_GAUSSIAN_SYMMETRIC:  = (fann_type)fann_gaussian_symmetric_real(); break; case FANN_ELLIOT:  = (fann_type)fann_elliot_real(); break; case FANN_ELLIOT_SYMMETRIC:  = (fann_type)fann_elliot_symmetric_real(); break; }annactivation_functionresultc:\Documents and Settings\stn\Desktop\fann\src\include\fann_cascade.hfann_set_cascade_candidate_limitfann_type cascade_candidate_limitfann_set_cascade_output_change_fractionfloat cascade_output_change_fractionfann_set_cascade_max_out_epochsunsigned int cascade_max_out_epochsfann_get_cascade_output_stagnation_epochsfann_get_cascade_candidate_limitfann_get_cascade_max_out_epochsfann_set_cascade_output_stagnation_epochsunsigned int cascade_output_stagnation_epochsfann_get_cascade_candidate_stagnation_epochsfann_set_cascade_candidate_change_fractionfloat cascade_candidate_change_fractionfann_get_cascade_output_change_fractionfann_get_cascade_max_cand_epochsfann_get_cascade_candidate_change_fractionfann_get_cascade_activation_functions_countfann_get_cascade_weight_multiplierfann_set_cascade_weight_multiplierfann_type cascade_weight_multiplierFANN_EXTERNAL enum enum fann_activationfunc_enum *fann_set_cascade_candidate_stagnation_epochsunsigned int cascade_candidate_stagnation_epochsfann_set_cascade_max_cand_epochsunsigned int cascade_max_cand_epochsc:\Documents and Settings\stn\Desktop\fann\src\include\fann_error.hfann_errno_enumFANN_E_INDEX_OUT_OF_BOUND17FANN_E_TRAIN_DATA_SUBSET16FANN_E_CANT_USE_TRAIN_ALG15c:\Documents and Settings\stn\Desktop\fann\src\include\fann_io.hc:\documents and settings\stn\desktop\fann\src\include\config.h"2.0.0"Release|Win32fann_get_train_error_functiontype valuefann_get_callbackfann_callback_type value(E¨ ²=È2ó%B#B©Ø Ò–Ì"-0( ò3݉q4466‡7;BDO/¦E`ÇOø1 Å'.;D¤¢ ¡NÕ.o’ ¸•Dy8Q: A\D–Ç(c3 RFJ¾4Œ}6ÐNÛ0YØ6«',-#šÀš ö ."§9aC,q:1CÌdP:#Ä„ŒkMù8~_0Fù;NLH!È@ð8ªM«#˜4P6 Âc!Áz/;@ eà,°4JR Lÿ¸ ìƒï?ÒM1 n‹VOÇsÛ¥ ÂNúWV:8E)å05š,ˆ:~í¼Ï:/ Ÿ0ttøÝ êA8ÞANù-ìQ´Cz)Ó&¬ýy3dÇ0XûAÝmBt'à±E9¦O4*q0% +ÚK!@¨/là>¸!u1 ²2h 2ƒçkÂê€!!a!Î!è!"ì†2x<£Qÿ>i#| éBIÖ9:QiV!V cƒ"5Y##iM¨~O› ]/ÇCf k ›¿ ¼/6?ê"%ù'_1`d"‹8¾ ‡+:ŠR#Ù "ï1!Ma%K8/Õ7¬"˜p;0÷Mô» ÷³+|.)@#ÔW°ù !'º1¶.„ …WMõ< FRO q(¡+ “"ª@«%­@…Q4µ¨)#×Crå4и’ o~ Þ-ÔíA6Bäõ'*|áAÍ=/BÈþŽ?„3ÄO5HBvDôn? 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To build the library and examples with MSVC++ 6.0. First, navigate to the MicrosoftVisualC++6.0 directory in the FANN distribution and open the all.dsw workspace. In the Visual Studio menu bar, choose "Build" -> "Batch build...", select the project configurations that you would like to build (by default, all are selected), and press "rebuild all" When the build process is complete, the library and examples can be found in the MicrosoftVisualC++6.0\Debug directorie and the examples are automatically copied into the examples where they are supposed to be run. If all of the examples are not copied to the correct location, please compile them separately. libfann-2.1.0~beta~dfsg/MicrosoftVisualC++6.0/all.dsw000077500000000000000000000036661140315177100223320ustar00rootroot00000000000000Microsoft Developer Studio Workspace File, Format Version 6.00 # WARNING: DO NOT EDIT OR DELETE THIS WORKSPACE FILE! ############################################################################### Project: "libfann"=".\libfann.dsp" - Package Owner=<4> Package=<5> {{{ }}} Package=<4> {{{ }}} ############################################################################### Project: "simple_test"=".\simple_test.dsp" - Package Owner=<4> Package=<5> {{{ }}} Package=<4> {{{ Begin Project Dependency Project_Dep_Name libfann End Project Dependency }}} ############################################################################### Project: "simple_train"=".\simple_train.dsp" - Package Owner=<4> Package=<5> {{{ }}} Package=<4> {{{ Begin Project Dependency Project_Dep_Name libfann End Project Dependency }}} ############################################################################### Project: "steepness_train"=".\steepness_train.dsp" - Package Owner=<4> Package=<5> {{{ }}} Package=<4> {{{ Begin Project Dependency Project_Dep_Name libfann End Project Dependency }}} ############################################################################### Project: "xor_test"=".\xor_test.dsp" - Package Owner=<4> Package=<5> {{{ }}} Package=<4> {{{ Begin Project Dependency Project_Dep_Name libfann End Project Dependency }}} ############################################################################### Project: "xor_train"=".\xor_train.dsp" - Package Owner=<4> Package=<5> {{{ }}} Package=<4> {{{ Begin Project Dependency Project_Dep_Name libfann End Project Dependency }}} ############################################################################### Global: Package=<5> {{{ }}} Package=<3> {{{ }}} ############################################################################### libfann-2.1.0~beta~dfsg/MicrosoftVisualC++6.0/libfann.dsp000077500000000000000000000076621140315177100231640ustar00rootroot00000000000000# Microsoft Developer Studio Project File - Name="libfann" - Package Owner=<4> # Microsoft Developer Studio Generated Build File, Format Version 6.00 # ** DO NOT EDIT ** # TARGTYPE "Win32 (x86) Static Library" 0x0104 CFG=libfann - Win32 Debug !MESSAGE This is not a valid makefile. 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Documents\Visual Studio Projects\fann\examples\xor_test.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\examples\xor_train.c/ncb/target/__NcbPseudoTarget__/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\windll\examples\simple_train.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\windll\examples\xor_test.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\windll\examples\xor_train.c&QãðX-íÏ X ÁE -6…P 'à ùž¬BAŒì@Œ,x@`*+&'() !" #$%  ,libfann-2.1.0~beta~dfsg/MicrosoftWindowsDll/examples/examples.sln000066400000000000000000000045701140315177100253530ustar00rootroot00000000000000Microsoft Visual Studio Solution File, Format Version 8.00 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "simple_test", "simple_test.vcproj", "{B7385AAC-B6EA-448B-91F5-E392F50BC89A}" ProjectSection(ProjectDependencies) = postProject EndProjectSection EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "simple_train", "simple_train.vcproj", "{C549447E-FB28-4463-9461-AB253543CD3C}" ProjectSection(ProjectDependencies) = postProject EndProjectSection EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "xor_test", "xor_test.vcproj", "{7F209747-4D1E-48DB-BEF1-97C24DB3FB07}" ProjectSection(ProjectDependencies) = postProject EndProjectSection EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "xor_train", "xor_train.vcproj", "{1FEB58E4-6D09-42C1-A15D-5ABD804CA1D9}" ProjectSection(ProjectDependencies) = postProject EndProjectSection EndProject Global GlobalSection(SolutionConfiguration) = preSolution Debug = Debug Release = Release EndGlobalSection GlobalSection(ProjectConfiguration) = postSolution {B7385AAC-B6EA-448B-91F5-E392F50BC89A}.Debug.ActiveCfg = Debug|Win32 {B7385AAC-B6EA-448B-91F5-E392F50BC89A}.Debug.Build.0 = Debug|Win32 {B7385AAC-B6EA-448B-91F5-E392F50BC89A}.Release.ActiveCfg = Release|Win32 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libfann-2.1.0~beta~dfsg/MicrosoftWindowsDll/examples/simple_train.vcproj000066400000000000000000000110641140315177100267260ustar00rootroot00000000000000 libfann-2.1.0~beta~dfsg/MicrosoftWindowsDll/examples/xor_test.vcproj000066400000000000000000000107221140315177100261070ustar00rootroot00000000000000 libfann-2.1.0~beta~dfsg/MicrosoftWindowsDll/examples/xor_train.vcproj000066400000000000000000000107341140315177100262500ustar00rootroot00000000000000 libfann-2.1.0~beta~dfsg/MicrosoftWindowsDll/makefile000066400000000000000000000527211140315177100227020ustar00rootroot00000000000000################################################################ # # Makefile to rebuild fann win32 dll's with command line Microsoft C++ 2003 7.1 # # Use vcvars32 command file to set environment for the compiler tools. # # To rebuild all dll's run: nmake # To rebuild all debug dll's run: nmake "Debug" # To rebuild all release dll's run: nmake "Release" # To rebuild just one dll (for example "Release Fixed") run: nmake "Release Fixed" # # To delete all intermediary directories named "Debug Double" and so forth # and ***ALL*** their content run: nmake "Clean" # ################################################################ "All" : "Debug" "Release" "Debug" : "Debug Double" "Debug Double Multithread" "Debug Fixed" "Debug Fixed Multithread" "Debug Float" "Debug Float Multithread" "Release" : "Release Double" "Release Double Multithread" "Release Fixed" "Release Fixed Multithread" "Release Float" "Release Float Multithread" ################################################################ "Clean" : @echo Removing all intermediary directories... @if exist "Debug Double" erase /Q "Debug Double" @if exist "Debug Double" rmdir "Debug Double" @if exist "Debug Double Multithread" erase /Q "Debug Double Multithread" @if exist "Debug Double Multithread" rmdir "Debug Double Multithread" @if exist "Debug Fixed" erase /Q "Debug Fixed" @if exist "Debug Fixed" rmdir "Debug Fixed" @if exist "Debug Fixed Multithread" erase /Q "Debug Fixed Multithread" @if exist "Debug Fixed Multithread" rmdir "Debug Fixed Multithread" @if exist "Debug Float" erase /Q "Debug Float" @if exist "Debug Float" rmdir "Debug Float" @if exist "Debug Float Multithread" erase /Q "Debug Float Multithread" @if exist "Debug Float Multithread" rmdir "Debug Float Multithread" @if exist "Release Double" erase /Q "Release Double" @if exist "Release Double" rmdir "Release Double" @if exist "Release Double Multithread" erase /Q "Release Double Multithread" @if exist "Release Double Multithread" rmdir "Release Double Multithread" @if exist "Release Fixed" erase /Q "Release Fixed" @if exist "Release Fixed" rmdir "Release Fixed" @if exist "Release Fixed Multithread" erase /Q "Release Fixed Multithread" @if exist "Release Fixed Multithread" rmdir "Release Fixed Multithread" @if exist "Release Float" erase /Q "Release Float" @if exist "Release Float" rmdir "Release Float" @if exist "Release Float Multithread" erase /Q "Release Float Multithread" @if exist "Release Float Multithread" rmdir "Release Float Multithread" ################################################################ SOURCE = "../src/fann.c" "../src/fann_error.c" "../src/fann_io.c" "../src/fann_cascade.c" "../src/fann_train.c" "../src/fann_train_data.c" ################################################################ # "Debug Double" - fanndoubled.dll DDBLOBJ = "Debug Double/fann.obj" "Debug Double/fann_error.obj" "Debug Double/fann_io.obj" "Debug Double/fann_cascade.obj" "Debug Double/fann_train.obj" "Debug Double/fann_train_data.obj" "Debug Double Compile" : @echo Compiling Debug Double... @if not exist "Debug Double" mkdir "Debug Double" @cl @<< /Od /I "../src/include" /D "WIN32" /D "_DEBUG" /D "_WINDOWS" /D "_USRDLL" /D "FANN_DLL_EXPORTS" /D "_WINDLL" /D "_MBCS" /Gm /RTC1 /ML /Fo"Debug Double/" /Fd"Debug Double/vc70.pdb" /W3 /nologo /c /ZI /TC /FI "doublefann.h" $(SOURCE) </dapÍX /names/ncb/targetinfo/ncb/moduleinfo/ncb/storeinfo/ncb/iinstdefs/ncb/referenceInfo/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_error.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_io.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_options.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_train.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_train_data.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\compat_time.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\doublefann.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\fann.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\fann_activation.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\fann_data.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\fann_errno.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\fann_internal.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\fixedfann.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\floatfann.h/ncb/versioninfo/ncb/module/c:\Documents and Settings\stn\Desktop\fann\src\fann.c/ncb/module/c:\Documents and Settings\stn\Desktop\fann\src\fann_error.c/ncb/module/c:\Documents and Settings\stn\Desktop\fann\src\fann_io.c/ncb/module/c:\Documents and 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Documents\Visual Studio Projects\fann\win32\windll.vcproj|Debug Fixed|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Debug Float|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Release Double|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Release Fixed|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Release Float|Win32C:\Program Files\Microsoft Visual Studio .NET 2003\Vc7\vcpackages\prebuilt.ncbc:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann.c"config.h""fann.h""fann_errno.h"fann_create_arrayintfloat connection_ratefloat learning_rateunsigned int num_layersunsigned int *layersfann_randomize_weightsfann *annfann_type min_weightfann_type max_weightfann_destroyfann_print_connectionsfann_allocate_structurefann *fann_allocate_connectionsvoidfann_allocate_neuronsfann_runfann_type *inputfann_init_weightsfann_train_data *train_datafann_create_forward_arrayfann_seed_randfann_create...FANN_APIFANN_EXTERNALfann_create_forwardc:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_error.cvsnprintf_vsnprintfsnprintf_snprintffann_reset_errnoFANN_EXTERNAL void FANN_APIfann_error *errdatfann_reset_errstrfann_get_errstrfann_errorconst unsigned int errnofann_init_error_datafann_print_errorfann_set_error_logFILE *log_fileFANN_EXTERNAL char *fann_get_errnoFANN_EXTERNAL unsigned int FANN_APIc:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_io.cfann_save_internal_fdFILE *confconst char *configuration_fileunsigned int save_as_fixedfann_savefann_save_internalfann_save_train_internalfann_train_data *datachar *filenameunsigned int decimal_pointfann_save_to_fixedfann_create_from_fdfann_create_from_filefann_save_train_internal_fdFILE *filec:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_options.cfann_set_rprop_increase_factorfloat rprop_increase_factorfann_get_use_tanh_error_functionfann_get_connectionsfann_neuron **fann_get_activation_function_hiddenfann_get_quickprop_muFANN_EXTERNAL float FANN_APIfann_get_activation_steepness_hiddenFANN_EXTERNAL fann_type FANN_APIfann_get_rprop_increase_factorfann_get_total_connectionsfann_get_decimal_pointfann_get_num_outputfann_set_quickprop_decayfloat quickprop_decayfann_set_activation_function_outputunsigned int activation_functionfann_get_num_inputfann_get_rprop_delta_minfann_get_activation_function_outputfann_set_activation_steepness_outputfann_type steepnessfann_get_rprop_decrease_factorfann_get_multiplierfann_set_activation_function_hiddenfann_update_stepwise_hiddenfann_get_rprop_delta_maxfann_set_activation_output_steepnessfann_get_weightsfann_type *fann_get_quickprop_decayfann_get_learning_ratefann_set_training_algorithmunsigned int training_algorithmfann_print_parametersfann_get_activation_output_steepnessfann_set_use_tanh_error_functionunsigned int use_tanh_error_functionfann_get_training_algorithmfann_set_rprop_delta_maxfloat rprop_delta_maxfann_get_activation_steepness_outputfann_set_rprop_delta_minfloat rprop_delta_minfann_set_quickprop_mufloat quickprop_mufann_get_total_neuronsfann_update_stepwise_outputfann_set_rprop_decrease_factorfloat rprop_decrease_factorfann_get_activation_hidden_steepnessfann_set_learning_ratefann_set_activation_steepness_hiddenfann_set_activation_hidden_steepnessc:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_train.cfann_compute_MSEfann_type *desired_outputfann_trainfann_get_errorfann_update_slopes_batchfann_clear_train_arraysfann_get_MSEfann_update_weights_quickpropunsigned int num_datafann_backpropagate_MSEfann_update_weights_batchfann_reset_errorfann_reset_MSEfann_update_weightsfann_update_weights_irpropmFANN_EXTERNAL fann_type *fann_activation_derivedfann_typefann_type valuefann_testc:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_train_data.cfann_read_train_from_fdfann_train_data *fann_train_on_file_callbackint (*%)(unsigned int, float)unsigned int max_epochsunsigned int epochs_between_reportsfloat desired_errorint FANN_APIfann_train_epoch_irpropmfloatfann_train_on_data_callbackfann_test_datafann_train_on_filefann_train_epoch_quickpropfann_save_trainfann_train_epoch_batchfann_shuffle_train_datafann_train_on_datafann_save_train_to_fixedfann_merge_train_datafann_train_data *data1fann_train_data *data2fann_duplicate_train_datafann_train_epochfann_train_epoch_incrementalfann_destroy_trainfann_read_train_from_filechar *configuration_filec:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\compat_time.h_TIMEVAL_HWIN32_LEAN_AND_MEANEPOCHFILETIME(116444736000000000i64)gettimeofdaytimeval *tvtimezone 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settings\stn\desktop\fann\src\include\config.hHAVE_DLFCN_HHAVE_GETTIMEOFDAYHAVE_INTTYPES_HHAVE_MEMORY_HHAVE_STDINT_HHAVE_STDLIB_HHAVE_STRINGS_HHAVE_STRING_HHAVE_SYS_STAT_HHAVE_SYS_TYPES_HHAVE_UNISTD_HPACKAGE"fann"PACKAGE_BUGREPORT""PACKAGE_NAMEPACKAGE_STRINGPACKAGE_TARNAMEPACKAGE_VERSIONSTDC_HEADERSTIME_WITH_SYS_TIMEVERSION"2.0.0"C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Debug Float Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Release Float Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Debug Fixed Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Release Fixed Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Debug Double Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Release Double Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Debug Double|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Debug Fixed|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Debug Float|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Release Double|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Release Fixed|Win32C:\Documents and Settings\stn\Desktop\fann\MicrosoftWindowsDll\windll.vcproj|Release Float|Win32(8‘eJµ@¬S]ü¢LéÁ 93Œ6±PA/8_uk¸#„CÕDfO[2ÃRðª?\¤6å1äGô á1/'ý9"7ÇE¸På6ç"€+Ž+(Ë"ñA !ËCoNšWç3½ú5S 3/ Ý €'a&D6NG$S/PHÑ'mŒ&P1SF ‹? F& ûS‰k3AÖ¶MÙE—FIMS°AÒ× )yR2%C †2sƒ ˜çˆ ^$Ò HyãAˆR„Ž$|#qMœï < õNH"ô!‚f( BM éQ·| 5H¬ Á7ä,^(¦/cBgHÑ%®HSr«3.(f$`EÞé"1‹Qw=6ˆ¥ 5¦ôÓ3È5ÞV7ìFEOĬ}3µ¬.e%´2Õ“K…%4N«Ÿ@›²i úGx™ ¤éiÐ,Àns@)")²KBQ÷)½ñÙÐ#¹`S}GÙP¡ ì$Á ÞBzRPS§#Óˆ ¯* OÞi2P6©® !È2éKáRå&‰ç&ä+k4 [RÌ c.œ2FˆS¥¯$å%z%?Æ4™E2:,#El&­"7 ‡)–M„‘$¿.ˆ1H5> d@p'Ï$ 5Ð Â1Ç  ‘)’8¨I¥Qä#’å­ ŠRâÌÓTÁ IS¬ `6ýBx H h Œ ê03¬U p'h#v!´€J¿N,¾&ALíH‚ÿ6 A‚B­ €ôå 's$ô$2§gDBE=03ŸGC6Këü ˜HÓmS”#BENÖ›/eJ$7AÏKnNyYZ*P4–)Ô.Ò1¸ 3Ú5’¯8´RÎIÙV%í8¹Ç'80’7ÅB°D( “ §øBN<€è@V#%NØ CB'&@œ0Q L#Þ2ò& ÈA‘O óJ[GŽIZ$í?5C¬^&Ì$÷1»*Û'£?‘5ú31"„A%( €Ç&<-oQAhU!|.™IM] Db/¾"¦š ZÀL˜RˆQÙ"‡"g"ê-þu6ÊHòR;›% ûWô¬1ë’1ýÚ~#èN.ÒÆ@Þ (¤2—!PIÑR'L@UJÆ&g  Ø0¡Å|ï•BªB÷3}&¨QÚM®(m›"^05q*ÇHê.áIzV¸~ O%ä'1ö— Æ54…L‡P'AZP!Sý-zÛ!J/‡DÕJ@S¬q‘¸Å¿e19'\CË%#K(éCÝ k%f z“-€ªA–AûÊRn ã{ œÄ5»J´<7EKàO'PîEþP, 72hRSDDK h —.¬&- 2“(ÊNKEzH8W—¬$+X8H7‚Qõ%à €S_'My¦"é&u®C›J ˆFÂ3yFs'lQº2Ô7 Iæ s8ò hAãF01!hT<@!‚E9B!v'YB0N37wK.ÚÒ½O1«õFRQÎ4þ?gI 0#Q¦R¿+Lü »êŒMJ¶î Îa@ ³E³%NÈ64Eˆ@O@3.7¹!.B!!7üÀ”.1ùá@ ö]À7j8•K·>/dapÍX /names/ncb/targetinfo/ncb/moduleinfo/ncb/storeinfo/ncb/iinstdefs/ncb/referenceInfo/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_error.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_io.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_options.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_train.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\fann_train_data.c/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\compat_time.h/ncb/module/c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\doublefann.h/ncb/module/c:\Documents and Settings\sonþïþïSC:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Debug Float Multithread|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Release Float Multithread|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Debug Fixed Multithread|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Release Fixed Multithread|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Debug Double Multithread|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Release Double Multithread|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Debug Double|Win32C:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\win32\windll.vcproj|Debug Fixed|Win32C:\Documen) : -())fann_mult((*) >> decimal_point)fann_div((() << decimal_point)/)fann_random_weight(fann_type)(fann_rand(-multiplier/10,multiplier/10))fann_update_output_weightsc:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\fixedfann.hFIXEDFANN"%d"c:\Documents and Settings\sony user\My Documents\Visual Studio Projects\fann\src\include\floatfann.hFLOATFANN"%f"C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Debug Float Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Release Float Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Debug Fixed Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Release Fixed Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Debug Double Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Release Double Multithread|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Debug Double|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Debug Fixed|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Debug Float|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Release Double|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Release Fixed|Win32C:\Documents and Settings\stn\Desktop\fann\win32_dll\windll.vcproj|Release Float|Win32Debug Double|Win32c:\Documents and Settings\stn\Desktop\fann\src\fann.cfann_create_standard_arrayfann_create_standardfann_create_sparse_arrayfann_create_shortcut_arrayfann_create_shortcutfann_create_sparsefann_update_stepwisec:\Documents and Settings\stn\Desktop\fann\src\fann_error.cconst enum fann_errno_enum errno_ffann_default_error_logFANN_EXTERNAL FANN_APIc:\Documents and Settings\stn\Desktop\fann\src\fann_io.cfann_scanf{ if(fscanf(conf, "=""\n", ) != 1) { fann_error(NULL, FANN_E_CANT_READ_CONFIG, , configuration_file); fann_destroy(ann); return NULL; } }typenamevalfann_create_from_fd_1_1c:\Documents and Settings\stn\Desktop\fann\src\fann_options.cc:\Documents and Settings\stn\Desktop\fann\src\fann_train.cfann_get_bit_failFANN_EXTERNAL unsigned intfann_layer *layer_beginfann_layer *layer_endenum fann_activationfunc_enum activation_functionfann_set_activation_function_layerint layerunsigned int first_weightunsigned int past_endfann_activationfann_get_layerfann_get_neuron_layerfann_layer *layerint neuronfann_get_neuronunsigned int layerfann_set_activation_steepness_layerfann_type sumfann_set_activation_functionfann_set_activation_steepnessfann_update_MSEfann_neuron *neuronfann_type neuron_diffc:\Documents and Settings\stn\Desktop\fann\src\fann_train_data.cfann_num_input_train_dataconst char *filenamefann_scale_output_train_datafann_type new_minfann_type new_maxfann_scale_input_train_datafann_subset_train_dataunsigned int posunsigned int lengthfann_scale_datafann_type **dataunsigned int num_elemfann_length_train_datafann_num_output_train_datafann_desired_error_reachedfann_scale_train_datac:\Documents and Settings\stn\Desktop\fann\src\include\compat_time.hc:\Documents and Settings\stn\Desktop\fann\src\include\doublefann.hc:\Documents and Settings\stn\Desktop\fann\src\include\fann.hc:\Documents and Settings\stn\Desktop\fann\src\include\fann_activation.h((((()-()) * (()-()))/(()-())) + ())sumfann_sigmoid_real(1.0f/(1.0f + exp(-2.0f * )))fann_sigmoid_symmetric_real(2.0f/(1.0f + exp(-2.0f * )) - 1.0f)fann_gaussian_real(exp(- * ))fann_gaussian_derive(-2.0f *  *  * )fann_gaussian_symmetric((exp(- *  *  * )*2.0)-1.0)fann_gaussian_symmetric_real((exp(- * )*2.0)-1.0)fann_gaussian_symmetric_derive(-2.0f *  * (+1.0f) * )fann_elliot((( * ) / 2.0f) / (1.0f + fann_abs( * )) + 0.5f)fann_elliot_real((() / 2.0f) / (1.0f + fann_abs()) + 0.5f)fann_elliot_derive( * 1.0f / (2.0f * (1.0f + fann_abs()) * (1.0f + fann_abs())))fann_elliot_symmetric(( * ) / (1.0f + fann_abs( * )))fann_elliot_symmetric_real(() / (1.0f + fann_abs()))fann_elliot_symmetric_derive( * 1.0f / ((1.0f + fann_abs()) * (1.0f + fann_abs())))fann_activation_switchswitch() { case FANN_LINEAR:  = (fann_type); break; case FANN_LINEAR_PIECE:  = (fann_type)(( < 0) ? 0 : ( > 1) ? 1 : ); break; case FANN_LINEAR_PIECE_SYMMETRIC:  = (fann_type)(( < -1) ? -1 : ( > 1) ? 1 : ); break; case FANN_SIGMOID:  = (fann_type)fann_sigmoid_real(); break; case FANN_SIGMOID_SYMMETRIC:  = (fann_type)fann_sigmoid_symmetric_real(); break; case FANN_SIGMOID_SYMMETRIC_STEPWISE:  = (fann_type)fann_stepwise(-2.64665293693542480469e+00, -1.47221934795379638672e+00, -5.49306154251098632812e-01, 5.49306154251098632812e-01, 1.47221934795379638672e+00, 2.64665293693542480469e+00, -9.90000009536743164062e-01, -8.99999976158142089844e-01, -5.00000000000000000000e-01, 5.00000000000000000000e-01, 8.99999976158142089844e-01, 9.90000009536743164062e-01, -1, 1, ); break; case FANN_SIGMOID_STEPWISE:  = (fann_type)fann_stepwise(-2.64665246009826660156e+00, -1.47221946716308593750e+00, -5.49306154251098632812e-01, 5.49306154251098632812e-01, 1.47221934795379638672e+00, 2.64665293693542480469e+00, 4.99999988824129104614e-03, 5.00000007450580596924e-02, 2.50000000000000000000e-01, 7.50000000000000000000e-01, 9.49999988079071044922e-01, 9.95000004768371582031e-01, 0, 1, ); break; case FANN_THRESHOLD:  = (fann_type)(( < 0) ? 0 : 1); break; case FANN_THRESHOLD_SYMMETRIC:  = (fann_type)(( < 0) ? -1 : 1); break; case FANN_GAUSSIAN:  = (fann_type)fann_gaussian_real(); break; case FANN_GAUSSIAN_SYMMETRIC:  = (fann_type)fann_gaussian_symmetric_real(); break; case FANN_ELLIOT:  = (fann_type)fann_elliot_real(); break; case FANN_ELLIOT_SYMMETRIC:  = (fann_type)fann_elliot_symmetric_real(); break; }annactivation_functionresultc:\Documents and Settings\stn\Desktop\fann\src\include\fann_data.hFANN_TRAIN_NAMESfann_errorfunc_enumFANN_ERRORFUNC_TANHFANN_ERRORFUNC_LINEARfann_stopfunc_enumFANN_STOPFUNC_BITFANN_STOPFUNC_MSEenum fann_errno_enumfann_neuron::first_confann_neuron::last_confann_neuron::sumfann_neuron::activation_steepnessfann_neuron::activation_functionenum fann_activationfunc_enumfann_train_enumfann_activationfunc_enumFANN_GAUSSIAN_SYMMETRICFANN_LINEAR_PIECE_SYMMETRICFANN_LINEAR_PIECEFANN_ACTIVATIONFUNC_NAMESfann::learning_momentumfann::shortcut_connectionsfann::weightsfann::connectionsenum fann_train_enumfann::sigmoid_resultsfann::sigmoid_valuesfann::sigmoid_symmetric_resultsfann::sigmoid_symmetric_valuesfann::num_bit_failfann::bit_fail_limitfann::train_error_functionenum fann_errorfunc_enumfann::train_stop_functionenum fann_stopfunc_enumfann::cascade_output_change_fractionfann::cascade_output_stagnation_epochsfann::cascade_candidate_change_fractionfann::cascade_candidate_stagnation_epochsfann::cascade_best_candidatefann::cascade_candidate_limitfann::cascade_weight_multiplierfann::cascade_max_out_epochsfann::cascade_max_cand_epochsfann::cascade_activation_functionsenum fann_activationfunc_enum *fann::cascade_activation_functions_countfann::cascade_activation_steepnessesfann::cascade_activation_steepnesses_countfann::cascade_num_candidate_groupsfann::cascade_candidate_scoresfann::total_neurons_allocatedfann::total_connections_allocatedfann::rprop_delta_zerofann::prev_weights_deltasFANN_STOPFUNC_NAMESFANN_EXTERNAL intFANN_ERRORFUNC_NAMESc:\Documents and Settings\stn\Desktop\fann\src\include\fann_errno.hc:\Documents and Settings\stn\Desktop\fann\src\include\fann_internal.h"FANN_FIX_2.0""FANN_FLO_2.0"FANN_GETFANN_EXTERNAL  FANN_API fann_get_(struct fann *ann) { return ann->; }FANN_SETFANN_EXTERNAL void FANN_API fann_set_(struct fann *ann,  value) { ann-> = value; }FANN_GET_SETFANN_GET(, ) FANN_SET(, ){if() { free();  = NULL; }}(((float)())+(((float)()-((float)()))*rand()/(RAND_MAX+1.0f)))(fann_type)(fann_rand((0-multiplier)/10,multiplier/10))fann_train_outputs_epochfann_initialize_candidatesfann_install_candidatefann_train_outputsfann_set_shortcut_connectionsfann_train_candidatesfann_train_candidates_epochc:\Documents and Settings\stn\Desktop\fann\src\include\fixedfann.hc:\Documents and Settings\stn\Desktop\fann\src\include\floatfann.hc:\Documents and Settings\stn\Desktop\fann\src\fann_cascade.c"string.h"fann_add_layerfann_set_cascade_activation_functionsFANN_EXTERNAL voidenum fann_activationfunc_enum *cascade_activation_functionsunsigned int cascade_activation_functions_countfann_reallocate_neuronsunsigned int total_neuronsfann_print_connections_rawfann_get_cascade_num_candidatesfann_add_candidate_neuronfann_update_candidate_weightsfann_set_cascade_activation_steepnessesfann_type *cascade_activation_steepnessesunsigned int cascade_activation_steepnesses_countfann_reallocate_connectionsunsigned int total_connectionsfann_update_candidate_slopesfann_cascadetrain_on_fileunsigned int max_neuronsunsigned int neurons_between_reportsfann_cascadetrain_on_datac:\Documents and Settings\stn\Desktop\fann\src\include\fann_train.hfann_get_train_stop_functionfann_get_train_error_functionc:\Documents and Settings\stn\Desktop\fann\src\include\fann_cascade.hfann_set_cascade_candidate_limitfann_type cascade_candidate_limitfann_set_cascade_output_change_fractionfloat cascade_output_change_fractionfann_set_cascade_max_out_epochsunsigned int cascade_max_out_epochsfann_get_cascade_output_stagnation_epochsfann_get_cascade_candidate_limitfann_get_cascade_max_out_epochsfann_set_cascade_output_stagnation_epochsunsigned int cascade_output_stagnation_epochsfann_get_cascade_candidate_stagnation_epochsfann_set_cascade_candidate_change_fractionfloat cascade_candidate_change_fractionfann_get_cascade_output_change_fractionfann_get_cascade_max_cand_epochsfann_get_cascade_candidate_change_fractionfann_get_cascade_activation_functions_countfann_get_cascade_weight_multiplierfann_set_cascade_weight_multiplierfann_type cascade_weight_multiplierFANN_EXTERNAL enum enum fann_activationfunc_enum *fann_set_cascade_candidate_stagnation_epochsunsigned int cascade_candidate_stagnation_epochsfann_set_cascade_max_cand_epochsunsigned int cascade_max_cand_epochsc:\Documents and Settings\stn\Desktop\fann\src\include\fann_error.hfann_errno_enumFANN_E_INDEX_OUT_OF_BOUND17FANN_E_TRAIN_DATA_SUBSET16FANN_E_CANT_USE_TRAIN_ALG15c:\Documents and Settings\stn\Desktop\fann\src\include\fann_io.hDebug Float Multithread|Win32__NcbPseudoTarget__c:\documents and 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Multithread;Q{CB0A9F5C-36EC-4824-948E-982C8F1B2125}.Release Float Multithread|Win32.fBatchBld=;Q{CB0A9F5C-36EC-4824-948E-982C8F1B2125}.Debug Float Multithread|Win32.BatchBldCtx=Debug Float Multithread;O{CB0A9F5C-36EC-4824-948E-982C8F1B2125}.Debug Floalibfann-2.1.0~beta~dfsg/MicrosoftWindowsDll/windll.vcproj000066400000000000000000000605651140315177100237250ustar00rootroot00000000000000 libfann-2.1.0~beta~dfsg/NEWS000066400000000000000000000000541140315177100157150ustar00rootroot00000000000000For news, go to http://fann.sourceforge.net libfann-2.1.0~beta~dfsg/README000066400000000000000000000026201140315177100160770ustar00rootroot00000000000000Fast Artificial Neural Network Library (fann) Fast Artificial Neural Network Library implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to other programming languages and a GUI are also available. A reference manual accompanies the library with examples and recommendations on how to use the library. The reference manual is available at: http://leenissen.dk/fann/html/index.html A university report about the implementation of version 1.0 is also available, please see: http://prdownloads.sourceforge.net/fann/fann_doc_complete_1.0.pdf?download An article about how to use the library is available at: doc/fann_en.pdf For installation do the following: type command: ./configure type command: make Now you would like to be root in order to install type command: su type command: make install type command: exit Now you are not root anymore, and you can test the library. type command: cd examples type command: make runtest For further installation instructions see: INSTALL For example programs please see: examples/simple_train.c examples/simple_test.c For more information and downloads, please go to: http://fann.sourceforge.net/ libfann-2.1.0~beta~dfsg/TODO000066400000000000000000000033141140315177100157100ustar00rootroot00000000000000For a look at current TODO items look at: http://sourceforge.net/tracker/?group_id=93562&atid=604725 If you want to contribute to the library, please contact me at lukesky@diku.dk Things TODO when releasing a new version (mostly for fann developers) * All Makefile.am and configure.in should be updated to include relevant files and to reflect the new version number * No compile warnings cd examples make compiletest * Everything should compile and install ok ./configure && make sudo make install cd examples make runtest * All of the examples should work fine cd examples make simple_train && ./simple_train make simple_test && ./simple_test make steepness_train && ./steepness_train make mushroom && ./mushroom make robot && ./robot make cascade_train && ./cascade_train make momentums && ./momentums * No memory leaks must exist cd examples make rundebug * Documentation should be updated * Benchmarks should be updated * Changelog should be updated and timestamp should be set * All relevant files should be in CVS (all other removed) * TGZ/BZ/ZIP packages should be created * Debian packages should be created (from tgz package) #Example of make command rm -rf fann-2.0.0.* test && mkdir test && ./configure && make distclean && ./configure && make dist && cp fann-2.0.0.*gz test && cd test && tar xvzf *gz && cd fann-2.0.0 su pbuilder update --distribution unstable pdebuild exit cd .. && cp /var/cache/pbuilder/result/*deb . && lintian *deb * Windows DLL's should be created * ZIP package with win dll should be created * RPM packages should be created * Commit for CVS * CVS tag for release * Upload to sf * Update sf site documentation etc. * Update fm site * Send out release info libfann-2.1.0~beta~dfsg/aclocal.m4000066400000000000000000010000631140315177100170570ustar00rootroot00000000000000# generated automatically by aclocal 1.9.6 -*- Autoconf -*- # Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, # 2005 Free Software Foundation, Inc. # This file is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; without # even the implied warranty of MERCHANTABILITY or FITNESS FOR A # PARTICULAR PURPOSE. # libtool.m4 - Configure libtool for the host system. -*-Autoconf-*- # serial 48 Debian 1.5.22-2 AC_PROG_LIBTOOL # AC_PROVIDE_IFELSE(MACRO-NAME, IF-PROVIDED, IF-NOT-PROVIDED) # ----------------------------------------------------------- # If this macro is not defined by Autoconf, define it here. m4_ifdef([AC_PROVIDE_IFELSE], [], [m4_define([AC_PROVIDE_IFELSE], [m4_ifdef([AC_PROVIDE_$1], [$2], [$3])])]) # AC_PROG_LIBTOOL # --------------- AC_DEFUN([AC_PROG_LIBTOOL], [AC_REQUIRE([_AC_PROG_LIBTOOL])dnl dnl If AC_PROG_CXX has already been expanded, run AC_LIBTOOL_CXX dnl immediately, otherwise, hook it in at the end of AC_PROG_CXX. 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esac ;; gnu*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}${major} ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH hardcode_into_libs=yes ;; hpux9* | hpux10* | hpux11*) # Give a soname corresponding to the major version so that dld.sl refuses to # link against other versions. version_type=sunos need_lib_prefix=no need_version=no case $host_cpu in ia64*) shrext_cmds='.so' hardcode_into_libs=yes dynamic_linker="$host_os dld.so" shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes # Unless +noenvvar is specified. library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' if test "X$HPUX_IA64_MODE" = X32; then sys_lib_search_path_spec="/usr/lib/hpux32 /usr/local/lib/hpux32 /usr/local/lib" else sys_lib_search_path_spec="/usr/lib/hpux64 /usr/local/lib/hpux64" fi sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; hppa*64*) shrext_cmds='.sl' hardcode_into_libs=yes dynamic_linker="$host_os dld.sl" shlibpath_var=LD_LIBRARY_PATH # How should we handle SHLIB_PATH shlibpath_overrides_runpath=yes # Unless +noenvvar is specified. library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' sys_lib_search_path_spec="/usr/lib/pa20_64 /usr/ccs/lib/pa20_64" sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; *) shrext_cmds='.sl' dynamic_linker="$host_os dld.sl" shlibpath_var=SHLIB_PATH shlibpath_overrides_runpath=no # +s is required to enable SHLIB_PATH library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' ;; esac # HP-UX runs *really* slowly unless shared libraries are mode 555. postinstall_cmds='chmod 555 $lib' ;; interix3*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='Interix 3.x ld.so.1 (PE, like ELF)' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no hardcode_into_libs=yes ;; irix5* | irix6* | nonstopux*) case $host_os in nonstopux*) version_type=nonstopux ;; *) if test "$lt_cv_prog_gnu_ld" = yes; then version_type=linux else version_type=irix fi ;; esac need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${release}${shared_ext} $libname${shared_ext}' case $host_os in irix5* | nonstopux*) libsuff= shlibsuff= ;; *) case $LD in # libtool.m4 will add one of these switches to LD *-32|*"-32 "|*-melf32bsmip|*"-melf32bsmip ") libsuff= shlibsuff= libmagic=32-bit;; *-n32|*"-n32 "|*-melf32bmipn32|*"-melf32bmipn32 ") libsuff=32 shlibsuff=N32 libmagic=N32;; *-64|*"-64 "|*-melf64bmip|*"-melf64bmip ") libsuff=64 shlibsuff=64 libmagic=64-bit;; *) libsuff= shlibsuff= libmagic=never-match;; esac ;; esac shlibpath_var=LD_LIBRARY${shlibsuff}_PATH shlibpath_overrides_runpath=no sys_lib_search_path_spec="/usr/lib${libsuff} /lib${libsuff} /usr/local/lib${libsuff}" sys_lib_dlsearch_path_spec="/usr/lib${libsuff} /lib${libsuff}" hardcode_into_libs=yes ;; # No shared lib support for Linux oldld, aout, or coff. linux*oldld* | linux*aout* | linux*coff*) dynamic_linker=no ;; # This must be Linux ELF. linux*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' finish_cmds='PATH="\$PATH:/sbin" ldconfig -n $libdir' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no # This implies no fast_install, which is unacceptable. # Some rework will be needed to allow for fast_install # before this can be enabled. hardcode_into_libs=yes # Append ld.so.conf contents to the search path if test -f /etc/ld.so.conf; then lt_ld_extra=`awk '/^include / { system(sprintf("cd /etc; cat %s", \[$]2)); skip = 1; } { if (!skip) print \[$]0; skip = 0; }' < /etc/ld.so.conf | $SED -e 's/#.*//;s/[:, ]/ /g;s/=[^=]*$//;s/=[^= ]* / /g;/^$/d' | tr '\n' ' '` sys_lib_dlsearch_path_spec="/lib /usr/lib $lt_ld_extra" fi # We used to test for /lib/ld.so.1 and disable shared libraries on # powerpc, because MkLinux only supported shared libraries with the # GNU dynamic linker. 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netbsd*) version_type=sunos need_lib_prefix=no need_version=no if echo __ELF__ | $CC -E - | grep __ELF__ >/dev/null; then library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${shared_ext}$versuffix' finish_cmds='PATH="\$PATH:/sbin" ldconfig -m $libdir' dynamic_linker='NetBSD (a.out) ld.so' else library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='NetBSD ld.elf_so' fi shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes hardcode_into_libs=yes ;; newsos6) version_type=linux library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes ;; nto-qnx*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes ;; openbsd*) version_type=sunos sys_lib_dlsearch_path_spec="/usr/lib" need_lib_prefix=no # Some older versions of OpenBSD (3.3 at least) *do* need versioned libs. case $host_os in openbsd3.3 | openbsd3.3.*) need_version=yes ;; *) need_version=no ;; esac library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${shared_ext}$versuffix' finish_cmds='PATH="\$PATH:/sbin" ldconfig -m $libdir' shlibpath_var=LD_LIBRARY_PATH if test -z "`echo __ELF__ | $CC -E - | grep __ELF__`" || test "$host_os-$host_cpu" = "openbsd2.8-powerpc"; then case $host_os in openbsd2.[[89]] | openbsd2.[[89]].*) shlibpath_overrides_runpath=no ;; *) shlibpath_overrides_runpath=yes ;; esac else shlibpath_overrides_runpath=yes fi ;; os2*) libname_spec='$name' shrext_cmds=".dll" need_lib_prefix=no library_names_spec='$libname${shared_ext} $libname.a' dynamic_linker='OS/2 ld.exe' shlibpath_var=LIBPATH ;; osf3* | osf4* | osf5*) version_type=osf need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' shlibpath_var=LD_LIBRARY_PATH sys_lib_search_path_spec="/usr/shlib /usr/ccs/lib /usr/lib/cmplrs/cc /usr/lib /usr/local/lib /var/shlib" sys_lib_dlsearch_path_spec="$sys_lib_search_path_spec" ;; 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AC_MSG_CHECKING([command to parse $NM output from $compiler object]) AC_CACHE_VAL([lt_cv_sys_global_symbol_pipe], [ # These are sane defaults that work on at least a few old systems. # [They come from Ultrix. 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Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.9.1 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. EOF elif $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then _LT_AC_TAGVAR(archive_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' _LT_AC_TAGVAR(archive_expsym_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else _LT_AC_TAGVAR(ld_shlibs, $1)=no fi ;; sysv5* | sco3.2v5* | sco5v6* | unixware* | OpenUNIX*) case `$LD -v 2>&1` in *\ [[01]].* | *\ 2.[[0-9]].* | *\ 2.1[[0-5]].*) _LT_AC_TAGVAR(ld_shlibs, $1)=no cat <<_LT_EOF 1>&2 *** Warning: Releases of the GNU linker prior to 2.16.91.0.3 can not *** reliably create shared libraries on SCO systems. Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.16.91.0.3 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. _LT_EOF ;; *) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then _LT_AC_TAGVAR(hardcode_libdir_flag_spec, $1)='`test -z "$SCOABSPATH" && echo ${wl}-rpath,$libdir`' _LT_AC_TAGVAR(archive_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname,\${SCOABSPATH:+${install_libdir}/}$soname -o $lib' _LT_AC_TAGVAR(archive_expsym_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname,\${SCOABSPATH:+${install_libdir}/}$soname,-retain-symbols-file,$export_symbols -o $lib' else _LT_AC_TAGVAR(ld_shlibs, $1)=no fi ;; esac ;; sunos4*) _LT_AC_TAGVAR(archive_cmds, $1)='$LD -assert pure-text -Bshareable -o $lib $libobjs $deplibs $linker_flags' wlarc= _LT_AC_TAGVAR(hardcode_direct, $1)=yes _LT_AC_TAGVAR(hardcode_shlibpath_var, $1)=no ;; *) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then _LT_AC_TAGVAR(archive_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' _LT_AC_TAGVAR(archive_expsym_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else _LT_AC_TAGVAR(ld_shlibs, $1)=no fi ;; esac if test "$_LT_AC_TAGVAR(ld_shlibs, $1)" = no; then runpath_var= _LT_AC_TAGVAR(hardcode_libdir_flag_spec, $1)= _LT_AC_TAGVAR(export_dynamic_flag_spec, $1)= _LT_AC_TAGVAR(whole_archive_flag_spec, $1)= fi else # PORTME fill in a description of your system's linker (not GNU ld) case $host_os in aix3*) _LT_AC_TAGVAR(allow_undefined_flag, $1)=unsupported _LT_AC_TAGVAR(always_export_symbols, $1)=yes _LT_AC_TAGVAR(archive_expsym_cmds, $1)='$LD -o $output_objdir/$soname $libobjs $deplibs $linker_flags -bE:$export_symbols -T512 -H512 -bM:SRE~$AR $AR_FLAGS $lib $output_objdir/$soname' # Note: this linker hardcodes the directories in LIBPATH if there # are no directories specified by -L. _LT_AC_TAGVAR(hardcode_minus_L, $1)=yes if test "$GCC" = yes && test -z "$lt_prog_compiler_static"; then # Neither direct hardcoding nor static linking is supported with a # broken collect2. _LT_AC_TAGVAR(hardcode_direct, $1)=unsupported fi ;; aix4* | aix5*) if test "$host_cpu" = ia64; then # On IA64, the linker does run time linking by default, so we don't # have to do anything special. aix_use_runtimelinking=no exp_sym_flag='-Bexport' no_entry_flag="" else # If we're using GNU nm, then we don't want the "-C" option. # -C means demangle to AIX nm, but means don't demangle with GNU nm if $NM -V 2>&1 | grep 'GNU' > /dev/null; then _LT_AC_TAGVAR(export_symbols_cmds, $1)='$NM -Bpg $libobjs $convenience | awk '\''{ if (((\[$]2 == "T") || (\[$]2 == "D") || (\[$]2 == "B")) && ([substr](\[$]3,1,1) != ".")) { print \[$]3 } }'\'' | sort -u > $export_symbols' else _LT_AC_TAGVAR(export_symbols_cmds, $1)='$NM -BCpg $libobjs $convenience | awk '\''{ if (((\[$]2 == "T") || (\[$]2 == "D") || (\[$]2 == "B")) && ([substr](\[$]3,1,1) != ".")) { print \[$]3 } }'\'' | sort -u > $export_symbols' fi aix_use_runtimelinking=no # Test if we are trying to use run time linking or normal # AIX style linking. If -brtl is somewhere in LDFLAGS, we # need to do runtime linking. case $host_os in aix4.[[23]]|aix4.[[23]].*|aix5*) for ld_flag in $LDFLAGS; do if (test $ld_flag = "-brtl" || test $ld_flag = "-Wl,-brtl"); then aix_use_runtimelinking=yes break fi done ;; esac exp_sym_flag='-bexport' no_entry_flag='-bnoentry' fi # When large executables or shared objects are built, AIX ld can # have problems creating the table of contents. If linking a library # or program results in "error TOC overflow" add -mminimal-toc to # CXXFLAGS/CFLAGS for g++/gcc. In the cases where that is not # enough to fix the problem, add -Wl,-bbigtoc to LDFLAGS. _LT_AC_TAGVAR(archive_cmds, $1)='' _LT_AC_TAGVAR(hardcode_direct, $1)=yes _LT_AC_TAGVAR(hardcode_libdir_separator, $1)=':' _LT_AC_TAGVAR(link_all_deplibs, $1)=yes if test "$GCC" = yes; then case $host_os in aix4.[[012]]|aix4.[[012]].*) # We only want to do this on AIX 4.2 and lower, the check # below for broken collect2 doesn't work under 4.3+ collect2name=`${CC} -print-prog-name=collect2` if test -f "$collect2name" && \ strings "$collect2name" | grep resolve_lib_name >/dev/null then # We have reworked collect2 _LT_AC_TAGVAR(hardcode_direct, $1)=yes else # We have old collect2 _LT_AC_TAGVAR(hardcode_direct, $1)=unsupported # It fails to find uninstalled libraries when the uninstalled # path is not listed in the libpath. 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The following line is correct: shared_flag='-G' else if test "$aix_use_runtimelinking" = yes; then shared_flag='${wl}-G' else shared_flag='${wl}-bM:SRE' fi fi fi # It seems that -bexpall does not export symbols beginning with # underscore (_), so it is better to generate a list of symbols to export. _LT_AC_TAGVAR(always_export_symbols, $1)=yes if test "$aix_use_runtimelinking" = yes; then # Warning - without using the other runtime loading flags (-brtl), # -berok will link without error, but may produce a broken library. _LT_AC_TAGVAR(allow_undefined_flag, $1)='-berok' # Determine the default libpath from the value encoded in an empty executable. _LT_AC_SYS_LIBPATH_AIX _LT_AC_TAGVAR(hardcode_libdir_flag_spec, $1)='${wl}-blibpath:$libdir:'"$aix_libpath" _LT_AC_TAGVAR(archive_expsym_cmds, $1)="\$CC"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags `if test "x${allow_undefined_flag}" != "x"; then echo "${wl}${allow_undefined_flag}"; else :; fi` '"\${wl}$exp_sym_flag:\$export_symbols $shared_flag" else if test "$host_cpu" = ia64; then _LT_AC_TAGVAR(hardcode_libdir_flag_spec, $1)='${wl}-R $libdir:/usr/lib:/lib' _LT_AC_TAGVAR(allow_undefined_flag, $1)="-z nodefs" _LT_AC_TAGVAR(archive_expsym_cmds, $1)="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags ${wl}${allow_undefined_flag} '"\${wl}$exp_sym_flag:\$export_symbols" else # Determine the default libpath from the value encoded in an empty executable. _LT_AC_SYS_LIBPATH_AIX _LT_AC_TAGVAR(hardcode_libdir_flag_spec, $1)='${wl}-blibpath:$libdir:'"$aix_libpath" # Warning - without using the other run time loading flags, # -berok will link without error, but may produce a broken library. _LT_AC_TAGVAR(no_undefined_flag, $1)=' ${wl}-bernotok' _LT_AC_TAGVAR(allow_undefined_flag, $1)=' ${wl}-berok' # Exported symbols can be pulled into shared objects from archives _LT_AC_TAGVAR(whole_archive_flag_spec, $1)='$convenience' _LT_AC_TAGVAR(archive_cmds_need_lc, $1)=yes # This is similar to how AIX traditionally builds its shared libraries. _LT_AC_TAGVAR(archive_expsym_cmds, $1)="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs ${wl}-bnoentry $compiler_flags ${wl}-bE:$export_symbols${allow_undefined_flag}~$AR $AR_FLAGS $output_objdir/$libname$release.a $output_objdir/$soname' fi fi ;; amigaos*) _LT_AC_TAGVAR(archive_cmds, $1)='$rm $output_objdir/a2ixlibrary.data~$echo "#define NAME $libname" > $output_objdir/a2ixlibrary.data~$echo "#define LIBRARY_ID 1" >> $output_objdir/a2ixlibrary.data~$echo "#define VERSION $major" >> $output_objdir/a2ixlibrary.data~$echo "#define REVISION $revision" >> $output_objdir/a2ixlibrary.data~$AR $AR_FLAGS $lib $libobjs~$RANLIB $lib~(cd $output_objdir && a2ixlibrary -32)' _LT_AC_TAGVAR(hardcode_libdir_flag_spec, $1)='-L$libdir' _LT_AC_TAGVAR(hardcode_minus_L, $1)=yes # see comment about different semantics on the GNU ld section _LT_AC_TAGVAR(ld_shlibs, $1)=no ;; bsdi[[45]]*) _LT_AC_TAGVAR(export_dynamic_flag_spec, $1)=-rdynamic ;; cygwin* | mingw* | pw32*) # When not using gcc, we currently assume that we are using # Microsoft Visual C++. # hardcode_libdir_flag_spec is actually meaningless, as there is # no search path for DLLs. _LT_AC_TAGVAR(hardcode_libdir_flag_spec, $1)=' ' _LT_AC_TAGVAR(allow_undefined_flag, $1)=unsupported # Tell ltmain to make .lib files, not .a files. libext=lib # Tell ltmain to make .dll files, not .so files. shrext_cmds=".dll" # FIXME: Setting linknames here is a bad hack. _LT_AC_TAGVAR(archive_cmds, $1)='$CC -o $lib $libobjs $compiler_flags `echo "$deplibs" | $SED -e '\''s/ -lc$//'\''` -link -dll~linknames=' # The linker will automatically build a .lib file if we build a DLL. _LT_AC_TAGVAR(old_archive_From_new_cmds, $1)='true' # FIXME: Should let the user specify the lib program. _LT_AC_TAGVAR(old_archive_cmds, $1)='lib /OUT:$oldlib$oldobjs$old_deplibs' _LT_AC_TAGVAR(fix_srcfile_path, $1)='`cygpath -w "$srcfile"`' _LT_AC_TAGVAR(enable_shared_with_static_runtimes, $1)=yes ;; darwin* | rhapsody*) case $host_os in rhapsody* | darwin1.[[012]]) _LT_AC_TAGVAR(allow_undefined_flag, $1)='${wl}-undefined ${wl}suppress' ;; *) # Darwin 1.3 on if test -z ${MACOSX_DEPLOYMENT_TARGET} ; then _LT_AC_TAGVAR(allow_undefined_flag, $1)='${wl}-flat_namespace ${wl}-undefined ${wl}suppress' else case ${MACOSX_DEPLOYMENT_TARGET} in 10.[[012]]) _LT_AC_TAGVAR(allow_undefined_flag, $1)='${wl}-flat_namespace ${wl}-undefined ${wl}suppress' ;; 10.*) _LT_AC_TAGVAR(allow_undefined_flag, $1)='${wl}-undefined ${wl}dynamic_lookup' ;; esac fi ;; esac _LT_AC_TAGVAR(archive_cmds_need_lc, $1)=no _LT_AC_TAGVAR(hardcode_direct, $1)=no _LT_AC_TAGVAR(hardcode_automatic, $1)=yes _LT_AC_TAGVAR(hardcode_shlibpath_var, $1)=unsupported _LT_AC_TAGVAR(whole_archive_flag_spec, $1)='' _LT_AC_TAGVAR(link_all_deplibs, $1)=yes if test "$GCC" = yes ; then output_verbose_link_cmd='echo' _LT_AC_TAGVAR(archive_cmds, $1)='$CC -dynamiclib $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags -install_name $rpath/$soname $verstring' _LT_AC_TAGVAR(module_cmds, $1)='$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags' # Don't fix this by using the ld -exported_symbols_list flag, it doesn't exist in older darwin lds _LT_AC_TAGVAR(archive_expsym_cmds, $1)='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC -dynamiclib $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags -install_name $rpath/$soname $verstring~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' _LT_AC_TAGVAR(module_expsym_cmds, $1)='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' else case $cc_basename in xlc*) output_verbose_link_cmd='echo' _LT_AC_TAGVAR(archive_cmds, $1)='$CC -qmkshrobj $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-install_name ${wl}`echo $rpath/$soname` $verstring' _LT_AC_TAGVAR(module_cmds, $1)='$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags' # Don't fix this by using the ld -exported_symbols_list flag, it doesn't exist in older darwin lds _LT_AC_TAGVAR(archive_expsym_cmds, $1)='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC -qmkshrobj $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-install_name ${wl}$rpath/$soname $verstring~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' _LT_AC_TAGVAR(module_expsym_cmds, $1)='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' ;; *) _LT_AC_TAGVAR(ld_shlibs, $1)=no ;; esac fi ;; dgux*) _LT_AC_TAGVAR(archive_cmds, $1)='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' _LT_AC_TAGVAR(hardcode_libdir_flag_spec, $1)='-L$libdir' _LT_AC_TAGVAR(hardcode_shlibpath_var, $1)=no ;; freebsd1*) _LT_AC_TAGVAR(ld_shlibs, $1)=no ;; # FreeBSD 2.2.[012] allows us to include c++rt0.o to get C++ constructor # support. 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We just wanted to have am__tar # and am__untar set. test -n "${am_cv_prog_tar_$1}" && break # tar/untar a dummy directory, and stop if the command works rm -rf conftest.dir mkdir conftest.dir echo GrepMe > conftest.dir/file AM_RUN_LOG([tardir=conftest.dir && eval $am__tar_ >conftest.tar]) rm -rf conftest.dir if test -s conftest.tar; then AM_RUN_LOG([$am__untar /dev/null 2>&1 && break fi done rm -rf conftest.dir AC_CACHE_VAL([am_cv_prog_tar_$1], [am_cv_prog_tar_$1=$_am_tool]) AC_MSG_RESULT([$am_cv_prog_tar_$1])]) AC_SUBST([am__tar]) AC_SUBST([am__untar]) ]) # _AM_PROG_TAR libfann-2.1.0~beta~dfsg/benchmarks/000077500000000000000000000000001140315177100173345ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/benchmarks/.cvsignore000066400000000000000000000002041140315177100213300ustar00rootroot00000000000000*.net *_fixed_test_* *_fixed_train_* *.out parity performance *_fixed quality shuffle two-spirals allplots.ps allplots_old.ps *.ps libfann-2.1.0~beta~dfsg/benchmarks/Makefile000066400000000000000000000026021140315177100207740ustar00rootroot00000000000000# The jneural library (http://voltar-confed.org/jneural/) and lwneuralnet library (http://sourceforge.net/projects/lwneuralnet/) needs to be installed in order to run the benchmarks GCC = gcc G++ = g++ TARGETS = quality quality_fixed performance performance_fixed shuffle parity two-spirals CFLAGS = -O3 -finline-functions -funroll-loops -Werror LFLAGS = -lm JNEURALDIR = ../../libraries/jneural/ LWNNDIR = ../../libraries/lwneuralnet-0.8/source/ all: $(TARGETS) quality: quality.cc $(G++) $(CFLAGS) $(LFLAGS) -I$(JNEURALDIR)include/ -L$(JNEURALDIR) -I$(LWNNDIR) -L$(LWNNDIR) $< -o $@ -lfloatfann -llwneuralnet -ljneural -lfl quality_fixed: quality_fixed.c $(GCC) $(CFLAGS) $(LFLAGS) $< -o $@ -lfixedfann quality_fixed_debug: quality_fixed.c $(GCC) -ggdb -lm -DDEBUG -Wall -ansi -I../src/ -I../src/include/ ../src/fixedfann.c $< -o $@ performance: performance.cc $(G++) $(CFLAGS) $(LFLAGS) -I$(JNEURALDIR)include/ -L$(JNEURALDIR) -I$(LWNNDIR) -L$(LWNNDIR) $< -o $@ -lfloatfann -llwneuralnet -ljneural -lfl performance_fixed: performance.cc $(G++) $(CFLAGS) $(LFLAGS) -DFIXEDFANN $< -o $@ -lfixedfann shuffle: shuffle.c $(GCC) $(CFLAGS) $(LFLAGS) $< -o $@ -lfloatfann two-spirals: two-spirals.c $(GCC) $(CFLAGS) $(LFLAGS) $< -o $@ parity: parity.c $(GCC) $(CFLAGS) $(LFLAGS) $< -o $@ clean: rm -rf -- $(TARGETS) *~ distclean: clean rm -rf *.ps *.out *.net *fixed_test* *fixed_train* libfann-2.1.0~beta~dfsg/benchmarks/README000066400000000000000000000022721140315177100202170ustar00rootroot00000000000000Programs used to generate benchmarks. performance - Program to benchmark the performance of running the libraries quality - Program to benchmark the quality of the training algorithms benchmark.sh - Shell file that runs a series of benchmarks gnuplot - File containing gnuplot commands for displaying graphs In order to run benchmarks, run: make ./benchmarks.sh gnuplot < gnuplot A file named allplots.ps will display the benchmark graphs. In order to generate benchmarks against other libraries (jneural and lwneuralnet), these libraries need to be installed (they can be downloaded from http://voltar-confed.org/jneural/ and http://sourceforge.net/projects/lwneuralnet/). The Makefile need to be altered to point to these libraries and the performance.cc and quality.cc files need to be altered in order to use the libraries (just uncomment a few lines in the top). Pregenerated benchmarks are available in the benchmark.pdf file. These benchmarks are generated on a AMD Athlon XP 1600+ machine (actually only 1400 MHz) with a 256 KB L2 cache and a 128 KB L1 cache. An earlier version of the library is benchmarked and commented in the full report (this benchmark also include benchmarks on an iPAQ). libfann-2.1.0~beta~dfsg/benchmarks/benchmark.sh000077500000000000000000000065231140315177100216330ustar00rootroot00000000000000#!/bin/sh date; #rm -f summary.txt; max_seconds_training=300; secs_between_reports=0.1; number_of_runs=4; function benchmark_algorithm() { ./quality $algo datasets/$prob.train datasets/$prob.test $prob.$algo.train.out $prob.$algo.test.out $n1 $n2 $sec_train $secs_between_reports $number_of_runs date; } function benchmark_problem() { rm -f *_fixed.net #algo="fann_rprop_stepwise"; benchmark_algorithm; #echo "./quality_fixed $prob.$algo.train.out_fixed_train $prob.$algo.train.out_fixed_test $prob.$algo.fixed_train.out $prob.$algo.fixed_test.out *_fixed.net" algo="fann_cascade_rprop_one_activation"; benchmark_algorithm; algo="fann_cascade_rprop_multi_activation"; benchmark_algorithm; algo="fann_cascade_quickprop_one_activation"; benchmark_algorithm; algo="fann_cascade_quickprop_multi_activation"; benchmark_algorithm; #algo="fann_cascade_batch_one_activation"; benchmark_algorithm; # algo="fann_cascade_batch_multi_activation"; benchmark_algorithm; algo="fann_rprop"; benchmark_algorithm; algo="fann_quickprop"; benchmark_algorithm; #algo="fann_quickprop_stepwise"; benchmark_algorithm; algo="fann_batch"; benchmark_algorithm; #algo="fann_batch_stepwise"; benchmark_algorithm; #algo="fann_incremental"; benchmark_algorithm; algo="fann_incremental_momentum"; benchmark_algorithm; #algo="fann_incremental_stepwise"; benchmark_algorithm; #comment out two following lines if the libraries are not available algo="lwnn"; benchmark_algorithm; algo="jneural"; benchmark_algorithm; } prob="two-spiral"; n1=20; n2=10; sec_train=$max_seconds_training; benchmark_problem; exit; #comment out some of the lines below if some of the problems should not be benchmarked prob="abelone"; n1=16; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="bank32fm"; n1=16; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="bank32nh"; n1=16; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="kin32fm"; n1=16; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="census-house"; n1=16; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="building"; n1=16; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="diabetes"; n1=4; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="gene"; n1=4; n2=2; sec_train=$max_seconds_training; benchmark_problem; prob="mushroom"; n1=32; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="parity8"; n1=16; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="parity13"; n1=26; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="pumadyn-32fm"; n1=10; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="robot"; n1=96; n2=0; sec_train=$max_seconds_training; benchmark_problem; prob="soybean"; n1=16; n2=8; sec_train=$max_seconds_training; benchmark_problem; prob="thyroid"; n1=16; n2=8; sec_train=$max_seconds_training; benchmark_problem; prob="two-spiral"; n1=20; n2=10; sec_train=$max_seconds_training; benchmark_problem; #./performance fann fann_performance.out 1 2048 2 20 #./performance fann_stepwise fann_stepwise_performance.out 1 2048 2 20 #./performance_fixed fann fann_fixed_performance.out 1 2048 2 20 #./performance lwnn lwnn_performance.out 1 2048 2 20 #./performance jneural jneural_performance.out 1 256 2 20 ./gnuplot.pl | gnuplot libfann-2.1.0~beta~dfsg/benchmarks/benchmarks.pdf000066400000000000000000023635701140315177100221640ustar00rootroot00000000000000%PDF-1.2 %Çì¢ 6 0 obj <> stream xœ˜Ko7„ïû+x´šðÍá5@à›[ºÅ9(ò*‘!­lÉ‚œŸî®&{c­”¬±·Pþ¸CöÔð1_œ_‚óüÑ͗ÍOï³ûó~ó› ÞEï~'ÿã&.Å=n‚»Ú”@fˆ}u7Ð-×è®7§Ô,µ{|Š…TaÖd4O½?ÄÉ?Ý|á‹óG¿.nÜÏgÔ³î‚;»ÜŸÝ ýyWB]*a}i1»³›Í+^Ÿ}ÚüræÞmr¥ßÖN@§È­P¸s9—8yÑʳ<4øØÚäE+ÏzðÐàC“­<ëÁCƒ÷ÉxÑʳ<´ð©§Ùhð¢•W ~-uò¢•g=xhðmÍ“­<ëÁC 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newEnd = endTime - (clock_t)(timePerIteration * (double)(i)); return (newEnd > beginTime) ? newEnd : beginTime; } void start_timer() { middleEnd = 0; middleBegin = 0; endTime = 0; curtime = 0; startTime = getBeginTime(); } void middle_time() { middleEnd = getEndTime(); middleBegin = getBeginTime(); } double time_elapsed() { return (double)(clock() - startTime) / (double)(CLOCKS_PER_SEC); } void stop_timer() { endTime = getEndTime(); if(middleEnd){ curtime = (endTime - middleEnd) - (middleBegin - startTime); }else{ curtime = endTime - startTime; } } double getSecs() { return (double)(curtime) / (double)(CLOCKS_PER_SEC); } double getNanoPerN(unsigned int N) { return (getSecs() / (double)(N)) * (double)(1.0e9); } void calibrate_timer() { /* Calculating timePerIteration this will take arround a second. */ clock_t real_begin; clock_t real_end; unsigned int i = 0; clock_t lastTime = getBeginTime(); real_begin = lastTime; real_end = real_begin + CLOCKS_PER_SEC; /* Looks a lot like the loop in getEndTime */ while((lastTime = clock()) < real_end) ++i; timePerIteration = (double)(i) / (double)(lastTime - real_begin); } void printStatus() { printf("startTime = %lu, middleEnd = %lu, middleBegin = %lu, endTime = %lu, curtime = %lu, timePerIteration = %f\n", startTime, middleEnd, middleBegin, endTime, curtime, timePerIteration); } libfann-2.1.0~beta~dfsg/benchmarks/datasets/000077500000000000000000000000001140315177100211445ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/benchmarks/datasets/abelone.test000066400000000000000000005427531140315177100234720ustar00rootroot000000000000002089 10 1 0 0 0.353920 0.148646 0.115024 0.040701 0.111131 0.045833 0.022474 0.035392 0.250000 0 0.353920 0 0.192886 0.145107 0.044240 0.231463 0.104229 0.046540 0.072554 0.321429 0.353920 0 0 0.222969 0.176960 0.061936 0.447531 0.199434 0.108476 0.121217 0.321429 0.353920 0 0 0.221200 0.164573 0.049549 0.422934 0.170766 0.072554 0.141568 0.428571 0.353920 0 0 0.199965 0.155725 0.040701 0.325075 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0.260870 1 0.740000 0.587100 0.505519 0.571965 0.509826 0.449215 0.701571 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.875000 0.130435 1 0.824000 0.764000 0.505000 0.659107 0.482672 0.552880 0.277487 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.625000 0.391304 1 0.881000 0.860000 0.517312 0.609285 0.791561 0.702618 0.198952 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.541667 0.478261 1 0.839000 0.752000 0.727971 0.614107 0.812316 0.702618 0.293194 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.833333 0.869565 0 0.774000 0.554000 0.505189 0.568928 0.501735 0.506807 0.670158 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.625000 0.652174 0 0.960000 0.762000 0.720849 0.595715 0.841055 0.863875 0.073298 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.791667 0.826087 0 0.861500 0.754000 0.504670 0.598215 0.519449 0.575917 0.293194 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.708333 0.739130 0 0.926000 0.764000 0.543208 0.590000 0.875269 0.771728 0.120419 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.666667 0.695652 0 0.875000 0.600000 0.692547 0.577679 0.690436 0.564398 0.308900 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.583333 0.434783 1 0.839000 0.792000 0.555754 0.638393 0.852676 0.610471 0.198952 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.833333 0.869565 0 0.910500 0.552000 0.508443 0.712500 0.536152 0.645027 0.073298 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.916667 0.086957 1 0.780000 0.598000 0.505472 0.534822 0.498258 0.518325 0.418848 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.708333 0.304348 1 0.872500 0.842000 0.505660 0.614107 0.560563 0.621990 0.151833 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 1 0 1 0.786500 0.619860 0.505283 0.640000 0.534153 0.472252 0.465969 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.583333 0.434783 1 0.758000 0.560000 0.575896 0.618035 0.744960 0.518325 0.575917 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.583333 0.434783 1 0.802000 0.700760 0.529198 0.600893 0.747447 0.668063 0.387435 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.833333 0.869565 0 0.877500 0.732000 0.504906 0.615357 0.530464 0.645027 0.293194 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.625000 0.391304 1 0.716000 0.562000 0.519387 0.541250 0.788510 0.437697 0.481675 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.791667 0.826087 0 0.932500 0.586000 0.560849 0.601964 0.631099 0.702618 0.089006 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.666667 0.695652 0 0.784000 0.589500 0.701038 0.709464 0.705003 0.621990 0.528796 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.625000 0.391304 1 0.846500 0.676000 0.588207 0.640179 0.447584 0.610471 0.214660 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.791667 0.217391 1 0.889000 0.890000 0.504623 0.549107 0.447851 0.783246 0.104712 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.708333 0.739130 0 0.847500 0.630000 0.559481 0.668750 0.474729 0.587435 0.481675 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.625000 0.652174 0 0.957000 0.750000 0.762028 0.628214 0.497630 0.783246 0.104712 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.708333 0.739130 0 0.672500 0.520000 0.512500 0.606250 0.524637 0.357068 0.670158 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.708333 0.739130 0 0.835500 0.633580 0.668349 0.686607 0.749361 0.621990 0.418848 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.541667 0.565217 0 0.884500 0.714000 0.802122 0.632321 0.495388 0.587435 0.277487 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.708333 0.304348 1 0.847500 0.812000 0.505000 0.576786 0.477504 0.668063 0.293194 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.916667 0.086957 1 0.720000 0.600000 0.505708 0.570179 0.515834 0.449215 0.560210 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.833333 0.173913 1 0.796500 0.586000 0.505424 0.554643 0.448648 0.506807 0.324608 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.791667 0.217391 1 0.856000 0.786000 0.505047 0.628393 0.440355 0.633508 0.183246 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.708333 0.739130 0 0.908000 0.534000 0.776887 0.640535 0.818864 0.679581 0.120419 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.521739 0 0.822500 0.760000 0.508820 0.563750 0.878299 0.714137 0.246073 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.666667 0.695652 0 0.910500 0.542000 0.825000 0.619643 0.822373 0.794765 0.120419 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.666667 0.347826 1 0.550500 0.520000 0.510896 0.759107 0.400632 0.241885 0.937173 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.875000 0.913043 0 0.861500 0.626000 0.505000 0.702143 0.523350 0.633508 0.246073 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.791667 0.217391 1 0.786500 0.696000 0.504906 0.596785 0.422790 0.621990 0.591623 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.541667 0.565217 0 0.898000 0.596000 0.886132 0.556965 0.846711 0.656545 0.198952 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.625000 0.652174 0 0.810000 0.706000 0.563726 0.718036 0.841671 0.598953 0.418848 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.521739 0 0.644000 0.512000 0.780472 0.656607 0.300241 0.264922 0.811519 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.791667 0.217391 1 0.835000 0.755820 0.504906 0.619107 0.468275 0.633508 0.450262 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.708333 0.739130 0 0.816000 0.728000 0.520095 0.562500 0.373573 0.483770 0.560210 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.583333 0.434783 1 0.824500 0.634000 0.691981 0.608214 0.814825 0.621990 0.277487 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.750000 0.260870 1 0.838500 0.760000 0.505189 0.546607 0.578107 0.668063 0.261781 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 1 0 1 0.860000 0.698000 0.505000 0.677500 0.529974 0.575917 0.214660 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.916667 0.086957 1 0.834000 0.742940 0.504906 0.614643 0.497641 0.610471 0.465969 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.958333 1 0 0.891000 0.798000 0.504858 0.603215 0.523892 0.737173 0.151833 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.791667 0.217391 1 0.886000 0.908000 0.504717 0.570357 0.433252 0.794765 0.120419 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0 1 0.751000 0.620000 0.505424 0.663215 0.534600 0.437697 0.591623 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.791667 0.826087 0 0.863000 0.667760 0.530330 0.611607 0.596054 0.621990 0.277487 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.708333 0.304348 1 0.802500 0.608000 0.505424 0.576786 0.496386 0.518325 0.277487 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.833333 0.869565 0 0.858500 0.834000 0.504575 0.621786 0.588974 0.702618 0.340314 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.583333 0.608696 0 0.900000 0.546000 0.926887 0.671072 0.870431 0.691100 0.167539 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.583333 0.434783 1 0.655500 0.568000 0.537264 0.552143 0.682856 0.380105 0.795812 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.833333 0.869565 0 0.928500 0.672000 0.504953 0.577322 0.624687 0.748691 0.104712 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.625000 0.391304 1 0.878500 0.870000 0.533444 0.568214 0.761855 0.702618 0.120419 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.750000 0.260870 1 0.844000 0.696000 0.505189 0.547143 0.412605 0.575917 0.167539 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.750000 0.260870 1 0.784000 0.648140 0.505141 0.660000 0.426192 0.506807 0.513089 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.833333 0.173913 1 0.833000 0.598000 0.505189 0.659285 0.427276 0.552880 0.198952 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.791667 0.826087 0 0.814000 0.578000 0.505802 0.636786 0.592418 0.714137 0.356021 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.583333 0.434783 1 0.752000 0.602000 0.597122 0.645000 0.848933 0.518325 0.371727 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.708333 0.304348 1 0.725500 0.594180 0.505283 0.664822 0.415985 0.541362 0.623037 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.541667 0.478261 1 0.807500 0.616000 0.743632 0.773214 0.874014 0.633508 0.198952 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.833333 0.173913 1 0.843500 0.660000 0.505189 0.572321 0.488413 0.598953 0.261781 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.875000 0.130435 1 0.882000 0.864000 0.504717 0.622321 0.484648 0.748691 0.151833 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.875000 0.130435 1 0.703500 0.604000 0.505754 0.683750 0.337264 0.426178 0.748691 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.583333 0.434783 1 0.777500 0.630520 0.523679 0.649822 0.778908 0.587435 0.481675 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.625000 0.652174 0 0.938000 0.554000 0.875095 0.625179 0.870686 0.714137 0.120419 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.875000 0.913043 0 0.877500 0.812000 0.504670 0.708571 0.682251 0.621990 0.136125 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.875000 0.913043 0 0.878000 0.686000 0.504906 0.594107 0.525307 0.621990 0.120419 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.750000 0.260870 1 0.683000 0.568000 0.505991 0.630715 0.473677 0.437697 0.575917 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.708333 0.304348 1 0.764500 0.582000 0.505754 0.577322 0.465235 0.495288 0.403142 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.666667 0.695652 0 0.884000 0.760000 0.597122 0.691607 0.857151 0.737173 0.340314 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.958333 0.043478 1 0.869000 0.694000 0.505000 0.552857 0.471848 0.645027 0.167539 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.833333 0.869565 0 0.798000 0.610020 0.505189 0.536785 0.592194 0.541362 0.387435 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.625000 0.652174 0 0.851000 0.626000 0.724293 0.682143 0.479588 0.610471 0.497383 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.583333 0.608696 0 0.720000 0.570000 0.568774 0.570179 0.901010 0.495288 0.465969 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.916667 0.086957 1 0.707500 0.560000 0.505754 0.582322 0.339709 0.414660 0.748691 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.666667 0.347826 1 0.872500 0.816000 0.509057 0.592678 0.591866 0.679581 0.167539 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.666667 0.347826 1 0.811000 0.734200 0.506792 0.556250 0.652203 0.598953 0.293194 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.666667 0.347826 1 0.759000 0.612000 0.514812 0.553393 0.551335 0.518325 0.638744 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.708333 0.739130 0 0.713500 0.566820 0.511934 0.668929 0.463086 0.529843 0.732985 0.500000 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.583333 0.434783 1 0.611000 0.516000 0.663538 0.596250 0.283720 0.276440 0.874346 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.958333 1 0 0.649000 0.510000 0.506085 0.550536 0.491676 0.345550 0.827225 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.583333 0.608696 0 0.881000 0.634000 0.949528 0.651964 0.834919 0.702618 0.136125 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.583333 0.608696 0 0.821000 0.756000 0.576368 0.560357 0.400909 0.495288 0.481675 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.583333 0.608696 0 0.870000 0.656000 0.776556 0.647857 0.887421 0.679581 0.277487 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.916667 0.086957 1 0.818000 0.704120 0.505000 0.602143 0.448085 0.564398 0.497383 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.541667 0.478261 1 0.749500 0.590000 0.512264 0.685714 0.790795 0.633508 0.623037 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.541667 0.478261 1 0.714500 0.558000 0.773349 0.623214 0.490868 0.426178 0.497383 0.500000 1 0.500000 0.500000 0.500000 0.500000 0.500000 0.750000 0.782609 0 0.867500 0.740000 0.505943 0.606250 0.510783 0.575917 0.277487 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.833333 0.869565 0 0.970500 0.658000 0.516746 0.632857 0.560181 0.829320 0.057592 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.500000 0.625000 0.391304 1 0.788000 0.622000 0.564529 0.734643 0.791444 0.598953 0.261781 0.500000 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.708333 0.739130 0 0.910500 0.856000 0.549434 0.587143 0.791922 0.852356 0.073298 0.500000 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0.878137 0.500000 0.866223 0.500000 0.500000 0.866211 0.852477 0.891439 0.500000 0.500000 0.500000 0.539607 0.322353 0.382418 0.300974 0.271514 0.333057 0.288302 0.883693 0.424462 0.500000 0 0 0 0.500000 0 0 0.500000 0.871898 0.500000 0.870200 0.889742 0.846178 0.895977 0.500000 0.500000 0.914945 0.915790 0.872462 0.500000 0.867772 0.868722 0.867003 0.500000 0.599205 0.556189 0.875856 0.711538 0.622790 0.235601 0.748677 0.818126 0.848679 1 1 1 0 0.500000 0 0.500000 0.500000 0.876861 0.884242 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.888424 0.500000 0.867767 0.894132 0.886882 0.884543 0.500000 0.500000 0.402329 0.235277 0.899258 0.424235 0.721130 0.235259 0.601505 0.895393 0.267768 0.500000 0.500000 0 1 0.500000 1 1 0 0.500000 0.876287 0.873334 0.862704 0.878193 0.500000 0.924824 0.866911 0.874225 0.500000 0.910378 0.882798 0.500000 0.874636 0.900794 0.500000 0.651045 0.593772 0.531437 0.869800 0.527008 0.817443 0.559488 0.311169 0.416858 1 0 1 0.500000 1 1 0 0 0.857275 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0.524181 0.502835 0 0.500000 0.500000 0 0.500000 0.500000 1 0 0.924772 0.883249 0.893184 0.896368 0.884705 0.846503 0.500000 0.500000 0.879870 0.860312 0.866107 0.861438 0.500000 0.500000 0.855017 0.855404 0.670324 0.489648 0.122394 0.477048 0.337361 0.621885 0.225516 0.844458 0.206275 1 1 0 0 0 0 1 0.500000 0.900677 0.870682 0.926969 0.921002 0.880844 0.500000 0.500000 0.879750 0.500000 0.906195 0.500000 0.500000 0.875214 0.500000 0.500000 0.863237 0.612434 0.797038 0.818537 0.547674 0.154246 0.615525 0.526309 0.274264 0.148970 1 1 1 1 1 0 1 0.500000 0.849556 0.866453 0.500000 0.500000 0.500000 0.871977 0.859388 0.903371 0.500000 0.500000 0.500000 0.500000 0.500000 0.864974 0.500000 0.892539 0.392304 0.239442 0.500452 0.895357 0.460943 0.868779 0.164249 0.740312 0.224233 1 0 0.500000 1 1 0 1 0.500000 0.877870 0.885635 0.500000 0.500000 0.876821 0.851060 0.500000 0.855679 0.500000 0.500000 0.500000 0.896605 0.500000 0.500000 0.500000 0.500000 0.312545 0.486511 0.185050 0.730359 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0.162697 0.251342 0.500977 0.115249 0.392525 0.561963 0.312360 0.434338 0 1 1 1 1 0 1 1 0.888048 0.876361 0.876437 0.500000 0.903932 0.869928 0.500000 0.500000 0.500000 0.871646 0.500000 0.500000 0.894631 0.838692 0.500000 0.500000 0.418293 0.521130 0.431282 0.754001 0.696768 0.381771 0.630410 0.303453 0.328867 0 0 1 0 1 0 1 0 0.883508 0.878181 0.500000 0.500000 0.868748 0.500000 0.891834 0.500000 0.500000 0.863080 0.888984 0.904225 0.500000 0.500000 0.500000 0.920032 0.350121 0.433307 0.393004 0.610451 0.848161 0.840061 0.705433 0.411939 0.460121 1 0 0.500000 0 0.500000 1 0.500000 0.500000 0.867701 0.922498 0.500000 0.875999 0.856712 0.900138 0.500000 0.500000 0.500000 0.867274 0.882277 0.875048 0.887672 0.885981 0.875720 0.500000 0.537344 0.550777 0.306886 0.426677 0.156663 0.427563 0.651051 0.813099 0.357657 0 0 0.500000 0.500000 0.500000 0.500000 1 0.500000 0.882221 0.873176 0.873307 0.873446 0.500000 0.893753 0.500000 0.877758 0.866937 0.906652 0.868215 0.897978 0.891335 0.500000 0.500000 0.876587 0.640620 0.779077 0.542840 0.504387 0.627952 0.582464 0.879893 0.569879 0.552748 1 0 0 0 1 0.500000 0 0.500000 0.500000 0.892876 0.872653 0.867414 0.500000 0.895185 0.500000 0.857835 0.891616 0.892346 0.500000 0.500000 0.830424 0.500000 0.869446 0.885068 0.378531 0.646678 0.113048 0.202969 0.291690 0.230534 0.391112 0.812933 0.767463 0.500000 0 0.500000 0 0.500000 0.500000 0.500000 1 0.851992 0.862320 0.870593 0.899452 0.871353 0.886698 0.868039 0.905909 0.500000 0.500000 0.500000 0.500000 0.500000 0.912413 0.883931 0.881116 0.752891 0.583584 0.488412 0.139251 0.597575 0.447323 0.754359 0.309085 0.594307 1 0.500000 0.500000 0 0.500000 0.500000 0.500000 1 0.500000 0.874628 0.898998 0.853894 0.500000 0.500000 0.500000 0.895714 0.500000 0.500000 0.888125 0.914071 0.864909 0.500000 0.500000 0.500000 0.338018 0.184589 0.184964 0.890056 0.173401 0.142036 0.195156 0.189388 0.839412 0 0 0 0 1 0 1 1 0.500000 0.851813 0.500000 0.865260 0.871795 0.500000 0.894341 0.887512 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0.881492 0.921689 0.500000 0.868789 0.563431 0.748907 0.672826 0.387168 0.125802 0.153392 0.655352 0.299731 0.177982 0 0 1 0 1 0.500000 1 0 0.833251 0.500000 0.887331 0.869180 0.500000 0.902976 0.892445 0.880004 0.500000 0.881785 0.899460 0.500000 0.500000 0.500000 0.500000 0.904256 0.395619 0.520939 0.428087 0.652379 0.737047 0.555017 0.376841 0.162170 0.195436 1 0 0 0.500000 1 1 1 1 0.500000 0.887297 0.916072 0.875805 0.876758 0.920162 0.879393 0.912253 0.500000 0.500000 0.857282 0.500000 0.500000 0.878443 0.500000 0.891959 0.604644 0.349030 0.744288 0.640628 0.826786 0.786089 0.796002 0.204083 0.615276 0 0.500000 0 0.500000 0.500000 0.500000 1 0 0.853816 0.500000 0.911164 0.889626 0.891368 0.897953 0.871646 0.865497 0.500000 0.886751 0.886112 0.500000 0.923990 0.500000 0.500000 0.885696 0.590208 0.498653 0.253865 0.512823 0.816825 0.571610 0.857269 0.317551 0.485132 0 0 0 0 1 0 0 1 0.869541 0.865626 0.875698 0.903652 0.500000 0.500000 0.895236 0.500000 0.500000 0.875449 0.891942 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0.904933 0.500000 0.899643 0.889995 0.883474 0.500000 0.566237 0.183658 0.537798 0.568675 0.320000 0.837083 0.250605 0.115672 0.302750 0.500000 1 1 0 0 0.500000 0 0.500000 0.500000 0.859441 0.893018 0.882115 0.500000 0.907665 0.883294 0.500000 0.913754 0.500000 0.868944 0.870887 0.880912 0.500000 0.500000 0.922982 0.620437 0.734437 0.683246 0.321495 0.370766 0.471441 0.505665 0.506370 0.363345 0.500000 0 0.500000 1 0.500000 0.500000 0 0.500000 0.899640 0.887174 0.858923 0.894111 0.864748 0.899308 0.918333 0.907649 0.887848 0.884695 0.500000 0.500000 0.855395 0.891138 0.500000 0.500000 0.810486 0.853333 0.562485 0.292313 0.302279 0.603949 0.378873 0.713177 0.172712 0.500000 0 0 0.500000 1 0.500000 1 1 0.870738 0.864894 0.877218 0.893866 0.500000 0.870920 0.500000 0.854780 0.500000 0.875478 0.866701 0.856097 0.500000 0.869303 0.890512 0.500000 0.601932 0.331112 0.401145 0.639541 0.163133 0.372297 0.589222 0.854705 0.432769 0 0 0.500000 1 0.500000 0.500000 0 0.500000 0.874536 0.500000 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0.500000 0 0 1 0 0.854185 0.500000 0.922464 0.873757 0.500000 0.874819 0.876185 0.890807 0.895926 0.500000 0.875903 0.500000 0.898784 0.874908 0.500000 0.900922 0.654296 0.783211 0.434330 0.792903 0.113309 0.584032 0.830427 0.391245 0.662720 0 1 1 1 0.500000 1 0.500000 1 0.500000 0.862737 0.870562 0.868042 0.903493 0.888872 0.880664 0.500000 0.500000 0.500000 0.867039 0.879331 0.885119 0.500000 0.886139 0.500000 0.561701 0.206475 0.832158 0.681034 0.730858 0.526410 0.373193 0.456329 0.121381 0 0.500000 1 0 0 1 0.500000 1 0.899371 0.500000 0.500000 0.865809 0.849033 0.907601 0.893892 0.887399 0.872841 0.870999 0.868562 0.889620 0.875963 0.500000 0.500000 0.500000 0.572750 0.216873 0.388215 0.136391 0.527990 0.531627 0.847500 0.131131 0.117266 1 0 1 0.500000 0 1 0 1 0.859266 0.894228 0.889270 0.927370 0.500000 0.879054 0.871353 0.863471 0.868957 0.500000 0.888753 0.500000 0.500000 0.903043 0.894450 0.500000 0.696396 0.833939 0.220790 0.623709 0.473014 0.552106 0.244408 0.763177 0.508531 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0.895197 0.885131 0.777742 0.869478 0.714291 0.483163 0.718658 0.279931 0.195464 0.562768 0.242900 0 1 1 0 1 0.500000 1 0 0.869804 0.888604 0.500000 0.860930 0.500000 0.859538 0.500000 0.881090 0.500000 0.850426 0.500000 0.500000 0.500000 0.874994 0.865248 0.500000 0.342087 0.218194 0.192527 0.794507 0.672791 0.592389 0.474824 0.566842 0.713006 0.500000 1 0 1 0 0 0.500000 1 0.860779 0.859872 0.879329 0.916700 0.500000 0.854499 0.500000 0.899809 0.500000 0.870893 0.888915 0.912359 0.500000 0.500000 0.869260 0.861988 0.583855 0.641823 0.121721 0.369083 0.185396 0.147448 0.764346 0.157307 0.862489 1 1 1 0 0.500000 1 1 1 0.926099 0.500000 0.878308 0.859629 0.500000 0.857894 0.855214 0.867414 0.500000 0.896422 0.922741 0.886474 0.884761 0.500000 0.905257 0.500000 0.721968 0.221594 0.524872 0.122426 0.217192 0.149347 0.678802 0.823021 0.708115 1 0 0.500000 0 0 0 0 0 0.914970 0.500000 0.500000 0.875026 0.914454 0.500000 0.887366 0.847142 0.873379 0.500000 0.500000 0.500000 0.500000 0.500000 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0.500000 0.888539 0.500000 0.874818 0.864245 0.500000 0.858572 0.903636 0.876282 0.500000 0.500000 0.898437 0.888246 0.580430 0.846603 0.254601 0.879094 0.729423 0.415652 0.456958 0.295400 0.190763 1 1 0.500000 0 1 1 0 0 0.900661 0.872334 0.883626 0.847794 0.867083 0.500000 0.500000 0.871469 0.500000 0.500000 0.885350 0.894923 0.873661 0.500000 0.848924 0.500000 0.495127 0.345366 0.763891 0.515734 0.144599 0.879313 0.407886 0.333611 0.807717 0 1 1 0 1 0.500000 0 0.500000 0.500000 0.892230 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.901199 0.856127 0.880656 0.500000 0.500000 0.867110 0.863741 0.899565 0.393217 0.345544 0.557433 0.183087 0.548949 0.786333 0.892699 0.596210 0.709969 1 0.500000 0 0.500000 0 1 1 0.500000 0.500000 0.500000 0.876512 0.500000 0.867580 0.834338 0.893353 0.886049 0.500000 0.500000 0.500000 0.500000 0.888820 0.890980 0.919073 0.500000 0.346867 0.177883 0.283625 0.686165 0.572369 0.810031 0.787049 0.137920 0.618279 0.500000 1 1 0 1 1 1 0.500000 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0.835151 0.500000 0 1 1 1 0 1 0 0.856592 0.500000 0.899124 0.500000 0.890131 0.500000 0.905254 0.882114 0.500000 0.500000 0.891203 0.500000 0.890912 0.500000 0.500000 0.500000 0.367925 0.377216 0.860920 0.880556 0.511191 0.591668 0.359629 0.491717 0.492185 0 0.500000 0 1 1 0.500000 0.500000 1 0.888530 0.853473 0.872346 0.860771 0.860778 0.500000 0.500000 0.500000 0.881103 0.869852 0.500000 0.500000 0.500000 0.869564 0.863628 0.905830 0.508684 0.379503 0.113894 0.823068 0.707551 0.805002 0.264090 0.353201 0.447976 0.500000 1 1 1 0 1 1 0.500000 0.500000 0.862946 0.873345 0.859556 0.899860 0.868847 0.867415 0.500000 0.500000 0.857953 0.865192 0.500000 0.500000 0.500000 0.862797 0.886012 0.589478 0.641283 0.649651 0.479507 0.879762 0.758247 0.857503 0.250377 0.286436 1 0.500000 0 0.500000 0.500000 0 0 0 0.883687 0.828257 0.862459 0.888286 0.866657 0.886672 0.852199 0.876212 0.500000 0.922255 0.500000 0.500000 0.865442 0.500000 0.873170 0.500000 0.618417 0.417303 0.207586 0.290454 0.261485 0.584888 0.816959 0.437094 0.837727 1 0 1 0 0.500000 0 0 1 0.826457 0.882868 0.500000 0.500000 0.882334 0.894113 0.873617 0.858935 0.902222 0.500000 0.500000 0.500000 0.500000 0.856760 0.885231 0.500000 0.471783 0.767724 0.131174 0.833560 0.246864 0.439210 0.404449 0.179345 0.855770 1 0 0 0.500000 0 1 0.500000 0.500000 0.500000 0.864940 0.921384 0.888939 0.869388 0.915493 0.500000 0.896055 0.866819 0.500000 0.500000 0.500000 0.897033 0.500000 0.889619 0.905758 0.606551 0.882587 0.238619 0.271896 0.357191 0.314019 0.311521 0.729549 0.125140 0 1 0.500000 1 0.500000 1 0.500000 0.500000 0.924780 0.880634 0.873061 0.876403 0.873732 0.894440 0.865362 0.500000 0.886263 0.500000 0.500000 0.869322 0.862332 0.902445 0.500000 0.500000 0.651263 0.472255 0.594510 0.137172 0.848541 0.835325 0.314557 0.562855 0.896601 0.500000 0.500000 0 0 0.500000 0.500000 0.500000 0.500000 0.902868 0.890450 0.500000 0.868108 0.500000 0.874748 0.910521 0.890810 0.884309 0.500000 0.879665 0.500000 0.500000 0.500000 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0.500000 0.344286 0.554330 0.820123 0.813147 0.361438 0.488146 0.467590 0.577402 0.599402 1 1 0.500000 0.500000 0.500000 0.500000 0.500000 0 0.500000 0.871626 0.859546 0.500000 0.863672 0.909364 0.867060 0.902457 0.862802 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.910454 0.469722 0.571090 0.105453 0.554266 0.549282 0.781592 0.414350 0.175575 0.696884 1 0.500000 1 0 1 1 0 0.500000 0.885004 0.866992 0.500000 0.907877 0.846031 0.500000 0.866109 0.500000 0.500000 0.500000 0.500000 0.874716 0.881657 0.500000 0.942804 0.500000 0.433044 0.494146 0.509975 0.158583 0.702345 0.516530 0.727760 0.454570 0.158166 1 1 1 0 0.500000 1 0.500000 0 0.868084 0.500000 0.863797 0.500000 0.500000 0.849809 0.500000 0.500000 0.500000 0.857476 0.891016 0.880634 0.500000 0.500000 0.500000 0.848297 0.345934 0.836991 0.302931 0.360222 0.843559 0.302372 0.564634 0.402742 0.290655 0.500000 1 0.500000 1 0 1 1 0.500000 0.500000 0.500000 0.900338 0.500000 0.500000 0.883100 0.900414 0.500000 0.500000 0.896030 0.500000 0.500000 0.500000 0.856943 0.861270 0.897421 0.344379 0.299271 0.444095 0.106982 0.651091 0.692741 0.699180 0.113023 0.569488 0.500000 0.500000 0 0 0.500000 0.500000 0.500000 0 0.892480 0.500000 0.500000 0.864168 0.877111 0.886503 0.869222 0.500000 0.886710 0.900456 0.500000 0.500000 0.896482 0.874162 0.500000 0.877807 0.536265 0.834645 0.870715 0.704427 0.304803 0.617612 0.397215 0.192698 0.474848 0 1 1 1 0 1 0.500000 0.500000 0.500000 0.891288 0.879248 0.500000 0.893065 0.910651 0.500000 0.912008 0.500000 0.892678 0.871296 0.892063 0.500000 0.890003 0.895975 0.868418 0.586285 0.459364 0.725284 0.452409 0.630001 0.620449 0.884786 0.391110 0.779205 0 0 0.500000 0.500000 0.500000 0 0.500000 0 0.888866 0.500000 0.886823 0.500000 0.876463 0.898815 0.838623 0.500000 0.500000 0.911442 0.500000 0.500000 0.891365 0.500000 0.853612 0.500000 0.374487 0.566301 0.413493 0.180866 0.820355 0.699077 0.788316 0.629269 0.330332 1 1 1 0 1 1 1 0.500000 0.899308 0.500000 0.500000 0.893520 0.500000 0.847630 0.883527 0.882584 0.500000 0.896133 0.500000 0.500000 0.500000 0.878008 0.841232 0.870425 0.473210 0.541482 0.890226 0.629693 0.257506 0.100092 0.840774 0.691737 0.617538 1 0 0 0 1 0 1 1 0.880784 0.883267 0.886888 0.500000 0.912757 0.500000 0.917285 0.901237 0.500000 0.886377 0.843260 0.500000 0.500000 0.868334 0.500000 0.500000 0.488705 0.382167 0.885184 0.695171 0.743298 0.806719 0.326306 0.418150 0.849336 0.500000 1 0.500000 0.500000 0 0.500000 1 0 0.886433 0.500000 0.846432 0.882016 0.856721 0.896942 0.866444 0.888497 0.878779 0.500000 0.884396 0.894481 0.888124 0.878212 0.898056 0.907346 0.843325 0.613659 0.368840 0.889857 0.822913 0.499241 0.543272 0.567151 0.306435 1 0 0.500000 0.500000 0.500000 0.500000 0.500000 0 0.895289 0.892280 0.888172 0.892619 0.897282 0.885504 0.869379 0.500000 0.850240 0.859646 0.868425 0.500000 0.851646 0.500000 0.500000 0.911151 0.677239 0.624944 0.437117 0.426447 0.297818 0.826662 0.308826 0.246733 0.648434 1 0.500000 1 1 0.500000 0 0.500000 0 0.869516 0.500000 0.500000 0.908850 0.884507 0.912083 0.916394 0.906695 0.500000 0.872828 0.500000 0.500000 0.500000 0.500000 0.890177 0.863816 0.565406 0.198781 0.310197 0.875211 0.277483 0.652203 0.179118 0.237215 0.678949 1 1 0 1 1 0.500000 1 0 0.876165 0.500000 0.880256 0.889553 0.898711 0.887320 0.899556 0.870116 0.500000 0.500000 0.500000 0.863985 0.500000 0.875822 0.500000 0.869509 0.665894 0.807156 0.465029 0.749790 0.315116 0.351956 0.318900 0.744937 0.364780 1 0 0.500000 1 0.500000 0 0.500000 0 0.899151 0.500000 0.882015 0.876020 0.908395 0.500000 0.864958 0.500000 0.500000 0.855487 0.895403 0.500000 0.500000 0.500000 0.878082 0.874652 0.515008 0.309926 0.332862 0.208007 0.177012 0.845591 0.847187 0.469212 0.653088 1 1 0.500000 1 0 0.500000 0.500000 0 0.864849 0.876098 0.881784 0.887402 0.500000 0.881910 0.875994 0.872065 0.905902 0.500000 0.500000 0.500000 0.500000 0.872416 0.500000 0.500000 0.634901 0.364388 0.472421 0.185882 0.320382 0.856208 0.892138 0.756094 0.671752 1 0.500000 1 0.500000 0 0 1 1 0.500000 0.500000 0.872156 0.854776 0.875328 0.859744 0.922014 0.852022 0.500000 0.500000 0.901264 0.500000 0.864059 0.866989 0.500000 0.860882 0.559180 0.696775 0.500413 0.238048 0.275653 0.297947 0.590810 0.338004 0.230427 0 1 1 1 0.500000 0.500000 1 0 0.870655 0.500000 0.899269 0.500000 0.860104 0.500000 0.500000 0.912805 0.864819 0.500000 0.858405 0.500000 0.873032 0.500000 0.500000 0.500000 0.297569 0.104644 0.553739 0.496112 0.751335 0.687335 0.247104 0.476565 0.224452 0.500000 0 0 1 0 0 0 1 0.883884 0.895552 0.500000 0.851666 0.868511 0.500000 0.856538 0.905096 0.869907 0.850461 0.500000 0.500000 0.500000 0.500000 0.500000 0.885707 0.436362 0.884444 0.252083 0.161001 0.545319 0.583841 0.823313 0.815499 0.695854 0 1 1 0 0 0.500000 1 1 0.500000 0.855851 0.873781 0.500000 0.918816 0.875660 0.878999 0.889178 0.879334 0.886403 0.862818 0.916075 0.500000 0.890246 0.891234 0.500000 0.641346 0.719390 0.592563 0.200678 0.190173 0.438045 0.828302 0.668021 0.427489 0 0.500000 0 0 0 0 1 0 0.885563 0.900683 0.871805 0.871459 0.910325 0.500000 0.853325 0.873631 0.878102 0.884539 0.875423 0.500000 0.500000 0.865145 0.878258 0.500000 0.580787 0.301633 0.142852 0.318920 0.289185 0.533314 0.413850 0.572284 0.174380 0.500000 0 0.500000 0.500000 0 0.500000 0.500000 0 0.849863 0.500000 0.876896 0.922381 0.870102 0.500000 0.900881 0.500000 0.500000 0.500000 0.921596 0.868654 0.861277 0.500000 0.896747 0.883631 0.509542 0.829003 0.456418 0.810732 0.577963 0.423715 0.785938 0.658236 0.771360 0.500000 1 1 1 0.500000 1 1 0 0.500000 0.860734 0.871212 0.500000 0.904320 0.875340 0.870133 0.500000 0.500000 0.915285 0.500000 0.500000 0.500000 0.500000 0.500000 0.903034 0.386934 0.766947 0.679363 0.652562 0.612472 0.423325 0.626313 0.250330 0.304194 1 0 0.500000 0 0.500000 0.500000 1 1 0.873682 0.859703 0.500000 0.853828 0.500000 0.500000 0.908360 0.885208 0.500000 0.500000 0.500000 0.500000 0.901096 0.500000 0.879406 0.895431 0.377062 0.745553 0.318096 0.843417 0.317245 0.139701 0.151656 0.277971 0.860668 1 0 0.500000 0 0 1 1 1 0.500000 0.872283 0.879524 0.851067 0.890076 0.500000 0.890287 0.879854 0.885997 0.500000 0.863446 0.880795 0.863051 0.500000 0.500000 0.500000 0.576877 0.164842 0.304276 0.560146 0.179020 0.189483 0.839185 0.374597 0.850484 1 0.500000 0 0.500000 0.500000 1 0.500000 0 0.500000 0.862775 0.868504 0.500000 0.884599 0.882700 0.885476 0.500000 0.500000 0.904944 0.500000 0.880677 0.883047 0.890427 0.869286 0.500000 0.564020 0.843586 0.749733 0.169834 0.207443 0.299116 0.248164 0.685418 0.819530 0.500000 0.500000 1 0 0 0.500000 0.500000 0.500000 0.863178 0.905525 0.893481 0.889349 0.500000 0.884298 0.899487 0.881570 0.866220 0.500000 0.873301 0.890505 0.907948 0.500000 0.879013 0.500000 0.751301 0.818948 0.254238 0.379685 0.764381 0.759368 0.496225 0.250382 0.363323 0 0 0 1 0 1 1 1 0.885345 0.886943 0.873201 0.500000 0.500000 0.500000 0.500000 0.890061 0.905023 0.500000 0.500000 0.500000 0.903697 0.931290 0.500000 0.500000 0.303852 0.899824 0.350266 0.728570 0.472002 0.332958 0.233075 0.683342 0.334692 0.500000 0 0.500000 0 0 1 0.500000 0 0.500000 0.899385 0.500000 0.921344 0.500000 0.848983 0.897528 0.901037 0.865483 0.500000 0.883978 0.500000 0.500000 0.875770 0.500000 0.866860 0.487726 0.728280 0.405484 0.768298 0.185438 0.861351 0.125689 0.661345 0.226763 0 0 1 1 0 0.500000 0.500000 1 0.881567 0.500000 0.903609 0.872529 0.869766 0.867117 0.882026 0.500000 0.874961 0.500000 0.888176 0.500000 0.500000 0.896502 0.878427 0.500000 0.507325 0.408802 0.528067 0.226534 0.353314 0.756351 0.199653 0.571178 0.782984 0.500000 1 1 0 0.500000 0.500000 0.500000 1 0.889039 0.890528 0.884878 0.500000 0.875717 0.897119 0.870012 0.897240 0.886807 0.500000 0.859785 0.890767 0.851355 0.890946 0.500000 0.875177 0.725540 0.848668 0.569533 0.332772 0.302827 0.219136 0.821058 0.519975 0.221686 0.500000 0 0 0 1 0.500000 1 0.500000 0.864986 0.853747 0.856448 0.500000 0.500000 0.914793 0.879187 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0.889018 0.890188 0.500000 0.860193 0.500000 0.882262 0.500000 0.500000 0.500000 0.500000 0.500000 0.892384 0.500000 0.432407 0.615808 0.483301 0.646502 0.353485 0.420076 0.423407 0.398927 0.168536 1 0 0 1 1 0 0 1 0.875451 0.500000 0.849515 0.838048 0.846426 0.864148 0.875542 0.500000 0.500000 0.882283 0.500000 0.836756 0.883501 0.500000 0.888718 0.862489 0.529781 0.739551 0.234756 0.151299 0.682772 0.347583 0.432241 0.877712 0.701454 1 0.500000 0 1 0.500000 0.500000 1 0 0.929461 0.867240 0.500000 0.877806 0.500000 0.500000 0.870777 0.878445 0.891920 0.877257 0.912673 0.500000 0.905114 0.500000 0.500000 0.500000 0.460144 0.599596 0.806922 0.538258 0.306118 0.323179 0.669365 0.616269 0.238626 0.500000 0 1 0 1 0 1 1 0.890683 0.899429 0.884897 0.895139 0.888842 0.500000 0.869018 0.500000 0.888936 0.500000 0.500000 0.500000 0.911583 0.907965 0.500000 0.500000 0.529649 0.662146 0.884286 0.792101 0.234179 0.839802 0.345444 0.478158 0.593421 0 0 0.500000 0 0 1 1 0 0.500000 0.896403 0.884252 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0.500000 0.633842 0.267086 0.310334 0.775463 0.601411 0.114692 0.622852 0.469070 0.660986 0.500000 0.500000 0.500000 1 0 1 1 0.500000 0.863997 0.892815 0.881103 0.913406 0.883610 0.880467 0.867536 0.841403 0.500000 0.500000 0.882733 0.500000 0.500000 0.885234 0.845627 0.886403 0.754475 0.296386 0.756613 0.599550 0.842449 0.832343 0.582730 0.545213 0.391828 1 0 0.500000 1 1 1 0.500000 0 0.885251 0.500000 0.884776 0.500000 0.872486 0.500000 0.868835 0.885471 0.500000 0.864625 0.500000 0.500000 0.898443 0.500000 0.860851 0.905374 0.452251 0.480053 0.449030 0.641922 0.176754 0.507297 0.736154 0.740466 0.412721 1 0.500000 0 0.500000 1 1 1 1 0.870638 0.858384 0.899956 0.500000 0.857709 0.862619 0.870896 0.500000 0.877698 0.891052 0.500000 0.500000 0.913866 0.500000 0.500000 0.883631 0.544314 0.539848 0.628129 0.359352 0.634833 0.246103 0.452288 0.198814 0.860447 0 1 1 1 0 0 0.500000 0 0.873825 0.904924 0.857675 0.500000 0.899680 0.874847 0.893371 0.500000 0.864511 0.875221 0.912139 0.869632 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0.500000 0.500000 0.878536 0.888212 0.849255 0.500000 0.500000 0.859250 0.835649 0.611865 0.787671 0.189235 0.664875 0.348532 0.368933 0.855412 0.315902 0.765472 1 0.500000 1 0 0 0 1 0.500000 0.500000 0.876519 0.872503 0.885301 0.909453 0.500000 0.854702 0.880673 0.500000 0.500000 0.500000 0.500000 0.874292 0.891299 0.500000 0.500000 0.504346 0.351798 0.867420 0.520768 0.748884 0.299496 0.224213 0.542965 0.208259 0.500000 0.500000 1 0.500000 1 1 0 0.500000 0.891723 0.874171 0.858926 0.889268 0.855455 0.912842 0.500000 0.915788 0.500000 0.500000 0.500000 0.500000 0.500000 0.879977 0.906258 0.892699 0.697345 0.447002 0.167256 0.871678 0.898575 0.149582 0.624738 0.176428 0.626365 0 1 0.500000 0 1 1 1 0 0.500000 0.500000 0.908086 0.880318 0.877549 0.890840 0.910119 0.864373 0.897276 0.892141 0.860877 0.872940 0.500000 0.880776 0.882520 0.500000 0.676819 0.292860 0.702771 0.470784 0.661117 0.400663 0.550600 0.534813 0.793784 0.500000 0 1 0.500000 1 0.500000 0.500000 0 0.881332 0.500000 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0.878730 0.885642 0.500000 0.905514 0.889579 0.935996 0.500000 0.500000 0.888387 0.871258 0.892182 0.879755 0.893643 0.500000 0.500000 0.652481 0.648897 0.603582 0.595653 0.136270 0.890154 0.217120 0.529222 0.442861 0 1 0 1 1 0 1 1 0.874852 0.865864 0.875516 0.840720 0.883195 0.885728 0.891303 0.883275 0.862334 0.880162 0.869698 0.500000 0.500000 0.500000 0.500000 0.931956 0.686736 0.382966 0.702837 0.783768 0.197354 0.331291 0.202401 0.350538 0.492088 0 0.500000 0 0 1 1 0.500000 0.500000 0.879100 0.889446 0.500000 0.500000 0.924142 0.899671 0.887256 0.500000 0.865951 0.500000 0.860801 0.891139 0.500000 0.900359 0.500000 0.901079 0.591110 0.806875 0.554062 0.324976 0.474379 0.293066 0.165294 0.703323 0.154310 1 0.500000 1 0.500000 0.500000 0 1 0.500000 0.901962 0.886323 0.881154 0.874488 0.500000 0.873116 0.856169 0.500000 0.500000 0.870150 0.898297 0.500000 0.879865 0.862154 0.862315 0.500000 0.641015 0.466683 0.537583 0.156818 0.641232 0.589858 0.348562 0.888034 0.588594 1 0 1 0 0 0 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0.856514 0.433555 0.559255 0.405210 0.158473 0.500000 0 1 1 0 1 1 1 0.500000 0.500000 0.877275 0.907808 0.866587 0.879330 0.886056 0.862450 0.878453 0.500000 0.862504 0.500000 0.893964 0.500000 0.500000 0.879537 0.593048 0.737875 0.872474 0.679049 0.181070 0.251272 0.129413 0.844976 0.107182 1 1 0 0 0 0 0 0.500000 0.500000 0.500000 0.891766 0.500000 0.831678 0.863339 0.877580 0.877368 0.500000 0.852803 0.877115 0.500000 0.886011 0.500000 0.500000 0.500000 0.394798 0.572870 0.425669 0.823568 0.300518 0.602855 0.389190 0.520075 0.302308 0 1 0.500000 0 0.500000 0 1 0.500000 0.875742 0.900423 0.858088 0.834108 0.500000 0.867239 0.899241 0.877901 0.872387 0.880816 0.500000 0.874615 0.858730 0.892814 0.864560 0.907973 0.844504 0.148165 0.803424 0.148520 0.368188 0.337454 0.590509 0.881318 0.507975 0 0.500000 1 0.500000 0.500000 1 0.500000 0.500000 0.844010 0.914338 0.902606 0.883235 0.500000 0.871328 0.867115 0.500000 0.500000 0.500000 0.500000 0.899907 0.861738 0.880966 0.878004 0.849984 0.571430 0.564024 0.556109 0.325526 0.107723 0.302920 0.176440 0.820603 0.875954 1 0 0 0.500000 0 0 1 0 0.923164 0.889233 0.887154 0.909557 0.870154 0.500000 0.500000 0.889112 0.897088 0.500000 0.500000 0.888536 0.921995 0.871010 0.500000 0.897546 0.627871 0.883265 0.437914 0.217234 0.843496 0.838768 0.579225 0.839456 0.137727 0.500000 0 1 1 0.500000 1 0 0.500000 0.858621 0.500000 0.891168 0.500000 0.911886 0.890193 0.500000 0.500000 0.888930 0.500000 0.901790 0.500000 0.500000 0.855765 0.500000 0.500000 0.327179 0.600886 0.896446 0.366821 0.555246 0.212864 0.198881 0.392540 0.827690 1 0 0.500000 0 0.500000 0.500000 0.500000 0 0.500000 0.500000 0.500000 0.861758 0.500000 0.500000 0.884323 0.895300 0.836729 0.881436 0.500000 0.500000 0.891582 0.890335 0.500000 0.893633 0.396771 0.294423 0.570189 0.160840 0.242567 0.229314 0.685830 0.545861 0.884300 0 1 0.500000 0.500000 0.500000 1 0 1 0.500000 0.873908 0.900967 0.901427 0.884826 0.890495 0.884502 0.871400 0.881067 0.885100 0.500000 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0.868353 0.891617 0.500000 0.838694 0.867158 0.883854 0.500000 0.500000 0.867719 0.500000 0.500000 0.510524 0.861620 0.647690 0.332571 0.128116 0.640579 0.819580 0.483850 0.267555 0 0 0 0.500000 0 0 1 1 0.848457 0.876784 0.846909 0.838778 0.905694 0.898375 0.500000 0.877464 0.876344 0.890583 0.903723 0.848851 0.500000 0.892533 0.500000 0.868453 0.629250 0.841715 0.507333 0.537351 0.252430 0.395635 0.829143 0.405349 0.899703 1 1 0.500000 0 0 0.500000 1 0 0.874183 0.500000 0.870743 0.500000 0.500000 0.500000 0.894909 0.892868 0.500000 0.500000 0.500000 0.500000 0.857662 0.872342 0.500000 0.500000 0.260776 0.677247 0.293572 0.671418 0.727133 0.323401 0.405210 0.169292 0.687607 1 0.500000 0.500000 0.500000 0 1 0.500000 0 0.860339 0.874767 0.887636 0.500000 0.892329 0.878695 0.863285 0.500000 0.864851 0.500000 0.863559 0.500000 0.879721 0.500000 0.881739 0.500000 0.558183 0.710037 0.185824 0.136883 0.786369 0.700445 0.578996 0.183416 0.479969 1 1 1 1 0 0.500000 1 0.500000 0.907517 0.930292 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0.796222 0.200251 0.223216 0.592550 0.783649 0.594693 0.800219 0.676683 0.419421 0.500000 0.500000 1 1 1 1 1 0 0.889374 0.880512 0.844419 0.868323 0.887712 0.886829 0.500000 0.884145 0.863837 0.500000 0.500000 0.500000 0.500000 0.880741 0.878230 0.500000 0.630874 0.741095 0.590666 0.321697 0.558946 0.800223 0.752781 0.187642 0.303338 1 0 1 0.500000 1 1 0.500000 1 0.890880 0.500000 0.883020 0.870975 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.500000 0.881717 0.500000 0.500000 0.500000 0.878051 0.179268 0.559391 0.292469 0.529673 0.807422 0.143829 0.732425 0.475091 0.246412 1 0.500000 0 0.500000 0.500000 1 0.500000 1 0.500000 0.500000 0.500000 0.903965 0.898800 0.888494 0.893013 0.875573 0.881973 0.500000 0.867319 0.895506 0.887224 0.500000 0.500000 0.500000 0.466471 0.772783 0.364356 0.734994 0.443816 0.209633 0.302029 0.405265 0.291520 0.500000 1 0.500000 0.500000 0 0.500000 0 0.500000 0.921523 0.859246 0.889631 0.886545 0.876103 0.869862 0.500000 0.899383 0.500000 0.500000 0.905257 0.913106 0.500000 0.500000 0.860404 0.500000 0.601243 0.475515 0.178939 0.423418 0.591800 0.185787 0.321098 0.887967 0.269210 1 1 0.500000 1 1 0.500000 1 0 0.881727 0.875466 0.500000 0.919647 0.500000 0.877347 0.500000 0.883158 0.839744 0.500000 0.500000 0.866337 0.862007 0.500000 0.500000 0.855277 0.436078 0.206457 0.528727 0.108372 0.810274 0.877335 0.774292 0.121091 0.182792 0.500000 1 1 0.500000 0.500000 0.500000 0 1 0.884861 0.500000 0.500000 0.892980 0.500000 0.875729 0.886051 0.500000 0.500000 0.500000 0.500000 0.863172 0.500000 0.907503 0.500000 0.500000 0.340854 0.776730 0.300531 0.817461 0.860571 0.622494 0.855845 0.390068 0.868204 0 1 0 1 1 1 0 0.500000 0.500000 0.859519 0.911277 0.871122 0.867263 0.500000 0.895843 0.867823 0.500000 0.832575 0.500000 0.873412 0.500000 0.883311 0.500000 0.500000 0.451689 0.744423 0.515202 0.801539 0.458636 0.500383 0.533185 0.636015 0.109823 0.500000 0.500000 0.500000 0.500000 0.500000 1 1 1 0.500000 0.875683 0.883073 0.862393 0.863602 0.846049 0.500000 0.500000 0.500000 0.500000 0.909082 0.500000 0.873665 0.500000 0.500000 0.500000 0.341738 0.482165 0.751820 0.836427 0.422249 0.135342 0.486296 0.573243 0.293433 1 0 0 1 1 1 1 1 0.875072 0.886341 0.500000 0.864852 0.911230 0.843962 0.500000 0.890048 0.500000 0.500000 0.873997 0.500000 0.897972 0.900896 0.500000 0.895173 0.531963 0.137863 0.458384 0.262855 0.397482 0.676913 0.281634 0.816434 0.401705 0.500000 1 1 1 0.500000 0 0.500000 0.500000 0.884946 0.909385 0.892878 0.883423 0.876709 0.881727 0.915079 0.863379 0.500000 0.500000 0.500000 0.500000 0.500000 0.914749 0.875609 0.500000 0.702813 0.674813 0.176625 0.739680 0.809429 0.272494 0.804503 0.674760 0.894111 0 1 0.500000 0 1 1 1 0.500000 0.863842 0.862002 0.500000 0.894149 0.902941 0.500000 0.875887 0.500000 0.500000 0.904011 0.500000 0.883108 0.500000 0.881224 0.500000 0.873538 0.476701 0.312783 0.343161 0.498651 0.222435 0.257066 0.705851 0.232681 0.464043 0 0 0 0 0 1 0.500000 1 0.891168 0.865170 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0.207928 0.450687 0.494480 0.519961 0.781735 0.428684 0 1 0.500000 0.500000 0.500000 0 0 0.500000 0.500000 0.890521 0.500000 0.500000 0.884076 0.859541 0.859625 0.895262 0.500000 0.885359 0.500000 0.500000 0.875775 0.500000 0.920930 0.862114 0.434055 0.835352 0.758450 0.270507 0.217023 0.513635 0.855099 0.657840 0.523116 0 0.500000 0.500000 0.500000 0 0 0.500000 0 0.886545 0.857901 0.894105 0.877081 0.851046 0.884863 0.866606 0.500000 0.500000 0.864403 0.908073 0.500000 0.861937 0.500000 0.500000 0.874825 0.578233 0.822470 0.247799 0.151792 0.171279 0.484271 0.141985 0.149545 0.804915 0.500000 1 1 1 0 1 1 0 0.884782 0.900437 0.500000 0.881337 0.930586 0.500000 0.500000 0.500000 0.500000 0.500000 0.865052 0.500000 0.500000 0.500000 0.897616 0.860154 0.325288 0.413583 0.892858 0.868721 0.802483 0.324603 0.597399 0.693457 0.730976 0 0 0 0.500000 1 1 0.500000 0 0.895052 0.865116 0.858904 0.500000 0.889547 0.861412 0.901863 0.500000 0.881841 0.882891 0.500000 0.841116 0.903875 0.899980 0.500000 0.500000 0.505835 0.230059 0.398754 0.466149 0.363110 0.596075 0.836208 0.751262 0.262277 0 0 1 0 0 0 1 0.500000 0.500000 0.500000 0.840262 0.873512 0.500000 0.856444 0.897146 0.500000 0.866444 0.500000 0.500000 0.862735 0.500000 0.897930 0.500000 0.500000 0.224097 0.105809 0.130557 0.867071 0.259431 0.458128 0.752629 0.833572 0.451094 0 0.500000 1 0 0 0.500000 0 0.500000 0.877304 0.500000 0.862429 0.911138 0.853810 0.500000 0.500000 0.901505 0.867353 0.500000 0.859590 0.874369 0.889165 0.500000 0.500000 0.500000 0.429887 0.847475 0.517661 0.523303 0.156158 0.752256 0.170436 0.715525 0.424475 1 1 1 0 0.500000 0 1 0.500000 0.880754 0.500000 0.500000 0.867551 0.880452 0.895805 0.500000 0.500000 0.500000 0.500000 0.881236 0.500000 0.500000 0.500000 0.914039 0.870242 0.353723 0.531989 0.531488 0.114115 0.528823 0.124085 0.102640 0.466955 0.309047 1 1 0.500000 1 0.500000 1 1 0 0.892019 0.891173 0.886706 0.894615 0.500000 0.500000 0.865752 0.890255 0.500000 0.851931 0.500000 0.865602 0.890830 0.894736 0.500000 0.500000 0.601721 0.146106 0.606796 0.220047 0.124834 0.689364 0.735970 0.843803 0.403471 0.500000 1 0 1 1 0.500000 0 0 0.870292 0.841957 0.872998 0.500000 0.892709 0.889723 0.873921 0.843167 0.867615 0.500000 0.851503 0.906446 0.885776 0.901872 0.500000 0.500000 0.729420 0.154361 0.142527 0.455275 0.812210 0.617532 0.654473 0.727922 0.789899 1 0.500000 0.500000 0.500000 0.500000 0 0 0 0.500000 0.882151 0.500000 0.833717 0.500000 0.879465 0.500000 0.884036 0.500000 0.912743 0.865726 0.891238 0.877705 0.892791 0.916939 0.500000 0.511464 0.861802 0.514475 0.583161 0.589313 0.778994 0.753308 0.254098 0.432144 1 0.500000 1 1 0.500000 0 1 0 0.500000 0.905767 0.853023 0.857177 0.905527 0.886001 0.897322 0.881928 0.879583 0.853184 0.500000 0.893082 0.500000 0.500000 0.500000 0.500000 0.675778 0.726570 0.456074 0.636412 0.376950 0.399063 0.850924 0.885618 0.783017 0.500000 0 1 0.500000 0 0 1 0 0.864963 0.875313 0.868566 0.904093 0.876047 0.872119 0.890334 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0.121000 0 0 1 0.510000 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0.000460 0.020100 0.186000 0.115000 0.162000 0 0 1 0.360000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.002320 0.020100 0.103000 0.081000 0.127000 0 0 1 0.630000 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.003200 0.019000 0.114000 0.100000 0.114000 0 0 1 0.390000 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0.001500 0.028000 0.131000 0.126000 0.104000 0 0 1 libfann-2.1.0~beta~dfsg/benchmarks/datasets/two-spiral.test000077500000000000000000000102201140315177100241440ustar00rootroot00000000000000193 2 1 0.485007 0.652173 1 0.544131 0.486615 1 0.637044 0.333021 1 0.603195 0.415316 0 0.396805 0.584684 1 0.158572 0.603566 0 0.023860 0.355564 0 0.618358 0.488347 0 0.395483 0.468291 1 0.434470 0.716004 0 0.613526 0.511186 0 0.292136 0.246708 0 0.843348 0.218225 0 0.077295 0.458364 1 0.768115 0.526413 0 0.614840 0.878591 0 0.818945 0.329526 1 0.318050 0.597247 0 0.569746 0.729943 0 0.690821 0.518800 1 0.915753 0.373886 0 0.492152 0.420289 1 0.181055 0.670474 0 0.887441 0.292911 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0.373710 0 0.604517 0.531709 0 0.363040 0.387593 0 0.515458 0.657006 1 0.193588 0.126633 0 0.455869 0.513385 0 0.866033 0.695653 0 0.677347 0.168216 1 0.492622 0.574879 0 0.538298 0.888889 0 0.922705 0.541636 0 0.457670 0.420798 1 0.817077 0.760224 0 0.636960 0.612407 1 0.432491 0.626290 1 0.386549 0.560636 1 0.232895 0.581019 1 0.112559 0.707089 1 0.696998 0.661680 0 0.827490 0.599348 1 0.845411 0.534025 1 0.461166 0.499998 0 0.362956 0.666979 0 0.604123 0.305212 1 0.456846 0.535413 0 0.339461 0.414184 0 0.713958 0.099718 0 0.681950 0.402753 1 0.065469 0.267737 0 0.462169 0.884058 0 0.541063 0.495957 1 0.543154 0.464587 1 0.841428 0.396434 1 0.757138 0.813330 0 0.692781 0.441527 1 0.227053 0.526877 1 0.461504 0.546903 0 0.407708 0.195733 0 0.560048 0.573175 0 0.660539 0.585816 1 0.381544 0.535929 1 0.303002 0.338320 1 0.439952 0.426825 1 0.461702 0.111111 1 0.276727 0.683227 1 0.530894 0.442207 1 0.784858 0.152902 0 0.757038 0.710952 1 0.927537 0.457894 0 0.286042 0.900282 1 0.976140 0.644436 1 0.322653 0.831784 0 0.537831 0.115942 1 0.390681 0.366786 0 0.588076 0.209672 0 0.366835 0.938977 1 0.156652 0.781775 1 0.772947 0.473123 0 0.640736 0.236715 0 0.336766 0.633955 0 0.182923 0.239776 1 0.476929 0.265700 1 0.304347 0.519264 0 0.341409 0.306746 1 0.806412 0.873367 1 0.615554 0.716199 0 0.723273 0.316773 0 0.686316 0.272982 0 0.084247 0.626114 1 libfann-2.1.0~beta~dfsg/benchmarks/datasets/two-spiral.train000077500000000000000000000102201140315177100243020ustar00rootroot00000000000000193 2 1 0.499995 0.653846 1 0.544418 0.481604 1 0.620200 0.320118 1 0.595191 0.404816 0 0.404809 0.595184 1 0.171310 0.636142 0 0.014323 0.403392 0 0.617884 0.476556 0 0.391548 0.478424 1 0.455912 0.721618 0 0.615385 0.500005 0 0.268835 0.268827 0 0.812761 0.187243 0 0.076923 0.499997 1 0.769231 0.500006 0 0.650862 0.864223 0 0.799812 0.299678 1 0.328106 0.614848 0 0.591985 0.722088 0 0.692308 0.500005 1 0.899757 0.334418 0 0.484058 0.419839 1 0.200188 0.700322 0 0.863769 0.256940 0 0.384615 0.499995 1 0.457562 0.508439 0 0.515942 0.580161 0 0.844219 0.431535 1 0.456027 0.529379 0 0.235571 0.104252 0 0.260149 0.400644 1 0.500003 0.423077 1 0.544088 0.278382 1 0.597716 0.540480 0 0.562549 0.651021 1 0.574101 0.127491 1 0.545953 0.731052 0 0.649585 0.350424 1 0.607934 0.427886 0 0.499995 0.807692 1 0.437451 0.348979 0 0.382116 0.523444 1 1 0.500000 1 0.731165 0.731173 1 0.500002 0.038462 0 0.683896 0.536585 1 0.910232 0.581604 0 0.499998 0.961538 1 0.903742 0.769772 1 0.543973 0.470621 1 0.593481 0.639914 1 0.240659 0.448408 1 0.425899 0.872509 0 0 0.500000 0 0.500006 0.269231 1 0.155781 0.568465 0 0.096258 0.230228 0 0.583945 0.556095 0 0.550746 0.575954 0 0.680302 0.935290 1 0.693329 0.461550 1 0.500005 0.192308 0 0.230769 0.499994 1 0.721691 0.831791 0 0.621423 0.793156 1 0.735853 0.342415 0 0.402284 0.459520 1 0.589105 0.052045 0 0.189081 0.371208 0 0.533114 0.579952 0 0.251594 0.871762 1 0.764429 0.895748 1 0.499994 0.730769 0 0.415362 0.704317 0 0.422537 0.615923 1 0.337064 0.743842 1 0.560960 0.806496 1 0.810919 0.628792 1 0.319698 0.064710 0 0.757622 0.393295 0 0.577463 0.384077 0 0.349138 0.135777 1 0.165214 0.433402 0 0.241631 0.758362 0 0.118012 0.341772 1 0.514072 0.429271 1 0.676772 0.676781 0 0.294328 0.807801 0 0.153846 0.499995 0 0.500005 0.346154 0 0.307692 0.499995 0 0.615487 0.452168 0 0.466886 0.420048 1 0.440905 0.797064 1 0.485928 0.570729 0 0.470919 0.646174 1 0.224179 0.315696 0 0.439040 0.193504 0 0.408015 0.277912 1 0.316104 0.463415 0 0.278309 0.168209 1 0.214440 0.214435 1 0.089768 0.418396 1 0.678953 0.767832 1 0.080336 0.583473 1 0.363783 0.296127 1 0.474240 0.562183 0 0.313445 0.577267 0 0.416055 0.443905 1 0.529081 0.353826 0 0.953056 0.687662 1 0.534725 0.448035 1 0.469053 0.344394 0 0.759341 0.551592 0 0.705672 0.192199 1 0.385925 0.775385 1 0.590978 0.957385 1 0.406519 0.360086 0 0.409022 0.042615 0 0.264147 0.657585 1 0.758369 0.241638 1 0.622380 0.622388 1 0.321047 0.232168 0 0.739851 0.599356 0 0.555199 0.366750 0 0.608452 0.521576 0 0.352098 0.401168 0 0.530947 0.655606 1 0.160045 0.160044 0 0.455582 0.518396 0 0.881988 0.658228 0 0.643511 0.153547 1 0.499997 0.576923 0 0.575968 0.881942 0 0.923077 0.500003 0 0.449254 0.424046 1 0.839782 0.727039 0 0.647902 0.598832 1 0.444801 0.633250 1 0.392066 0.572114 1 0.242378 0.606705 1 0.136231 0.743060 1 0.711862 0.641568 0 0.834786 0.566598 1 0.846154 0.500005 1 0.538462 0.500002 1 0.379800 0.679882 0 0.584638 0.295683 1 0.459204 0.540793 0 0.331216 0.430082 0 0.672945 0.082478 0 0.671894 0.385152 1 0.046944 0.312338 0 0.499995 0.884615 0 0.542438 0.491561 1 0.540796 0.459207 1 0.828690 0.363858 1 0.785560 0.785565 0 0.686555 0.422733 1 0.231226 0.553456 1 0.465275 0.551965 0 0.378577 0.206844 0 0.567988 0.567994 0 0.668784 0.569918 1 0.384513 0.547832 1 0.288138 0.358432 1 0.432012 0.432006 1 0.424032 0.118058 1 0.296023 0.703969 1 0.525760 0.437817 1 0.748406 0.128238 0 0.775821 0.684304 1 0.919664 0.416527 0 0.327055 0.917522 1 0.985677 0.596608 1 0.356489 0.846453 0 0.500005 0.115385 1 0.377620 0.377612 0 0.559095 0.202936 0 0.410895 0.947955 1 0.187239 0.812757 1 0.768774 0.446544 0 0.614075 0.224615 0 0.350415 0.649576 0 0.160218 0.272961 1 0.454047 0.268948 1 0.306671 0.538450 0 0.323228 0.323219 1 0.839955 0.839956 1 0.636217 0.703873 0 0.703977 0.296031 0 0.662936 0.256158 0 0.100243 0.665582 1 libfann-2.1.0~beta~dfsg/benchmarks/gnuplot000066400000000000000000000400661140315177100207550ustar00rootroot00000000000000set terminal postscript color solid set output "allplots.ps" set grid set xlabel "Number of neurons in the four layers" set ylabel "Nanoseconds per connection" set logscale #set output "performance.ps" set title "Performance of execution" plot "fann_performance.out" title "fann" with lines -1, \ "fann_stepwise_performance.out" title "fann (stepwise)" with lines 2, \ "fann_fixed_performance.out" title "fann (fix)" with lines 1, \ "lwnn_performance.out" title "lwnn" with lines 8, \ "jneural_performance.out" title "jneural" with lines 9 set nologscale set xlabel "Seconds of training" set ylabel "Mean square error" set logscale x #set output "building.ps" set title "building" plot "building.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "building.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "building.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "building.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "building.fann_batch.test.out" title "fann batch test" with lines 4, \ "building.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "building.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "building.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "building.jneural.test.out" title "jneural incremental test" with lines 9, \ "building.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "building.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "building.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "building.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "building.fann_batch.train.out" title "fann batch train" with lines 4, \ "building.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "building.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "building.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "building.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale x #set output "diabetes.ps" set title "diabetes" plot "diabetes.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "diabetes.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "diabetes.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "diabetes.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "diabetes.fann_batch.test.out" title "fann batch test" with lines 4, \ "diabetes.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "diabetes.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "diabetes.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "diabetes.jneural.test.out" title "jneural incremental test" with lines 9, \ "diabetes.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "diabetes.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "diabetes.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "diabetes.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "diabetes.fann_batch.train.out" title "fann batch train" with lines 4, \ "diabetes.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "diabetes.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "diabetes.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "diabetes.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale x #set output "gene.ps" set title "gene" plot "gene.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "gene.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "gene.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "gene.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "gene.fann_batch.test.out" title "fann batch test" with lines 4, \ "gene.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "gene.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "gene.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "gene.jneural.test.out" title "jneural incremental test" with lines 9, \ "gene.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "gene.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "gene.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "gene.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "gene.fann_batch.train.out" title "fann batch train" with lines 4, \ "gene.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "gene.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "gene.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "gene.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale x #set output "mushroom.ps" set title "mushroom" plot "mushroom.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "mushroom.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "mushroom.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "mushroom.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "mushroom.fann_batch.test.out" title "fann batch test" with lines 4, \ "mushroom.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "mushroom.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "mushroom.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "mushroom.jneural.test.out" title "jneural incremental test" with lines 9, \ "mushroom.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "mushroom.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "mushroom.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "mushroom.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "mushroom.fann_batch.train.out" title "fann batch train" with lines 4, \ "mushroom.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "mushroom.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "mushroom.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "mushroom.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale x #set output "parity8.ps" set title "parity8" plot "parity8.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "parity8.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "parity8.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "parity8.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "parity8.fann_batch.train.out" title "fann batch train" with lines 4, \ "parity8.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "parity8.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "parity8.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "parity8.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale x #set output "parity13.ps" set title "parity13" plot "parity13.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "parity13.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "parity13.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "parity13.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "parity13.fann_batch.train.out" title "fann batch train" with lines 4, \ "parity13.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "parity13.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "parity13.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "parity13.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale y #set output "pumadyn-32fm.ps" set title "pumadyn-32fm" plot "pumadyn-32fm.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "pumadyn-32fm.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "pumadyn-32fm.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "pumadyn-32fm.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "pumadyn-32fm.fann_batch.test.out" title "fann batch test" with lines 4, \ "pumadyn-32fm.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "pumadyn-32fm.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "pumadyn-32fm.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "pumadyn-32fm.jneural.test.out" title "jneural incremental test" with lines 9, \ "pumadyn-32fm.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "pumadyn-32fm.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "pumadyn-32fm.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "pumadyn-32fm.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "pumadyn-32fm.fann_batch.train.out" title "fann batch train" with lines 4, \ "pumadyn-32fm.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "pumadyn-32fm.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "pumadyn-32fm.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "pumadyn-32fm.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale #set output "robot.ps" set title "robot" plot "robot.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "robot.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "robot.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "robot.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "robot.fann_batch.test.out" title "fann batch test" with lines 4, \ "robot.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "robot.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "robot.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "robot.jneural.test.out" title "jneural incremental test" with lines 9, \ "robot.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "robot.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "robot.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "robot.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "robot.fann_batch.train.out" title "fann batch train" with lines 4, \ "robot.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "robot.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "robot.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "robot.jneural.train.out" title "jneural incremental train" with lines 9 set logscale x #set output "soybean.ps" set title "soybean" plot "soybean.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "soybean.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "soybean.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "soybean.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "soybean.fann_batch.test.out" title "fann batch test" with lines 4, \ "soybean.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "soybean.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "soybean.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "soybean.jneural.test.out" title "jneural incremental test" with lines 9, \ "soybean.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "soybean.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "soybean.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "soybean.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "soybean.fann_batch.train.out" title "fann batch train" with lines 4, \ "soybean.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "soybean.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "soybean.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "soybean.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale y #set output "thyroid.ps" set title "thyroid" plot "thyroid.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "thyroid.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "thyroid.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "thyroid.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "thyroid.fann_batch.test.out" title "fann batch test" with lines 4, \ "thyroid.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "thyroid.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "thyroid.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "thyroid.jneural.test.out" title "jneural incremental test" with lines 9, \ "thyroid.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "thyroid.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "thyroid.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "thyroid.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "thyroid.fann_batch.train.out" title "fann batch train" with lines 4, \ "thyroid.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "thyroid.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "thyroid.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "thyroid.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set logscale x #set output "two-spiral.ps" set title "two-spiral" plot "two-spiral.fann_rprop.test.out" title "fann rprop test" with lines -1, \ "two-spiral.fann_rprop_stepwise.test.out" title "fann rprop (stepwise) test" with lines 2, \ "two-spiral.fann_cascade_rprop_multi_activation.test.out" title "fann cascade test" with lines 1, \ "two-spiral.fann_quickprop.test.out" title "fann quickprop test" with lines 3, \ "two-spiral.fann_batch.test.out" title "fann batch test" with lines 4, \ "two-spiral.fann_incremental.test.out" title "fann incremental test" with lines 5, \ "two-spiral.fann_incremental_momentum.test.out" title "fann incr mom test" with lines 6, \ "two-spiral.lwnn.test.out" title "lwnn incremental test" with lines 8, \ "two-spiral.jneural.test.out" title "jneural incremental test" with lines 9, \ "two-spiral.fann_rprop.train.out" title "fann rprop train" with lines -1, \ "two-spiral.fann_rprop_stepwise.train.out" title "fann rprop (stepwise) train" with lines 2, \ "two-spiral.fann_cascade_rprop_multi_activation.train.out" title "fann cascade train" with lines 1, \ "two-spiral.fann_quickprop.train.out" title "fann quickprop train" with lines 3, \ "two-spiral.fann_batch.train.out" title "fann batch train" with lines 4, \ "two-spiral.fann_incremental.train.out" title "fann incremental train" with lines 5, \ "two-spiral.fann_incremental_momentum.train.out" title "fann incr mom train" with lines 6, \ "two-spiral.lwnn.train.out" title "lwnn incremental train" with lines 8, \ "two-spiral.jneural.train.out" title "jneural incremental train" with lines 9 set nologscale set output set terminal x11 libfann-2.1.0~beta~dfsg/benchmarks/gnuplot.pl000077500000000000000000000105521140315177100213670ustar00rootroot00000000000000#!/usr/bin/perl $own_file = 0; print ' set terminal postscript color solid set output "allplots.ps" set style line 1 lt 1 lw 1 set style line 2 lt 2 lw 1 set style line 3 lt 3 lw 1 set style line 4 lt 4 lw 1 set style line 5 lt 5 lw 1 set style line 6 lt 6 lw 1 set style line 7 lt 7 lw 1 set style line 8 lt 8 lw 1 set style line 9 lt 9 lw 1 set grid set xlabel "Number of neurons in the four layers" set ylabel "Nanoseconds per connection" set logscale '; if($own_file) { print 'set output "performance.ps"'; } print ' set title "Performance of execution" plot "fann_performance.out" title "fann" with lines -1, \ "fann_stepwise_performance.out" title "fann (stepwise)" with lines 2, \ "fann_fixed_performance.out" title "fann (fix)" with lines 1, \ "lwnn_performance.out" title "lwnn" with lines 8, \ "jneural_performance.out" title "jneural" with lines 9 set nologscale set xlabel "Seconds of training" set ylabel "Mean square error" '; sub print_plot_line { local($filename, $title, $extra_title, $width, $last) = @_; print " \"$filename\" title \"$title $extra_title\" w l lt $linestyle lw $width"; if(!$last) { print ", \\\n"; } $linestyle++; } sub print_plot_lines { local($train_file, $train_test, $extra_title, $width) = @_; $linestyle = 1; print_plot_line("$train_file.fann_cascade_rprop_one_activation.$train_test.out", "Cascade2 RPROP One", $extra_title, $width, 0); print_plot_line("$train_file.fann_cascade_rprop_multi_activation.$train_test.out", "Cascade2 RPROP Multi", $extra_title, $width, 0); print_plot_line("$train_file.fann_cascade_quickprop_one_activation.$train_test.out", "Cascade2 Quickprop One", $extra_title, $width, 0); print_plot_line("$train_file.fann_cascade_quickprop_multi_activation.$train_test.out", "Cascade2 Quickprop Multi", $extra_title, $width, 0); print_plot_line("$train_file.fann_rprop.$train_test.out", "iRPROP-", $extra_title, $width, 0); #print_plot_line("$train_file.fann_rprop_stepwise.$train_test.out", "fann rprop (stepwise)", $extra_title, $width, 0); print_plot_line("$train_file.fann_quickprop.$train_test.out", "Quickprop", $extra_title, $width, 0); print_plot_line("$train_file.fann_batch.$train_test.out", "Batch", $extra_title, $width, 0); #print_plot_line("$train_file.fann_incremental.$train_test.out", "fann incremental", $extra_title, $width, 0); print_plot_line("$train_file.fann_incremental_momentum.$train_test.out", "Incremental", $extra_title, $width, 0); print_plot_line("$train_file.lwnn.$train_test.out", "(External) Lwnn Incremental", $extra_title, $width, 0); print_plot_line("$train_file.jneural.$train_test.out", "(External) Jneural Incremental", $extra_title, $width, 1); } sub print_plot { local($train_file, $logscale, $have_test, $have_train, $own_file) = @_; print "\nset $logscale\n"; if($own_file) { print "set output \"$train_file.ps\"\n"; } print "set title \"$train_file\"\n"; print "plot "; if($have_train) { if($have_test) { &print_plot_lines($train_file, "train", "Train", 2); } else { &print_plot_lines($train_file, "train", "", 2); } } if($have_test) { print ", \\\n"; if($have_test) { &print_plot_lines($train_file, "test", "Test", 1); } else { &print_plot_lines($train_file, "test", "", 1); } } print "\nset nologscale\n"; } for($own_file = 0; $own_file < 2; $own_file++) { &print_plot("abelone", "logscale x", 1, 1, $own_file); &print_plot("bank32fm", "logscale x", 1, 1, $own_file); &print_plot("bank32nh", "logscale x", 1, 1, $own_file); &print_plot("building", "logscale x", 1, 1, $own_file); &print_plot("census-house", "logscale x", 1, 1, $own_file); &print_plot("diabetes", "logscale x", 1, 1, $own_file); &print_plot("gene", "logscale x", 1, 1, $own_file); &print_plot("kin32fm", "logscale x", 1, 1, $own_file); &print_plot("mushroom", "logscale x", 1, 1, $own_file); &print_plot("parity8", "logscale x", 0, 1, $own_file); &print_plot("parity13", "logscale x", 0, 1, $own_file); &print_plot("pumadyn-32fm", "logscale y", 1, 1, $own_file); &print_plot("robot", "logscale", 1, 1, $own_file); &print_plot("soybean", "logscale x", 1, 1, $own_file); &print_plot("thyroid", "logscale y", 1, 1, $own_file); &print_plot("two-spiral", "logscale x", 1, 1, $own_file); } print "\nset output\n"; print "set terminal x11\n" libfann-2.1.0~beta~dfsg/benchmarks/parity.c000077500000000000000000000052621140315177100210200ustar00rootroot00000000000000/* Parity Benchmark Data Set Generator v1.0 Matt White (mwhite+@cmu.edu) 9/19/93 QUESTIONS/COMMENTS: neural-bench@cs.cmu.edu Usage: a.out <# of inputs> # of inputs defaults to 2, or XOR. Care should be taken not to input a number greater than the number of bits, usually 32, of the machine being used, minus one. Thus, for most computers, the maximum input is 31. Description ~~~~~~~~~~~ This program generates parity benchmark data in the CMU Neural Network Benchmark format. */ #include #include #include #include /* Function Prototypes */ void getCommandLine(int *, int, char **); void printHeader(int); void printSet(int); int main(int argc, char **argv) { int inputs; /* Number of inputs into the neural network */ getCommandLine(&inputs, argc, argv); printHeader(inputs); printSet(inputs); } /* getCommandLine - This function reads the values from the command line and interprets them. The only argument should be the number of inputs to generate, which defaults to 2. Extra arguments are ignored. Invalid arguments are flagged and an error is produced. */ void getCommandLine(int *inputs, int argc, char **argv) { if(argc < 2) *inputs = 2; else if((**(argv + 1) >= '0') && (**(argv + 1) <= '9')) *inputs = atoi(*(argv + 1)); else { fprintf(stderr, "Invalid argument: %s\n", *(argv + 1)); fprintf(stderr, "Value should be an integer.\n"); exit(1); } } /* printHeader - This function prints out the header information for the data set. This includes generation time (local) and number of inputs. It also prints the $SETUP segment for the data set. */ void printHeader(int inputs) { unsigned int num_points; num_points = pow(2, inputs); printf("%d %d 1\n", num_points, inputs); } /* printSet - Generates and prints out a data set having the specified number of inputs. */ void printSet(int inputs) { unsigned int num_points, /* Number of points of data to generate */ num_pos, /* Number of positive bits in this number */ this_point, /* Number being analyzed */ i, /* General indexing variables */ j; num_points = pow(2, inputs); /* Figure out how many points to generate */ for(i = 0; i < num_points; i++) { num_pos = 0; this_point = i; for(j = 0; j < inputs; j++) { /* Analyze the number */ if((this_point & 1) == 1) { /* Get a bit and use it */ printf("1 "); num_pos++; } else printf("-1 "); this_point >>= 1; /* Shift to the next bit */ } if((num_pos % 2) == 1) /* Print the expected output */ printf("1\n"); else printf("0\n"); } } libfann-2.1.0~beta~dfsg/benchmarks/performance.cc000066400000000000000000000154031140315177100221470ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ //uncomment lines below to benchmark the libraries #define JNEURAL #define LWNN #include #include #include #include "ctimer.h" #include "fann.h" #ifndef FIXEDFANN #ifdef JNEURAL #include "nets/backprop.h" #endif #ifdef LWNN #include "lwneuralnet.h" #endif #ifdef JNEURAL void performance_benchmark_jneural(FILE * out, fann_type * input, unsigned int num_neurons, unsigned int seconds_per_test) { unsigned int i, total_connections; fann_type *output; backprop *ann = new backprop(0.7, num_neurons, num_neurons, 2, num_neurons, num_neurons); total_connections = (num_neurons + 1) * num_neurons * 3; start_timer(); for(i = 0; time_elapsed() < (double) seconds_per_test; i++) { ann->set_input(input); output = ann->query_output(); } stop_timer(); fprintf(out, "%d %.20e\n", num_neurons, getNanoPerN(i) / total_connections); fprintf(stderr, "%d ", num_neurons); delete ann; } #endif #ifdef LWNN void performance_benchmark_lwnn(FILE * out, fann_type * input, unsigned int num_neurons, unsigned int seconds_per_test) { unsigned int i, total_connections; fann_type *output; output = (fann_type *) calloc(num_neurons, sizeof(fann_type)); network_t *ann = net_allocate(4, num_neurons, num_neurons, num_neurons, num_neurons); total_connections = num_neurons * num_neurons * 3; start_timer(); for(i = 0; time_elapsed() < (double) seconds_per_test; i++) { net_compute(ann, input, output); } stop_timer(); fprintf(out, "%d %.20e\n", num_neurons, getNanoPerN(i) / total_connections); fprintf(stderr, "%d ", num_neurons); net_free(ann); free(output); } #endif void performance_benchmark_fann_noopt(FILE * out, fann_type * input, unsigned int num_neurons, unsigned int seconds_per_test) { unsigned int i, total_connections; fann_type *output; struct fann *ann = fann_create_standard(4, num_neurons, num_neurons, num_neurons, num_neurons); //just to fool the optimizer into thinking that the network is not fully connected ann->connection_rate = 0.9; total_connections = (num_neurons + 1) * num_neurons * 3; start_timer(); for(i = 0; time_elapsed() < (double) seconds_per_test; i++) { output = fann_run(ann, input); } stop_timer(); fprintf(out, "%d %.20e\n", num_neurons, getNanoPerN(i) / total_connections); fprintf(stderr, "%d ", num_neurons); fann_destroy(ann); } void performance_benchmark_fann_thres(FILE * out, fann_type * input, unsigned int num_neurons, unsigned int seconds_per_test) { unsigned int i, total_connections; fann_type *output; struct fann *ann = fann_create_standard(4, num_neurons, num_neurons, num_neurons, num_neurons); fann_set_activation_function_hidden(ann, FANN_THRESHOLD); fann_set_activation_function_output(ann, FANN_THRESHOLD); total_connections = (num_neurons + 1) * num_neurons * 3; start_timer(); for(i = 0; time_elapsed() < (double) seconds_per_test; i++) { output = fann_run(ann, input); } stop_timer(); fprintf(out, "%d %.20e\n", num_neurons, getNanoPerN(i) / total_connections); fprintf(stderr, "%d ", num_neurons); fann_destroy(ann); } #endif void performance_benchmark_fann(bool stepwise, FILE * out, fann_type * input, unsigned int num_neurons, unsigned int seconds_per_test) { unsigned int i, total_connections; fann_type *output; struct fann *ann = fann_create_standard(4, num_neurons, num_neurons, num_neurons, num_neurons); if(stepwise) { fann_set_activation_function_hidden(ann, FANN_SIGMOID_STEPWISE); fann_set_activation_function_output(ann, FANN_SIGMOID_STEPWISE); } else { fann_set_activation_function_hidden(ann, FANN_SIGMOID); fann_set_activation_function_output(ann, FANN_SIGMOID); } total_connections = (num_neurons + 1) * num_neurons * 3; start_timer(); for(i = 0; time_elapsed() < (double) seconds_per_test; i++) { output = fann_run(ann, input); } stop_timer(); fprintf(out, "%d %.20e\n", num_neurons, getNanoPerN(i) / total_connections); fprintf(stderr, "%d ", num_neurons); fann_destroy(ann); } int main(int argc, char *argv[]) { /* parameters */ unsigned int num_neurons_first; unsigned int num_neurons_last; double multiplier; unsigned int seconds_per_test; FILE *out; fann_type *input; unsigned int num_neurons, i; if(argc != 7) { printf ("usage %s net file_out num_neurons_first num_neurons_last multiplier seconds_per_test\n", argv[0]); return -1; } calibrate_timer(); num_neurons_first = atoi(argv[3]); num_neurons_last = atoi(argv[4]); multiplier = atof(argv[5]); seconds_per_test = atoi(argv[6]); if(strlen(argv[2]) == 1 && argv[2][0] == '-') { out = stdout; } else { out = fopen(argv[2], "w"); } fprintf(stderr, "Performance test of %s %s ", argv[1], argv[2]); input = (fann_type *) calloc(num_neurons_last, sizeof(fann_type)); for(i = 0; i < num_neurons_last; i++) { input[i] = fann_random_weight(); //fill input with random variables } for(num_neurons = num_neurons_first; num_neurons <= num_neurons_last; num_neurons = (int) (num_neurons * multiplier)) { #ifndef FIXEDFANN if(strcmp(argv[1], "fann") == 0) { #endif performance_benchmark_fann(false, out, input, num_neurons, seconds_per_test); #ifndef FIXEDFANN } else if(strcmp(argv[1], "fann_noopt") == 0) { performance_benchmark_fann_noopt(out, input, num_neurons, seconds_per_test); } else if(strcmp(argv[1], "fann_stepwise") == 0) { performance_benchmark_fann(true, out, input, num_neurons, seconds_per_test); } else if(strcmp(argv[1], "fann_thres") == 0) { performance_benchmark_fann_thres(out, input, num_neurons, seconds_per_test); #ifdef LWNN } else if(strcmp(argv[1], "lwnn") == 0) { performance_benchmark_lwnn(out, input, num_neurons, seconds_per_test); #endif #ifdef JNEURAL } else if(strcmp(argv[1], "jneural") == 0) { performance_benchmark_jneural(out, input, num_neurons, seconds_per_test); #endif } else { printf("unrecognized option %s\n", argv[1]); } #endif } fprintf(stderr, "\n"); free(input); return 0; } libfann-2.1.0~beta~dfsg/benchmarks/quality.cc000066400000000000000000000517201140315177100213400ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ //uncomment lines below to benchmark the libraries #define JNEURAL #define LWNN #include #include #include #ifdef JNEURAL #include "nets/backprop.h" #endif #ifdef LWNN #include "lwneuralnet.h" #endif #include "ctimer.h" #include "floatfann.h" #include "benchdata.h" unsigned int num_errors = 0; unsigned int num_bit_fail = 0; double error_value = 0; BenchDataCollector *train_collector = new BenchDataCollector(); BenchDataCollector *test_collector = new BenchDataCollector(); void clear_error() { num_errors = 0; error_value = 0; num_bit_fail = 0; } void update_error(fann_type * output, fann_type * desired_output, unsigned int num_output) { unsigned int i = 0; double error_val = 0; /* calculate the error */ for(i = 0; i < num_output; i++) { error_val = (desired_output[i] - output[i]) * (desired_output[i] - output[i]); error_value += error_val; if(error_val >= 0.25) { num_bit_fail++; } num_errors++; } } double mean_error() { return error_value / (double) num_errors; } double mean_bit_fail() { return num_bit_fail / (double) num_errors; } void report_error(double time, unsigned int epochs, FILE * out, BenchDataCollector *collector, unsigned int numNeurons) { BenchData *data = new BenchData(time, epochs, mean_error(), num_bit_fail, mean_bit_fail(), numNeurons); data->printBench(out); collector->addBench(data); } #ifdef JNEURAL void quality_benchmark_jneural(struct fann_train_data *train_data, struct fann_train_data *test_data, FILE * train_out, FILE * test_out, unsigned int num_input, unsigned int num_neurons_hidden1, unsigned int num_neurons_hidden2, unsigned int num_output, unsigned int seconds_of_training, double seconds_between_reports) { float train_error, test_error; unsigned int i, train_bit_fail, test_bit_fail; unsigned int epochs = 0; double elapsed = 0; double total_elapsed = 0; fann_type *output; struct backprop *ann; if(num_neurons_hidden2) { ann = new backprop(0.7, num_input, num_output, 2, num_neurons_hidden1, num_neurons_hidden2); } else { ann = new backprop(0.7, num_input, num_output, 1, num_neurons_hidden1); } calibrate_timer(); while(total_elapsed < (double) seconds_of_training) { /* train */ elapsed = 0; start_timer(); while(elapsed < (double) seconds_between_reports) { for(i = 0; i != train_data->num_data; i++) { ann->set_input(train_data->input[i]); ann->train_on(train_data->output[i]); } elapsed = time_elapsed(); epochs++; } stop_timer(); total_elapsed += getSecs(); /* make report */ clear_error(); for(i = 0; i != train_data->num_data; i++) { ann->set_input(train_data->input[i]); output = ann->query_output(); update_error(output, train_data->output[i], train_data->num_output); } train_error = mean_error(); train_bit_fail = num_bit_fail; report_error(total_elapsed, epochs, train_out, train_collector, num_neurons_hidden1+num_neurons_hidden2); clear_error(); for(i = 0; i != test_data->num_data; i++) { ann->set_input(test_data->input[i]); output = ann->query_output(); update_error(output, test_data->output[i], test_data->num_output); } test_error = mean_error(); test_bit_fail = num_bit_fail; report_error(total_elapsed, epochs, test_out, test_collector, num_neurons_hidden1+num_neurons_hidden2); fprintf(stderr, "secs: %8.2f, train: %8.6f (%4d), test: %8.6f (%4d), epochs: %5d\r", total_elapsed, train_error, train_bit_fail, test_error, test_bit_fail, epochs); } fprintf(stdout, "\nepochs: %d, epochs/sec: %f\n", epochs, epochs / total_elapsed); delete ann; } #endif void quality_benchmark_fann(bool stepwise, fann_train_enum training_algorithm, char *filename, struct fann_train_data *train_data, struct fann_train_data *test_data, FILE * train_out, FILE * test_out, unsigned int num_input, unsigned int num_neurons_hidden1, unsigned int num_neurons_hidden2, unsigned int num_output, unsigned int seconds_of_training, double seconds_between_reports, float momentum = 0) { float train_error, test_error; unsigned int i, decimal_point, j, train_bit_fail, test_bit_fail; unsigned int epochs = 0; double elapsed = 0; double total_elapsed = 0; fann_type *output; struct fann *ann; char fixed_point_file[256]; if(num_neurons_hidden2) { ann = fann_create_standard(4, num_input, num_neurons_hidden1, num_neurons_hidden2, num_output); } else { ann = fann_create_standard(3, num_input, num_neurons_hidden1, num_output); } fann_set_training_algorithm(ann, training_algorithm); fann_set_learning_momentum(ann, momentum); if(stepwise) { fann_set_activation_function_hidden(ann, FANN_SIGMOID_STEPWISE); fann_set_activation_function_output(ann, FANN_SIGMOID_STEPWISE); } else { fann_set_activation_function_hidden(ann, FANN_SIGMOID); fann_set_activation_function_output(ann, FANN_SIGMOID); } if(training_algorithm == FANN_TRAIN_INCREMENTAL || training_algorithm == FANN_TRAIN_QUICKPROP) { fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR); } calibrate_timer(); while(total_elapsed < (double) seconds_of_training) { /* train */ elapsed = 0; start_timer(); while(elapsed < (double) seconds_between_reports) { fann_train_epoch(ann, train_data); elapsed = time_elapsed(); epochs++; } stop_timer(); total_elapsed += getSecs(); /* make report */ clear_error(); for(i = 0; i != test_data->num_data; i++) { output = fann_run(ann, test_data->input[i]); update_error(output, test_data->output[i], test_data->num_output); } test_error = mean_error(); test_bit_fail = num_bit_fail; report_error(total_elapsed, epochs, test_out, test_collector, num_neurons_hidden1+num_neurons_hidden2); clear_error(); for(i = 0; i != train_data->num_data; i++) { output = fann_run(ann, train_data->input[i]); update_error(output, train_data->output[i], train_data->num_output); } train_error = mean_error(); train_bit_fail = num_bit_fail; report_error(total_elapsed, epochs, train_out, train_collector, num_neurons_hidden1+num_neurons_hidden2); fprintf(stderr, "secs: %8.2f, train: %8.6f (%4d), test: %8.6f (%4d), epochs: %5d\r", total_elapsed, train_error, train_bit_fail, test_error, test_bit_fail, epochs); /* Save the data as fixed point, to allow for drawing of * a fixed point graph */ if(filename) { /* buffer overflow could occur here */ sprintf(fixed_point_file, "%08d_%f_%s_fixed.net", epochs, total_elapsed, filename); decimal_point = fann_save_to_fixed(ann, fixed_point_file); sprintf(fixed_point_file, "%s_fixed_train_%d", filename, decimal_point); fann_save_train_to_fixed(train_data, fixed_point_file, decimal_point); sprintf(fixed_point_file, "%s_fixed_test_%d", filename, decimal_point); fann_save_train_to_fixed(test_data, fixed_point_file, decimal_point); } } fprintf(stdout, "\nepochs: %d, epochs/sec: %f\n", epochs, epochs / total_elapsed); fann_destroy(ann); } void quality_benchmark_cascade(struct fann_train_data *train_data, struct fann_train_data *test_data, FILE * train_out, FILE * test_out, unsigned int num_input, unsigned int num_output, unsigned int seconds_of_training, double seconds_between_reports, enum fann_train_enum train_function, bool multi) { float train_error = 0; float test_error = 0; unsigned int i, train_bit_fail, test_bit_fail; unsigned int epochs = 0; double elapsed = 0; double total_elapsed = 0; fann_type *output; struct fann *ann; ann = fann_create_shortcut(2, num_input, num_output); fann_set_training_algorithm(ann, train_function); fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(ann, FANN_LINEAR); fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR); if(!multi) { fann_type steepness = 0.5; fann_set_cascade_activation_steepnesses(ann, &steepness, 1); fann_activationfunc_enum activation = FANN_SIGMOID; fann_set_cascade_activation_functions(ann, &activation, 1); fann_set_cascade_num_candidate_groups(ann, 8); } /* fann_set_activation_steepness_hidden(ann, 0.5); fann_set_activation_steepness_output(ann, 0.5); fann_set_rprop_increase_factor(ann, 1.2); fann_set_rprop_decrease_factor(ann, 0.5); fann_set_rprop_delta_min(ann, 0.0); fann_set_rprop_delta_max(ann, 50.0); fann_set_cascade_weight_multiplier(ann, 0.4); fann_set_cascade_candidate_limit(ann, 1000.0); fann_set_cascade_max_out_epochs(ann, 150); fann_set_cascade_max_cand_epochs(ann, 150); */ calibrate_timer(); while(total_elapsed < (double) seconds_of_training) { /* Train with FANN_LINEAR */ fann_set_activation_function_output(ann, FANN_LINEAR); /* train */ elapsed = 0; start_timer(); while(elapsed < (double) seconds_between_reports) { epochs += fann_train_outputs(ann, train_data, 0); if(fann_initialize_candidates(ann) == -1) break; epochs += fann_train_candidates(ann, train_data); fann_install_candidate(ann); elapsed = time_elapsed(); } stop_timer(); total_elapsed += getSecs(); /* make report */ /* But test with FANN_LINEAR_PIECE */ fann_set_activation_function_output(ann, FANN_LINEAR_PIECE); clear_error(); for(i = 0; i != test_data->num_data; i++) { output = fann_run(ann, test_data->input[i]); update_error(output, test_data->output[i], test_data->num_output); } test_error = mean_error(); test_bit_fail = num_bit_fail; report_error(total_elapsed, epochs, test_out, test_collector, ann->total_neurons-ann->num_input-ann->num_output); clear_error(); for(i = 0; i != train_data->num_data; i++) { output = fann_run(ann, train_data->input[i]); update_error(output, train_data->output[i], train_data->num_output); } train_error = mean_error(); train_bit_fail = num_bit_fail; report_error(total_elapsed, epochs, train_out, train_collector, ann->total_neurons-ann->num_input-ann->num_output); fprintf(stderr, "secs: %8.2f, train: %8.6f (%4d), test: %8.6f (%4d), epochs: %5d, neurons: %d\r", total_elapsed, train_error, train_bit_fail, test_error, test_bit_fail, epochs, ann->total_neurons-ann->num_input-ann->num_output); } fprintf(stdout, "\nepochs: %d, epochs/sec: %f\n", epochs, epochs / total_elapsed); fann_destroy(ann); } #ifdef LWNN void quality_benchmark_lwnn(struct fann_train_data *train_data, struct fann_train_data *test_data, FILE * train_out, FILE * test_out, unsigned int num_input, unsigned int num_neurons_hidden1, unsigned int num_neurons_hidden2, unsigned int num_output, unsigned int seconds_of_training, double seconds_between_reports) { float train_error = 0; float test_error = 0; unsigned int i, train_bit_fail, test_bit_fail; unsigned int epochs = 0; double elapsed = 0; double total_elapsed = 0; fann_type *output; network_t *ann; if(num_neurons_hidden2) { ann = net_allocate(4, num_input, num_neurons_hidden1, num_neurons_hidden2, num_output); } else { ann = net_allocate(3, num_input, num_neurons_hidden1, num_output); } net_set_learning_rate(ann, 0.7); calibrate_timer(); output = (fann_type *) calloc(num_output, sizeof(fann_type)); while(total_elapsed < (double) seconds_of_training) { /* train */ elapsed = 0; start_timer(); while(elapsed < (double) seconds_between_reports) { for(i = 0; i != train_data->num_data; i++) { /* compute the outputs for inputs(i) */ net_compute(ann, train_data->input[i], output); /* find the error with respect to targets(i) */ net_compute_output_error(ann, train_data->output[i]); /* train the network one step */ net_train(ann); } elapsed = time_elapsed(); epochs++; } stop_timer(); total_elapsed += getSecs(); /* make report */ clear_error(); for(i = 0; i != train_data->num_data; i++) { net_compute(ann, train_data->input[i], output); update_error(output, train_data->output[i], train_data->num_output); } train_error = mean_error(); train_bit_fail = num_bit_fail; report_error(total_elapsed, epochs, train_out, train_collector, num_neurons_hidden1+num_neurons_hidden2); clear_error(); for(i = 0; i != test_data->num_data; i++) { net_compute(ann, test_data->input[i], output); update_error(output, test_data->output[i], test_data->num_output); } test_error = mean_error(); test_bit_fail = num_bit_fail; report_error(total_elapsed, epochs, test_out, test_collector, num_neurons_hidden1+num_neurons_hidden2); fprintf(stderr, "secs: %8.2f, train: %8.6f (%4d), test: %8.6f (%4d), epochs: %5d\r", total_elapsed, train_error, train_bit_fail, test_error, test_bit_fail, epochs); } fprintf(stdout, "\nepochs: %d, epochs/sec: %f\n", epochs, epochs / total_elapsed); net_free(ann); } #endif int main(int argc, char *argv[]) { /* parameters */ char output_file[256]; if(argc != 11) { printf ("usage %s net train_file test_file train_file_out test_file_out num_hidden1 num_hidden2 seconds_of_training seconds_between_reports number_of_runs\n", argv[0]); return -1; } unsigned int num_neurons_hidden1 = atoi(argv[6]); unsigned int num_neurons_hidden2 = atoi(argv[7]); unsigned int seconds_of_training = atoi(argv[8]); double seconds_between_reports = atof(argv[9]); unsigned int number_of_runs = atoi(argv[10]); fann_train_data *train_data = fann_read_train_from_file(argv[2]); fann_train_data *test_data = fann_read_train_from_file(argv[3]); char *train_out_file = argv[4]; char *test_out_file = argv[5]; FILE *train_out = 0; FILE *test_out = 0; for(int i = 0; i < number_of_runs; i++) { test_collector->newCollection(); train_collector->newCollection(); if(strlen(train_out_file) == 1 && train_out_file[0] == '-') train_out = stdout; else { if(number_of_runs == 1) train_out = fopen(train_out_file, "w"); else { sprintf(output_file, "%s_%d_run", train_out_file, i); train_out = fopen(output_file, "w"); } } if(strlen(test_out_file) == 1 && test_out_file[0] == '-') test_out = stdout; else { if(number_of_runs == 1) test_out = fopen(test_out_file, "w"); { sprintf(output_file, "%s_%d_run", test_out_file, i); test_out = fopen(output_file, "w"); } } fprintf(stdout, "Quality test of %s %s\n", argv[1], argv[2]); if(strcmp(argv[1], "fann_incremental") == 0) { quality_benchmark_fann(false, FANN_TRAIN_INCREMENTAL, NULL, train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); } else if(strcmp(argv[1], "fann_incremental_stepwise") == 0) { quality_benchmark_fann(true, FANN_TRAIN_INCREMENTAL, NULL, train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); } else if(strcmp(argv[1], "fann_incremental_momentum") == 0) { quality_benchmark_fann(false, FANN_TRAIN_INCREMENTAL, NULL, train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports, 0.4); } else if(strcmp(argv[1], "fann_quickprop") == 0) { quality_benchmark_fann(false, FANN_TRAIN_QUICKPROP, NULL, train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); } else if(strcmp(argv[1], "fann_quickprop_stepwise") == 0) { quality_benchmark_fann(true, FANN_TRAIN_QUICKPROP, NULL, train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); } else if(strcmp(argv[1], "fann_batch") == 0) { quality_benchmark_fann(false, FANN_TRAIN_BATCH, NULL, train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); } else if(strcmp(argv[1], "fann_batch_stepwise") == 0) { quality_benchmark_fann(true, FANN_TRAIN_BATCH, NULL, train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); } else if(strcmp(argv[1], "fann_rprop") == 0) { quality_benchmark_fann(false, FANN_TRAIN_RPROP, NULL, train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); } else if(strcmp(argv[1], "fann_rprop_stepwise") == 0) { quality_benchmark_fann(true, FANN_TRAIN_RPROP, argv[4], train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); } else if(strcmp(argv[1], "fann_cascade_rprop_one_activation") == 0) { quality_benchmark_cascade(train_data, test_data, train_out, test_out, train_data->num_input, train_data->num_output, seconds_of_training, seconds_between_reports, FANN_TRAIN_RPROP, false); } else if(strcmp(argv[1], "fann_cascade_rprop_multi_activation") == 0) { quality_benchmark_cascade(train_data, test_data, train_out, test_out, train_data->num_input, train_data->num_output, seconds_of_training, seconds_between_reports, FANN_TRAIN_RPROP, true); } else if(strcmp(argv[1], "fann_cascade_quickprop_one_activation") == 0) { quality_benchmark_cascade(train_data, test_data, train_out, test_out, train_data->num_input, train_data->num_output, seconds_of_training, seconds_between_reports, FANN_TRAIN_QUICKPROP, false); } else if(strcmp(argv[1], "fann_cascade_quickprop_multi_activation") == 0) { quality_benchmark_cascade(train_data, test_data, train_out, test_out, train_data->num_input, train_data->num_output, seconds_of_training, seconds_between_reports, FANN_TRAIN_QUICKPROP, true); } else if(strcmp(argv[1], "fann_cascade_batch_one_activation") == 0) { quality_benchmark_cascade(train_data, test_data, train_out, test_out, train_data->num_input, train_data->num_output, seconds_of_training, seconds_between_reports, FANN_TRAIN_BATCH, false); } else if(strcmp(argv[1], "fann_cascade_batch_multi_activation") == 0) { quality_benchmark_cascade(train_data, test_data, train_out, test_out, train_data->num_input, train_data->num_output, seconds_of_training, seconds_between_reports, FANN_TRAIN_BATCH, true); #ifdef LWNN } else if(strcmp(argv[1], "lwnn") == 0) { quality_benchmark_lwnn(train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); #endif #ifdef JNEURAL } else if(strcmp(argv[1], "jneural") == 0) { quality_benchmark_jneural(train_data, test_data, train_out, test_out, train_data->num_input, num_neurons_hidden1, num_neurons_hidden2, train_data->num_output, seconds_of_training, seconds_between_reports); #endif } else { printf("unrecognized option %s\n", argv[1]); } fclose(train_out); fclose(test_out); } if(strlen(train_out_file) == 1 && train_out_file[0] == '-') train_out = stdout; else train_out = fopen(train_out_file, "w"); if(strlen(test_out_file) == 1 && test_out_file[0] == '-') test_out = stdout; else test_out = fopen(test_out_file, "w"); train_collector->printAvgCollection(train_out, train_out_file); test_collector->printAvgCollection(test_out, test_out_file); fann_destroy_train(train_data); fann_destroy_train(test_data); return 0; } libfann-2.1.0~beta~dfsg/benchmarks/quality_fixed.c000066400000000000000000000044001140315177100223450ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include #include "fixedfann.h" int main(int argc, char *argv[]) { struct fann_train_data *train_data, *test_data; FILE *train_out, *test_out; struct fann *ann; float train_error, test_error; unsigned int j; unsigned int epochs = 0; double total_elapsed = 0; char file[256]; if(argc < 6) { printf("usage %s train_file test_file train_file_out test_file_out fixed_conf_files\n", argv[0]); return -1; } if(strlen(argv[3]) == 1 && argv[3][0] == '-') { train_out = stdout; } else { train_out = fopen(argv[3], "w"); } if(strlen(argv[4]) == 1 && argv[4][0] == '-') { test_out = stdout; } else { test_out = fopen(argv[4], "w"); } fprintf(stderr, "Quality test of fixed point "); for(j = 5; j < argc; j++) { ann = fann_create_from_file(argv[j]); sprintf(file, "%s_%d", argv[1], fann_get_decimal_point(ann)); train_data = fann_read_train_from_file(file); train_error = fann_test_data(ann, train_data); fann_destroy_train(train_data); sprintf(file, "%s_%d", argv[2], fann_get_decimal_point(ann)); test_data = fann_read_train_from_file(file); test_error = fann_test_data(ann, test_data); fann_destroy_train(test_data); sscanf(argv[j], "%d_%lf", &epochs, &total_elapsed); fprintf(train_out, "%f %.20e %d\n", total_elapsed, train_error, epochs); fprintf(test_out, "%f %.20e %d\n", total_elapsed, test_error, epochs); fprintf(stderr, "."); fann_destroy(ann); } fprintf(stderr, "\n"); return 0; } libfann-2.1.0~beta~dfsg/benchmarks/shuffle.c000066400000000000000000000037221140315177100211400ustar00rootroot00000000000000#include "fann.h" int main(int argc, char *argv[]) { struct fann_train_data *train, *test, *all; unsigned int i, j; unsigned int length, half_length; if(argc == 2) { train = fann_read_train_from_file(argv[1]); fann_shuffle_train_data(train); fann_scale_train_data(train, 0, 1); fann_save_train(train, argv[1]); return 0; } else if(argc == 3) { train = fann_read_train_from_file(argv[1]); test = fann_read_train_from_file(argv[2]); all = fann_merge_train_data(train, test); fann_shuffle_train_data(all); for(i = 0; i < train->num_data; i++) { for(j = 0; j < train->num_input; j++) { train->input[i][j] = all->input[i][j]; } for(j = 0; j < train->num_output; j++) { train->output[i][j] = all->output[i][j]; } } for(i = 0; i < test->num_data; i++) { for(j = 0; j < test->num_input; j++) { test->input[i][j] = all->input[i + train->num_data][j]; } for(j = 0; j < test->num_output; j++) { test->output[i][j] = all->output[i + train->num_data][j]; } } fann_save_train(train, argv[1]); fann_save_train(test, argv[2]); } else if(argc == 4) { all = fann_read_train_from_file(argv[1]); fann_shuffle_train_data(all); fann_scale_train_data(all, 0, 1); length = fann_length_train_data(all); half_length = length/2; train = fann_subset_train_data(all, 0, half_length); test = fann_subset_train_data(all, half_length, length-half_length); fann_save_train(train, argv[2]); fann_save_train(test, argv[3]); } else { printf("usage: %s train_file\n", argv[0]); printf("will shuffle the data in the file.\n"); printf("usage: %s train_file test_file\n", argv[0]); printf("will shuffle the data in the two files and save the new data back to them.\n"); printf("usage: %s input_file train_file test_file\n", argv[0]); printf("will shuffle the data in the input_file, and split it in two files and save the new data back to them.\n\n"); return -1; } return 0; } libfann-2.1.0~beta~dfsg/benchmarks/summary.txt000066400000000000000000001267521140315177100216070ustar00rootroot00000000000000abelone.fann_cascade_rprop_one_activation.test.out 2.897500 5.96264795783181533739e-03 681.750000 0.000000 0.00000000000000000000e+00 4.000000 4 abelone.fann_cascade_rprop_one_activation.train.out 301.874855 4.54656391234015480607e-03 13840.000000 0.750000 3.59195402298850569728e-04 55.250000 4 abelone.fann_cascade_rprop_multi_activation.test.out 17.907201 5.68820446917702240985e-03 684.500000 0.000000 0.00000000000000000000e+00 5.000000 4 abelone.fann_cascade_rprop_multi_activation.train.out 301.719378 2.00646878431104663115e-03 6497.000000 0.000000 0.00000000000000000000e+00 35.000000 4 abelone.fann_cascade_quickprop_one_activation.test.out 292.755829 6.26235374482114054429e-03 13808.500000 0.000000 0.00000000000000000000e+00 84.500000 4 abelone.fann_cascade_quickprop_one_activation.train.out 285.252852 6.52104226100370162195e-03 13641.500000 1.000000 4.78927203065134074900e-04 83.500000 4 abelone.fann_cascade_quickprop_multi_activation.test.out 76.015090 6.00258629836296526971e-03 5249.250000 0.000000 0.00000000000000000000e+00 31.500000 4 abelone.fann_cascade_quickprop_multi_activation.train.out 299.808436 5.14772042715242079958e-03 12006.500000 0.500000 2.39463601532567037450e-04 75.250000 4 abelone.fann_rprop.test.out 2.887128 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bank32fm.fann_cascade_rprop_multi_activation.train.out 298.331648 8.78287029522796915473e-04 5928.500000 0.000000 0.00000000000000000000e+00 27.500000 4 bank32fm.fann_cascade_quickprop_one_activation.test.out 117.128177 2.73982969229382995052e-03 8324.500000 0.000000 0.00000000000000000000e+00 50.000000 4 bank32fm.fann_cascade_quickprop_one_activation.train.out 117.128177 2.79426879232498159150e-03 8324.500000 0.000000 0.00000000000000000000e+00 50.000000 4 bank32fm.fann_cascade_quickprop_multi_activation.test.out 218.136751 2.37181127351066651718e-03 8884.250000 0.000000 0.00000000000000000000e+00 51.250000 4 bank32fm.fann_cascade_quickprop_multi_activation.train.out 300.178922 2.10300311290789439592e-03 10714.000000 0.000000 0.00000000000000000000e+00 62.000000 4 bank32fm.fann_rprop.test.out 168.240702 1.80114388467667138789e-03 13014.250000 0.000000 0.00000000000000000000e+00 16.000000 4 bank32fm.fann_rprop.train.out 299.970626 1.33816443009190889565e-03 23165.000000 0.000000 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16.000000 4 bank32nh.fann_quickprop.train.out 300.031711 1.68496693927135503255e-02 23237.500000 13.000000 6.34765625000000000000e-03 16.000000 4 bank32nh.fann_batch.test.out 300.053956 1.54224471432798538456e-02 23321.750000 16.000000 7.81250000000000000000e-03 16.000000 4 bank32nh.fann_batch.train.out 300.053956 1.61764584965364242863e-02 23321.750000 11.000000 5.37109375000000000000e-03 16.000000 4 bank32nh.fann_incremental_momentum.test.out 2.425967 1.45653705575216618762e-02 158.250000 9.750000 4.76074218750000000000e-03 16.000000 4 bank32nh.fann_incremental_momentum.train.out 300.032679 6.14826143933647900619e-03 19420.750000 0.000000 0.00000000000000000000e+00 16.000000 4 bank32nh.lwnn.train.out 297.721935 4.87433199341777336627e-03 23922.000000 0.000000 0.00000000000000000000e+00 16.000000 4 bank32nh.lwnn.test.out 1.686583 1.47969770169485669020e-02 135.000000 10.000000 4.88281250000000000000e-03 16.000000 4 bank32nh.jneural.train.out 299.947367 1.06873294498545517994e-02 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pumadyn-32fm.fann_cascade_rprop_multi_activation.test.out 2.447500 3.96767472447460967100e-03 263.500000 0.000000 0.00000000000000000000e+00 3.000000 4 pumadyn-32fm.fann_cascade_rprop_multi_activation.train.out 133.861296 3.25672661613097678385e-06 10887.000000 0.000000 0.00000000000000000000e+00 46.000000 4 pumadyn-32fm.fann_cascade_quickprop_one_activation.test.out 0.756288 3.93468772249060474988e-03 1020.500000 0.000000 0.00000000000000000000e+00 8.250000 4 pumadyn-32fm.fann_cascade_quickprop_one_activation.train.out 167.377034 3.21991094190996699625e-03 19978.250000 0.000000 0.00000000000000000000e+00 109.250000 4 pumadyn-32fm.fann_cascade_quickprop_multi_activation.test.out 2.178739 3.96765351048453205479e-03 610.500000 0.000000 0.00000000000000000000e+00 5.750000 4 pumadyn-32fm.fann_cascade_quickprop_multi_activation.train.out 230.309779 1.38867662491677342287e-03 18884.750000 0.000000 0.00000000000000000000e+00 125.500000 4 pumadyn-32fm.fann_rprop.test.out 1.430000 4.19255271461634916597e-03 659.000000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.fann_rprop.train.out 299.982716 2.42263143185888303252e-03 135762.500000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.fann_quickprop.test.out 300.091654 4.16187167262660126510e-03 143322.750000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.fann_quickprop.train.out 300.091654 3.21218041642164523972e-03 143322.750000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.fann_batch.test.out 300.090789 4.24326094437869980741e-03 140951.500000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.fann_batch.train.out 300.090789 3.13814201344696837997e-03 140951.500000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.fann_incremental_momentum.test.out 4.290000 4.49500806382311084847e-03 1713.000000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.fann_incremental_momentum.train.out 299.995368 2.05056880181601810381e-03 119256.250000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.lwnn.train.out 299.783035 1.86371522239929575740e-03 148374.000000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.lwnn.test.out 1.871118 4.40116223911898776094e-03 933.250000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.jneural.train.out 300.025736 2.69703298492740550785e-03 18760.000000 0.000000 0.00000000000000000000e+00 10.000000 4 pumadyn-32fm.jneural.test.out 0.115000 4.41571854066193092425e-03 7.000000 0.000000 0.00000000000000000000e+00 10.000000 4 robot.fann_cascade_rprop_one_activation.test.out 0.270179 1.23644331992864130870e-02 233.750000 18.250000 1.02413019079685748552e-02 3.000000 4 robot.fann_cascade_rprop_one_activation.train.out 283.533187 5.77805760417992409850e-05 27557.750000 0.000000 0.00000000000000000000e+00 106.000000 4 robot.fann_cascade_rprop_multi_activation.test.out 14.403543 9.95027588699355994284e-03 1719.000000 14.500000 8.13692480359146964874e-03 9.500000 4 robot.fann_cascade_rprop_multi_activation.train.out 199.218968 1.04167063592821534795e-06 15787.000000 0.000000 0.00000000000000000000e+00 60.500000 4 robot.fann_cascade_quickprop_one_activation.test.out 1.318839 1.17663312998648253438e-02 1376.250000 20.750000 1.16442199775533099249e-02 10.000000 4 robot.fann_cascade_quickprop_one_activation.train.out 26.748900 5.82725704058324051049e-03 8314.750000 5.500000 4.90196078431372542217e-03 52.000000 4 robot.fann_cascade_quickprop_multi_activation.test.out 37.876616 1.00442248424774845106e-02 6453.000000 15.000000 8.41750841750841735656e-03 36.000000 4 robot.fann_cascade_quickprop_multi_activation.train.out 287.346755 2.86814703601912283307e-04 22554.750000 0.000000 0.00000000000000000000e+00 127.750000 4 robot.fann_rprop.test.out 0.677500 9.03386516329479812015e-03 42.750000 14.500000 8.13692480359147138347e-03 96.000000 4 robot.fann_rprop.train.out 241.307368 3.99536809082343881986e-06 14521.500000 0.000000 0.00000000000000000000e+00 96.000000 4 robot.fann_quickprop.test.out 299.977410 7.90337375163609190398e-03 18732.000000 12.000000 6.73400673400673371177e-03 96.000000 4 robot.fann_quickprop.train.out 299.977410 2.33096207678801844304e-03 18732.000000 0.000000 0.00000000000000000000e+00 96.000000 4 robot.fann_batch.test.out 299.907554 7.58095199038197938707e-03 18893.500000 13.500000 7.57575757575757596785e-03 96.000000 4 robot.fann_batch.train.out 300.017554 1.99598178895693013912e-03 18900.500000 0.000000 0.00000000000000000000e+00 96.000000 4 robot.fann_incremental_momentum.test.out 21.043849 5.82616801074206311362e-03 992.250000 9.750000 5.47138047138047076134e-03 96.000000 4 robot.fann_incremental_momentum.train.out 299.957050 6.00328589571843212027e-05 14098.750000 0.000000 0.00000000000000000000e+00 96.000000 4 robot.lwnn.train.out 286.390423 4.40509114935102512149e-04 11690.500000 0.000000 0.00000000000000000000e+00 96.000000 4 robot.lwnn.test.out 286.510423 7.43031472096836696772e-03 11695.500000 11.750000 6.59371492704826072523e-03 96.000000 4 robot.jneural.train.out 300.404151 9.20271330067451518581e-04 526.750000 0.000000 0.00000000000000000000e+00 96.000000 4 robot.jneural.test.out 300.404151 6.26697718319900801132e-03 526.750000 10.250000 5.75196408529741760179e-03 96.000000 4 soybean.fann_cascade_rprop_one_activation.test.out 5.321948 1.01819524174841195135e-02 1442.750000 61.250000 9.45361938570767230960e-03 6.000000 4 soybean.fann_cascade_rprop_one_activation.train.out 301.564940 7.68048737759943430693e-04 26256.500000 2.250000 3.46260387811634403991e-04 90.750000 4 soybean.fann_cascade_rprop_multi_activation.test.out 16.657500 1.04036092410420832888e-02 1169.000000 66.000000 1.01867572156196933841e-02 5.000000 4 soybean.fann_cascade_rprop_multi_activation.train.out 288.432388 5.28809846320405885049e-04 15510.000000 2.000000 3.07787011388119409981e-04 54.000000 4 soybean.fann_cascade_quickprop_one_activation.test.out 7.073143 1.16055374082332050240e-02 1815.000000 70.000000 1.08041364408087651322e-02 12.000000 4 soybean.fann_cascade_quickprop_one_activation.train.out 7.073143 8.35668198636280341873e-03 1815.000000 44.250000 6.80978762696214223044e-03 12.000000 4 soybean.fann_cascade_quickprop_multi_activation.test.out 20.540943 1.08467820194441469384e-02 3305.250000 66.500000 1.02639296187683297379e-02 21.000000 4 soybean.fann_cascade_quickprop_multi_activation.train.out 20.540943 7.60855581436977942977e-03 3305.250000 39.500000 6.07879347491535864528e-03 21.000000 4 soybean.fann_rprop.test.out 2.640268 4.38164441874325794046e-02 386.500000 320.750000 4.95060966198487412138e-02 24.000000 4 soybean.fann_rprop.train.out 1.650255 4.19136634630113483202e-02 243.750000 314.750000 4.84379809172052963606e-02 24.000000 4 soybean.fann_quickprop.test.out 300.048168 1.21226363758294465589e-02 51079.250000 90.000000 1.38910325667541308114e-02 24.000000 4 soybean.fann_quickprop.train.out 300.048168 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soybean.jneural.test.out 10.429912 9.30209136405481772092e-03 244.500000 75.750000 1.16916190770180589142e-02 24.000000 4 thyroid.fann_cascade_rprop_one_activation.test.out 39.989840 1.87363000027275447557e-02 2488.250000 255.750000 2.36805555555555553859e-02 13.500000 4 thyroid.fann_cascade_rprop_one_activation.train.out 306.741883 1.27893549009963775065e-02 8802.000000 171.750000 1.59027777777777795665e-02 39.750000 4 thyroid.fann_cascade_rprop_multi_activation.test.out 77.274686 9.60319933422722031569e-03 1094.250000 129.250000 1.19675925925925921711e-02 9.000000 4 thyroid.fann_cascade_rprop_multi_activation.train.out 302.393737 7.00753235319021786781e-04 3648.333333 5.000000 4.62962962962962980631e-04 22.500000 6 thyroid.fann_cascade_quickprop_one_activation.test.out 273.851757 3.61298309553120949866e-02 9580.750000 476.250000 4.40972222222222320909e-02 58.750000 4 thyroid.fann_cascade_quickprop_one_activation.train.out 273.851757 3.79971392148817782575e-02 9580.750000 508.250000 4.70601851851851771258e-02 58.750000 4 thyroid.fann_cascade_quickprop_multi_activation.test.out 221.249814 2.95987885615262812100e-02 6017.500000 376.500000 3.48611111111111135474e-02 35.000000 4 thyroid.fann_cascade_quickprop_multi_activation.train.out 308.417420 2.90667148163319677412e-02 7469.500000 386.000000 3.57407407407407395894e-02 43.000000 4 thyroid.fann_rprop.test.out 7.808709 1.08959961017801639643e-02 244.000000 143.000000 1.32407407407407404221e-02 24.000000 4 thyroid.fann_rprop.train.out 9.145926 4.12015431021693114000e-03 284.000000 50.500000 4.67592592592592625617e-03 24.000000 4 thyroid.fann_quickprop.test.out 299.931932 4.42729699811515997609e-02 9648.000000 506.000000 4.68518518518518528593e-02 24.000000 4 thyroid.fann_quickprop.train.out 299.931932 4.88408905917870478364e-02 9648.000000 562.000000 5.20370370370370344260e-02 24.000000 4 thyroid.fann_batch.test.out 300.047349 4.53473495537719054060e-02 9394.000000 506.000000 4.68518518518518528593e-02 24.000000 4 thyroid.fann_batch.train.out 300.047349 4.95553748135874813641e-02 9394.000000 562.000000 5.20370370370370344260e-02 24.000000 4 thyroid.fann_incremental_momentum.test.out 197.491289 1.40010208186225598020e-02 4621.500000 176.750000 1.63657407407407397282e-02 24.000000 4 thyroid.fann_incremental_momentum.train.out 292.203655 3.67097202712467641367e-03 6813.000000 51.500000 4.76851851851851828851e-03 24.000000 4 thyroid.lwnn.train.out 290.599281 3.26900334464936062617e-03 8196.750000 45.750000 4.23611111111111063310e-03 24.000000 4 thyroid.lwnn.test.out 164.372165 1.36575171566259111200e-02 4698.000000 168.500000 1.56018518518518528593e-02 24.000000 4 thyroid.jneural.train.out 288.499261 6.19221403001681772876e-03 1777.000000 82.750000 7.66203703703703664640e-03 24.000000 4 thyroid.jneural.test.out 252.205204 1.35795972177160367139e-02 1560.500000 169.750000 1.57175925925925920323e-02 24.000000 4 two-spiral.fann_cascade_rprop_one_activation.test.out 14.200190 1.43576619504884123169e-01 9422.750000 33.000000 1.70984455958549219634e-01 45.750000 4 two-spiral.fann_cascade_rprop_one_activation.train.out 294.335919 4.62513294914917305820e-07 40812.250000 0.000000 0.00000000000000000000e+00 163.750000 4 two-spiral.fann_cascade_rprop_multi_activation.test.out 2.733568 1.99565649691583357761e-01 992.000000 66.000000 3.41968911917098439268e-01 8.000000 4 two-spiral.fann_cascade_rprop_multi_activation.train.out 65.066055 4.95185592734650998310e-08 12825.250000 0.000000 0.00000000000000000000e+00 60.500000 4 two-spiral.fann_cascade_quickprop_one_activation.test.out 239.986907 2.43684297469496963240e-01 41766.000000 88.750000 4.59844559585492196341e-01 198.500000 4 two-spiral.fann_cascade_quickprop_one_activation.train.out 239.986907 2.43832751074098741384e-01 41766.000000 87.750000 4.54663212435233110575e-01 198.500000 4 two-spiral.fann_cascade_quickprop_multi_activation.test.out 0.985588 2.34522965907656005813e-01 911.000000 82.000000 4.24870466321243478447e-01 7.750000 4 two-spiral.fann_cascade_quickprop_multi_activation.train.out 301.296024 2.21351772001419065412e-05 30244.750000 0.000000 0.00000000000000000000e+00 173.250000 4 two-spiral.fann_rprop.test.out 110.114609 2.03635494137086814348e-01 58973.250000 60.500000 3.13471502590673578581e-01 30.000000 4 two-spiral.fann_rprop.train.out 38.612717 1.46328576333805443488e-01 20578.250000 40.250000 2.08549222797927480411e-01 30.000000 4 two-spiral.fann_quickprop.test.out 300.091942 2.46777289425367890585e-01 185172.750000 95.000000 4.92227979274611371352e-01 30.000000 4 two-spiral.fann_quickprop.train.out 300.091942 2.46717653601167236088e-01 185172.750000 93.750000 4.85751295336787514145e-01 30.000000 4 two-spiral.fann_batch.test.out 300.087093 2.45793508653533443464e-01 186090.750000 93.000000 4.81865284974093255332e-01 30.000000 4 two-spiral.fann_batch.train.out 300.087093 2.45793043960691709460e-01 186090.750000 93.000000 4.81865284974093199821e-01 30.000000 4 two-spiral.fann_incremental_momentum.test.out 46.095293 2.02472728717701638823e-01 25108.750000 65.250000 3.38082901554404180455e-01 30.000000 4 two-spiral.fann_incremental_momentum.train.out 53.575627 1.71653168602657169028e-01 28751.000000 55.250000 2.86269430051813489335e-01 30.000000 4 two-spiral.lwnn.test.out 288.314963 1.60326033980209142760e-01 297319.500000 57.500000 2.97927461139896376796e-01 30.000000 4 two-spiral.lwnn.train.out 7.590000 1.72290950102615664230e-01 8129.500000 63.750000 3.30310880829015551807e-01 30.000000 4 two-spiral.jneural.test.out 300.006334 2.42606312772428944768e-01 44013.750000 90.000000 4.66321243523316053547e-01 30.000000 4 two-spiral.jneural.train.out 85.393573 2.44508295409398501707e-01 12508.500000 92.000000 4.76683937823834169567e-01 30.000000 4 END libfann-2.1.0~beta~dfsg/benchmarks/summary2tex.pl000077500000000000000000000122511140315177100221750ustar00rootroot00000000000000#!/usr/bin/perl use Data::Dumper; use POSIX; use Math::BigFloat; $path = "/home/lukesky/rl-thesis/doc"; @algorithms = ({ALGO => 'fann_cascade_rprop_one_activation', NAME => 'C2 RPROP One'}, {ALGO => 'fann_cascade_rprop_multi_activation', NAME => 'C2 RPROP Multi'}, {ALGO => 'fann_cascade_quickprop_one_activation', NAME => 'C2 Quickprop One'}, {ALGO => 'fann_cascade_quickprop_multi_activation', NAME => 'C2 Quickprop Multi'}, {ALGO => 'fann_rprop', NAME => 'iRPROP$^{-}$'}, {ALGO => 'fann_quickprop', NAME => 'Quickprop'}, {ALGO => 'fann_batch', NAME => 'Batch'}, {ALGO => 'fann_incremental_momentum', NAME => 'Incremental'}, {ALGO => 'lwnn', NAME => 'Lwnn Incr.'}, {ALGO => 'jneural', NAME => 'Jneural Incr.'}); #This is not used yet %dataSetType = {'abelone' => 'R', 'bank32fm' => 'R', 'bank32nh' => 'R', 'kin32fm' => 'R', 'census-house' => 'R', 'building' => 'R', 'diabetes' => 'C', 'gene' => 'C', 'mushroom' => 'C', 'parity8' => 'C', 'parity13' => 'C', 'pumadyn-32fm' => 'R', 'robot' => 'R', 'soybean' => 'C', 'thyroid' => 'C', 'two-spiral' => 'C'}; $oldDataSet = ""; $numDataSet = 0; while(<>) { chop; @data = split / /; @words = split(/\./, @data[0]); $dataSet = @words[0]; $algoritm = @words[1]; $test_train = @words[2]; $mse = Math::BigFloat->new(@data[2]); $meanbitfail = Math::BigFloat->new(@data[5]); if($oldDataSet ne $dataSet && $oldDataSet ne "") { $numDataSet++; print "**$oldDataSet**\n"; @test = keys(%summary); #calculate min and max mse $min_test_mse = 100; $min_train_mse = 100; $max_test_mse = -100; $max_train_mse = -100; while(($algo, $foo) = each(%summary)) { if($foo->{'test'} < $min_test_mse) { $min_test_mse = $foo->{'test'}; } if($foo->{'train'} < $min_train_mse) { $min_train_mse = $foo->{'train'}; } if($foo->{'test'} > $max_test_mse) { $max_test_mse = $foo->{'test'}; } if($foo->{'train'} > $max_train_mse) { $max_train_mse = $foo->{'train'}; } } #Adds rank to each set and calculates percent etc. for($i = 1; $i <= scalar(%summary)+2; $i++) { $min_algo_test = 0; $min_algo_train = 0; while(($algo, $foo) = each(%summary)) { if(!exists($foo->{'test_rank'}) && (!$min_algo_test || $foo->{'test'} < $summary{$min_algo_test}->{'test'})) { $min_algo_test = $algo; } if(!exists($foo->{'train_rank'}) && (!$min_algo_train || $foo->{'train'} < $summary{$min_algo_train}->{'train'})) { $min_algo_train = $algo; } } $summary{$min_algo_test}->{'test_rank'} = $i; $summary{$min_algo_train}->{'train_rank'} = $i; $summary{$min_algo_test}->{'test_percent'} = (($summary{$min_algo_test}->{'test'}-$min_test_mse)*100)/($max_test_mse-$min_test_mse); $summary{$min_algo_train}->{'train_percent'} = (($summary{$min_algo_train}->{'train'}-$min_train_mse)*100)/($max_train_mse-$min_train_mse); } #write tex to file open(OUT, "> $path/".$oldDataSet."_table.tex") or die("unable to open file $path/".$oldDataSet."_table.tex"); print OUT '\begin{tabular} {|l|r|r|r|r|r|r|}'."\n"; print OUT '\hline'."\n"; print OUT '& \multicolumn{3}{c|}{\textbf{Best Train}} & \multicolumn{3}{c|}{\textbf{Best Test}} \\\\'."\n"; print OUT '\hline'."\n"; print OUT '\textbf{Configuration} & \textbf{MSE} & \textbf{Rank} & \textbf{\%} & \textbf{MSE} & \textbf{Rank} & \textbf{\%} \\\\'."\n"; print OUT '\hline'."\n"; foreach $algo (@algorithms) { $foo = $summary{$algo->{ALGO}}; printf OUT ("%s & %.8f & %d & %.2f & %.8f & %d & %.2f\\\\\n\\hline\n", $algo->{NAME}, $foo->{'train'}, $foo->{'train_rank'}, $foo->{'train_percent'}, $foo->{'test'}, $foo->{'test_rank'}, $foo->{'test_percent'}); $total{$algo->{ALGO}}->{'train'} += $foo->{'train'}; $total{$algo->{ALGO}}->{'test'} += $foo->{'test'}; $total{$algo->{ALGO}}->{'train_percent'} += $foo->{'train_percent'}; $total{$algo->{ALGO}}->{'test_percent'} += $foo->{'test_percent'}; $total{$algo->{ALGO}}->{'train_rank'} += $foo->{'train_rank'}; $total{$algo->{ALGO}}->{'test_rank'} += $foo->{'test_rank'}; } print OUT '\end{tabular}'."\n"; close(OUT); undef(%summary); } $summary{$algoritm}->{$test_train} = $mse; $oldDataSet = $dataSet; } print "**Average**\n"; open(OUT, "> $path/average_table.tex") or die("unable to open file $path/average_table.tex"); print OUT '\begin{tabular} {|l|r|r|r|r|r|r|}'."\n"; print OUT '\hline'."\n"; print OUT '& \multicolumn{3}{c|}{\textbf{Best Train}} & \multicolumn{3}{c|}{\textbf{Best Test}} \\\\'."\n"; print OUT '\hline'."\n"; print OUT '\textbf{Configuration} & \textbf{MSE} & \textbf{Rank} & \textbf{\%} & \textbf{MSE} & \textbf{Rank} & \textbf{\%} \\\\'."\n"; print OUT '\hline'."\n"; foreach $algo (@algorithms) { $foo = $total{$algo->{ALGO}}; printf OUT ("%s & %.6f & %.2f & %.2f & %.6f & %.2f & %.2f\\\\\n\\hline\n", $algo->{NAME}, $foo->{'train'}/$numDataSet, $foo->{'train_rank'}/$numDataSet, $foo->{'train_percent'}/$numDataSet, $foo->{'test'}/$numDataSet, $foo->{'test_rank'}/$numDataSet, $foo->{'test_percent'}/$numDataSet); } print OUT '\end{tabular}'."\n"; close(OUT); libfann-2.1.0~beta~dfsg/benchmarks/two-spirals.c000077500000000000000000000102361140315177100217710ustar00rootroot00000000000000/* Two-Spirals Benchmark Data Set Generator v1.0 By: Matt White (mwhite+@cmu.edu) Based on code by Alexis Wieland of MITRE Corporation Usage: a.out Density defaults to '1'. Radius defaults to '6.5'. These are the values used in the original benchmark. Changing the density changes the number of data points generated. Changing the radius changes the range of numbers generated. Description ~~~~~~~~~~~ This program generates two sets of points, each with 96 * density + 1 data points (3 revolutions of 32 times the density plus one end point). The output of this program is in the standard CMU Neural Network Benchmark format. For more information, see the accompanying database file, 'two-spirals'. */ #include #include #include #include #ifndef PI /* PI is defined on some systems, if it is not, define */ #define PI 3.1416 /* it to four decimal places. Good enough for our */ #endif /* purposes. */ /* Function Prototypes */ void getCommandLine(int *, double *, int, char **); void printHeader(int, double); void printSet(int, double); int main(int argc, char **argv) { int density; /* Density of the data set to generate */ double maxRadius; /* Maximum radius of the data set to generate */ getCommandLine(&density, &maxRadius, argc, argv); printHeader(density, maxRadius); printSet(density, maxRadius); return 0; } /* getCommandLine - This function reads the values from the command line and interprets them. The first argument is read as the density and the second as the radius. If there is no value for radius, it defaults to 6.5 while density defaults to 1. Extra arguments are ignored. */ void getCommandLine(int *density, double *maxRadius, int argc, char **argv) { if(argc < 2) *density = 1; else *density = atoi(*(argv + 1)); if(argc < 3) *maxRadius = 6.5; else *maxRadius = atof(*(argv + 2)); } /* printHeader - This function prints out the header information for the data set. This includes generation time (local), density and radius. It also prints the $SETUP segment for the data set. */ void printHeader(int density, double maxRadius) { time_t genTime; time(&genTime); /* * printf (";Two-Spirals Benchmark\n"); * printf (";Generated: %s", asctime( localtime( &genTime ) )); * printf (";Density: %d\n", density ); * printf (";Radius: %.2f\n\n", maxRadius ); * printf (";Program by: Matt White (mwhite+@cmu.edu)\n"); * printf (";Benchmark by: Alexis Wieland of MITRE Corporation\n"); * printf ("; Any questions should be directed to neural-bench@cs.cmu.edu."); * printf ("\n\n"); * * printf ("$SETUP\n\n"); * printf ("PROTOCOL: IO;\n"); * printf ("OFFSET: 0;\n"); * printf ("INPUTS: 2;\n"); * printf ("OUTPUTS: 1;\n\n"); * printf ("IN [1]: CONT {%.2f,-%.2f};\n", maxRadius, maxRadius ); * printf ("IN [2]: CONT {%.2f,-%.2f};\n\n", maxRadius, maxRadius ); * printf ("OUT [1]: BINARY;\n\n\n"); * * printf ("$TRAIN\n\n"); */ printf("%d 2 1\n", (density * 96 + 1) * 2); } /* printSet - Generates and prints out a data set having the specified density and radius. */ void printSet(int density, double maxRadius) { int points, /* Number of interior data points to generate */ i; /* Indexing variable */ double x, /* x coordinate */ y, /* y coordinate */ angle, /* Angle to calculate */ radius; /* Radius at current iteration */ points = (96 * density); for(i = 0; i <= points; i++) { /* Angle is based on the iteration * PI/16, divided by point density */ angle = (i * PI) / (16.0 * density); /* Radius is the maximum radius * the fraction of iterations left */ radius = maxRadius * ((104 * density) - i) / (104 * density); /* x and y are based upon cos and sin of the current radius */ x = radius * cos(angle); y = radius * sin(angle); /* printf ("%8.5f, %8.5f => +;\n", x, y ); */ /* printf ("%8.5f, %8.5f => -;\n", -x, -y ); */ if(i % 2 == 1) { printf("%8.5f %8.5f 1\n", x, y); printf("%8.5f %8.5f 0\n", -x, -y); } } } libfann-2.1.0~beta~dfsg/config.guess000077500000000000000000001247531140315177100175530ustar00rootroot00000000000000#! /bin/sh # Attempt to guess a canonical system name. # Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, # 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. timestamp='2005-08-03' # This file is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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Otherwise, it exits with 1. # # The plan is that this can be called by configure scripts if you # don't specify an explicit build system type. me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] Output the configuration name of the system \`$me' is run on. Operation modes: -h, --help print this help, then exit -t, --time-stamp print date of last modification, then exit -v, --version print version number, then exit Report bugs and patches to ." version="\ GNU config.guess ($timestamp) Originally written by Per Bothner. Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE." help=" Try \`$me --help' for more information." # Parse command line while test $# -gt 0 ; do case $1 in --time-stamp | --time* | -t ) echo "$timestamp" ; exit ;; --version | -v ) echo "$version" ; exit ;; --help | --h* | -h ) echo "$usage"; exit ;; -- ) # Stop option processing shift; break ;; - ) # Use stdin as input. break ;; -* ) echo "$me: invalid option $1$help" >&2 exit 1 ;; * ) break ;; esac done if test $# != 0; then echo "$me: too many arguments$help" >&2 exit 1 fi trap 'exit 1' 1 2 15 # CC_FOR_BUILD -- compiler used by this script. Note that the use of a # compiler to aid in system detection is discouraged as it requires # temporary files to be created and, as you can see below, it is a # headache to deal with in a portable fashion. # Historically, `CC_FOR_BUILD' used to be named `HOST_CC'. We still # use `HOST_CC' if defined, but it is deprecated. # Portable tmp directory creation inspired by the Autoconf team. set_cc_for_build=' trap "exitcode=\$?; (rm -f \$tmpfiles 2>/dev/null; rmdir \$tmp 2>/dev/null) && exit \$exitcode" 0 ; trap "rm -f \$tmpfiles 2>/dev/null; rmdir \$tmp 2>/dev/null; exit 1" 1 2 13 15 ; : ${TMPDIR=/tmp} ; { tmp=`(umask 077 && mktemp -d -q "$TMPDIR/cgXXXXXX") 2>/dev/null` && test -n "$tmp" && test -d "$tmp" ; } || { test -n "$RANDOM" && tmp=$TMPDIR/cg$$-$RANDOM && (umask 077 && mkdir $tmp) ; } || { tmp=$TMPDIR/cg-$$ && (umask 077 && mkdir $tmp) && echo "Warning: creating insecure temp directory" >&2 ; } || { echo "$me: cannot create a temporary directory in $TMPDIR" >&2 ; exit 1 ; } ; dummy=$tmp/dummy ; tmpfiles="$dummy.c $dummy.o $dummy.rel $dummy" ; case $CC_FOR_BUILD,$HOST_CC,$CC in ,,) echo "int x;" > $dummy.c ; for c in cc gcc c89 c99 ; do if ($c -c -o $dummy.o $dummy.c) >/dev/null 2>&1 ; then CC_FOR_BUILD="$c"; break ; fi ; done ; if test x"$CC_FOR_BUILD" = x ; then CC_FOR_BUILD=no_compiler_found ; fi ;; ,,*) CC_FOR_BUILD=$CC ;; ,*,*) CC_FOR_BUILD=$HOST_CC ;; esac ; set_cc_for_build= ;' # This is needed to find uname on a Pyramid OSx when run in the BSD universe. # (ghazi@noc.rutgers.edu 1994-08-24) if (test -f /.attbin/uname) >/dev/null 2>&1 ; then PATH=$PATH:/.attbin ; export PATH fi UNAME_MACHINE=`(uname -m) 2>/dev/null` || UNAME_MACHINE=unknown UNAME_RELEASE=`(uname -r) 2>/dev/null` || UNAME_RELEASE=unknown UNAME_SYSTEM=`(uname -s) 2>/dev/null` || UNAME_SYSTEM=unknown UNAME_VERSION=`(uname -v) 2>/dev/null` || UNAME_VERSION=unknown # Note: order is significant - the case branches are not exclusive. case "${UNAME_MACHINE}:${UNAME_SYSTEM}:${UNAME_RELEASE}:${UNAME_VERSION}" in *:NetBSD:*:*) # NetBSD (nbsd) targets should (where applicable) match one or # more of the tupples: *-*-netbsdelf*, *-*-netbsdaout*, # *-*-netbsdecoff* and *-*-netbsd*. For targets that recently # switched to ELF, *-*-netbsd* would select the old # object file format. This provides both forward # compatibility and a consistent mechanism for selecting the # object file format. # # Note: NetBSD doesn't particularly care about the vendor # portion of the name. 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However, they do not need # kernel version information, so it can be replaced with a # suitable tag, in the style of linux-gnu. case "${UNAME_VERSION}" in Debian*) release='-gnu' ;; *) release=`echo ${UNAME_RELEASE}|sed -e 's/[-_].*/\./'` ;; esac # Since CPU_TYPE-MANUFACTURER-KERNEL-OPERATING_SYSTEM: # contains redundant information, the shorter form: # CPU_TYPE-MANUFACTURER-OPERATING_SYSTEM is used. echo "${machine}-${os}${release}" exit ;; *:OpenBSD:*:*) UNAME_MACHINE_ARCH=`arch | sed 's/OpenBSD.//'` echo ${UNAME_MACHINE_ARCH}-unknown-openbsd${UNAME_RELEASE} exit ;; *:ekkoBSD:*:*) echo ${UNAME_MACHINE}-unknown-ekkobsd${UNAME_RELEASE} exit ;; macppc:MirBSD:*:*) echo powerppc-unknown-mirbsd${UNAME_RELEASE} exit ;; *:MirBSD:*:*) echo ${UNAME_MACHINE}-unknown-mirbsd${UNAME_RELEASE} exit ;; alpha:OSF1:*:*) case $UNAME_RELEASE in *4.0) UNAME_RELEASE=`/usr/sbin/sizer -v | awk '{print $3}'` ;; *5.*) UNAME_RELEASE=`/usr/sbin/sizer -v | awk '{print $4}'` ;; esac # According to Compaq, /usr/sbin/psrinfo has been available on # OSF/1 and Tru64 systems produced since 1995. I hope that # covers most systems running today. This code pipes the CPU # types through head -n 1, so we only detect the type of CPU 0. ALPHA_CPU_TYPE=`/usr/sbin/psrinfo -v | sed -n -e 's/^ The alpha \(.*\) processor.*$/\1/p' | head -n 1` case "$ALPHA_CPU_TYPE" in "EV4 (21064)") UNAME_MACHINE="alpha" ;; "EV4.5 (21064)") UNAME_MACHINE="alpha" ;; "LCA4 (21066/21068)") UNAME_MACHINE="alpha" ;; "EV5 (21164)") UNAME_MACHINE="alphaev5" ;; "EV5.6 (21164A)") UNAME_MACHINE="alphaev56" ;; "EV5.6 (21164PC)") UNAME_MACHINE="alphapca56" ;; "EV5.7 (21164PC)") UNAME_MACHINE="alphapca57" ;; "EV6 (21264)") UNAME_MACHINE="alphaev6" ;; "EV6.7 (21264A)") UNAME_MACHINE="alphaev67" ;; "EV6.8CB (21264C)") UNAME_MACHINE="alphaev68" ;; "EV6.8AL (21264B)") UNAME_MACHINE="alphaev68" ;; "EV6.8CX (21264D)") UNAME_MACHINE="alphaev68" ;; "EV6.9A (21264/EV69A)") UNAME_MACHINE="alphaev69" ;; "EV7 (21364)") UNAME_MACHINE="alphaev7" ;; "EV7.9 (21364A)") UNAME_MACHINE="alphaev79" ;; esac # A Pn.n version is a patched version. # A Vn.n version is a released version. # A Tn.n version is a released field test version. # A Xn.n version is an unreleased experimental baselevel. # 1.2 uses "1.2" for uname -r. echo ${UNAME_MACHINE}-dec-osf`echo ${UNAME_RELEASE} | sed -e 's/^[PVTX]//' | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz'` exit ;; Alpha\ *:Windows_NT*:*) # How do we know it's Interix rather than the generic POSIX subsystem? # Should we change UNAME_MACHINE based on the output of uname instead # of the specific Alpha model? echo alpha-pc-interix exit ;; 21064:Windows_NT:50:3) echo alpha-dec-winnt3.5 exit ;; Amiga*:UNIX_System_V:4.0:*) echo m68k-unknown-sysv4 exit ;; *:[Aa]miga[Oo][Ss]:*:*) echo ${UNAME_MACHINE}-unknown-amigaos exit ;; *:[Mm]orph[Oo][Ss]:*:*) echo ${UNAME_MACHINE}-unknown-morphos exit ;; *:OS/390:*:*) echo i370-ibm-openedition exit ;; *:z/VM:*:*) echo s390-ibm-zvmoe exit ;; *:OS400:*:*) echo powerpc-ibm-os400 exit ;; arm:RISC*:1.[012]*:*|arm:riscix:1.[012]*:*) echo arm-acorn-riscix${UNAME_RELEASE} exit ;; arm:riscos:*:*|arm:RISCOS:*:*) echo arm-unknown-riscos exit ;; SR2?01:HI-UX/MPP:*:* | SR8000:HI-UX/MPP:*:*) echo hppa1.1-hitachi-hiuxmpp exit ;; Pyramid*:OSx*:*:* | MIS*:OSx*:*:* | MIS*:SMP_DC-OSx*:*:*) # akee@wpdis03.wpafb.af.mil (Earle F. Ake) contributed MIS and NILE. if test "`(/bin/universe) 2>/dev/null`" = att ; then echo pyramid-pyramid-sysv3 else echo pyramid-pyramid-bsd fi exit ;; NILE*:*:*:dcosx) echo pyramid-pyramid-svr4 exit ;; DRS?6000:unix:4.0:6*) echo sparc-icl-nx6 exit ;; DRS?6000:UNIX_SV:4.2*:7* | DRS?6000:isis:4.2*:7*) case `/usr/bin/uname -p` in sparc) echo sparc-icl-nx7; exit ;; esac ;; sun4H:SunOS:5.*:*) echo sparc-hal-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:5.*:* | tadpole*:SunOS:5.*:*) echo sparc-sun-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; i86pc:SunOS:5.*:*) echo i386-pc-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:6*:*) # According to config.sub, this is the proper way to canonicalize # SunOS6. Hard to guess exactly what SunOS6 will be like, but # it's likely to be more like Solaris than SunOS4. echo sparc-sun-solaris3`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:*:*) case "`/usr/bin/arch -k`" in Series*|S4*) UNAME_RELEASE=`uname -v` ;; esac # Japanese Language versions have a version number like `4.1.3-JL'. echo sparc-sun-sunos`echo ${UNAME_RELEASE}|sed -e 's/-/_/'` exit ;; sun3*:SunOS:*:*) echo m68k-sun-sunos${UNAME_RELEASE} exit ;; sun*:*:4.2BSD:*) UNAME_RELEASE=`(sed 1q /etc/motd | awk '{print substr($5,1,3)}') 2>/dev/null` test "x${UNAME_RELEASE}" = "x" && UNAME_RELEASE=3 case "`/bin/arch`" in sun3) echo m68k-sun-sunos${UNAME_RELEASE} ;; sun4) echo sparc-sun-sunos${UNAME_RELEASE} ;; esac exit ;; aushp:SunOS:*:*) echo sparc-auspex-sunos${UNAME_RELEASE} exit ;; # The situation for MiNT is a little confusing. The machine name # can be virtually everything (everything which is not # "atarist" or "atariste" at least should have a processor # > m68000). The system name ranges from "MiNT" over "FreeMiNT" # to the lowercase version "mint" (or "freemint"). Finally # the system name "TOS" denotes a system which is actually not # MiNT. But MiNT is downward compatible to TOS, so this should # be no problem. atarist[e]:*MiNT:*:* | atarist[e]:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; atari*:*MiNT:*:* | atari*:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; *falcon*:*MiNT:*:* | *falcon*:*mint:*:* | *falcon*:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; milan*:*MiNT:*:* | milan*:*mint:*:* | *milan*:*TOS:*:*) echo m68k-milan-mint${UNAME_RELEASE} exit ;; hades*:*MiNT:*:* | hades*:*mint:*:* | *hades*:*TOS:*:*) echo m68k-hades-mint${UNAME_RELEASE} exit ;; *:*MiNT:*:* | *:*mint:*:* | *:*TOS:*:*) echo m68k-unknown-mint${UNAME_RELEASE} exit ;; m68k:machten:*:*) echo m68k-apple-machten${UNAME_RELEASE} exit ;; powerpc:machten:*:*) echo powerpc-apple-machten${UNAME_RELEASE} exit ;; RISC*:Mach:*:*) echo mips-dec-mach_bsd4.3 exit ;; RISC*:ULTRIX:*:*) echo mips-dec-ultrix${UNAME_RELEASE} exit ;; VAX*:ULTRIX*:*:*) echo vax-dec-ultrix${UNAME_RELEASE} exit ;; 2020:CLIX:*:* | 2430:CLIX:*:*) echo clipper-intergraph-clix${UNAME_RELEASE} exit ;; mips:*:*:UMIPS | mips:*:*:RISCos) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #ifdef __cplusplus #include /* for printf() prototype */ int main (int argc, char *argv[]) { #else int main (argc, argv) int argc; char *argv[]; { #endif #if defined (host_mips) && defined (MIPSEB) #if defined (SYSTYPE_SYSV) printf ("mips-mips-riscos%ssysv\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_SVR4) printf ("mips-mips-riscos%ssvr4\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_BSD43) || defined(SYSTYPE_BSD) printf ("mips-mips-riscos%sbsd\n", argv[1]); exit (0); #endif #endif exit (-1); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && dummyarg=`echo "${UNAME_RELEASE}" | sed -n 's/\([0-9]*\).*/\1/p'` && SYSTEM_NAME=`$dummy $dummyarg` && { echo "$SYSTEM_NAME"; exit; } echo mips-mips-riscos${UNAME_RELEASE} exit ;; Motorola:PowerMAX_OS:*:*) echo powerpc-motorola-powermax exit ;; Motorola:*:4.3:PL8-*) echo powerpc-harris-powermax exit ;; Night_Hawk:*:*:PowerMAX_OS | Synergy:PowerMAX_OS:*:*) echo powerpc-harris-powermax exit ;; Night_Hawk:Power_UNIX:*:*) echo powerpc-harris-powerunix exit ;; m88k:CX/UX:7*:*) echo m88k-harris-cxux7 exit ;; m88k:*:4*:R4*) echo m88k-motorola-sysv4 exit ;; m88k:*:3*:R3*) echo m88k-motorola-sysv3 exit ;; AViiON:dgux:*:*) # DG/UX returns AViiON for all architectures UNAME_PROCESSOR=`/usr/bin/uname -p` if [ $UNAME_PROCESSOR = mc88100 ] || [ $UNAME_PROCESSOR = mc88110 ] then if [ ${TARGET_BINARY_INTERFACE}x = m88kdguxelfx ] || \ [ ${TARGET_BINARY_INTERFACE}x = x ] then echo m88k-dg-dgux${UNAME_RELEASE} else echo m88k-dg-dguxbcs${UNAME_RELEASE} fi else echo i586-dg-dgux${UNAME_RELEASE} fi exit ;; M88*:DolphinOS:*:*) # DolphinOS (SVR3) echo m88k-dolphin-sysv3 exit ;; M88*:*:R3*:*) # Delta 88k system running SVR3 echo m88k-motorola-sysv3 exit ;; XD88*:*:*:*) # Tektronix XD88 system running UTekV (SVR3) echo m88k-tektronix-sysv3 exit ;; Tek43[0-9][0-9]:UTek:*:*) # Tektronix 4300 system running UTek (BSD) echo m68k-tektronix-bsd exit ;; *:IRIX*:*:*) echo mips-sgi-irix`echo ${UNAME_RELEASE}|sed -e 's/-/_/g'` exit ;; ????????:AIX?:[12].1:2) # AIX 2.2.1 or AIX 2.1.1 is RT/PC AIX. echo romp-ibm-aix # uname -m gives an 8 hex-code CPU id exit ;; # Note that: echo "'`uname -s`'" gives 'AIX ' i*86:AIX:*:*) echo i386-ibm-aix exit ;; ia64:AIX:*:*) if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${UNAME_MACHINE}-ibm-aix${IBM_REV} exit ;; *:AIX:2:3) if grep bos325 /usr/include/stdio.h >/dev/null 2>&1; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include main() { if (!__power_pc()) exit(1); puts("powerpc-ibm-aix3.2.5"); exit(0); } EOF if $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` then echo "$SYSTEM_NAME" else echo rs6000-ibm-aix3.2.5 fi elif grep bos324 /usr/include/stdio.h >/dev/null 2>&1; then echo rs6000-ibm-aix3.2.4 else echo rs6000-ibm-aix3.2 fi exit ;; *:AIX:*:[45]) IBM_CPU_ID=`/usr/sbin/lsdev -C -c processor -S available | sed 1q | awk '{ print $1 }'` if /usr/sbin/lsattr -El ${IBM_CPU_ID} | grep ' POWER' >/dev/null 2>&1; then IBM_ARCH=rs6000 else IBM_ARCH=powerpc fi if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${IBM_ARCH}-ibm-aix${IBM_REV} exit ;; *:AIX:*:*) echo rs6000-ibm-aix exit ;; ibmrt:4.4BSD:*|romp-ibm:BSD:*) echo romp-ibm-bsd4.4 exit ;; ibmrt:*BSD:*|romp-ibm:BSD:*) # covers RT/PC BSD and echo romp-ibm-bsd${UNAME_RELEASE} # 4.3 with uname added to exit ;; # report: romp-ibm BSD 4.3 *:BOSX:*:*) echo rs6000-bull-bosx exit ;; DPX/2?00:B.O.S.:*:*) echo m68k-bull-sysv3 exit ;; 9000/[34]??:4.3bsd:1.*:*) echo m68k-hp-bsd exit ;; hp300:4.4BSD:*:* | 9000/[34]??:4.3bsd:2.*:*) echo m68k-hp-bsd4.4 exit ;; 9000/[34678]??:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` case "${UNAME_MACHINE}" in 9000/31? ) HP_ARCH=m68000 ;; 9000/[34]?? ) HP_ARCH=m68k ;; 9000/[678][0-9][0-9]) if [ -x /usr/bin/getconf ]; then sc_cpu_version=`/usr/bin/getconf SC_CPU_VERSION 2>/dev/null` sc_kernel_bits=`/usr/bin/getconf SC_KERNEL_BITS 2>/dev/null` case "${sc_cpu_version}" in 523) HP_ARCH="hppa1.0" ;; # CPU_PA_RISC1_0 528) HP_ARCH="hppa1.1" ;; # CPU_PA_RISC1_1 532) # CPU_PA_RISC2_0 case "${sc_kernel_bits}" in 32) HP_ARCH="hppa2.0n" ;; 64) HP_ARCH="hppa2.0w" ;; '') HP_ARCH="hppa2.0" ;; # HP-UX 10.20 esac ;; esac fi if [ "${HP_ARCH}" = "" ]; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #define _HPUX_SOURCE #include #include int main () { #if defined(_SC_KERNEL_BITS) long bits = sysconf(_SC_KERNEL_BITS); #endif long cpu = sysconf (_SC_CPU_VERSION); switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0"); break; case CPU_PA_RISC1_1: puts ("hppa1.1"); break; case CPU_PA_RISC2_0: #if defined(_SC_KERNEL_BITS) switch (bits) { case 64: puts ("hppa2.0w"); break; case 32: puts ("hppa2.0n"); break; default: puts ("hppa2.0"); break; } break; #else /* !defined(_SC_KERNEL_BITS) */ puts ("hppa2.0"); break; #endif default: puts ("hppa1.0"); break; } exit (0); } EOF (CCOPTS= $CC_FOR_BUILD -o $dummy $dummy.c 2>/dev/null) && HP_ARCH=`$dummy` test -z "$HP_ARCH" && HP_ARCH=hppa fi ;; esac if [ ${HP_ARCH} = "hppa2.0w" ] then eval $set_cc_for_build # hppa2.0w-hp-hpux* has a 64-bit kernel and a compiler generating # 32-bit code. hppa64-hp-hpux* has the same kernel and a compiler # generating 64-bit code. GNU and HP use different nomenclature: # # $ CC_FOR_BUILD=cc ./config.guess # => hppa2.0w-hp-hpux11.23 # $ CC_FOR_BUILD="cc +DA2.0w" ./config.guess # => hppa64-hp-hpux11.23 if echo __LP64__ | (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | grep __LP64__ >/dev/null then HP_ARCH="hppa2.0w" else HP_ARCH="hppa64" fi fi echo ${HP_ARCH}-hp-hpux${HPUX_REV} exit ;; ia64:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` echo ia64-hp-hpux${HPUX_REV} exit ;; 3050*:HI-UX:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include int main () { long cpu = sysconf (_SC_CPU_VERSION); /* The order matters, because CPU_IS_HP_MC68K erroneously returns true for CPU_PA_RISC1_0. CPU_IS_PA_RISC returns correct results, however. */ if (CPU_IS_PA_RISC (cpu)) { switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0-hitachi-hiuxwe2"); break; case CPU_PA_RISC1_1: puts ("hppa1.1-hitachi-hiuxwe2"); break; case CPU_PA_RISC2_0: puts ("hppa2.0-hitachi-hiuxwe2"); break; default: puts ("hppa-hitachi-hiuxwe2"); break; } } else if (CPU_IS_HP_MC68K (cpu)) puts ("m68k-hitachi-hiuxwe2"); else puts ("unknown-hitachi-hiuxwe2"); exit (0); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` && { echo "$SYSTEM_NAME"; exit; } echo unknown-hitachi-hiuxwe2 exit ;; 9000/7??:4.3bsd:*:* | 9000/8?[79]:4.3bsd:*:* ) echo hppa1.1-hp-bsd exit ;; 9000/8??:4.3bsd:*:*) echo hppa1.0-hp-bsd exit ;; *9??*:MPE/iX:*:* | *3000*:MPE/iX:*:*) echo hppa1.0-hp-mpeix exit ;; hp7??:OSF1:*:* | hp8?[79]:OSF1:*:* ) echo hppa1.1-hp-osf exit ;; hp8??:OSF1:*:*) echo hppa1.0-hp-osf exit ;; i*86:OSF1:*:*) if [ -x /usr/sbin/sysversion ] ; then echo ${UNAME_MACHINE}-unknown-osf1mk else echo ${UNAME_MACHINE}-unknown-osf1 fi exit ;; parisc*:Lites*:*:*) echo hppa1.1-hp-lites exit ;; C1*:ConvexOS:*:* | convex:ConvexOS:C1*:*) echo c1-convex-bsd exit ;; C2*:ConvexOS:*:* | convex:ConvexOS:C2*:*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; C34*:ConvexOS:*:* | convex:ConvexOS:C34*:*) echo c34-convex-bsd exit ;; C38*:ConvexOS:*:* | convex:ConvexOS:C38*:*) echo c38-convex-bsd exit ;; C4*:ConvexOS:*:* | convex:ConvexOS:C4*:*) echo c4-convex-bsd exit ;; CRAY*Y-MP:*:*:*) echo ymp-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*[A-Z]90:*:*:*) echo ${UNAME_MACHINE}-cray-unicos${UNAME_RELEASE} \ | sed -e 's/CRAY.*\([A-Z]90\)/\1/' \ -e y/ABCDEFGHIJKLMNOPQRSTUVWXYZ/abcdefghijklmnopqrstuvwxyz/ \ -e 's/\.[^.]*$/.X/' exit ;; CRAY*TS:*:*:*) echo t90-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*T3E:*:*:*) echo alphaev5-cray-unicosmk${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*SV1:*:*:*) echo sv1-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; *:UNICOS/mp:*:*) echo craynv-cray-unicosmp${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; F30[01]:UNIX_System_V:*:* | F700:UNIX_System_V:*:*) FUJITSU_PROC=`uname -m | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz'` FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | sed -e 's/ /_/'` echo "${FUJITSU_PROC}-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; 5000:UNIX_System_V:4.*:*) FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/ /_/'` echo "sparc-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; i*86:BSD/386:*:* | i*86:BSD/OS:*:* | *:Ascend\ Embedded/OS:*:*) echo ${UNAME_MACHINE}-pc-bsdi${UNAME_RELEASE} exit ;; sparc*:BSD/OS:*:*) echo sparc-unknown-bsdi${UNAME_RELEASE} exit ;; *:BSD/OS:*:*) echo ${UNAME_MACHINE}-unknown-bsdi${UNAME_RELEASE} exit ;; *:FreeBSD:*:*) echo ${UNAME_MACHINE}-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` exit ;; i*:CYGWIN*:*) echo ${UNAME_MACHINE}-pc-cygwin exit ;; i*:MINGW*:*) echo ${UNAME_MACHINE}-pc-mingw32 exit ;; i*:windows32*:*) # uname -m includes "-pc" on this system. echo ${UNAME_MACHINE}-mingw32 exit ;; i*:PW*:*) echo ${UNAME_MACHINE}-pc-pw32 exit ;; x86:Interix*:[34]*) echo i586-pc-interix${UNAME_RELEASE}|sed -e 's/\..*//' exit ;; [345]86:Windows_95:* | [345]86:Windows_98:* | [345]86:Windows_NT:*) echo i${UNAME_MACHINE}-pc-mks exit ;; i*:Windows_NT*:* | Pentium*:Windows_NT*:*) # How do we know it's Interix rather than the generic POSIX subsystem? # It also conflicts with pre-2.0 versions of AT&T UWIN. Should we # UNAME_MACHINE based on the output of uname instead of i386? echo i586-pc-interix exit ;; i*:UWIN*:*) echo ${UNAME_MACHINE}-pc-uwin exit ;; amd64:CYGWIN*:*:* | x86_64:CYGWIN*:*:*) echo x86_64-unknown-cygwin exit ;; p*:CYGWIN*:*) echo powerpcle-unknown-cygwin exit ;; prep*:SunOS:5.*:*) echo powerpcle-unknown-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; *:GNU:*:*) # the GNU system echo `echo ${UNAME_MACHINE}|sed -e 's,[-/].*$,,'`-unknown-gnu`echo ${UNAME_RELEASE}|sed -e 's,/.*$,,'` exit ;; *:GNU/*:*:*) # other systems with GNU libc and userland echo ${UNAME_MACHINE}-unknown-`echo ${UNAME_SYSTEM} | sed 's,^[^/]*/,,' | tr '[A-Z]' '[a-z]'``echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'`-gnu exit ;; i*86:Minix:*:*) echo ${UNAME_MACHINE}-pc-minix exit ;; arm*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; cris:Linux:*:*) echo cris-axis-linux-gnu exit ;; crisv32:Linux:*:*) echo crisv32-axis-linux-gnu exit ;; frv:Linux:*:*) echo frv-unknown-linux-gnu exit ;; ia64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; m32r*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; m68*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; mips:Linux:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #undef CPU #undef mips #undef mipsel #if defined(__MIPSEL__) || defined(__MIPSEL) || defined(_MIPSEL) || defined(MIPSEL) CPU=mipsel #else #if defined(__MIPSEB__) || defined(__MIPSEB) || defined(_MIPSEB) || defined(MIPSEB) CPU=mips #else CPU= #endif #endif EOF eval `$CC_FOR_BUILD -E $dummy.c 2>/dev/null | grep ^CPU=` test x"${CPU}" != x && { echo "${CPU}-unknown-linux-gnu"; exit; } ;; mips64:Linux:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #undef CPU #undef mips64 #undef mips64el #if defined(__MIPSEL__) || defined(__MIPSEL) || defined(_MIPSEL) || defined(MIPSEL) CPU=mips64el #else #if defined(__MIPSEB__) || defined(__MIPSEB) || defined(_MIPSEB) || defined(MIPSEB) CPU=mips64 #else CPU= #endif #endif EOF eval `$CC_FOR_BUILD -E $dummy.c 2>/dev/null | grep ^CPU=` test x"${CPU}" != x && { echo "${CPU}-unknown-linux-gnu"; exit; } ;; or32:Linux:*:*) echo or32-unknown-linux-gnu exit ;; ppc:Linux:*:*) echo powerpc-unknown-linux-gnu exit ;; ppc64:Linux:*:*) echo powerpc64-unknown-linux-gnu exit ;; alpha:Linux:*:*) case `sed -n '/^cpu model/s/^.*: \(.*\)/\1/p' < /proc/cpuinfo` in EV5) UNAME_MACHINE=alphaev5 ;; EV56) UNAME_MACHINE=alphaev56 ;; PCA56) UNAME_MACHINE=alphapca56 ;; PCA57) UNAME_MACHINE=alphapca56 ;; EV6) UNAME_MACHINE=alphaev6 ;; EV67) UNAME_MACHINE=alphaev67 ;; EV68*) UNAME_MACHINE=alphaev68 ;; esac objdump --private-headers /bin/sh | grep ld.so.1 >/dev/null if test "$?" = 0 ; then LIBC="libc1" ; else LIBC="" ; fi echo ${UNAME_MACHINE}-unknown-linux-gnu${LIBC} exit ;; parisc:Linux:*:* | hppa:Linux:*:*) # Look for CPU level case `grep '^cpu[^a-z]*:' /proc/cpuinfo 2>/dev/null | cut -d' ' -f2` in PA7*) echo hppa1.1-unknown-linux-gnu ;; PA8*) echo hppa2.0-unknown-linux-gnu ;; *) echo hppa-unknown-linux-gnu ;; esac exit ;; parisc64:Linux:*:* | hppa64:Linux:*:*) echo hppa64-unknown-linux-gnu exit ;; s390:Linux:*:* | s390x:Linux:*:*) echo ${UNAME_MACHINE}-ibm-linux exit ;; sh64*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; sh*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; sparc:Linux:*:* | sparc64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; x86_64:Linux:*:*) echo x86_64-unknown-linux-gnu exit ;; i*86:Linux:*:*) # The BFD linker knows what the default object file format is, so # first see if it will tell us. cd to the root directory to prevent # problems with other programs or directories called `ld' in the path. # Set LC_ALL=C to ensure ld outputs messages in English. ld_supported_targets=`cd /; LC_ALL=C ld --help 2>&1 \ | sed -ne '/supported targets:/!d s/[ ][ ]*/ /g s/.*supported targets: *// s/ .*// p'` case "$ld_supported_targets" in elf32-i386) TENTATIVE="${UNAME_MACHINE}-pc-linux-gnu" ;; a.out-i386-linux) echo "${UNAME_MACHINE}-pc-linux-gnuaout" exit ;; coff-i386) echo "${UNAME_MACHINE}-pc-linux-gnucoff" exit ;; "") # Either a pre-BFD a.out linker (linux-gnuoldld) or # one that does not give us useful --help. echo "${UNAME_MACHINE}-pc-linux-gnuoldld" exit ;; esac # Determine whether the default compiler is a.out or elf eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include #ifdef __ELF__ # ifdef __GLIBC__ # if __GLIBC__ >= 2 LIBC=gnu # else LIBC=gnulibc1 # endif # else LIBC=gnulibc1 # endif #else #ifdef __INTEL_COMPILER LIBC=gnu #else LIBC=gnuaout #endif #endif #ifdef __dietlibc__ LIBC=dietlibc #endif EOF eval `$CC_FOR_BUILD -E $dummy.c 2>/dev/null | grep ^LIBC=` test x"${LIBC}" != x && { echo "${UNAME_MACHINE}-pc-linux-${LIBC}" exit } test x"${TENTATIVE}" != x && { echo "${TENTATIVE}"; exit; } ;; i*86:DYNIX/ptx:4*:*) # ptx 4.0 does uname -s correctly, with DYNIX/ptx in there. # earlier versions are messed up and put the nodename in both # sysname and nodename. echo i386-sequent-sysv4 exit ;; i*86:UNIX_SV:4.2MP:2.*) # Unixware is an offshoot of SVR4, but it has its own version # number series starting with 2... # I am not positive that other SVR4 systems won't match this, # I just have to hope. -- rms. # Use sysv4.2uw... so that sysv4* matches it. echo ${UNAME_MACHINE}-pc-sysv4.2uw${UNAME_VERSION} exit ;; i*86:OS/2:*:*) # If we were able to find `uname', then EMX Unix compatibility # is probably installed. echo ${UNAME_MACHINE}-pc-os2-emx exit ;; i*86:XTS-300:*:STOP) echo ${UNAME_MACHINE}-unknown-stop exit ;; i*86:atheos:*:*) echo ${UNAME_MACHINE}-unknown-atheos exit ;; i*86:syllable:*:*) echo ${UNAME_MACHINE}-pc-syllable exit ;; i*86:LynxOS:2.*:* | i*86:LynxOS:3.[01]*:* | i*86:LynxOS:4.0*:*) echo i386-unknown-lynxos${UNAME_RELEASE} exit ;; i*86:*DOS:*:*) echo ${UNAME_MACHINE}-pc-msdosdjgpp exit ;; i*86:*:4.*:* | i*86:SYSTEM_V:4.*:*) UNAME_REL=`echo ${UNAME_RELEASE} | sed 's/\/MP$//'` if grep Novell /usr/include/link.h >/dev/null 2>/dev/null; then echo ${UNAME_MACHINE}-univel-sysv${UNAME_REL} else echo ${UNAME_MACHINE}-pc-sysv${UNAME_REL} fi exit ;; i*86:*:5:[678]*) # UnixWare 7.x, OpenUNIX and OpenServer 6. case `/bin/uname -X | grep "^Machine"` in *486*) UNAME_MACHINE=i486 ;; *Pentium) UNAME_MACHINE=i586 ;; *Pent*|*Celeron) UNAME_MACHINE=i686 ;; esac echo ${UNAME_MACHINE}-unknown-sysv${UNAME_RELEASE}${UNAME_SYSTEM}${UNAME_VERSION} exit ;; i*86:*:3.2:*) if test -f /usr/options/cb.name; then UNAME_REL=`sed -n 's/.*Version //p' /dev/null >/dev/null ; then UNAME_REL=`(/bin/uname -X|grep Release|sed -e 's/.*= //')` (/bin/uname -X|grep i80486 >/dev/null) && UNAME_MACHINE=i486 (/bin/uname -X|grep '^Machine.*Pentium' >/dev/null) \ && UNAME_MACHINE=i586 (/bin/uname -X|grep '^Machine.*Pent *II' >/dev/null) \ && UNAME_MACHINE=i686 (/bin/uname -X|grep '^Machine.*Pentium Pro' >/dev/null) \ && UNAME_MACHINE=i686 echo ${UNAME_MACHINE}-pc-sco$UNAME_REL else echo ${UNAME_MACHINE}-pc-sysv32 fi exit ;; pc:*:*:*) # Left here for compatibility: # uname -m prints for DJGPP always 'pc', but it prints nothing about # the processor, so we play safe by assuming i386. echo i386-pc-msdosdjgpp exit ;; Intel:Mach:3*:*) echo i386-pc-mach3 exit ;; paragon:*:*:*) echo i860-intel-osf1 exit ;; i860:*:4.*:*) # i860-SVR4 if grep Stardent /usr/include/sys/uadmin.h >/dev/null 2>&1 ; then echo i860-stardent-sysv${UNAME_RELEASE} # Stardent Vistra i860-SVR4 else # Add other i860-SVR4 vendors below as they are discovered. echo i860-unknown-sysv${UNAME_RELEASE} # Unknown i860-SVR4 fi exit ;; 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TSUNAMI:LynxOS:2.*:*) echo sparc-unknown-lynxos${UNAME_RELEASE} exit ;; rs6000:LynxOS:2.*:*) echo rs6000-unknown-lynxos${UNAME_RELEASE} exit ;; PowerPC:LynxOS:2.*:* | PowerPC:LynxOS:3.[01]*:* | PowerPC:LynxOS:4.0*:*) echo powerpc-unknown-lynxos${UNAME_RELEASE} exit ;; SM[BE]S:UNIX_SV:*:*) echo mips-dde-sysv${UNAME_RELEASE} exit ;; RM*:ReliantUNIX-*:*:*) echo mips-sni-sysv4 exit ;; RM*:SINIX-*:*:*) echo mips-sni-sysv4 exit ;; *:SINIX-*:*:*) if uname -p 2>/dev/null >/dev/null ; then UNAME_MACHINE=`(uname -p) 2>/dev/null` echo ${UNAME_MACHINE}-sni-sysv4 else echo ns32k-sni-sysv fi exit ;; PENTIUM:*:4.0*:*) # Unisys `ClearPath HMP IX 4000' SVR4/MP effort # says echo i586-unisys-sysv4 exit ;; *:UNIX_System_V:4*:FTX*) # From Gerald Hewes . # How about differentiating between stratus architectures? -djm echo hppa1.1-stratus-sysv4 exit ;; *:*:*:FTX*) # From seanf@swdc.stratus.com. echo i860-stratus-sysv4 exit ;; i*86:VOS:*:*) # From Paul.Green@stratus.com. echo ${UNAME_MACHINE}-stratus-vos exit ;; *:VOS:*:*) # From Paul.Green@stratus.com. echo hppa1.1-stratus-vos exit ;; mc68*:A/UX:*:*) echo m68k-apple-aux${UNAME_RELEASE} exit ;; news*:NEWS-OS:6*:*) echo mips-sony-newsos6 exit ;; R[34]000:*System_V*:*:* | R4000:UNIX_SYSV:*:* | R*000:UNIX_SV:*:*) if [ -d /usr/nec ]; then echo mips-nec-sysv${UNAME_RELEASE} else echo mips-unknown-sysv${UNAME_RELEASE} fi exit ;; BeBox:BeOS:*:*) # BeOS running on hardware made by Be, PPC only. echo powerpc-be-beos exit ;; BeMac:BeOS:*:*) # BeOS running on Mac or Mac clone, PPC only. echo powerpc-apple-beos exit ;; BePC:BeOS:*:*) # BeOS running on Intel PC compatible. echo i586-pc-beos exit ;; SX-4:SUPER-UX:*:*) echo sx4-nec-superux${UNAME_RELEASE} exit ;; SX-5:SUPER-UX:*:*) echo sx5-nec-superux${UNAME_RELEASE} exit ;; SX-6:SUPER-UX:*:*) echo sx6-nec-superux${UNAME_RELEASE} exit ;; Power*:Rhapsody:*:*) echo powerpc-apple-rhapsody${UNAME_RELEASE} exit ;; *:Rhapsody:*:*) echo ${UNAME_MACHINE}-apple-rhapsody${UNAME_RELEASE} exit ;; *:Darwin:*:*) UNAME_PROCESSOR=`uname -p` || UNAME_PROCESSOR=unknown case $UNAME_PROCESSOR in *86) UNAME_PROCESSOR=i686 ;; unknown) UNAME_PROCESSOR=powerpc ;; esac echo ${UNAME_PROCESSOR}-apple-darwin${UNAME_RELEASE} exit ;; *:procnto*:*:* | *:QNX:[0123456789]*:*) UNAME_PROCESSOR=`uname -p` if test "$UNAME_PROCESSOR" = "x86"; then UNAME_PROCESSOR=i386 UNAME_MACHINE=pc fi echo ${UNAME_PROCESSOR}-${UNAME_MACHINE}-nto-qnx${UNAME_RELEASE} exit ;; *:QNX:*:4*) echo i386-pc-qnx exit ;; NSE-?:NONSTOP_KERNEL:*:*) echo nse-tandem-nsk${UNAME_RELEASE} exit ;; NSR-?:NONSTOP_KERNEL:*:*) echo nsr-tandem-nsk${UNAME_RELEASE} exit ;; *:NonStop-UX:*:*) echo mips-compaq-nonstopux exit ;; BS2000:POSIX*:*:*) echo bs2000-siemens-sysv exit ;; DS/*:UNIX_System_V:*:*) echo ${UNAME_MACHINE}-${UNAME_SYSTEM}-${UNAME_RELEASE} exit ;; *:Plan9:*:*) # "uname -m" is not consistent, so use $cputype instead. 386 # is converted to i386 for consistency with other x86 # operating systems. if test "$cputype" = "386"; then UNAME_MACHINE=i386 else UNAME_MACHINE="$cputype" fi echo ${UNAME_MACHINE}-unknown-plan9 exit ;; *:TOPS-10:*:*) echo pdp10-unknown-tops10 exit ;; *:TENEX:*:*) echo pdp10-unknown-tenex exit ;; KS10:TOPS-20:*:* | KL10:TOPS-20:*:* | TYPE4:TOPS-20:*:*) echo pdp10-dec-tops20 exit ;; XKL-1:TOPS-20:*:* | TYPE5:TOPS-20:*:*) echo pdp10-xkl-tops20 exit ;; *:TOPS-20:*:*) echo pdp10-unknown-tops20 exit ;; *:ITS:*:*) echo pdp10-unknown-its exit ;; SEI:*:*:SEIUX) echo mips-sei-seiux${UNAME_RELEASE} exit ;; *:DragonFly:*:*) echo ${UNAME_MACHINE}-unknown-dragonfly`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` exit ;; *:*VMS:*:*) UNAME_MACHINE=`(uname -p) 2>/dev/null` case "${UNAME_MACHINE}" in A*) echo alpha-dec-vms ; exit ;; I*) echo ia64-dec-vms ; exit ;; V*) echo vax-dec-vms ; exit ;; esac ;; *:XENIX:*:SysV) echo i386-pc-xenix exit ;; i*86:skyos:*:*) echo ${UNAME_MACHINE}-pc-skyos`echo ${UNAME_RELEASE}` | sed -e 's/ .*$//' exit ;; esac #echo '(No uname command or uname output not recognized.)' 1>&2 #echo "${UNAME_MACHINE}:${UNAME_SYSTEM}:${UNAME_RELEASE}:${UNAME_VERSION}" 1>&2 eval $set_cc_for_build cat >$dummy.c < # include #endif main () { #if defined (sony) #if defined (MIPSEB) /* BFD wants "bsd" instead of "newsos". Perhaps BFD should be changed, I don't know.... */ printf ("mips-sony-bsd\n"); exit (0); #else #include printf ("m68k-sony-newsos%s\n", #ifdef NEWSOS4 "4" #else "" #endif ); exit (0); #endif #endif #if defined (__arm) && defined (__acorn) && defined (__unix) printf ("arm-acorn-riscix\n"); exit (0); #endif #if defined (hp300) && !defined (hpux) printf ("m68k-hp-bsd\n"); exit (0); #endif #if defined (NeXT) #if !defined (__ARCHITECTURE__) #define __ARCHITECTURE__ "m68k" #endif int version; version=`(hostinfo | sed -n 's/.*NeXT Mach \([0-9]*\).*/\1/p') 2>/dev/null`; if (version < 4) printf ("%s-next-nextstep%d\n", __ARCHITECTURE__, version); else printf ("%s-next-openstep%d\n", __ARCHITECTURE__, version); exit (0); #endif #if defined (MULTIMAX) || defined (n16) #if defined (UMAXV) printf ("ns32k-encore-sysv\n"); exit (0); #else #if defined (CMU) printf ("ns32k-encore-mach\n"); exit (0); #else printf ("ns32k-encore-bsd\n"); exit (0); #endif #endif #endif #if defined (__386BSD__) printf ("i386-pc-bsd\n"); exit (0); #endif #if defined (sequent) #if defined (i386) printf ("i386-sequent-dynix\n"); exit (0); #endif #if defined (ns32000) printf ("ns32k-sequent-dynix\n"); exit (0); #endif #endif #if defined (_SEQUENT_) struct utsname un; uname(&un); if (strncmp(un.version, "V2", 2) == 0) { printf ("i386-sequent-ptx2\n"); exit (0); } if (strncmp(un.version, "V1", 2) == 0) { /* XXX is V1 correct? */ printf ("i386-sequent-ptx1\n"); exit (0); } printf ("i386-sequent-ptx\n"); exit (0); #endif #if defined (vax) # if !defined (ultrix) # include # if defined (BSD) # if BSD == 43 printf ("vax-dec-bsd4.3\n"); exit (0); # else # if BSD == 199006 printf ("vax-dec-bsd4.3reno\n"); exit (0); # else printf ("vax-dec-bsd\n"); exit (0); # endif # endif # else printf ("vax-dec-bsd\n"); exit (0); # endif # else printf ("vax-dec-ultrix\n"); exit (0); # endif #endif #if defined (alliant) && defined (i860) printf ("i860-alliant-bsd\n"); exit (0); #endif exit (1); } EOF $CC_FOR_BUILD -o $dummy $dummy.c 2>/dev/null && SYSTEM_NAME=`$dummy` && { echo "$SYSTEM_NAME"; exit; } # Apollos put the system type in the environment. test -d /usr/apollo && { echo ${ISP}-apollo-${SYSTYPE}; exit; } # Convex versions that predate uname can use getsysinfo(1) if [ -x /usr/convex/getsysinfo ] then case `getsysinfo -f cpu_type` in c1*) echo c1-convex-bsd exit ;; c2*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; c34*) echo c34-convex-bsd exit ;; c38*) echo c38-convex-bsd exit ;; c4*) echo c4-convex-bsd exit ;; esac fi cat >&2 < in order to provide the needed information to handle your system. config.guess timestamp = $timestamp uname -m = `(uname -m) 2>/dev/null || echo unknown` uname -r = `(uname -r) 2>/dev/null || echo unknown` uname -s = `(uname -s) 2>/dev/null || echo unknown` uname -v = `(uname -v) 2>/dev/null || echo unknown` /usr/bin/uname -p = `(/usr/bin/uname -p) 2>/dev/null` /bin/uname -X = `(/bin/uname -X) 2>/dev/null` hostinfo = `(hostinfo) 2>/dev/null` /bin/universe = `(/bin/universe) 2>/dev/null` /usr/bin/arch -k = `(/usr/bin/arch -k) 2>/dev/null` /bin/arch = `(/bin/arch) 2>/dev/null` /usr/bin/oslevel = `(/usr/bin/oslevel) 2>/dev/null` /usr/convex/getsysinfo = `(/usr/convex/getsysinfo) 2>/dev/null` UNAME_MACHINE = ${UNAME_MACHINE} UNAME_RELEASE = ${UNAME_RELEASE} UNAME_SYSTEM = ${UNAME_SYSTEM} UNAME_VERSION = ${UNAME_VERSION} EOF exit 1 # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "timestamp='" # time-stamp-format: "%:y-%02m-%02d" # time-stamp-end: "'" # End: libfann-2.1.0~beta~dfsg/config.in000066400000000000000000000032541140315177100170200ustar00rootroot00000000000000/* config.in. 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It does not imply ALL GNU software can. # # This file is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street - Fifth Floor, Boston, MA # 02110-1301, USA. # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Please send patches to . Submit a context # diff and a properly formatted ChangeLog entry. # # Configuration subroutine to validate and canonicalize a configuration type. # Supply the specified configuration type as an argument. # If it is invalid, we print an error message on stderr and exit with code 1. # Otherwise, we print the canonical config type on stdout and succeed. # This file is supposed to be the same for all GNU packages # and recognize all the CPU types, system types and aliases # that are meaningful with *any* GNU software. # Each package is responsible for reporting which valid configurations # it does not support. The user should be able to distinguish # a failure to support a valid configuration from a meaningless # configuration. # The goal of this file is to map all the various variations of a given # machine specification into a single specification in the form: # CPU_TYPE-MANUFACTURER-OPERATING_SYSTEM # or in some cases, the newer four-part form: # CPU_TYPE-MANUFACTURER-KERNEL-OPERATING_SYSTEM # It is wrong to echo any other type of specification. me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] CPU-MFR-OPSYS $0 [OPTION] ALIAS Canonicalize a configuration name. Operation modes: -h, --help print this help, then exit -t, --time-stamp print date of last modification, then exit -v, --version print version number, then exit Report bugs and patches to ." version="\ GNU config.sub ($timestamp) Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. This is free software; see the source for copying conditions. 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We also ### recognize some manufacturers as not being operating systems, so we ### can provide default operating systems below. case $os in -sun*os*) # Prevent following clause from handling this invalid input. ;; -dec* | -mips* | -sequent* | -encore* | -pc532* | -sgi* | -sony* | \ -att* | -7300* | -3300* | -delta* | -motorola* | -sun[234]* | \ -unicom* | -ibm* | -next | -hp | -isi* | -apollo | -altos* | \ -convergent* | -ncr* | -news | -32* | -3600* | -3100* | -hitachi* |\ -c[123]* | -convex* | -sun | -crds | -omron* | -dg | -ultra | -tti* | \ -harris | -dolphin | -highlevel | -gould | -cbm | -ns | -masscomp | \ -apple | -axis | -knuth | -cray) os= basic_machine=$1 ;; -sim | -cisco | -oki | -wec | -winbond) os= basic_machine=$1 ;; -scout) ;; -wrs) os=-vxworks basic_machine=$1 ;; -chorusos*) os=-chorusos basic_machine=$1 ;; -chorusrdb) os=-chorusrdb basic_machine=$1 ;; -hiux*) os=-hiuxwe2 ;; -sco5) os=-sco3.2v5 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco4) os=-sco3.2v4 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2.[4-9]*) os=`echo $os | sed -e 's/sco3.2./sco3.2v/'` basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2v[4-9]*) # Don't forget version if it is 3.2v4 or newer. basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco*) os=-sco3.2v2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -udk*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -isc) os=-isc2.2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -clix*) basic_machine=clipper-intergraph ;; -isc*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -lynx*) os=-lynxos ;; -ptx*) basic_machine=`echo $1 | sed -e 's/86-.*/86-sequent/'` ;; -windowsnt*) os=`echo $os | sed -e 's/windowsnt/winnt/'` ;; -psos*) os=-psos ;; -mint | -mint[0-9]*) basic_machine=m68k-atari os=-mint ;; esac # Decode aliases for certain CPU-COMPANY combinations. case $basic_machine in # Recognize the basic CPU types without company name. # Some are omitted here because they have special meanings below. 1750a | 580 \ | a29k \ | alpha | alphaev[4-8] | alphaev56 | alphaev6[78] | alphapca5[67] \ | alpha64 | alpha64ev[4-8] | alpha64ev56 | alpha64ev6[78] | alpha64pca5[67] \ | am33_2.0 \ | arc | arm | arm[bl]e | arme[lb] | armv[2345] | armv[345][lb] | avr \ | bfin \ | c4x | clipper \ | d10v | d30v | dlx | dsp16xx \ | fr30 | frv \ | h8300 | h8500 | hppa | hppa1.[01] | hppa2.0 | hppa2.0[nw] | hppa64 \ | i370 | i860 | i960 | ia64 \ | ip2k | iq2000 \ | m32r | m32rle | m68000 | m68k | m88k | maxq | mcore \ | mips | mipsbe | mipseb | mipsel | mipsle \ | mips16 \ | mips64 | mips64el \ | mips64vr | mips64vrel \ | mips64orion | mips64orionel \ | mips64vr4100 | mips64vr4100el \ | mips64vr4300 | mips64vr4300el \ | mips64vr5000 | mips64vr5000el \ | mips64vr5900 | mips64vr5900el \ | mipsisa32 | mipsisa32el \ | mipsisa32r2 | mipsisa32r2el \ | mipsisa64 | mipsisa64el \ | mipsisa64r2 | mipsisa64r2el \ | mipsisa64sb1 | mipsisa64sb1el \ | mipsisa64sr71k | mipsisa64sr71kel \ | mipstx39 | mipstx39el \ | mn10200 | mn10300 \ | ms1 \ | msp430 \ | ns16k | ns32k \ | or32 \ | pdp10 | pdp11 | pj | pjl \ | powerpc | powerpc64 | powerpc64le | powerpcle | ppcbe \ | pyramid \ | sh | sh[1234] | sh[24]a | sh[23]e | sh[34]eb | shbe | shle | sh[1234]le | sh3ele \ | sh64 | sh64le \ | sparc | sparc64 | sparc64b | sparc86x | sparclet | sparclite \ | sparcv8 | sparcv9 | sparcv9b \ | strongarm \ | tahoe | thumb | tic4x | tic80 | tron \ | v850 | v850e \ | we32k \ | x86 | xscale | xscalee[bl] | xstormy16 | xtensa \ | z8k) basic_machine=$basic_machine-unknown ;; m32c) basic_machine=$basic_machine-unknown ;; m6811 | m68hc11 | m6812 | m68hc12) # Motorola 68HC11/12. basic_machine=$basic_machine-unknown os=-none ;; m88110 | m680[12346]0 | m683?2 | m68360 | m5200 | v70 | w65 | z8k) ;; # We use `pc' rather than `unknown' # because (1) that's what they normally are, and # (2) the word "unknown" tends to confuse beginning users. i*86 | x86_64) basic_machine=$basic_machine-pc ;; # Object if more than one company name word. *-*-*) echo Invalid configuration \`$1\': machine \`$basic_machine\' not recognized 1>&2 exit 1 ;; # Recognize the basic CPU types with company name. 580-* \ | a29k-* \ | alpha-* | alphaev[4-8]-* | alphaev56-* | alphaev6[78]-* \ | alpha64-* | alpha64ev[4-8]-* | alpha64ev56-* | alpha64ev6[78]-* \ | alphapca5[67]-* | alpha64pca5[67]-* | arc-* \ | arm-* | armbe-* | armle-* | armeb-* | armv*-* \ | avr-* \ | bfin-* | bs2000-* \ | c[123]* | c30-* | [cjt]90-* | c4x-* | c54x-* | c55x-* | c6x-* \ | clipper-* | craynv-* | cydra-* \ | d10v-* | d30v-* | dlx-* \ | elxsi-* \ | f30[01]-* | f700-* | fr30-* | frv-* | fx80-* \ | h8300-* | h8500-* \ | hppa-* | hppa1.[01]-* | hppa2.0-* | hppa2.0[nw]-* | hppa64-* \ | i*86-* | i860-* | i960-* | ia64-* \ | ip2k-* | iq2000-* \ | m32r-* | m32rle-* \ | m68000-* | m680[012346]0-* | m68360-* | m683?2-* | m68k-* \ | m88110-* | m88k-* | maxq-* | mcore-* \ | mips-* | mipsbe-* | mipseb-* | mipsel-* | mipsle-* \ | mips16-* \ | mips64-* | mips64el-* \ | mips64vr-* | mips64vrel-* \ | mips64orion-* | mips64orionel-* \ | mips64vr4100-* | mips64vr4100el-* \ | mips64vr4300-* | mips64vr4300el-* \ | mips64vr5000-* | mips64vr5000el-* \ | mips64vr5900-* | mips64vr5900el-* \ | mipsisa32-* | mipsisa32el-* \ | mipsisa32r2-* | mipsisa32r2el-* \ | mipsisa64-* | mipsisa64el-* \ | mipsisa64r2-* | mipsisa64r2el-* \ | mipsisa64sb1-* | mipsisa64sb1el-* \ | mipsisa64sr71k-* | mipsisa64sr71kel-* \ | mipstx39-* | mipstx39el-* \ | mmix-* \ | ms1-* \ | msp430-* \ | none-* | np1-* | ns16k-* | ns32k-* \ | orion-* \ | pdp10-* | pdp11-* | pj-* | pjl-* | pn-* | power-* \ | powerpc-* | powerpc64-* | powerpc64le-* | powerpcle-* | ppcbe-* \ | pyramid-* \ | romp-* | rs6000-* \ | sh-* | sh[1234]-* | sh[24]a-* | sh[23]e-* | sh[34]eb-* | shbe-* \ | shle-* | sh[1234]le-* | sh3ele-* | sh64-* | sh64le-* \ | sparc-* | sparc64-* | sparc64b-* | sparc86x-* | sparclet-* \ | sparclite-* \ | sparcv8-* | sparcv9-* | sparcv9b-* | strongarm-* | sv1-* | sx?-* \ | tahoe-* | thumb-* \ | tic30-* | tic4x-* | tic54x-* | tic55x-* | tic6x-* | tic80-* \ | tron-* \ | v850-* | v850e-* | vax-* \ | we32k-* \ | x86-* | x86_64-* | xps100-* | xscale-* | xscalee[bl]-* \ | xstormy16-* | xtensa-* \ | ymp-* \ | z8k-*) ;; m32c-*) ;; # Recognize the various machine names and aliases which stand # for a CPU type and a company and sometimes even an OS. 386bsd) basic_machine=i386-unknown os=-bsd ;; 3b1 | 7300 | 7300-att | att-7300 | pc7300 | safari | unixpc) basic_machine=m68000-att ;; 3b*) basic_machine=we32k-att ;; a29khif) basic_machine=a29k-amd os=-udi ;; abacus) basic_machine=abacus-unknown ;; adobe68k) basic_machine=m68010-adobe os=-scout ;; alliant | fx80) basic_machine=fx80-alliant ;; altos | altos3068) basic_machine=m68k-altos ;; am29k) basic_machine=a29k-none os=-bsd ;; amd64) basic_machine=x86_64-pc ;; amd64-*) basic_machine=x86_64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; amdahl) basic_machine=580-amdahl os=-sysv ;; amiga | amiga-*) basic_machine=m68k-unknown ;; amigaos | amigados) basic_machine=m68k-unknown os=-amigaos ;; amigaunix | amix) basic_machine=m68k-unknown os=-sysv4 ;; apollo68) basic_machine=m68k-apollo os=-sysv ;; apollo68bsd) basic_machine=m68k-apollo os=-bsd ;; aux) basic_machine=m68k-apple os=-aux ;; balance) basic_machine=ns32k-sequent os=-dynix ;; c90) basic_machine=c90-cray os=-unicos ;; convex-c1) basic_machine=c1-convex os=-bsd ;; convex-c2) basic_machine=c2-convex os=-bsd ;; convex-c32) basic_machine=c32-convex os=-bsd ;; convex-c34) basic_machine=c34-convex os=-bsd ;; convex-c38) basic_machine=c38-convex os=-bsd ;; cray | j90) basic_machine=j90-cray os=-unicos ;; craynv) basic_machine=craynv-cray os=-unicosmp ;; cr16c) basic_machine=cr16c-unknown os=-elf ;; crds | unos) basic_machine=m68k-crds ;; crisv32 | crisv32-* | etraxfs*) basic_machine=crisv32-axis ;; cris | cris-* | etrax*) basic_machine=cris-axis ;; crx) basic_machine=crx-unknown os=-elf ;; da30 | da30-*) basic_machine=m68k-da30 ;; decstation | decstation-3100 | pmax | pmax-* | pmin | dec3100 | decstatn) basic_machine=mips-dec ;; decsystem10* | dec10*) basic_machine=pdp10-dec os=-tops10 ;; decsystem20* | dec20*) basic_machine=pdp10-dec os=-tops20 ;; delta | 3300 | motorola-3300 | motorola-delta \ | 3300-motorola | delta-motorola) basic_machine=m68k-motorola ;; delta88) basic_machine=m88k-motorola os=-sysv3 ;; djgpp) basic_machine=i586-pc os=-msdosdjgpp ;; dpx20 | dpx20-*) basic_machine=rs6000-bull os=-bosx ;; dpx2* | dpx2*-bull) basic_machine=m68k-bull os=-sysv3 ;; ebmon29k) basic_machine=a29k-amd os=-ebmon ;; elxsi) basic_machine=elxsi-elxsi os=-bsd ;; encore | umax | mmax) basic_machine=ns32k-encore ;; es1800 | OSE68k | ose68k | ose | OSE) basic_machine=m68k-ericsson os=-ose ;; fx2800) basic_machine=i860-alliant ;; genix) basic_machine=ns32k-ns ;; gmicro) basic_machine=tron-gmicro os=-sysv ;; go32) basic_machine=i386-pc os=-go32 ;; h3050r* | hiux*) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; h8300hms) basic_machine=h8300-hitachi os=-hms ;; h8300xray) basic_machine=h8300-hitachi os=-xray ;; h8500hms) basic_machine=h8500-hitachi os=-hms ;; harris) basic_machine=m88k-harris os=-sysv3 ;; hp300-*) basic_machine=m68k-hp ;; hp300bsd) basic_machine=m68k-hp os=-bsd ;; hp300hpux) basic_machine=m68k-hp os=-hpux ;; hp3k9[0-9][0-9] | hp9[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k2[0-9][0-9] | hp9k31[0-9]) basic_machine=m68000-hp ;; hp9k3[2-9][0-9]) basic_machine=m68k-hp ;; hp9k6[0-9][0-9] | hp6[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k7[0-79][0-9] | hp7[0-79][0-9]) basic_machine=hppa1.1-hp ;; hp9k78[0-9] | hp78[0-9]) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[67]1 | hp8[67]1 | hp9k80[24] | hp80[24] | hp9k8[78]9 | hp8[78]9 | hp9k893 | hp893) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[0-9][13679] | hp8[0-9][13679]) basic_machine=hppa1.1-hp ;; hp9k8[0-9][0-9] | hp8[0-9][0-9]) basic_machine=hppa1.0-hp ;; hppa-next) os=-nextstep3 ;; hppaosf) basic_machine=hppa1.1-hp os=-osf ;; hppro) basic_machine=hppa1.1-hp os=-proelf ;; i370-ibm* | ibm*) basic_machine=i370-ibm ;; # I'm not sure what "Sysv32" means. Should this be sysv3.2? i*86v32) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv32 ;; i*86v4*) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv4 ;; i*86v) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv ;; i*86sol2) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-solaris2 ;; i386mach) basic_machine=i386-mach os=-mach ;; i386-vsta | vsta) basic_machine=i386-unknown os=-vsta ;; iris | iris4d) basic_machine=mips-sgi case $os in -irix*) ;; *) os=-irix4 ;; esac ;; isi68 | isi) basic_machine=m68k-isi os=-sysv ;; m88k-omron*) basic_machine=m88k-omron ;; magnum | m3230) basic_machine=mips-mips os=-sysv ;; merlin) basic_machine=ns32k-utek os=-sysv ;; mingw32) basic_machine=i386-pc os=-mingw32 ;; miniframe) basic_machine=m68000-convergent ;; *mint | -mint[0-9]* | *MiNT | *MiNT[0-9]*) basic_machine=m68k-atari os=-mint ;; mips3*-*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'` ;; mips3*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'`-unknown ;; monitor) basic_machine=m68k-rom68k os=-coff ;; morphos) basic_machine=powerpc-unknown os=-morphos ;; msdos) basic_machine=i386-pc os=-msdos ;; mvs) basic_machine=i370-ibm os=-mvs ;; ncr3000) basic_machine=i486-ncr os=-sysv4 ;; netbsd386) basic_machine=i386-unknown os=-netbsd ;; netwinder) basic_machine=armv4l-rebel os=-linux ;; news | news700 | news800 | news900) basic_machine=m68k-sony os=-newsos ;; news1000) basic_machine=m68030-sony os=-newsos ;; news-3600 | risc-news) basic_machine=mips-sony os=-newsos ;; necv70) basic_machine=v70-nec os=-sysv ;; next | m*-next ) basic_machine=m68k-next case $os in -nextstep* ) ;; -ns2*) os=-nextstep2 ;; *) os=-nextstep3 ;; esac ;; nh3000) basic_machine=m68k-harris os=-cxux ;; nh[45]000) basic_machine=m88k-harris os=-cxux ;; nindy960) basic_machine=i960-intel os=-nindy ;; mon960) basic_machine=i960-intel os=-mon960 ;; nonstopux) basic_machine=mips-compaq os=-nonstopux ;; np1) basic_machine=np1-gould ;; nsr-tandem) basic_machine=nsr-tandem ;; op50n-* | op60c-*) basic_machine=hppa1.1-oki os=-proelf ;; openrisc | openrisc-*) basic_machine=or32-unknown ;; os400) basic_machine=powerpc-ibm os=-os400 ;; OSE68000 | ose68000) basic_machine=m68000-ericsson os=-ose ;; os68k) basic_machine=m68k-none os=-os68k ;; pa-hitachi) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; paragon) basic_machine=i860-intel os=-osf ;; pbd) basic_machine=sparc-tti ;; pbb) basic_machine=m68k-tti ;; pc532 | pc532-*) basic_machine=ns32k-pc532 ;; pentium | p5 | k5 | k6 | nexgen | viac3) basic_machine=i586-pc ;; pentiumpro | p6 | 6x86 | athlon | athlon_*) basic_machine=i686-pc ;; pentiumii | pentium2 | pentiumiii | pentium3) basic_machine=i686-pc ;; pentium4) basic_machine=i786-pc ;; pentium-* | p5-* | k5-* | k6-* | nexgen-* | viac3-*) basic_machine=i586-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumpro-* | p6-* | 6x86-* | athlon-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumii-* | pentium2-* | pentiumiii-* | pentium3-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentium4-*) basic_machine=i786-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pn) basic_machine=pn-gould ;; power) basic_machine=power-ibm ;; ppc) basic_machine=powerpc-unknown ;; ppc-*) basic_machine=powerpc-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppcle | powerpclittle | ppc-le | powerpc-little) basic_machine=powerpcle-unknown ;; ppcle-* | powerpclittle-*) basic_machine=powerpcle-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64) basic_machine=powerpc64-unknown ;; ppc64-*) basic_machine=powerpc64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64le | powerpc64little | ppc64-le | powerpc64-little) basic_machine=powerpc64le-unknown ;; ppc64le-* | powerpc64little-*) basic_machine=powerpc64le-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ps2) basic_machine=i386-ibm ;; pw32) basic_machine=i586-unknown os=-pw32 ;; rom68k) basic_machine=m68k-rom68k os=-coff ;; rm[46]00) basic_machine=mips-siemens ;; rtpc | rtpc-*) basic_machine=romp-ibm ;; s390 | s390-*) basic_machine=s390-ibm ;; s390x | s390x-*) basic_machine=s390x-ibm ;; sa29200) basic_machine=a29k-amd os=-udi ;; sb1) basic_machine=mipsisa64sb1-unknown ;; sb1el) basic_machine=mipsisa64sb1el-unknown ;; sei) basic_machine=mips-sei os=-seiux ;; sequent) basic_machine=i386-sequent ;; sh) basic_machine=sh-hitachi os=-hms ;; sh64) basic_machine=sh64-unknown ;; sparclite-wrs | simso-wrs) basic_machine=sparclite-wrs os=-vxworks ;; sps7) basic_machine=m68k-bull os=-sysv2 ;; spur) basic_machine=spur-unknown ;; st2000) basic_machine=m68k-tandem ;; stratus) basic_machine=i860-stratus os=-sysv4 ;; sun2) basic_machine=m68000-sun ;; sun2os3) basic_machine=m68000-sun os=-sunos3 ;; sun2os4) basic_machine=m68000-sun os=-sunos4 ;; sun3os3) basic_machine=m68k-sun os=-sunos3 ;; sun3os4) basic_machine=m68k-sun os=-sunos4 ;; sun4os3) basic_machine=sparc-sun os=-sunos3 ;; sun4os4) basic_machine=sparc-sun os=-sunos4 ;; sun4sol2) basic_machine=sparc-sun os=-solaris2 ;; sun3 | sun3-*) basic_machine=m68k-sun ;; sun4) basic_machine=sparc-sun ;; sun386 | sun386i | roadrunner) basic_machine=i386-sun ;; sv1) basic_machine=sv1-cray os=-unicos ;; symmetry) basic_machine=i386-sequent os=-dynix ;; t3e) basic_machine=alphaev5-cray os=-unicos ;; t90) basic_machine=t90-cray os=-unicos ;; tic54x | c54x*) basic_machine=tic54x-unknown os=-coff ;; tic55x | c55x*) basic_machine=tic55x-unknown os=-coff ;; tic6x | c6x*) basic_machine=tic6x-unknown os=-coff ;; tx39) basic_machine=mipstx39-unknown ;; tx39el) basic_machine=mipstx39el-unknown ;; toad1) basic_machine=pdp10-xkl os=-tops20 ;; tower | tower-32) basic_machine=m68k-ncr ;; tpf) basic_machine=s390x-ibm os=-tpf ;; udi29k) basic_machine=a29k-amd os=-udi ;; ultra3) basic_machine=a29k-nyu os=-sym1 ;; v810 | necv810) basic_machine=v810-nec os=-none ;; vaxv) basic_machine=vax-dec os=-sysv ;; vms) basic_machine=vax-dec os=-vms ;; vpp*|vx|vx-*) basic_machine=f301-fujitsu ;; vxworks960) basic_machine=i960-wrs os=-vxworks ;; vxworks68) basic_machine=m68k-wrs os=-vxworks ;; vxworks29k) basic_machine=a29k-wrs os=-vxworks ;; w65*) basic_machine=w65-wdc os=-none ;; w89k-*) basic_machine=hppa1.1-winbond os=-proelf ;; xbox) basic_machine=i686-pc os=-mingw32 ;; xps | xps100) basic_machine=xps100-honeywell ;; ymp) basic_machine=ymp-cray os=-unicos ;; z8k-*-coff) basic_machine=z8k-unknown os=-sim ;; none) basic_machine=none-none os=-none ;; # Here we handle the default manufacturer of certain CPU types. It is in # some cases the only manufacturer, in others, it is the most popular. w89k) basic_machine=hppa1.1-winbond ;; op50n) basic_machine=hppa1.1-oki ;; op60c) basic_machine=hppa1.1-oki ;; romp) basic_machine=romp-ibm ;; mmix) basic_machine=mmix-knuth ;; rs6000) basic_machine=rs6000-ibm ;; vax) basic_machine=vax-dec ;; pdp10) # there are many clones, so DEC is not a safe bet basic_machine=pdp10-unknown ;; pdp11) basic_machine=pdp11-dec ;; we32k) basic_machine=we32k-att ;; sh[1234] | sh[24]a | sh[34]eb | sh[1234]le | sh[23]ele) basic_machine=sh-unknown ;; sparc | sparcv8 | sparcv9 | sparcv9b) basic_machine=sparc-sun ;; cydra) basic_machine=cydra-cydrome ;; orion) basic_machine=orion-highlevel ;; orion105) basic_machine=clipper-highlevel ;; mac | mpw | mac-mpw) basic_machine=m68k-apple ;; pmac | pmac-mpw) basic_machine=powerpc-apple ;; *-unknown) # Make sure to match an already-canonicalized machine name. ;; *) echo Invalid configuration \`$1\': machine \`$basic_machine\' not recognized 1>&2 exit 1 ;; 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PACKAGE=fann VERSION=2.1.0 cat >>confdefs.h <<_ACEOF #define PACKAGE "$PACKAGE" _ACEOF cat >>confdefs.h <<_ACEOF #define VERSION "$VERSION" _ACEOF # Some tools Automake needs. ACLOCAL=${ACLOCAL-"${am_missing_run}aclocal-${am__api_version}"} AUTOCONF=${AUTOCONF-"${am_missing_run}autoconf"} AUTOMAKE=${AUTOMAKE-"${am_missing_run}automake-${am__api_version}"} AUTOHEADER=${AUTOHEADER-"${am_missing_run}autoheader"} MAKEINFO=${MAKEINFO-"${am_missing_run}makeinfo"} install_sh=${install_sh-"$am_aux_dir/install-sh"} # Installed binaries are usually stripped using `strip' when the user # run `make install-strip'. However `strip' might not be the right # tool to use in cross-compilation environments, therefore Automake # will honor the `STRIP' environment variable to overrule this program. if test "$cross_compiling" != no; then if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}strip", so it can be a program name with args. set dummy ${ac_tool_prefix}strip; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_STRIP+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$STRIP"; then ac_cv_prog_STRIP="$STRIP" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_STRIP="${ac_tool_prefix}strip" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi STRIP=$ac_cv_prog_STRIP if test -n "$STRIP"; then echo "$as_me:$LINENO: result: $STRIP" >&5 echo "${ECHO_T}$STRIP" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi fi if test -z "$ac_cv_prog_STRIP"; then ac_ct_STRIP=$STRIP # Extract the first word of "strip", so it can be a program name with args. set dummy strip; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_ac_ct_STRIP+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$ac_ct_STRIP"; then ac_cv_prog_ac_ct_STRIP="$ac_ct_STRIP" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_STRIP="strip" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done test -z "$ac_cv_prog_ac_ct_STRIP" && ac_cv_prog_ac_ct_STRIP=":" fi fi ac_ct_STRIP=$ac_cv_prog_ac_ct_STRIP if test -n "$ac_ct_STRIP"; then echo "$as_me:$LINENO: result: $ac_ct_STRIP" >&5 echo "${ECHO_T}$ac_ct_STRIP" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi STRIP=$ac_ct_STRIP else STRIP="$ac_cv_prog_STRIP" fi fi INSTALL_STRIP_PROGRAM="\${SHELL} \$(install_sh) -c -s" # We need awk for the "check" target. The system "awk" is bad on # some platforms. # Always define AMTAR for backward compatibility. AMTAR=${AMTAR-"${am_missing_run}tar"} am__tar='${AMTAR} chof - "$$tardir"'; am__untar='${AMTAR} xf -' ac_config_headers="$ac_config_headers src/include/config.h:config.in" ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}gcc", so it can be a program name with args. set dummy ${ac_tool_prefix}gcc; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_CC+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_CC="${ac_tool_prefix}gcc" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi CC=$ac_cv_prog_CC if test -n "$CC"; then echo "$as_me:$LINENO: result: $CC" >&5 echo "${ECHO_T}$CC" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi fi if test -z "$ac_cv_prog_CC"; then ac_ct_CC=$CC # Extract the first word of "gcc", so it can be a program name with args. set dummy gcc; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_ac_ct_CC+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$ac_ct_CC"; then ac_cv_prog_ac_ct_CC="$ac_ct_CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_CC="gcc" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi ac_ct_CC=$ac_cv_prog_ac_ct_CC if test -n "$ac_ct_CC"; then echo "$as_me:$LINENO: result: $ac_ct_CC" >&5 echo "${ECHO_T}$ac_ct_CC" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi CC=$ac_ct_CC else CC="$ac_cv_prog_CC" fi if test -z "$CC"; then if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}cc", so it can be a program name with args. set dummy ${ac_tool_prefix}cc; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_CC+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_CC="${ac_tool_prefix}cc" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi CC=$ac_cv_prog_CC if test -n "$CC"; then echo "$as_me:$LINENO: result: $CC" >&5 echo "${ECHO_T}$CC" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi fi if test -z "$ac_cv_prog_CC"; then ac_ct_CC=$CC # Extract the first word of "cc", so it can be a program name with args. set dummy cc; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_ac_ct_CC+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$ac_ct_CC"; then ac_cv_prog_ac_ct_CC="$ac_ct_CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_CC="cc" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi ac_ct_CC=$ac_cv_prog_ac_ct_CC if test -n "$ac_ct_CC"; then echo "$as_me:$LINENO: result: $ac_ct_CC" >&5 echo "${ECHO_T}$ac_ct_CC" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi CC=$ac_ct_CC else CC="$ac_cv_prog_CC" fi fi if test -z "$CC"; then # Extract the first word of "cc", so it can be a program name with args. set dummy cc; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_CC+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else ac_prog_rejected=no as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then if test "$as_dir/$ac_word$ac_exec_ext" = "/usr/ucb/cc"; then ac_prog_rejected=yes continue fi ac_cv_prog_CC="cc" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done if test $ac_prog_rejected = yes; then # We found a bogon in the path, so make sure we never use it. set dummy $ac_cv_prog_CC shift if test $# != 0; then # We chose a different compiler from the bogus one. # However, it has the same basename, so the bogon will be chosen # first if we set CC to just the basename; use the full file name. shift ac_cv_prog_CC="$as_dir/$ac_word${1+' '}$@" fi fi fi fi CC=$ac_cv_prog_CC if test -n "$CC"; then echo "$as_me:$LINENO: result: $CC" >&5 echo "${ECHO_T}$CC" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi fi if test -z "$CC"; then if test -n "$ac_tool_prefix"; then for ac_prog in cl do # Extract the first word of "$ac_tool_prefix$ac_prog", so it can be a program name with args. set dummy $ac_tool_prefix$ac_prog; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_CC+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_CC="$ac_tool_prefix$ac_prog" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi CC=$ac_cv_prog_CC if test -n "$CC"; then echo "$as_me:$LINENO: result: $CC" >&5 echo "${ECHO_T}$CC" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi test -n "$CC" && break done fi if test -z "$CC"; then ac_ct_CC=$CC for ac_prog in cl do # Extract the first word of "$ac_prog", so it can be a program name with args. set dummy $ac_prog; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_ac_ct_CC+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$ac_ct_CC"; then ac_cv_prog_ac_ct_CC="$ac_ct_CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_CC="$ac_prog" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi ac_ct_CC=$ac_cv_prog_ac_ct_CC if test -n "$ac_ct_CC"; then echo "$as_me:$LINENO: result: $ac_ct_CC" >&5 echo "${ECHO_T}$ac_ct_CC" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi test -n "$ac_ct_CC" && break done CC=$ac_ct_CC fi fi test -z "$CC" && { { echo "$as_me:$LINENO: error: no acceptable C compiler found in \$PATH See \`config.log' for more details." >&5 echo "$as_me: error: no acceptable C compiler found in \$PATH See \`config.log' for more details." >&2;} { (exit 1); exit 1; }; } # Provide some information about the compiler. echo "$as_me:$LINENO:" \ "checking for C compiler version" >&5 ac_compiler=`set X $ac_compile; echo $2` { (eval echo "$as_me:$LINENO: \"$ac_compiler --version &5\"") >&5 (eval $ac_compiler --version &5) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } { (eval echo "$as_me:$LINENO: \"$ac_compiler -v &5\"") >&5 (eval $ac_compiler -v &5) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } { (eval echo "$as_me:$LINENO: \"$ac_compiler -V &5\"") >&5 (eval $ac_compiler -V &5) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ int main () { ; return 0; } _ACEOF ac_clean_files_save=$ac_clean_files ac_clean_files="$ac_clean_files a.out a.exe b.out" # Try to create an executable without -o first, disregard a.out. # It will help us diagnose broken compilers, and finding out an intuition # of exeext. echo "$as_me:$LINENO: checking for C compiler default output file name" >&5 echo $ECHO_N "checking for C compiler default output file name... $ECHO_C" >&6 ac_link_default=`echo "$ac_link" | sed 's/ -o *conftest[^ ]*//'` if { (eval echo "$as_me:$LINENO: \"$ac_link_default\"") >&5 (eval $ac_link_default) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; then # Find the output, starting from the most likely. This scheme is # not robust to junk in `.', hence go to wildcards (a.*) only as a last # resort. # Be careful to initialize this variable, since it used to be cached. # Otherwise an old cache value of `no' led to `EXEEXT = no' in a Makefile. ac_cv_exeext= # b.out is created by i960 compilers. for ac_file in a_out.exe a.exe conftest.exe a.out conftest a.* conftest.* b.out do test -f "$ac_file" || continue case $ac_file in *.$ac_ext | *.xcoff | *.tds | *.d | *.pdb | *.xSYM | *.bb | *.bbg | *.o | *.obj ) ;; conftest.$ac_ext ) # This is the source file. ;; [ab].out ) # We found the default executable, but exeext='' is most # certainly right. break;; *.* ) ac_cv_exeext=`expr "$ac_file" : '[^.]*\(\..*\)'` # FIXME: I believe we export ac_cv_exeext for Libtool, # but it would be cool to find out if it's true. Does anybody # maintain Libtool? --akim. export ac_cv_exeext break;; * ) break;; esac done else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 { { echo "$as_me:$LINENO: error: C compiler cannot create executables See \`config.log' for more details." >&5 echo "$as_me: error: C compiler cannot create executables See \`config.log' for more details." >&2;} { (exit 77); exit 77; }; } fi ac_exeext=$ac_cv_exeext echo "$as_me:$LINENO: result: $ac_file" >&5 echo "${ECHO_T}$ac_file" >&6 # Check the compiler produces executables we can run. 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SAVE_CFLAGS="$CFLAGS" CFLAGS="$CFLAGS -belf" echo "$as_me:$LINENO: checking whether the C compiler needs -belf" >&5 echo $ECHO_N "checking whether the C compiler needs -belf... $ECHO_C" >&6 if test "${lt_cv_cc_needs_belf+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ int main () { ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then lt_cv_cc_needs_belf=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 lt_cv_cc_needs_belf=no fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu fi echo "$as_me:$LINENO: result: $lt_cv_cc_needs_belf" >&5 echo "${ECHO_T}$lt_cv_cc_needs_belf" >&6 if test x"$lt_cv_cc_needs_belf" != x"yes"; then # this is probably gcc 2.8.0, egcs 1.0 or newer; no need for -belf CFLAGS="$SAVE_CFLAGS" fi ;; sparc*-*solaris*) # Find out which ABI we are using. echo 'int i;' > conftest.$ac_ext if { (eval echo "$as_me:$LINENO: \"$ac_compile\"") >&5 (eval $ac_compile) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; then case `/usr/bin/file conftest.o` in *64-bit*) case $lt_cv_prog_gnu_ld in yes*) LD="${LD-ld} -m elf64_sparc" ;; *) LD="${LD-ld} -64" ;; esac ;; esac fi rm -rf conftest* ;; esac need_locks="$enable_libtool_lock" ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu echo "$as_me:$LINENO: checking how to run the C preprocessor" >&5 echo $ECHO_N "checking how to run the C preprocessor... $ECHO_C" >&6 # On Suns, sometimes $CPP names a directory. if test -n "$CPP" && test -d "$CPP"; then CPP= fi if test -z "$CPP"; then if test "${ac_cv_prog_CPP+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else # Double quotes because CPP needs to be expanded for CPP in "$CC -E" "$CC -E -traditional-cpp" "/lib/cpp" do ac_preproc_ok=false for ac_c_preproc_warn_flag in '' yes do # Use a header file that comes with gcc, so configuring glibc # with a fresh cross-compiler works. # Prefer to if __STDC__ is defined, since # exists even on freestanding compilers. # On the NeXT, cc -E runs the code through the compiler's parser, # not just through cpp. "Syntax error" is here to catch this case. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ #ifdef __STDC__ # include #else # include #endif Syntax error _ACEOF if { (eval echo "$as_me:$LINENO: \"$ac_cpp conftest.$ac_ext\"") >&5 (eval $ac_cpp conftest.$ac_ext) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } >/dev/null; then if test -s conftest.err; then ac_cpp_err=$ac_c_preproc_warn_flag ac_cpp_err=$ac_cpp_err$ac_c_werror_flag else ac_cpp_err= fi else ac_cpp_err=yes fi if test -z "$ac_cpp_err"; then : else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 # Broken: fails on valid input. continue fi rm -f conftest.err conftest.$ac_ext # OK, works on sane cases. Now check whether non-existent headers # can be detected and how. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ #include _ACEOF if { (eval echo "$as_me:$LINENO: \"$ac_cpp conftest.$ac_ext\"") >&5 (eval $ac_cpp conftest.$ac_ext) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } >/dev/null; then if test -s conftest.err; then ac_cpp_err=$ac_c_preproc_warn_flag ac_cpp_err=$ac_cpp_err$ac_c_werror_flag else ac_cpp_err= fi else ac_cpp_err=yes fi if test -z "$ac_cpp_err"; then # Broken: success on invalid input. continue else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 # Passes both tests. ac_preproc_ok=: break fi rm -f conftest.err conftest.$ac_ext done # Because of `break', _AC_PREPROC_IFELSE's cleaning code was skipped. rm -f conftest.err conftest.$ac_ext if $ac_preproc_ok; then break fi done ac_cv_prog_CPP=$CPP fi CPP=$ac_cv_prog_CPP else ac_cv_prog_CPP=$CPP fi echo "$as_me:$LINENO: result: $CPP" >&5 echo "${ECHO_T}$CPP" >&6 ac_preproc_ok=false for ac_c_preproc_warn_flag in '' yes do # Use a header file that comes with gcc, so configuring glibc # with a fresh cross-compiler works. # Prefer to if __STDC__ is defined, since # exists even on freestanding compilers. # On the NeXT, cc -E runs the code through the compiler's parser, # not just through cpp. "Syntax error" is here to catch this case. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ #ifdef __STDC__ # include #else # include #endif Syntax error _ACEOF if { (eval echo "$as_me:$LINENO: \"$ac_cpp conftest.$ac_ext\"") >&5 (eval $ac_cpp conftest.$ac_ext) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } >/dev/null; then if test -s conftest.err; then ac_cpp_err=$ac_c_preproc_warn_flag ac_cpp_err=$ac_cpp_err$ac_c_werror_flag else ac_cpp_err= fi else ac_cpp_err=yes fi if test -z "$ac_cpp_err"; then : else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 # Broken: fails on valid input. continue fi rm -f conftest.err conftest.$ac_ext # OK, works on sane cases. Now check whether non-existent headers # can be detected and how. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ #include _ACEOF if { (eval echo "$as_me:$LINENO: \"$ac_cpp conftest.$ac_ext\"") >&5 (eval $ac_cpp conftest.$ac_ext) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } >/dev/null; then if test -s conftest.err; then ac_cpp_err=$ac_c_preproc_warn_flag ac_cpp_err=$ac_cpp_err$ac_c_werror_flag else ac_cpp_err= fi else ac_cpp_err=yes fi if test -z "$ac_cpp_err"; then # Broken: success on invalid input. continue else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 # Passes both tests. ac_preproc_ok=: break fi rm -f conftest.err conftest.$ac_ext done # Because of `break', _AC_PREPROC_IFELSE's cleaning code was skipped. rm -f conftest.err conftest.$ac_ext if $ac_preproc_ok; then : else { { echo "$as_me:$LINENO: error: C preprocessor \"$CPP\" fails sanity check See \`config.log' for more details." >&5 echo "$as_me: error: C preprocessor \"$CPP\" fails sanity check See \`config.log' for more details." >&2;} { (exit 1); exit 1; }; } fi ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu echo "$as_me:$LINENO: checking for ANSI C header files" >&5 echo $ECHO_N "checking for ANSI C header files... $ECHO_C" >&6 if test "${ac_cv_header_stdc+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ #include #include #include #include int main () { ; return 0; } _ACEOF rm -f conftest.$ac_objext if { (eval echo "$as_me:$LINENO: \"$ac_compile\"") >&5 (eval $ac_compile) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest.$ac_objext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then ac_cv_header_stdc=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_header_stdc=no fi rm -f conftest.err conftest.$ac_objext conftest.$ac_ext if test $ac_cv_header_stdc = yes; then # SunOS 4.x string.h does not declare mem*, contrary to ANSI. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ #include _ACEOF if (eval "$ac_cpp conftest.$ac_ext") 2>&5 | $EGREP "memchr" >/dev/null 2>&1; then : else ac_cv_header_stdc=no fi rm -f conftest* fi if test $ac_cv_header_stdc = yes; then # ISC 2.0.2 stdlib.h does not declare free, contrary to ANSI. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ #include _ACEOF if (eval "$ac_cpp conftest.$ac_ext") 2>&5 | $EGREP "free" >/dev/null 2>&1; then : else ac_cv_header_stdc=no fi rm -f conftest* fi if test $ac_cv_header_stdc = yes; then # /bin/cc in Irix-4.0.5 gets non-ANSI ctype macros unless using -ansi. if test "$cross_compiling" = yes; then : else cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ #include #if ((' ' & 0x0FF) == 0x020) # define ISLOWER(c) ('a' <= (c) && (c) <= 'z') # define TOUPPER(c) (ISLOWER(c) ? 'A' + ((c) - 'a') : (c)) #else # define ISLOWER(c) \ (('a' <= (c) && (c) <= 'i') \ || ('j' <= (c) && (c) <= 'r') \ || ('s' <= (c) && (c) <= 'z')) # define TOUPPER(c) (ISLOWER(c) ? ((c) | 0x40) : (c)) #endif #define XOR(e, f) (((e) && !(f)) || (!(e) && (f))) int main () { int i; for (i = 0; i < 256; i++) if (XOR (islower (i), ISLOWER (i)) || toupper (i) != TOUPPER (i)) exit(2); exit (0); } _ACEOF rm -f conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='./conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then : else echo "$as_me: program exited with status $ac_status" >&5 echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ( exit $ac_status ) ac_cv_header_stdc=no fi rm -f core *.core gmon.out bb.out conftest$ac_exeext conftest.$ac_objext conftest.$ac_ext fi fi fi echo "$as_me:$LINENO: result: $ac_cv_header_stdc" >&5 echo "${ECHO_T}$ac_cv_header_stdc" >&6 if test $ac_cv_header_stdc = yes; then cat >>confdefs.h <<\_ACEOF #define STDC_HEADERS 1 _ACEOF fi # On IRIX 5.3, sys/types and inttypes.h are conflicting. for ac_header in sys/types.h sys/stat.h stdlib.h string.h memory.h strings.h \ inttypes.h stdint.h unistd.h do as_ac_Header=`echo "ac_cv_header_$ac_header" | $as_tr_sh` echo "$as_me:$LINENO: checking for $ac_header" >&5 echo $ECHO_N "checking for $ac_header... $ECHO_C" >&6 if eval "test \"\${$as_ac_Header+set}\" = set"; then echo $ECHO_N "(cached) $ECHO_C" >&6 else cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ $ac_includes_default #include <$ac_header> _ACEOF rm -f conftest.$ac_objext if { (eval echo "$as_me:$LINENO: \"$ac_compile\"") >&5 (eval $ac_compile) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest.$ac_objext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then eval "$as_ac_Header=yes" else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 eval "$as_ac_Header=no" fi rm -f conftest.err conftest.$ac_objext conftest.$ac_ext fi echo "$as_me:$LINENO: result: `eval echo '${'$as_ac_Header'}'`" >&5 echo "${ECHO_T}`eval echo '${'$as_ac_Header'}'`" >&6 if test `eval echo '${'$as_ac_Header'}'` = yes; then cat >>confdefs.h <<_ACEOF #define `echo "HAVE_$ac_header" | $as_tr_cpp` 1 _ACEOF fi done for ac_header in dlfcn.h do as_ac_Header=`echo "ac_cv_header_$ac_header" | $as_tr_sh` if eval "test \"\${$as_ac_Header+set}\" = set"; then echo "$as_me:$LINENO: checking for $ac_header" >&5 echo $ECHO_N "checking for $ac_header... $ECHO_C" >&6 if eval "test \"\${$as_ac_Header+set}\" = set"; then echo $ECHO_N "(cached) $ECHO_C" >&6 fi echo "$as_me:$LINENO: result: `eval echo '${'$as_ac_Header'}'`" >&5 echo "${ECHO_T}`eval echo '${'$as_ac_Header'}'`" >&6 else # Is the header compilable? 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SHELL=${SHELL-${CONFIG_SHELL-/bin/sh}} while (test "X"`$SHELL $0 --fallback-echo "X$teststring" 2>/dev/null` \ = "XX$teststring") >/dev/null 2>&1 && new_result=`expr "X$teststring" : ".*" 2>&1` && lt_cv_sys_max_cmd_len=$new_result && test $i != 17 # 1/2 MB should be enough do i=`expr $i + 1` teststring=$teststring$teststring done teststring= # Add a significant safety factor because C++ compilers can tack on massive # amounts of additional arguments before passing them to the linker. # It appears as though 1/2 is a usable value. lt_cv_sys_max_cmd_len=`expr $lt_cv_sys_max_cmd_len \/ 2` ;; esac fi if test -n $lt_cv_sys_max_cmd_len ; then echo "$as_me:$LINENO: result: $lt_cv_sys_max_cmd_len" >&5 echo "${ECHO_T}$lt_cv_sys_max_cmd_len" >&6 else echo "$as_me:$LINENO: result: none" >&5 echo "${ECHO_T}none" >&6 fi # Check for command to grab the raw symbol name followed by C symbol from nm. echo "$as_me:$LINENO: checking command to parse $NM output from $compiler object" >&5 echo $ECHO_N "checking command to parse $NM output from $compiler object... $ECHO_C" >&6 if test "${lt_cv_sys_global_symbol_pipe+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else # These are sane defaults that work on at least a few old systems. # [They come from Ultrix. What could be older than Ultrix?!! ;)] # Character class describing NM global symbol codes. symcode='[BCDEGRST]' # Regexp to match symbols that can be accessed directly from C. sympat='\([_A-Za-z][_A-Za-z0-9]*\)' # Transform an extracted symbol line into a proper C declaration lt_cv_sys_global_symbol_to_cdecl="sed -n -e 's/^. .* \(.*\)$/extern int \1;/p'" # Transform an extracted symbol line into symbol name and symbol address lt_cv_sys_global_symbol_to_c_name_address="sed -n -e 's/^: \([^ ]*\) $/ {\\\"\1\\\", (lt_ptr) 0},/p' -e 's/^$symcode \([^ ]*\) \([^ ]*\)$/ {\"\2\", (lt_ptr) \&\2},/p'" # Define system-specific variables. case $host_os in aix*) symcode='[BCDT]' ;; cygwin* | mingw* | pw32*) symcode='[ABCDGISTW]' ;; hpux*) # Its linker distinguishes data from code symbols if test "$host_cpu" = ia64; then symcode='[ABCDEGRST]' fi lt_cv_sys_global_symbol_to_cdecl="sed -n -e 's/^T .* \(.*\)$/extern int \1();/p' -e 's/^$symcode* .* \(.*\)$/extern char \1;/p'" lt_cv_sys_global_symbol_to_c_name_address="sed -n -e 's/^: \([^ ]*\) $/ {\\\"\1\\\", (lt_ptr) 0},/p' -e 's/^$symcode* \([^ ]*\) \([^ ]*\)$/ {\"\2\", (lt_ptr) \&\2},/p'" ;; linux*) if test "$host_cpu" = ia64; then symcode='[ABCDGIRSTW]' lt_cv_sys_global_symbol_to_cdecl="sed -n -e 's/^T .* \(.*\)$/extern int \1();/p' -e 's/^$symcode* .* \(.*\)$/extern char \1;/p'" lt_cv_sys_global_symbol_to_c_name_address="sed -n -e 's/^: \([^ ]*\) $/ {\\\"\1\\\", (lt_ptr) 0},/p' -e 's/^$symcode* \([^ ]*\) \([^ ]*\)$/ {\"\2\", (lt_ptr) \&\2},/p'" fi ;; irix* | nonstopux*) symcode='[BCDEGRST]' ;; osf*) symcode='[BCDEGQRST]' ;; solaris*) symcode='[BDRT]' ;; sco3.2v5*) symcode='[DT]' ;; sysv4.2uw2*) symcode='[DT]' ;; sysv5* | sco5v6* | unixware* | OpenUNIX*) symcode='[ABDT]' ;; sysv4) symcode='[DFNSTU]' ;; esac # Handle CRLF in mingw tool chain opt_cr= case $build_os in mingw*) opt_cr=`echo 'x\{0,1\}' | tr x '\015'` # option cr in regexp ;; esac # If we're using GNU nm, then use its standard symbol codes. case `$NM -V 2>&1` in *GNU* | *'with BFD'*) symcode='[ABCDGIRSTW]' ;; esac # Try without a prefix undercore, then with it. for ac_symprfx in "" "_"; do # Transform symcode, sympat, and symprfx into a raw symbol and a C symbol. symxfrm="\\1 $ac_symprfx\\2 \\2" # Write the raw and C identifiers. lt_cv_sys_global_symbol_pipe="sed -n -e 's/^.*[ ]\($symcode$symcode*\)[ ][ ]*$ac_symprfx$sympat$opt_cr$/$symxfrm/p'" # Check to see that the pipe works correctly. pipe_works=no rm -f conftest* cat > conftest.$ac_ext <&5 (eval $ac_compile) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; then # Now try to grab the symbols. nlist=conftest.nm if { (eval echo "$as_me:$LINENO: \"$NM conftest.$ac_objext \| $lt_cv_sys_global_symbol_pipe \> $nlist\"") >&5 (eval $NM conftest.$ac_objext \| $lt_cv_sys_global_symbol_pipe \> $nlist) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && test -s "$nlist"; then # Try sorting and uniquifying the output. if sort "$nlist" | uniq > "$nlist"T; then mv -f "$nlist"T "$nlist" else rm -f "$nlist"T fi # Make sure that we snagged all the symbols we need. if grep ' nm_test_var$' "$nlist" >/dev/null; then if grep ' nm_test_func$' "$nlist" >/dev/null; then cat < conftest.$ac_ext #ifdef __cplusplus extern "C" { #endif EOF # Now generate the symbol file. eval "$lt_cv_sys_global_symbol_to_cdecl"' < "$nlist" | grep -v main >> conftest.$ac_ext' cat <> conftest.$ac_ext #if defined (__STDC__) && __STDC__ # define lt_ptr_t void * #else # define lt_ptr_t char * # define const #endif /* The mapping between symbol names and symbols. */ const struct { const char *name; lt_ptr_t address; } lt_preloaded_symbols[] = { EOF $SED "s/^$symcode$symcode* \(.*\) \(.*\)$/ {\"\2\", (lt_ptr_t) \&\2},/" < "$nlist" | grep -v main >> conftest.$ac_ext cat <<\EOF >> conftest.$ac_ext {0, (lt_ptr_t) 0} }; #ifdef __cplusplus } #endif EOF # Now try linking the two files. mv conftest.$ac_objext conftstm.$ac_objext lt_save_LIBS="$LIBS" lt_save_CFLAGS="$CFLAGS" LIBS="conftstm.$ac_objext" CFLAGS="$CFLAGS$lt_prog_compiler_no_builtin_flag" if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && test -s conftest${ac_exeext}; then pipe_works=yes fi LIBS="$lt_save_LIBS" CFLAGS="$lt_save_CFLAGS" else echo "cannot find nm_test_func in $nlist" >&5 fi else echo "cannot find nm_test_var in $nlist" >&5 fi else echo "cannot run $lt_cv_sys_global_symbol_pipe" >&5 fi else echo "$progname: failed program was:" >&5 cat conftest.$ac_ext >&5 fi rm -f conftest* conftst* # Do not use the global_symbol_pipe unless it works. if test "$pipe_works" = yes; then break else lt_cv_sys_global_symbol_pipe= fi done fi if test -z "$lt_cv_sys_global_symbol_pipe"; then lt_cv_sys_global_symbol_to_cdecl= fi if test -z "$lt_cv_sys_global_symbol_pipe$lt_cv_sys_global_symbol_to_cdecl"; then echo "$as_me:$LINENO: result: failed" >&5 echo "${ECHO_T}failed" >&6 else echo "$as_me:$LINENO: result: ok" >&5 echo "${ECHO_T}ok" >&6 fi echo "$as_me:$LINENO: checking for objdir" >&5 echo $ECHO_N "checking for objdir... $ECHO_C" >&6 if test "${lt_cv_objdir+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else rm -f .libs 2>/dev/null mkdir .libs 2>/dev/null if test -d .libs; then lt_cv_objdir=.libs else # MS-DOS does not allow filenames that begin with a dot. lt_cv_objdir=_libs fi rmdir .libs 2>/dev/null fi echo "$as_me:$LINENO: result: $lt_cv_objdir" >&5 echo "${ECHO_T}$lt_cv_objdir" >&6 objdir=$lt_cv_objdir case $host_os in aix3*) # AIX sometimes has problems with the GCC collect2 program. For some # reason, if we set the COLLECT_NAMES environment variable, the problems # vanish in a puff of smoke. if test "X${COLLECT_NAMES+set}" != Xset; then COLLECT_NAMES= export COLLECT_NAMES fi ;; esac # Sed substitution that helps us do robust quoting. It backslashifies # metacharacters that are still active within double-quoted strings. Xsed='sed -e 1s/^X//' sed_quote_subst='s/\([\\"\\`$\\\\]\)/\\\1/g' # Same as above, but do not quote variable references. double_quote_subst='s/\([\\"\\`\\\\]\)/\\\1/g' # Sed substitution to delay expansion of an escaped shell variable in a # double_quote_subst'ed string. delay_variable_subst='s/\\\\\\\\\\\$/\\\\\\$/g' # Sed substitution to avoid accidental globbing in evaled expressions no_glob_subst='s/\*/\\\*/g' # Constants: rm="rm -f" # Global variables: default_ofile=libtool can_build_shared=yes # All known linkers require a `.a' archive for static linking (except MSVC, # which needs '.lib'). libext=a ltmain="$ac_aux_dir/ltmain.sh" ofile="$default_ofile" with_gnu_ld="$lt_cv_prog_gnu_ld" if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}ar", so it can be a program name with args. set dummy ${ac_tool_prefix}ar; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_AR+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$AR"; then ac_cv_prog_AR="$AR" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_AR="${ac_tool_prefix}ar" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi AR=$ac_cv_prog_AR if test -n "$AR"; then echo "$as_me:$LINENO: result: $AR" >&5 echo "${ECHO_T}$AR" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi fi if test -z "$ac_cv_prog_AR"; then ac_ct_AR=$AR # Extract the first word of "ar", so it can be a program name with args. set dummy ar; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_ac_ct_AR+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$ac_ct_AR"; then ac_cv_prog_ac_ct_AR="$ac_ct_AR" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_AR="ar" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done test -z "$ac_cv_prog_ac_ct_AR" && ac_cv_prog_ac_ct_AR="false" fi fi ac_ct_AR=$ac_cv_prog_ac_ct_AR if test -n "$ac_ct_AR"; then echo "$as_me:$LINENO: result: $ac_ct_AR" >&5 echo "${ECHO_T}$ac_ct_AR" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi AR=$ac_ct_AR else AR="$ac_cv_prog_AR" fi if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}ranlib", so it can be a program name with args. set dummy ${ac_tool_prefix}ranlib; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_RANLIB+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$RANLIB"; then ac_cv_prog_RANLIB="$RANLIB" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_RANLIB="${ac_tool_prefix}ranlib" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi RANLIB=$ac_cv_prog_RANLIB if test -n "$RANLIB"; then echo "$as_me:$LINENO: result: $RANLIB" >&5 echo "${ECHO_T}$RANLIB" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi fi if test -z "$ac_cv_prog_RANLIB"; then ac_ct_RANLIB=$RANLIB # Extract the first word of "ranlib", so it can be a program name with args. set dummy ranlib; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_ac_ct_RANLIB+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$ac_ct_RANLIB"; then ac_cv_prog_ac_ct_RANLIB="$ac_ct_RANLIB" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_RANLIB="ranlib" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done test -z "$ac_cv_prog_ac_ct_RANLIB" && ac_cv_prog_ac_ct_RANLIB=":" fi fi ac_ct_RANLIB=$ac_cv_prog_ac_ct_RANLIB if test -n "$ac_ct_RANLIB"; then echo "$as_me:$LINENO: result: $ac_ct_RANLIB" >&5 echo "${ECHO_T}$ac_ct_RANLIB" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi RANLIB=$ac_ct_RANLIB else RANLIB="$ac_cv_prog_RANLIB" fi if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}strip", so it can be a program name with args. set dummy ${ac_tool_prefix}strip; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_STRIP+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$STRIP"; then ac_cv_prog_STRIP="$STRIP" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_STRIP="${ac_tool_prefix}strip" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done fi fi STRIP=$ac_cv_prog_STRIP if test -n "$STRIP"; then echo "$as_me:$LINENO: result: $STRIP" >&5 echo "${ECHO_T}$STRIP" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi fi if test -z "$ac_cv_prog_STRIP"; then ac_ct_STRIP=$STRIP # Extract the first word of "strip", so it can be a program name with args. set dummy strip; ac_word=$2 echo "$as_me:$LINENO: checking for $ac_word" >&5 echo $ECHO_N "checking for $ac_word... $ECHO_C" >&6 if test "${ac_cv_prog_ac_ct_STRIP+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test -n "$ac_ct_STRIP"; then ac_cv_prog_ac_ct_STRIP="$ac_ct_STRIP" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. for ac_exec_ext in '' $ac_executable_extensions; do if $as_executable_p "$as_dir/$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_STRIP="strip" echo "$as_me:$LINENO: found $as_dir/$ac_word$ac_exec_ext" >&5 break 2 fi done done test -z "$ac_cv_prog_ac_ct_STRIP" && ac_cv_prog_ac_ct_STRIP=":" fi fi ac_ct_STRIP=$ac_cv_prog_ac_ct_STRIP if test -n "$ac_ct_STRIP"; then echo "$as_me:$LINENO: result: $ac_ct_STRIP" >&5 echo "${ECHO_T}$ac_ct_STRIP" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi STRIP=$ac_ct_STRIP else STRIP="$ac_cv_prog_STRIP" fi old_CC="$CC" old_CFLAGS="$CFLAGS" # Set sane defaults for various variables test -z "$AR" && AR=ar test -z "$AR_FLAGS" && AR_FLAGS=cru test -z "$AS" && AS=as test -z "$CC" && CC=cc test -z "$LTCC" && LTCC=$CC test -z "$LTCFLAGS" && LTCFLAGS=$CFLAGS test -z "$DLLTOOL" && DLLTOOL=dlltool test -z "$LD" && LD=ld test -z "$LN_S" && LN_S="ln -s" test -z "$MAGIC_CMD" && MAGIC_CMD=file test -z "$NM" && NM=nm test -z "$SED" && SED=sed test -z "$OBJDUMP" && OBJDUMP=objdump test -z "$RANLIB" && RANLIB=: test -z "$STRIP" && STRIP=: test -z "$ac_objext" && ac_objext=o # Determine commands to create old-style static archives. old_archive_cmds='$AR $AR_FLAGS $oldlib$oldobjs$old_deplibs' old_postinstall_cmds='chmod 644 $oldlib' old_postuninstall_cmds= if test -n "$RANLIB"; then case $host_os in openbsd*) old_postinstall_cmds="$old_postinstall_cmds~\$RANLIB -t \$oldlib" ;; *) old_postinstall_cmds="$old_postinstall_cmds~\$RANLIB \$oldlib" ;; esac old_archive_cmds="$old_archive_cmds~\$RANLIB \$oldlib" fi for cc_temp in $compiler""; do case $cc_temp in compile | *[\\/]compile | ccache | *[\\/]ccache ) ;; distcc | *[\\/]distcc | purify | *[\\/]purify ) ;; \-*) ;; *) break;; esac done cc_basename=`$echo "X$cc_temp" | $Xsed -e 's%.*/%%' -e "s%^$host_alias-%%"` # Only perform the check for file, if the check method requires it case $deplibs_check_method in file_magic*) if test "$file_magic_cmd" = '$MAGIC_CMD'; then echo "$as_me:$LINENO: checking for ${ac_tool_prefix}file" >&5 echo $ECHO_N "checking for ${ac_tool_prefix}file... $ECHO_C" >&6 if test "${lt_cv_path_MAGIC_CMD+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else case $MAGIC_CMD in [\\/*] | ?:[\\/]*) lt_cv_path_MAGIC_CMD="$MAGIC_CMD" # Let the user override the test with a path. ;; *) lt_save_MAGIC_CMD="$MAGIC_CMD" lt_save_ifs="$IFS"; IFS=$PATH_SEPARATOR ac_dummy="/usr/bin$PATH_SEPARATOR$PATH" for ac_dir in $ac_dummy; do IFS="$lt_save_ifs" test -z "$ac_dir" && ac_dir=. if test -f $ac_dir/${ac_tool_prefix}file; then lt_cv_path_MAGIC_CMD="$ac_dir/${ac_tool_prefix}file" if test -n "$file_magic_test_file"; then case $deplibs_check_method in "file_magic "*) file_magic_regex=`expr "$deplibs_check_method" : "file_magic \(.*\)"` MAGIC_CMD="$lt_cv_path_MAGIC_CMD" if eval $file_magic_cmd \$file_magic_test_file 2> /dev/null | $EGREP "$file_magic_regex" > /dev/null; then : else cat <&2 *** Warning: the command libtool uses to detect shared libraries, *** $file_magic_cmd, produces output that libtool cannot recognize. *** The result is that libtool may fail to recognize shared libraries *** as such. This will affect the creation of libtool libraries that *** depend on shared libraries, but programs linked with such libtool *** libraries will work regardless of this problem. Nevertheless, you *** may want to report the problem to your system manager and/or to *** bug-libtool@gnu.org EOF fi ;; esac fi break fi done IFS="$lt_save_ifs" MAGIC_CMD="$lt_save_MAGIC_CMD" ;; esac fi MAGIC_CMD="$lt_cv_path_MAGIC_CMD" if test -n "$MAGIC_CMD"; then echo "$as_me:$LINENO: result: $MAGIC_CMD" >&5 echo "${ECHO_T}$MAGIC_CMD" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi if test -z "$lt_cv_path_MAGIC_CMD"; then if test -n "$ac_tool_prefix"; then echo "$as_me:$LINENO: checking for file" >&5 echo $ECHO_N "checking for file... $ECHO_C" >&6 if test "${lt_cv_path_MAGIC_CMD+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else case $MAGIC_CMD in [\\/*] | ?:[\\/]*) lt_cv_path_MAGIC_CMD="$MAGIC_CMD" # Let the user override the test with a path. ;; *) lt_save_MAGIC_CMD="$MAGIC_CMD" lt_save_ifs="$IFS"; IFS=$PATH_SEPARATOR ac_dummy="/usr/bin$PATH_SEPARATOR$PATH" for ac_dir in $ac_dummy; do IFS="$lt_save_ifs" test -z "$ac_dir" && ac_dir=. if test -f $ac_dir/file; then lt_cv_path_MAGIC_CMD="$ac_dir/file" if test -n "$file_magic_test_file"; then case $deplibs_check_method in "file_magic "*) file_magic_regex=`expr "$deplibs_check_method" : "file_magic \(.*\)"` MAGIC_CMD="$lt_cv_path_MAGIC_CMD" if eval $file_magic_cmd \$file_magic_test_file 2> /dev/null | $EGREP "$file_magic_regex" > /dev/null; then : else cat <&2 *** Warning: the command libtool uses to detect shared libraries, *** $file_magic_cmd, produces output that libtool cannot recognize. *** The result is that libtool may fail to recognize shared libraries *** as such. This will affect the creation of libtool libraries that *** depend on shared libraries, but programs linked with such libtool *** libraries will work regardless of this problem. Nevertheless, you *** may want to report the problem to your system manager and/or to *** bug-libtool@gnu.org EOF fi ;; esac fi break fi done IFS="$lt_save_ifs" MAGIC_CMD="$lt_save_MAGIC_CMD" ;; esac fi MAGIC_CMD="$lt_cv_path_MAGIC_CMD" if test -n "$MAGIC_CMD"; then echo "$as_me:$LINENO: result: $MAGIC_CMD" >&5 echo "${ECHO_T}$MAGIC_CMD" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi else MAGIC_CMD=: fi fi fi ;; esac enable_dlopen=no enable_win32_dll=no # Check whether --enable-libtool-lock or --disable-libtool-lock was given. if test "${enable_libtool_lock+set}" = set; then enableval="$enable_libtool_lock" fi; test "x$enable_libtool_lock" != xno && enable_libtool_lock=yes # Check whether --with-pic or --without-pic was given. if test "${with_pic+set}" = set; then withval="$with_pic" pic_mode="$withval" else pic_mode=default fi; test -z "$pic_mode" && pic_mode=default # Use C for the default configuration in the libtool script tagname= lt_save_CC="$CC" ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu # Source file extension for C test sources. ac_ext=c # Object file extension for compiled C test sources. objext=o objext=$objext # Code to be used in simple compile tests lt_simple_compile_test_code="int some_variable = 0;\n" # Code to be used in simple link tests lt_simple_link_test_code='int main(){return(0);}\n' # If no C compiler was specified, use CC. LTCC=${LTCC-"$CC"} # If no C compiler flags were specified, use CFLAGS. 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FIXME archive_cmds='$CC -nostart $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' else ld_shlibs=no fi ;; cygwin* | mingw* | pw32*) # _LT_AC_TAGVAR(hardcode_libdir_flag_spec, ) is actually meaningless, # as there is no search path for DLLs. hardcode_libdir_flag_spec='-L$libdir' allow_undefined_flag=unsupported always_export_symbols=no enable_shared_with_static_runtimes=yes export_symbols_cmds='$NM $libobjs $convenience | $global_symbol_pipe | $SED -e '\''/^[BCDGRS] /s/.* \([^ ]*\)/\1 DATA/'\'' | $SED -e '\''/^[AITW] /s/.* //'\'' | sort | uniq > $export_symbols' if $LD --help 2>&1 | grep 'auto-import' > /dev/null; then archive_cmds='$CC -shared $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' # If the export-symbols file already is a .def file (1st line # is EXPORTS), use it as is; otherwise, prepend... archive_expsym_cmds='if test "x`$SED 1q $export_symbols`" = xEXPORTS; then cp $export_symbols $output_objdir/$soname.def; else echo EXPORTS > $output_objdir/$soname.def; cat $export_symbols >> $output_objdir/$soname.def; fi~ $CC -shared $output_objdir/$soname.def $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' else ld_shlibs=no fi ;; interix3*) hardcode_direct=no hardcode_shlibpath_var=no hardcode_libdir_flag_spec='${wl}-rpath,$libdir' export_dynamic_flag_spec='${wl}-E' # Hack: On Interix 3.x, we cannot compile PIC because of a broken gcc. # Instead, shared libraries are loaded at an image base (0x10000000 by # default) and relocated if they conflict, which is a slow very memory # consuming and fragmenting process. 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Moving up from 0x10000000 also allows more sbrk(2) space. archive_cmds='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-h,$soname ${wl}--image-base,`expr ${RANDOM-$$} % 4096 / 2 \* 262144 + 1342177280` -o $lib' archive_expsym_cmds='sed "s,^,_," $export_symbols >$output_objdir/$soname.expsym~$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-h,$soname ${wl}--retain-symbols-file,$output_objdir/$soname.expsym ${wl}--image-base,`expr ${RANDOM-$$} % 4096 / 2 \* 262144 + 1342177280` -o $lib' ;; linux*) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then tmp_addflag= case $cc_basename,$host_cpu in pgcc*) # Portland Group C compiler whole_archive_flag_spec='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}--no-whole-archive' tmp_addflag=' $pic_flag' ;; pgf77* | pgf90* | pgf95*) # Portland Group f77 and f90 compilers whole_archive_flag_spec='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}--no-whole-archive' tmp_addflag=' $pic_flag -Mnomain' ;; ecc*,ia64* | icc*,ia64*) # Intel C compiler on ia64 tmp_addflag=' -i_dynamic' ;; efc*,ia64* | ifort*,ia64*) # Intel Fortran compiler on ia64 tmp_addflag=' -i_dynamic -nofor_main' ;; ifc* | ifort*) # Intel Fortran compiler tmp_addflag=' -nofor_main' ;; esac archive_cmds='$CC -shared'"$tmp_addflag"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' if test $supports_anon_versioning = yes; then archive_expsym_cmds='$echo "{ global:" > $output_objdir/$libname.ver~ cat $export_symbols | sed -e "s/\(.*\)/\1;/" >> $output_objdir/$libname.ver~ $echo "local: *; };" >> $output_objdir/$libname.ver~ $CC -shared'"$tmp_addflag"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-version-script ${wl}$output_objdir/$libname.ver -o $lib' fi link_all_deplibs=no else ld_shlibs=no fi ;; netbsd* | netbsdelf*-gnu | knetbsd*-gnu) if echo __ELF__ | $CC -E - | grep __ELF__ >/dev/null; then archive_cmds='$LD -Bshareable $libobjs $deplibs $linker_flags -o $lib' wlarc= else archive_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' fi ;; solaris*) if $LD -v 2>&1 | grep 'BFD 2\.8' > /dev/null; then ld_shlibs=no cat <&2 *** Warning: The releases 2.8.* of the GNU linker cannot reliably *** create shared libraries on Solaris systems. Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.9.1 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. EOF elif $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then archive_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else ld_shlibs=no fi ;; sysv5* | sco3.2v5* | sco5v6* | unixware* | OpenUNIX*) case `$LD -v 2>&1` in *\ [01].* | *\ 2.[0-9].* | *\ 2.1[0-5].*) ld_shlibs=no cat <<_LT_EOF 1>&2 *** Warning: Releases of the GNU linker prior to 2.16.91.0.3 can not *** reliably create shared libraries on SCO systems. Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.16.91.0.3 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. _LT_EOF ;; *) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then hardcode_libdir_flag_spec='`test -z "$SCOABSPATH" && echo ${wl}-rpath,$libdir`' archive_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname,\${SCOABSPATH:+${install_libdir}/}$soname -o $lib' archive_expsym_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname,\${SCOABSPATH:+${install_libdir}/}$soname,-retain-symbols-file,$export_symbols -o $lib' else ld_shlibs=no fi ;; esac ;; sunos4*) archive_cmds='$LD -assert pure-text -Bshareable -o $lib $libobjs $deplibs $linker_flags' wlarc= hardcode_direct=yes hardcode_shlibpath_var=no ;; *) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then archive_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else ld_shlibs=no fi ;; esac if test "$ld_shlibs" = no; then runpath_var= hardcode_libdir_flag_spec= export_dynamic_flag_spec= whole_archive_flag_spec= fi else # PORTME fill in a description of your system's linker (not GNU ld) case $host_os in aix3*) allow_undefined_flag=unsupported always_export_symbols=yes archive_expsym_cmds='$LD -o $output_objdir/$soname $libobjs $deplibs $linker_flags -bE:$export_symbols -T512 -H512 -bM:SRE~$AR $AR_FLAGS $lib $output_objdir/$soname' # Note: this linker hardcodes the directories in LIBPATH if there # are no directories specified by -L. hardcode_minus_L=yes if test "$GCC" = yes && test -z "$lt_prog_compiler_static"; then # Neither direct hardcoding nor static linking is supported with a # broken collect2. hardcode_direct=unsupported fi ;; aix4* | aix5*) if test "$host_cpu" = ia64; then # On IA64, the linker does run time linking by default, so we don't # have to do anything special. aix_use_runtimelinking=no exp_sym_flag='-Bexport' no_entry_flag="" else # If we're using GNU nm, then we don't want the "-C" option. # -C means demangle to AIX nm, but means don't demangle with GNU nm if $NM -V 2>&1 | grep 'GNU' > /dev/null; then export_symbols_cmds='$NM -Bpg $libobjs $convenience | awk '\''{ if (((\$2 == "T") || (\$2 == "D") || (\$2 == "B")) && (substr(\$3,1,1) != ".")) { print \$3 } }'\'' | sort -u > $export_symbols' else export_symbols_cmds='$NM -BCpg $libobjs $convenience | awk '\''{ if (((\$2 == "T") || (\$2 == "D") || (\$2 == "B")) && (substr(\$3,1,1) != ".")) { print \$3 } }'\'' | sort -u > $export_symbols' fi aix_use_runtimelinking=no # Test if we are trying to use run time linking or normal # AIX style linking. If -brtl is somewhere in LDFLAGS, we # need to do runtime linking. case $host_os in aix4.[23]|aix4.[23].*|aix5*) for ld_flag in $LDFLAGS; do if (test $ld_flag = "-brtl" || test $ld_flag = "-Wl,-brtl"); then aix_use_runtimelinking=yes break fi done ;; esac exp_sym_flag='-bexport' no_entry_flag='-bnoentry' fi # When large executables or shared objects are built, AIX ld can # have problems creating the table of contents. If linking a library # or program results in "error TOC overflow" add -mminimal-toc to # CXXFLAGS/CFLAGS for g++/gcc. In the cases where that is not # enough to fix the problem, add -Wl,-bbigtoc to LDFLAGS. archive_cmds='' hardcode_direct=yes hardcode_libdir_separator=':' link_all_deplibs=yes if test "$GCC" = yes; then case $host_os in aix4.[012]|aix4.[012].*) # We only want to do this on AIX 4.2 and lower, the check # below for broken collect2 doesn't work under 4.3+ collect2name=`${CC} -print-prog-name=collect2` if test -f "$collect2name" && \ strings "$collect2name" | grep resolve_lib_name >/dev/null then # We have reworked collect2 hardcode_direct=yes else # We have old collect2 hardcode_direct=unsupported # It fails to find uninstalled libraries when the uninstalled # path is not listed in the libpath. Setting hardcode_minus_L # to unsupported forces relinking hardcode_minus_L=yes hardcode_libdir_flag_spec='-L$libdir' hardcode_libdir_separator= fi ;; esac shared_flag='-shared' if test "$aix_use_runtimelinking" = yes; then shared_flag="$shared_flag "'${wl}-G' fi else # not using gcc if test "$host_cpu" = ia64; then # VisualAge C++, Version 5.5 for AIX 5L for IA-64, Beta 3 Release # chokes on -Wl,-G. The following line is correct: shared_flag='-G' else if test "$aix_use_runtimelinking" = yes; then shared_flag='${wl}-G' else shared_flag='${wl}-bM:SRE' fi fi fi # It seems that -bexpall does not export symbols beginning with # underscore (_), so it is better to generate a list of symbols to export. always_export_symbols=yes if test "$aix_use_runtimelinking" = yes; then # Warning - without using the other runtime loading flags (-brtl), # -berok will link without error, but may produce a broken library. allow_undefined_flag='-berok' # Determine the default libpath from the value encoded in an empty executable. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ int main () { ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then aix_libpath=`dump -H conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'` # Check for a 64-bit object if we didn't find anything. if test -z "$aix_libpath"; then aix_libpath=`dump -HX64 conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'`; fi else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext if test -z "$aix_libpath"; then aix_libpath="/usr/lib:/lib"; fi hardcode_libdir_flag_spec='${wl}-blibpath:$libdir:'"$aix_libpath" archive_expsym_cmds="\$CC"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags `if test "x${allow_undefined_flag}" != "x"; then echo "${wl}${allow_undefined_flag}"; else :; fi` '"\${wl}$exp_sym_flag:\$export_symbols $shared_flag" else if test "$host_cpu" = ia64; then hardcode_libdir_flag_spec='${wl}-R $libdir:/usr/lib:/lib' allow_undefined_flag="-z nodefs" archive_expsym_cmds="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags ${wl}${allow_undefined_flag} '"\${wl}$exp_sym_flag:\$export_symbols" else # Determine the default libpath from the value encoded in an empty executable. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ int main () { ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then aix_libpath=`dump -H conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'` # Check for a 64-bit object if we didn't find anything. if test -z "$aix_libpath"; then aix_libpath=`dump -HX64 conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'`; fi else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext if test -z "$aix_libpath"; then aix_libpath="/usr/lib:/lib"; fi hardcode_libdir_flag_spec='${wl}-blibpath:$libdir:'"$aix_libpath" # Warning - without using the other run time loading flags, # -berok will link without error, but may produce a broken library. no_undefined_flag=' ${wl}-bernotok' allow_undefined_flag=' ${wl}-berok' # Exported symbols can be pulled into shared objects from archives whole_archive_flag_spec='$convenience' archive_cmds_need_lc=yes # This is similar to how AIX traditionally builds its shared libraries. archive_expsym_cmds="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs ${wl}-bnoentry $compiler_flags ${wl}-bE:$export_symbols${allow_undefined_flag}~$AR $AR_FLAGS $output_objdir/$libname$release.a $output_objdir/$soname' fi fi ;; amigaos*) archive_cmds='$rm $output_objdir/a2ixlibrary.data~$echo "#define NAME $libname" > $output_objdir/a2ixlibrary.data~$echo "#define LIBRARY_ID 1" >> $output_objdir/a2ixlibrary.data~$echo "#define VERSION $major" >> $output_objdir/a2ixlibrary.data~$echo "#define REVISION $revision" >> $output_objdir/a2ixlibrary.data~$AR $AR_FLAGS $lib $libobjs~$RANLIB $lib~(cd $output_objdir && a2ixlibrary -32)' hardcode_libdir_flag_spec='-L$libdir' hardcode_minus_L=yes # see comment about different semantics on the GNU ld section ld_shlibs=no ;; bsdi[45]*) export_dynamic_flag_spec=-rdynamic ;; cygwin* | mingw* | pw32*) # When not using gcc, we currently assume that we are using # Microsoft Visual C++. # hardcode_libdir_flag_spec is actually meaningless, as there is # no search path for DLLs. hardcode_libdir_flag_spec=' ' allow_undefined_flag=unsupported # Tell ltmain to make .lib files, not .a files. libext=lib # Tell ltmain to make .dll files, not .so files. shrext_cmds=".dll" # FIXME: Setting linknames here is a bad hack. archive_cmds='$CC -o $lib $libobjs $compiler_flags `echo "$deplibs" | $SED -e '\''s/ -lc$//'\''` -link -dll~linknames=' # The linker will automatically build a .lib file if we build a DLL. old_archive_From_new_cmds='true' # FIXME: Should let the user specify the lib program. old_archive_cmds='lib /OUT:$oldlib$oldobjs$old_deplibs' fix_srcfile_path='`cygpath -w "$srcfile"`' enable_shared_with_static_runtimes=yes ;; darwin* | rhapsody*) case $host_os in rhapsody* | darwin1.[012]) allow_undefined_flag='${wl}-undefined ${wl}suppress' ;; *) # Darwin 1.3 on if test -z ${MACOSX_DEPLOYMENT_TARGET} ; then allow_undefined_flag='${wl}-flat_namespace ${wl}-undefined ${wl}suppress' else case ${MACOSX_DEPLOYMENT_TARGET} in 10.[012]) allow_undefined_flag='${wl}-flat_namespace ${wl}-undefined ${wl}suppress' ;; 10.*) allow_undefined_flag='${wl}-undefined ${wl}dynamic_lookup' ;; esac fi ;; esac archive_cmds_need_lc=no hardcode_direct=no hardcode_automatic=yes hardcode_shlibpath_var=unsupported whole_archive_flag_spec='' link_all_deplibs=yes if test "$GCC" = yes ; then output_verbose_link_cmd='echo' archive_cmds='$CC -dynamiclib $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags -install_name $rpath/$soname $verstring' module_cmds='$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags' # Don't fix this by using the ld -exported_symbols_list flag, it doesn't exist in older darwin lds archive_expsym_cmds='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC -dynamiclib $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags -install_name $rpath/$soname $verstring~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' module_expsym_cmds='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' else case $cc_basename in xlc*) output_verbose_link_cmd='echo' archive_cmds='$CC -qmkshrobj $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-install_name ${wl}`echo $rpath/$soname` $verstring' module_cmds='$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags' # Don't fix this by using the ld -exported_symbols_list flag, it doesn't exist in older darwin lds archive_expsym_cmds='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC -qmkshrobj $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-install_name ${wl}$rpath/$soname $verstring~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' module_expsym_cmds='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' ;; *) ld_shlibs=no ;; esac fi ;; dgux*) archive_cmds='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_libdir_flag_spec='-L$libdir' hardcode_shlibpath_var=no ;; freebsd1*) ld_shlibs=no ;; # FreeBSD 2.2.[012] allows us to include c++rt0.o to get C++ constructor # support. Future versions do this automatically, but an explicit c++rt0.o # does not break anything, and helps significantly (at the cost of a little # extra space). freebsd2.2*) archive_cmds='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags /usr/lib/c++rt0.o' hardcode_libdir_flag_spec='-R$libdir' hardcode_direct=yes hardcode_shlibpath_var=no ;; # Unfortunately, older versions of FreeBSD 2 do not have this feature. freebsd2*) archive_cmds='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' hardcode_direct=yes hardcode_minus_L=yes hardcode_shlibpath_var=no ;; # FreeBSD 3 and greater uses gcc -shared to do shared libraries. freebsd* | dragonfly*) archive_cmds='$CC -shared -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec='-R$libdir' hardcode_direct=yes hardcode_shlibpath_var=no ;; # GNU/kFreeBSD uses gcc -shared to do shared libraries. kfreebsd*-gnu) archive_cmds='$CC -shared -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec='-R$libdir' hardcode_direct=yes hardcode_shlibpath_var=no link_all_deplibs=no ;; hpux9*) if test "$GCC" = yes; then archive_cmds='$rm $output_objdir/$soname~$CC -shared -fPIC ${wl}+b ${wl}$install_libdir -o $output_objdir/$soname $libobjs $deplibs $compiler_flags~test $output_objdir/$soname = $lib || mv $output_objdir/$soname $lib' else archive_cmds='$rm $output_objdir/$soname~$LD -b +b $install_libdir -o $output_objdir/$soname $libobjs $deplibs $linker_flags~test $output_objdir/$soname = $lib || mv $output_objdir/$soname $lib' fi hardcode_libdir_flag_spec='${wl}+b ${wl}$libdir' hardcode_libdir_separator=: hardcode_direct=yes # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L=yes export_dynamic_flag_spec='${wl}-E' ;; hpux10*) if test "$GCC" = yes -a "$with_gnu_ld" = no; then archive_cmds='$CC -shared -fPIC ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds='$LD -b +h $soname +b $install_libdir -o $lib $libobjs $deplibs $linker_flags' fi if test "$with_gnu_ld" = no; then hardcode_libdir_flag_spec='${wl}+b ${wl}$libdir' hardcode_libdir_separator=: hardcode_direct=yes export_dynamic_flag_spec='${wl}-E' # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L=yes fi ;; hpux11*) if test "$GCC" = yes -a "$with_gnu_ld" = no; then case $host_cpu in hppa*64*) archive_cmds='$CC -shared ${wl}+h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' ;; ia64*) archive_cmds='$CC -shared ${wl}+h ${wl}$soname ${wl}+nodefaultrpath -o $lib $libobjs $deplibs $compiler_flags' ;; *) archive_cmds='$CC -shared -fPIC ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' ;; esac else case $host_cpu in hppa*64*) archive_cmds='$CC -b ${wl}+h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' ;; ia64*) archive_cmds='$CC -b ${wl}+h ${wl}$soname ${wl}+nodefaultrpath -o $lib $libobjs $deplibs $compiler_flags' ;; *) archive_cmds='$CC -b ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' ;; esac fi if test "$with_gnu_ld" = no; then hardcode_libdir_flag_spec='${wl}+b ${wl}$libdir' hardcode_libdir_separator=: case $host_cpu in hppa*64*|ia64*) hardcode_libdir_flag_spec_ld='+b $libdir' hardcode_direct=no hardcode_shlibpath_var=no ;; *) hardcode_direct=yes export_dynamic_flag_spec='${wl}-E' # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L=yes ;; esac fi ;; irix5* | irix6* | nonstopux*) if test "$GCC" = yes; then archive_cmds='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' else archive_cmds='$LD -shared $libobjs $deplibs $linker_flags -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' hardcode_libdir_flag_spec_ld='-rpath $libdir' fi hardcode_libdir_flag_spec='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator=: link_all_deplibs=yes ;; 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*) archive_cmds='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec='${wl}-rpath,$libdir' ;; esac fi ;; os2*) hardcode_libdir_flag_spec='-L$libdir' hardcode_minus_L=yes allow_undefined_flag=unsupported archive_cmds='$echo "LIBRARY $libname INITINSTANCE" > $output_objdir/$libname.def~$echo "DESCRIPTION \"$libname\"" >> $output_objdir/$libname.def~$echo DATA >> $output_objdir/$libname.def~$echo " SINGLE NONSHARED" >> $output_objdir/$libname.def~$echo EXPORTS >> $output_objdir/$libname.def~emxexp $libobjs >> $output_objdir/$libname.def~$CC -Zdll -Zcrtdll -o $lib $libobjs $deplibs $compiler_flags $output_objdir/$libname.def' old_archive_From_new_cmds='emximp -o $output_objdir/$libname.a $output_objdir/$libname.def' ;; osf3*) if test "$GCC" = yes; then allow_undefined_flag=' ${wl}-expect_unresolved ${wl}\*' archive_cmds='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' else allow_undefined_flag=' -expect_unresolved \*' archive_cmds='$LD -shared${allow_undefined_flag} $libobjs $deplibs $linker_flags -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' fi hardcode_libdir_flag_spec='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator=: ;; osf4* | osf5*) # as osf3* with the addition of -msym flag if test "$GCC" = yes; then allow_undefined_flag=' ${wl}-expect_unresolved ${wl}\*' archive_cmds='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags ${wl}-msym ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' hardcode_libdir_flag_spec='${wl}-rpath ${wl}$libdir' else allow_undefined_flag=' -expect_unresolved \*' archive_cmds='$LD -shared${allow_undefined_flag} $libobjs $deplibs $linker_flags -msym -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' archive_expsym_cmds='for i in `cat $export_symbols`; do printf "%s %s\\n" -exported_symbol "\$i" >> $lib.exp; done; echo "-hidden">> $lib.exp~ $LD -shared${allow_undefined_flag} -input $lib.exp $linker_flags $libobjs $deplibs -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib~$rm $lib.exp' # Both c and cxx compiler support -rpath directly hardcode_libdir_flag_spec='-rpath $libdir' fi hardcode_libdir_separator=: ;; solaris*) no_undefined_flag=' -z text' if test "$GCC" = yes; then wlarc='${wl}' archive_cmds='$CC -shared ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds='$echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~$echo "local: *; };" >> $lib.exp~ $CC -shared ${wl}-M ${wl}$lib.exp ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags~$rm $lib.exp' else wlarc='' archive_cmds='$LD -G${allow_undefined_flag} -h $soname -o $lib $libobjs $deplibs $linker_flags' archive_expsym_cmds='$echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~$echo "local: *; };" >> $lib.exp~ $LD -G${allow_undefined_flag} -M $lib.exp -h $soname -o $lib $libobjs $deplibs $linker_flags~$rm $lib.exp' fi hardcode_libdir_flag_spec='-R$libdir' hardcode_shlibpath_var=no case $host_os in solaris2.[0-5] | solaris2.[0-5].*) ;; *) # The compiler driver will combine linker options so we # cannot just pass the convience library names through # without $wl, iff we do not link with $LD. # Luckily, gcc supports the same syntax we need for Sun Studio. # Supported since Solaris 2.6 (maybe 2.5.1?) case $wlarc in '') whole_archive_flag_spec='-z allextract$convenience -z defaultextract' ;; *) whole_archive_flag_spec='${wl}-z ${wl}allextract`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}-z ${wl}defaultextract' ;; esac ;; esac link_all_deplibs=yes ;; sunos4*) if test "x$host_vendor" = xsequent; then # Use $CC to link under sequent, because it throws in some extra .o # files that make .init and .fini sections work. archive_cmds='$CC -G ${wl}-h $soname -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds='$LD -assert pure-text -Bstatic -o $lib $libobjs $deplibs $linker_flags' fi hardcode_libdir_flag_spec='-L$libdir' hardcode_direct=yes hardcode_minus_L=yes hardcode_shlibpath_var=no ;; sysv4) case $host_vendor in sni) archive_cmds='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct=yes # is this really true??? ;; siemens) ## LD is ld it makes a PLAMLIB ## CC just makes a GrossModule. archive_cmds='$LD -G -o $lib $libobjs $deplibs $linker_flags' reload_cmds='$CC -r -o $output$reload_objs' hardcode_direct=no ;; motorola) archive_cmds='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct=no #Motorola manual says yes, but my tests say they lie ;; esac runpath_var='LD_RUN_PATH' hardcode_shlibpath_var=no ;; sysv4.3*) archive_cmds='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_shlibpath_var=no export_dynamic_flag_spec='-Bexport' ;; sysv4*MP*) if test -d /usr/nec; then archive_cmds='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_shlibpath_var=no runpath_var=LD_RUN_PATH hardcode_runpath_var=yes ld_shlibs=yes fi ;; sysv4*uw2* | sysv5OpenUNIX* | sysv5UnixWare7.[01].[10]* | unixware7*) no_undefined_flag='${wl}-z,text' archive_cmds_need_lc=no hardcode_shlibpath_var=no runpath_var='LD_RUN_PATH' if test "$GCC" = yes; then archive_cmds='$CC -shared ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds='$CC -shared ${wl}-Bexport:$export_symbols ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds='$CC -G ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds='$CC -G ${wl}-Bexport:$export_symbols ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' fi ;; sysv5* | sco3.2v5* | sco5v6*) # Note: We can NOT use -z defs as we might desire, because we do not # link with -lc, and that would cause any symbols used from libc to # always be unresolved, which means just about no library would # ever link correctly. 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The import file would start with # the line `#! .'. This would cause the generated library to # depend on `.', always an invalid library. This was fixed in # development snapshots of GCC prior to 3.0. case $host_os in aix4 | aix4.[01] | aix4.[01].*) if { echo '#if __GNUC__ > 2 || (__GNUC__ == 2 && __GNUC_MINOR__ >= 97)' echo ' yes ' echo '#endif'; } | ${CC} -E - | grep yes > /dev/null; then : else can_build_shared=no fi ;; esac # AIX (on Power*) has no versioning support, so currently we can not hardcode correct # soname into executable. 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esac ;; gnu*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}${major} ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH hardcode_into_libs=yes ;; hpux9* | hpux10* | hpux11*) # Give a soname corresponding to the major version so that dld.sl refuses to # link against other versions. version_type=sunos need_lib_prefix=no need_version=no case $host_cpu in ia64*) shrext_cmds='.so' hardcode_into_libs=yes dynamic_linker="$host_os dld.so" shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes # Unless +noenvvar is specified. library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' if test "X$HPUX_IA64_MODE" = X32; then sys_lib_search_path_spec="/usr/lib/hpux32 /usr/local/lib/hpux32 /usr/local/lib" else sys_lib_search_path_spec="/usr/lib/hpux64 /usr/local/lib/hpux64" fi sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; hppa*64*) shrext_cmds='.sl' hardcode_into_libs=yes dynamic_linker="$host_os dld.sl" shlibpath_var=LD_LIBRARY_PATH # How should we handle SHLIB_PATH shlibpath_overrides_runpath=yes # Unless +noenvvar is specified. library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' sys_lib_search_path_spec="/usr/lib/pa20_64 /usr/ccs/lib/pa20_64" sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; *) shrext_cmds='.sl' dynamic_linker="$host_os dld.sl" shlibpath_var=SHLIB_PATH shlibpath_overrides_runpath=no # +s is required to enable SHLIB_PATH library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' ;; esac # HP-UX runs *really* slowly unless shared libraries are mode 555. postinstall_cmds='chmod 555 $lib' ;; interix3*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='Interix 3.x ld.so.1 (PE, like ELF)' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no hardcode_into_libs=yes ;; irix5* | irix6* | nonstopux*) case $host_os in nonstopux*) version_type=nonstopux ;; *) if test "$lt_cv_prog_gnu_ld" = yes; then version_type=linux else version_type=irix fi ;; esac need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${release}${shared_ext} $libname${shared_ext}' case $host_os in irix5* | nonstopux*) libsuff= shlibsuff= ;; *) case $LD in # libtool.m4 will add one of these switches to LD *-32|*"-32 "|*-melf32bsmip|*"-melf32bsmip ") libsuff= shlibsuff= libmagic=32-bit;; *-n32|*"-n32 "|*-melf32bmipn32|*"-melf32bmipn32 ") libsuff=32 shlibsuff=N32 libmagic=N32;; *-64|*"-64 "|*-melf64bmip|*"-melf64bmip ") libsuff=64 shlibsuff=64 libmagic=64-bit;; *) libsuff= shlibsuff= libmagic=never-match;; esac ;; esac shlibpath_var=LD_LIBRARY${shlibsuff}_PATH shlibpath_overrides_runpath=no sys_lib_search_path_spec="/usr/lib${libsuff} /lib${libsuff} /usr/local/lib${libsuff}" sys_lib_dlsearch_path_spec="/usr/lib${libsuff} /lib${libsuff}" hardcode_into_libs=yes ;; 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then striplib="$STRIP -x" echo "$as_me:$LINENO: result: yes" >&5 echo "${ECHO_T}yes" >&6 else echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 fi ;; *) echo "$as_me:$LINENO: result: no" >&5 echo "${ECHO_T}no" >&6 ;; esac fi if test "x$enable_dlopen" != xyes; then enable_dlopen=unknown enable_dlopen_self=unknown enable_dlopen_self_static=unknown else lt_cv_dlopen=no lt_cv_dlopen_libs= case $host_os in beos*) lt_cv_dlopen="load_add_on" lt_cv_dlopen_libs= lt_cv_dlopen_self=yes ;; mingw* | pw32*) lt_cv_dlopen="LoadLibrary" lt_cv_dlopen_libs= ;; cygwin*) lt_cv_dlopen="dlopen" lt_cv_dlopen_libs= ;; darwin*) # if libdl is installed we need to link against it echo "$as_me:$LINENO: checking for dlopen in -ldl" >&5 echo $ECHO_N "checking for dlopen in -ldl... $ECHO_C" >&6 if test "${ac_cv_lib_dl_dlopen+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-ldl $LIBS" cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ /* Override any gcc2 internal prototype to avoid an error. */ #ifdef __cplusplus extern "C" #endif /* We use char because int might match the return type of a gcc2 builtin and then its argument prototype would still apply. */ char dlopen (); int main () { dlopen (); ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then ac_cv_lib_dl_dlopen=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_lib_dl_dlopen=no fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi echo "$as_me:$LINENO: result: $ac_cv_lib_dl_dlopen" >&5 echo "${ECHO_T}$ac_cv_lib_dl_dlopen" >&6 if test $ac_cv_lib_dl_dlopen = yes; then lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-ldl" else lt_cv_dlopen="dyld" lt_cv_dlopen_libs= lt_cv_dlopen_self=yes fi ;; *) echo "$as_me:$LINENO: checking for shl_load" >&5 echo $ECHO_N "checking for shl_load... $ECHO_C" >&6 if test "${ac_cv_func_shl_load+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ /* Define shl_load to an innocuous variant, in case declares shl_load. For example, HP-UX 11i declares gettimeofday. */ #define shl_load innocuous_shl_load /* System header to define __stub macros and hopefully few prototypes, which can conflict with char shl_load (); below. Prefer to if __STDC__ is defined, since exists even on freestanding compilers. */ #ifdef __STDC__ # include #else # include #endif #undef shl_load /* Override any gcc2 internal prototype to avoid an error. */ #ifdef __cplusplus extern "C" { #endif /* We use char because int might match the return type of a gcc2 builtin and then its argument prototype would still apply. */ char shl_load (); /* The GNU C library defines this for functions which it implements to always fail with ENOSYS. Some functions are actually named something starting with __ and the normal name is an alias. */ #if defined (__stub_shl_load) || defined (__stub___shl_load) choke me #else char (*f) () = shl_load; #endif #ifdef __cplusplus } #endif int main () { return f != shl_load; ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then ac_cv_func_shl_load=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_func_shl_load=no fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext fi echo "$as_me:$LINENO: result: $ac_cv_func_shl_load" >&5 echo "${ECHO_T}$ac_cv_func_shl_load" >&6 if test $ac_cv_func_shl_load = yes; then lt_cv_dlopen="shl_load" else echo "$as_me:$LINENO: checking for shl_load in -ldld" >&5 echo $ECHO_N "checking for shl_load in -ldld... $ECHO_C" >&6 if test "${ac_cv_lib_dld_shl_load+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-ldld $LIBS" cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ /* Override any gcc2 internal prototype to avoid an error. */ #ifdef __cplusplus extern "C" #endif /* We use char because int might match the return type of a gcc2 builtin and then its argument prototype would still apply. */ char shl_load (); int main () { shl_load (); ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then ac_cv_lib_dld_shl_load=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_lib_dld_shl_load=no fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi echo "$as_me:$LINENO: result: $ac_cv_lib_dld_shl_load" >&5 echo "${ECHO_T}$ac_cv_lib_dld_shl_load" >&6 if test $ac_cv_lib_dld_shl_load = yes; then lt_cv_dlopen="shl_load" lt_cv_dlopen_libs="-dld" else echo "$as_me:$LINENO: checking for dlopen" >&5 echo $ECHO_N "checking for dlopen... $ECHO_C" >&6 if test "${ac_cv_func_dlopen+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ /* Define dlopen to an innocuous variant, in case declares dlopen. For example, HP-UX 11i declares gettimeofday. */ #define dlopen innocuous_dlopen /* System header to define __stub macros and hopefully few prototypes, which can conflict with char dlopen (); below. Prefer to if __STDC__ is defined, since exists even on freestanding compilers. */ #ifdef __STDC__ # include #else # include #endif #undef dlopen /* Override any gcc2 internal prototype to avoid an error. */ #ifdef __cplusplus extern "C" { #endif /* We use char because int might match the return type of a gcc2 builtin and then its argument prototype would still apply. */ char dlopen (); /* The GNU C library defines this for functions which it implements to always fail with ENOSYS. Some functions are actually named something starting with __ and the normal name is an alias. */ #if defined (__stub_dlopen) || defined (__stub___dlopen) choke me #else char (*f) () = dlopen; #endif #ifdef __cplusplus } #endif int main () { return f != dlopen; ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then ac_cv_func_dlopen=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_func_dlopen=no fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext fi echo "$as_me:$LINENO: result: $ac_cv_func_dlopen" >&5 echo "${ECHO_T}$ac_cv_func_dlopen" >&6 if test $ac_cv_func_dlopen = yes; then lt_cv_dlopen="dlopen" else echo "$as_me:$LINENO: checking for dlopen in -ldl" >&5 echo $ECHO_N "checking for dlopen in -ldl... $ECHO_C" >&6 if test "${ac_cv_lib_dl_dlopen+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-ldl $LIBS" cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ /* Override any gcc2 internal prototype to avoid an error. */ #ifdef __cplusplus extern "C" #endif /* We use char because int might match the return type of a gcc2 builtin and then its argument prototype would still apply. */ char dlopen (); int main () { dlopen (); ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then ac_cv_lib_dl_dlopen=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_lib_dl_dlopen=no fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi echo "$as_me:$LINENO: result: $ac_cv_lib_dl_dlopen" >&5 echo "${ECHO_T}$ac_cv_lib_dl_dlopen" >&6 if test $ac_cv_lib_dl_dlopen = yes; then lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-ldl" else echo "$as_me:$LINENO: checking for dlopen in -lsvld" >&5 echo $ECHO_N "checking for dlopen in -lsvld... $ECHO_C" >&6 if test "${ac_cv_lib_svld_dlopen+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-lsvld $LIBS" cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ /* Override any gcc2 internal prototype to avoid an error. */ #ifdef __cplusplus extern "C" #endif /* We use char because int might match the return type of a gcc2 builtin and then its argument prototype would still apply. */ char dlopen (); int main () { dlopen (); ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then ac_cv_lib_svld_dlopen=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_lib_svld_dlopen=no fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi echo "$as_me:$LINENO: result: $ac_cv_lib_svld_dlopen" >&5 echo "${ECHO_T}$ac_cv_lib_svld_dlopen" >&6 if test $ac_cv_lib_svld_dlopen = yes; then lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-lsvld" else echo "$as_me:$LINENO: checking for dld_link in -ldld" >&5 echo $ECHO_N "checking for dld_link in -ldld... $ECHO_C" >&6 if test "${ac_cv_lib_dld_dld_link+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else ac_check_lib_save_LIBS=$LIBS LIBS="-ldld $LIBS" cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ /* Override any gcc2 internal prototype to avoid an error. */ #ifdef __cplusplus extern "C" #endif /* We use char because int might match the return type of a gcc2 builtin and then its argument prototype would still apply. */ char dld_link (); int main () { dld_link (); ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then ac_cv_lib_dld_dld_link=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_lib_dld_dld_link=no fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext LIBS=$ac_check_lib_save_LIBS fi echo "$as_me:$LINENO: result: $ac_cv_lib_dld_dld_link" >&5 echo "${ECHO_T}$ac_cv_lib_dld_dld_link" >&6 if test $ac_cv_lib_dld_dld_link = yes; then lt_cv_dlopen="dld_link" lt_cv_dlopen_libs="-dld" fi fi fi fi fi fi ;; esac if test "x$lt_cv_dlopen" != xno; then enable_dlopen=yes else enable_dlopen=no fi case $lt_cv_dlopen in dlopen) save_CPPFLAGS="$CPPFLAGS" test "x$ac_cv_header_dlfcn_h" = xyes && CPPFLAGS="$CPPFLAGS -DHAVE_DLFCN_H" save_LDFLAGS="$LDFLAGS" wl=$lt_prog_compiler_wl eval LDFLAGS=\"\$LDFLAGS $export_dynamic_flag_spec\" save_LIBS="$LIBS" LIBS="$lt_cv_dlopen_libs $LIBS" echo "$as_me:$LINENO: checking whether a program can dlopen itself" >&5 echo $ECHO_N "checking whether a program can dlopen itself... $ECHO_C" >&6 if test "${lt_cv_dlopen_self+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test "$cross_compiling" = yes; then : lt_cv_dlopen_self=cross else lt_dlunknown=0; lt_dlno_uscore=1; lt_dlneed_uscore=2 lt_status=$lt_dlunknown cat > conftest.$ac_ext < #endif #include #ifdef RTLD_GLOBAL # define LT_DLGLOBAL RTLD_GLOBAL #else # ifdef DL_GLOBAL # define LT_DLGLOBAL DL_GLOBAL # else # define LT_DLGLOBAL 0 # endif #endif /* We may have to define LT_DLLAZY_OR_NOW in the command line if we find out it does not work in some platform. */ #ifndef LT_DLLAZY_OR_NOW # ifdef RTLD_LAZY # define LT_DLLAZY_OR_NOW RTLD_LAZY # else # ifdef DL_LAZY # define LT_DLLAZY_OR_NOW DL_LAZY # else # ifdef RTLD_NOW # define LT_DLLAZY_OR_NOW RTLD_NOW # else # ifdef DL_NOW # define LT_DLLAZY_OR_NOW DL_NOW # else # define LT_DLLAZY_OR_NOW 0 # endif # endif # endif # endif #endif #ifdef __cplusplus extern "C" void exit (int); #endif void fnord() { int i=42;} int main () { void *self = dlopen (0, LT_DLGLOBAL|LT_DLLAZY_OR_NOW); int status = $lt_dlunknown; if (self) { if (dlsym (self,"fnord")) status = $lt_dlno_uscore; else if (dlsym( self,"_fnord")) status = $lt_dlneed_uscore; /* dlclose (self); */ } else puts (dlerror ()); exit (status); } EOF if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && test -s conftest${ac_exeext} 2>/dev/null; then (./conftest; exit; ) >&5 2>/dev/null lt_status=$? case x$lt_status in x$lt_dlno_uscore) lt_cv_dlopen_self=yes ;; x$lt_dlneed_uscore) lt_cv_dlopen_self=yes ;; x$lt_dlunknown|x*) lt_cv_dlopen_self=no ;; esac else : # compilation failed lt_cv_dlopen_self=no fi fi rm -fr conftest* fi echo "$as_me:$LINENO: result: $lt_cv_dlopen_self" >&5 echo "${ECHO_T}$lt_cv_dlopen_self" >&6 if test "x$lt_cv_dlopen_self" = xyes; then wl=$lt_prog_compiler_wl eval LDFLAGS=\"\$LDFLAGS $lt_prog_compiler_static\" echo "$as_me:$LINENO: checking whether a statically linked program can dlopen itself" >&5 echo $ECHO_N "checking whether a statically linked program can dlopen itself... $ECHO_C" >&6 if test "${lt_cv_dlopen_self_static+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else if test "$cross_compiling" = yes; then : lt_cv_dlopen_self_static=cross else lt_dlunknown=0; lt_dlno_uscore=1; lt_dlneed_uscore=2 lt_status=$lt_dlunknown cat > conftest.$ac_ext < #endif #include #ifdef RTLD_GLOBAL # define LT_DLGLOBAL RTLD_GLOBAL #else # ifdef DL_GLOBAL # define LT_DLGLOBAL DL_GLOBAL # else # define LT_DLGLOBAL 0 # endif #endif /* We may have to define LT_DLLAZY_OR_NOW in the command line if we find out it does not work in some platform. */ #ifndef LT_DLLAZY_OR_NOW # ifdef RTLD_LAZY # define LT_DLLAZY_OR_NOW RTLD_LAZY # else # ifdef DL_LAZY # define LT_DLLAZY_OR_NOW DL_LAZY # else # ifdef RTLD_NOW # define LT_DLLAZY_OR_NOW RTLD_NOW # else # ifdef DL_NOW # define LT_DLLAZY_OR_NOW DL_NOW # else # define LT_DLLAZY_OR_NOW 0 # endif # endif # endif # endif #endif #ifdef __cplusplus extern "C" void exit (int); #endif void fnord() { int i=42;} int main () { void *self = dlopen (0, LT_DLGLOBAL|LT_DLLAZY_OR_NOW); int status = $lt_dlunknown; if (self) { if (dlsym (self,"fnord")) status = $lt_dlno_uscore; else if (dlsym( self,"_fnord")) status = $lt_dlneed_uscore; /* dlclose (self); */ } else puts (dlerror ()); exit (status); } EOF if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>&5 ac_status=$? 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If you *** really care for shared libraries, you may want to modify your PATH *** so that a non-GNU linker is found, and then restart. EOF fi ;; amigaos*) archive_cmds_F77='$rm $output_objdir/a2ixlibrary.data~$echo "#define NAME $libname" > $output_objdir/a2ixlibrary.data~$echo "#define LIBRARY_ID 1" >> $output_objdir/a2ixlibrary.data~$echo "#define VERSION $major" >> $output_objdir/a2ixlibrary.data~$echo "#define REVISION $revision" >> $output_objdir/a2ixlibrary.data~$AR $AR_FLAGS $lib $libobjs~$RANLIB $lib~(cd $output_objdir && a2ixlibrary -32)' hardcode_libdir_flag_spec_F77='-L$libdir' hardcode_minus_L_F77=yes # Samuel A. Falvo II reports # that the semantics of dynamic libraries on AmigaOS, at least up # to version 4, is to share data among multiple programs linked # with the same dynamic library. Since this doesn't match the # behavior of shared libraries on other platforms, we can't use # them. ld_shlibs_F77=no ;; beos*) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then allow_undefined_flag_F77=unsupported # Joseph Beckenbach says some releases of gcc # support --undefined. This deserves some investigation. FIXME archive_cmds_F77='$CC -nostart $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' else ld_shlibs_F77=no fi ;; cygwin* | mingw* | pw32*) # _LT_AC_TAGVAR(hardcode_libdir_flag_spec, F77) is actually meaningless, # as there is no search path for DLLs. hardcode_libdir_flag_spec_F77='-L$libdir' allow_undefined_flag_F77=unsupported always_export_symbols_F77=no enable_shared_with_static_runtimes_F77=yes export_symbols_cmds_F77='$NM $libobjs $convenience | $global_symbol_pipe | $SED -e '\''/^[BCDGRS] /s/.* \([^ ]*\)/\1 DATA/'\'' | $SED -e '\''/^[AITW] /s/.* //'\'' | sort | uniq > $export_symbols' if $LD --help 2>&1 | grep 'auto-import' > /dev/null; then archive_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' # If the export-symbols file already is a .def file (1st line # is EXPORTS), use it as is; otherwise, prepend... archive_expsym_cmds_F77='if test "x`$SED 1q $export_symbols`" = xEXPORTS; then cp $export_symbols $output_objdir/$soname.def; else echo EXPORTS > $output_objdir/$soname.def; cat $export_symbols >> $output_objdir/$soname.def; fi~ $CC -shared $output_objdir/$soname.def $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' else ld_shlibs_F77=no fi ;; interix3*) hardcode_direct_F77=no hardcode_shlibpath_var_F77=no hardcode_libdir_flag_spec_F77='${wl}-rpath,$libdir' export_dynamic_flag_spec_F77='${wl}-E' # Hack: On Interix 3.x, we cannot compile PIC because of a broken gcc. # Instead, shared libraries are loaded at an image base (0x10000000 by # default) and relocated if they conflict, which is a slow very memory # consuming and fragmenting process. To avoid this, we pick a random, # 256 KiB-aligned image base between 0x50000000 and 0x6FFC0000 at link # time. Moving up from 0x10000000 also allows more sbrk(2) space. archive_cmds_F77='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-h,$soname ${wl}--image-base,`expr ${RANDOM-$$} % 4096 / 2 \* 262144 + 1342177280` -o $lib' archive_expsym_cmds_F77='sed "s,^,_," $export_symbols >$output_objdir/$soname.expsym~$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-h,$soname ${wl}--retain-symbols-file,$output_objdir/$soname.expsym ${wl}--image-base,`expr ${RANDOM-$$} % 4096 / 2 \* 262144 + 1342177280` -o $lib' ;; linux*) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then tmp_addflag= case $cc_basename,$host_cpu in pgcc*) # Portland Group C compiler whole_archive_flag_spec_F77='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}--no-whole-archive' tmp_addflag=' $pic_flag' ;; pgf77* | pgf90* | pgf95*) # Portland Group f77 and f90 compilers whole_archive_flag_spec_F77='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}--no-whole-archive' tmp_addflag=' $pic_flag -Mnomain' ;; ecc*,ia64* | icc*,ia64*) # Intel C compiler on ia64 tmp_addflag=' -i_dynamic' ;; efc*,ia64* | ifort*,ia64*) # Intel Fortran compiler on ia64 tmp_addflag=' -i_dynamic -nofor_main' ;; ifc* | ifort*) # Intel Fortran compiler tmp_addflag=' -nofor_main' ;; esac archive_cmds_F77='$CC -shared'"$tmp_addflag"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' if test $supports_anon_versioning = yes; then archive_expsym_cmds_F77='$echo "{ global:" > $output_objdir/$libname.ver~ cat $export_symbols | sed -e "s/\(.*\)/\1;/" >> $output_objdir/$libname.ver~ $echo "local: *; };" >> $output_objdir/$libname.ver~ $CC -shared'"$tmp_addflag"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-version-script ${wl}$output_objdir/$libname.ver -o $lib' fi link_all_deplibs_F77=no else ld_shlibs_F77=no fi ;; netbsd* | netbsdelf*-gnu | knetbsd*-gnu) if echo __ELF__ | $CC -E - | grep __ELF__ >/dev/null; then archive_cmds_F77='$LD -Bshareable $libobjs $deplibs $linker_flags -o $lib' wlarc= else archive_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' fi ;; solaris*) if $LD -v 2>&1 | grep 'BFD 2\.8' > /dev/null; then ld_shlibs_F77=no cat <&2 *** Warning: The releases 2.8.* of the GNU linker cannot reliably *** create shared libraries on Solaris systems. Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.9.1 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. EOF elif $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then archive_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else ld_shlibs_F77=no fi ;; sysv5* | sco3.2v5* | sco5v6* | unixware* | OpenUNIX*) case `$LD -v 2>&1` in *\ [01].* | *\ 2.[0-9].* | *\ 2.1[0-5].*) ld_shlibs_F77=no cat <<_LT_EOF 1>&2 *** Warning: Releases of the GNU linker prior to 2.16.91.0.3 can not *** reliably create shared libraries on SCO systems. Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.16.91.0.3 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. _LT_EOF ;; *) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then hardcode_libdir_flag_spec_F77='`test -z "$SCOABSPATH" && echo ${wl}-rpath,$libdir`' archive_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname,\${SCOABSPATH:+${install_libdir}/}$soname -o $lib' archive_expsym_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname,\${SCOABSPATH:+${install_libdir}/}$soname,-retain-symbols-file,$export_symbols -o $lib' else ld_shlibs_F77=no fi ;; esac ;; sunos4*) archive_cmds_F77='$LD -assert pure-text -Bshareable -o $lib $libobjs $deplibs $linker_flags' wlarc= hardcode_direct_F77=yes hardcode_shlibpath_var_F77=no ;; *) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then archive_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else ld_shlibs_F77=no fi ;; esac if test "$ld_shlibs_F77" = no; then runpath_var= hardcode_libdir_flag_spec_F77= export_dynamic_flag_spec_F77= whole_archive_flag_spec_F77= fi else # PORTME fill in a description of your system's linker (not GNU ld) case $host_os in aix3*) allow_undefined_flag_F77=unsupported always_export_symbols_F77=yes archive_expsym_cmds_F77='$LD -o $output_objdir/$soname $libobjs $deplibs $linker_flags -bE:$export_symbols -T512 -H512 -bM:SRE~$AR $AR_FLAGS $lib $output_objdir/$soname' # Note: this linker hardcodes the directories in LIBPATH if there # are no directories specified by -L. hardcode_minus_L_F77=yes if test "$GCC" = yes && test -z "$lt_prog_compiler_static"; then # Neither direct hardcoding nor static linking is supported with a # broken collect2. hardcode_direct_F77=unsupported fi ;; aix4* | aix5*) if test "$host_cpu" = ia64; then # On IA64, the linker does run time linking by default, so we don't # have to do anything special. aix_use_runtimelinking=no exp_sym_flag='-Bexport' no_entry_flag="" else # If we're using GNU nm, then we don't want the "-C" option. # -C means demangle to AIX nm, but means don't demangle with GNU nm if $NM -V 2>&1 | grep 'GNU' > /dev/null; then export_symbols_cmds_F77='$NM -Bpg $libobjs $convenience | awk '\''{ if (((\$2 == "T") || (\$2 == "D") || (\$2 == "B")) && (substr(\$3,1,1) != ".")) { print \$3 } }'\'' | sort -u > $export_symbols' else export_symbols_cmds_F77='$NM -BCpg $libobjs $convenience | awk '\''{ if (((\$2 == "T") || (\$2 == "D") || (\$2 == "B")) && (substr(\$3,1,1) != ".")) { print \$3 } }'\'' | sort -u > $export_symbols' fi aix_use_runtimelinking=no # Test if we are trying to use run time linking or normal # AIX style linking. If -brtl is somewhere in LDFLAGS, we # need to do runtime linking. case $host_os in aix4.[23]|aix4.[23].*|aix5*) for ld_flag in $LDFLAGS; do if (test $ld_flag = "-brtl" || test $ld_flag = "-Wl,-brtl"); then aix_use_runtimelinking=yes break fi done ;; esac exp_sym_flag='-bexport' no_entry_flag='-bnoentry' fi # When large executables or shared objects are built, AIX ld can # have problems creating the table of contents. If linking a library # or program results in "error TOC overflow" add -mminimal-toc to # CXXFLAGS/CFLAGS for g++/gcc. In the cases where that is not # enough to fix the problem, add -Wl,-bbigtoc to LDFLAGS. archive_cmds_F77='' hardcode_direct_F77=yes hardcode_libdir_separator_F77=':' link_all_deplibs_F77=yes if test "$GCC" = yes; then case $host_os in aix4.[012]|aix4.[012].*) # We only want to do this on AIX 4.2 and lower, the check # below for broken collect2 doesn't work under 4.3+ collect2name=`${CC} -print-prog-name=collect2` if test -f "$collect2name" && \ strings "$collect2name" | grep resolve_lib_name >/dev/null then # We have reworked collect2 hardcode_direct_F77=yes else # We have old collect2 hardcode_direct_F77=unsupported # It fails to find uninstalled libraries when the uninstalled # path is not listed in the libpath. Setting hardcode_minus_L # to unsupported forces relinking hardcode_minus_L_F77=yes hardcode_libdir_flag_spec_F77='-L$libdir' hardcode_libdir_separator_F77= fi ;; esac shared_flag='-shared' if test "$aix_use_runtimelinking" = yes; then shared_flag="$shared_flag "'${wl}-G' fi else # not using gcc if test "$host_cpu" = ia64; then # VisualAge C++, Version 5.5 for AIX 5L for IA-64, Beta 3 Release # chokes on -Wl,-G. The following line is correct: shared_flag='-G' else if test "$aix_use_runtimelinking" = yes; then shared_flag='${wl}-G' else shared_flag='${wl}-bM:SRE' fi fi fi # It seems that -bexpall does not export symbols beginning with # underscore (_), so it is better to generate a list of symbols to export. always_export_symbols_F77=yes if test "$aix_use_runtimelinking" = yes; then # Warning - without using the other runtime loading flags (-brtl), # -berok will link without error, but may produce a broken library. allow_undefined_flag_F77='-berok' # Determine the default libpath from the value encoded in an empty executable. cat >conftest.$ac_ext <<_ACEOF program main end _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_f77_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then aix_libpath=`dump -H conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'` # Check for a 64-bit object if we didn't find anything. if test -z "$aix_libpath"; then aix_libpath=`dump -HX64 conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'`; fi else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext if test -z "$aix_libpath"; then aix_libpath="/usr/lib:/lib"; fi hardcode_libdir_flag_spec_F77='${wl}-blibpath:$libdir:'"$aix_libpath" archive_expsym_cmds_F77="\$CC"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags `if test "x${allow_undefined_flag}" != "x"; then echo "${wl}${allow_undefined_flag}"; else :; fi` '"\${wl}$exp_sym_flag:\$export_symbols $shared_flag" else if test "$host_cpu" = ia64; then hardcode_libdir_flag_spec_F77='${wl}-R $libdir:/usr/lib:/lib' allow_undefined_flag_F77="-z nodefs" archive_expsym_cmds_F77="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags ${wl}${allow_undefined_flag} '"\${wl}$exp_sym_flag:\$export_symbols" else # Determine the default libpath from the value encoded in an empty executable. cat >conftest.$ac_ext <<_ACEOF program main end _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_f77_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then aix_libpath=`dump -H conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'` # Check for a 64-bit object if we didn't find anything. if test -z "$aix_libpath"; then aix_libpath=`dump -HX64 conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'`; fi else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext if test -z "$aix_libpath"; then aix_libpath="/usr/lib:/lib"; fi hardcode_libdir_flag_spec_F77='${wl}-blibpath:$libdir:'"$aix_libpath" # Warning - without using the other run time loading flags, # -berok will link without error, but may produce a broken library. no_undefined_flag_F77=' ${wl}-bernotok' allow_undefined_flag_F77=' ${wl}-berok' # Exported symbols can be pulled into shared objects from archives whole_archive_flag_spec_F77='$convenience' archive_cmds_need_lc_F77=yes # This is similar to how AIX traditionally builds its shared libraries. archive_expsym_cmds_F77="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs ${wl}-bnoentry $compiler_flags ${wl}-bE:$export_symbols${allow_undefined_flag}~$AR $AR_FLAGS $output_objdir/$libname$release.a $output_objdir/$soname' fi fi ;; amigaos*) archive_cmds_F77='$rm $output_objdir/a2ixlibrary.data~$echo "#define NAME $libname" > $output_objdir/a2ixlibrary.data~$echo "#define LIBRARY_ID 1" >> $output_objdir/a2ixlibrary.data~$echo "#define VERSION $major" >> $output_objdir/a2ixlibrary.data~$echo "#define REVISION $revision" >> $output_objdir/a2ixlibrary.data~$AR $AR_FLAGS $lib $libobjs~$RANLIB $lib~(cd $output_objdir && a2ixlibrary -32)' hardcode_libdir_flag_spec_F77='-L$libdir' hardcode_minus_L_F77=yes # see comment about different semantics on the GNU ld section ld_shlibs_F77=no ;; bsdi[45]*) export_dynamic_flag_spec_F77=-rdynamic ;; cygwin* | mingw* | pw32*) # When not using gcc, we currently assume that we are using # Microsoft Visual C++. # hardcode_libdir_flag_spec is actually meaningless, as there is # no search path for DLLs. hardcode_libdir_flag_spec_F77=' ' allow_undefined_flag_F77=unsupported # Tell ltmain to make .lib files, not .a files. libext=lib # Tell ltmain to make .dll files, not .so files. shrext_cmds=".dll" # FIXME: Setting linknames here is a bad hack. archive_cmds_F77='$CC -o $lib $libobjs $compiler_flags `echo "$deplibs" | $SED -e '\''s/ -lc$//'\''` -link -dll~linknames=' # The linker will automatically build a .lib file if we build a DLL. old_archive_From_new_cmds_F77='true' # FIXME: Should let the user specify the lib program. old_archive_cmds_F77='lib /OUT:$oldlib$oldobjs$old_deplibs' fix_srcfile_path_F77='`cygpath -w "$srcfile"`' enable_shared_with_static_runtimes_F77=yes ;; darwin* | rhapsody*) case $host_os in rhapsody* | darwin1.[012]) allow_undefined_flag_F77='${wl}-undefined ${wl}suppress' ;; *) # Darwin 1.3 on if test -z ${MACOSX_DEPLOYMENT_TARGET} ; then allow_undefined_flag_F77='${wl}-flat_namespace ${wl}-undefined ${wl}suppress' else case ${MACOSX_DEPLOYMENT_TARGET} in 10.[012]) allow_undefined_flag_F77='${wl}-flat_namespace ${wl}-undefined ${wl}suppress' ;; 10.*) allow_undefined_flag_F77='${wl}-undefined ${wl}dynamic_lookup' ;; esac fi ;; esac archive_cmds_need_lc_F77=no hardcode_direct_F77=no hardcode_automatic_F77=yes hardcode_shlibpath_var_F77=unsupported whole_archive_flag_spec_F77='' link_all_deplibs_F77=yes if test "$GCC" = yes ; then output_verbose_link_cmd='echo' archive_cmds_F77='$CC -dynamiclib $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags -install_name $rpath/$soname $verstring' module_cmds_F77='$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags' # Don't fix this by using the ld -exported_symbols_list flag, it doesn't exist in older darwin lds archive_expsym_cmds_F77='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC -dynamiclib $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags -install_name $rpath/$soname $verstring~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' module_expsym_cmds_F77='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' else case $cc_basename in xlc*) output_verbose_link_cmd='echo' archive_cmds_F77='$CC -qmkshrobj $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-install_name ${wl}`echo $rpath/$soname` $verstring' module_cmds_F77='$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags' # Don't fix this by using the ld -exported_symbols_list flag, it doesn't exist in older darwin lds archive_expsym_cmds_F77='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC -qmkshrobj $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-install_name ${wl}$rpath/$soname $verstring~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' module_expsym_cmds_F77='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' ;; *) ld_shlibs_F77=no ;; esac fi ;; dgux*) archive_cmds_F77='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_libdir_flag_spec_F77='-L$libdir' hardcode_shlibpath_var_F77=no ;; freebsd1*) ld_shlibs_F77=no ;; # FreeBSD 2.2.[012] allows us to include c++rt0.o to get C++ constructor # support. Future versions do this automatically, but an explicit c++rt0.o # does not break anything, and helps significantly (at the cost of a little # extra space). freebsd2.2*) archive_cmds_F77='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags /usr/lib/c++rt0.o' hardcode_libdir_flag_spec_F77='-R$libdir' hardcode_direct_F77=yes hardcode_shlibpath_var_F77=no ;; # Unfortunately, older versions of FreeBSD 2 do not have this feature. freebsd2*) archive_cmds_F77='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' hardcode_direct_F77=yes hardcode_minus_L_F77=yes hardcode_shlibpath_var_F77=no ;; # FreeBSD 3 and greater uses gcc -shared to do shared libraries. freebsd* | dragonfly*) archive_cmds_F77='$CC -shared -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec_F77='-R$libdir' hardcode_direct_F77=yes hardcode_shlibpath_var_F77=no ;; # GNU/kFreeBSD uses gcc -shared to do shared libraries. kfreebsd*-gnu) archive_cmds_F77='$CC -shared -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec_F77='-R$libdir' hardcode_direct_F77=yes hardcode_shlibpath_var_F77=no link_all_deplibs_F77=no ;; hpux9*) if test "$GCC" = yes; then archive_cmds_F77='$rm $output_objdir/$soname~$CC -shared -fPIC ${wl}+b ${wl}$install_libdir -o $output_objdir/$soname $libobjs $deplibs $compiler_flags~test $output_objdir/$soname = $lib || mv $output_objdir/$soname $lib' else archive_cmds_F77='$rm $output_objdir/$soname~$LD -b +b $install_libdir -o $output_objdir/$soname $libobjs $deplibs $linker_flags~test $output_objdir/$soname = $lib || mv $output_objdir/$soname $lib' fi hardcode_libdir_flag_spec_F77='${wl}+b ${wl}$libdir' hardcode_libdir_separator_F77=: hardcode_direct_F77=yes # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L_F77=yes export_dynamic_flag_spec_F77='${wl}-E' ;; hpux10*) if test "$GCC" = yes -a "$with_gnu_ld" = no; then archive_cmds_F77='$CC -shared -fPIC ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds_F77='$LD -b +h $soname +b $install_libdir -o $lib $libobjs $deplibs $linker_flags' fi if test "$with_gnu_ld" = no; then hardcode_libdir_flag_spec_F77='${wl}+b ${wl}$libdir' hardcode_libdir_separator_F77=: hardcode_direct_F77=yes export_dynamic_flag_spec_F77='${wl}-E' # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L_F77=yes fi ;; hpux11*) if test "$GCC" = yes -a "$with_gnu_ld" = no; then case $host_cpu in hppa*64*) archive_cmds_F77='$CC -shared ${wl}+h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' ;; ia64*) archive_cmds_F77='$CC -shared ${wl}+h ${wl}$soname ${wl}+nodefaultrpath -o $lib $libobjs $deplibs $compiler_flags' ;; *) archive_cmds_F77='$CC -shared -fPIC ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' ;; esac else case $host_cpu in hppa*64*) archive_cmds_F77='$CC -b ${wl}+h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' ;; ia64*) archive_cmds_F77='$CC -b ${wl}+h ${wl}$soname ${wl}+nodefaultrpath -o $lib $libobjs $deplibs $compiler_flags' ;; *) archive_cmds_F77='$CC -b ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' ;; esac fi if test "$with_gnu_ld" = no; then hardcode_libdir_flag_spec_F77='${wl}+b ${wl}$libdir' hardcode_libdir_separator_F77=: case $host_cpu in hppa*64*|ia64*) hardcode_libdir_flag_spec_ld_F77='+b $libdir' hardcode_direct_F77=no hardcode_shlibpath_var_F77=no ;; *) hardcode_direct_F77=yes export_dynamic_flag_spec_F77='${wl}-E' # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L_F77=yes ;; esac fi ;; irix5* | irix6* | nonstopux*) if test "$GCC" = yes; then archive_cmds_F77='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' else archive_cmds_F77='$LD -shared $libobjs $deplibs $linker_flags -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' hardcode_libdir_flag_spec_ld_F77='-rpath $libdir' fi hardcode_libdir_flag_spec_F77='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator_F77=: link_all_deplibs_F77=yes ;; netbsd* | netbsdelf*-gnu | knetbsd*-gnu) if echo __ELF__ | $CC -E - | grep __ELF__ >/dev/null; then archive_cmds_F77='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' # a.out else archive_cmds_F77='$LD -shared -o $lib $libobjs $deplibs $linker_flags' # ELF fi hardcode_libdir_flag_spec_F77='-R$libdir' hardcode_direct_F77=yes hardcode_shlibpath_var_F77=no ;; newsos6) archive_cmds_F77='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct_F77=yes hardcode_libdir_flag_spec_F77='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator_F77=: hardcode_shlibpath_var_F77=no ;; openbsd*) hardcode_direct_F77=yes hardcode_shlibpath_var_F77=no if test -z "`echo __ELF__ | $CC -E - | grep __ELF__`" || test "$host_os-$host_cpu" = "openbsd2.8-powerpc"; then archive_cmds_F77='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds_F77='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-retain-symbols-file,$export_symbols' hardcode_libdir_flag_spec_F77='${wl}-rpath,$libdir' export_dynamic_flag_spec_F77='${wl}-E' else case $host_os in openbsd[01].* | openbsd2.[0-7] | openbsd2.[0-7].*) archive_cmds_F77='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' hardcode_libdir_flag_spec_F77='-R$libdir' ;; *) archive_cmds_F77='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec_F77='${wl}-rpath,$libdir' ;; esac fi ;; os2*) hardcode_libdir_flag_spec_F77='-L$libdir' hardcode_minus_L_F77=yes allow_undefined_flag_F77=unsupported archive_cmds_F77='$echo "LIBRARY $libname INITINSTANCE" > $output_objdir/$libname.def~$echo "DESCRIPTION \"$libname\"" >> $output_objdir/$libname.def~$echo DATA >> $output_objdir/$libname.def~$echo " SINGLE NONSHARED" >> $output_objdir/$libname.def~$echo EXPORTS >> $output_objdir/$libname.def~emxexp $libobjs >> $output_objdir/$libname.def~$CC -Zdll -Zcrtdll -o $lib $libobjs $deplibs $compiler_flags $output_objdir/$libname.def' old_archive_From_new_cmds_F77='emximp -o $output_objdir/$libname.a $output_objdir/$libname.def' ;; osf3*) if test "$GCC" = yes; then allow_undefined_flag_F77=' ${wl}-expect_unresolved ${wl}\*' archive_cmds_F77='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' else allow_undefined_flag_F77=' -expect_unresolved \*' archive_cmds_F77='$LD -shared${allow_undefined_flag} $libobjs $deplibs $linker_flags -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' fi hardcode_libdir_flag_spec_F77='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator_F77=: ;; osf4* | osf5*) # as osf3* with the addition of -msym flag if test "$GCC" = yes; then allow_undefined_flag_F77=' ${wl}-expect_unresolved ${wl}\*' archive_cmds_F77='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags ${wl}-msym ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' hardcode_libdir_flag_spec_F77='${wl}-rpath ${wl}$libdir' else allow_undefined_flag_F77=' -expect_unresolved \*' archive_cmds_F77='$LD -shared${allow_undefined_flag} $libobjs $deplibs $linker_flags -msym -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' archive_expsym_cmds_F77='for i in `cat $export_symbols`; do printf "%s %s\\n" -exported_symbol "\$i" >> $lib.exp; done; echo "-hidden">> $lib.exp~ $LD -shared${allow_undefined_flag} -input $lib.exp $linker_flags $libobjs $deplibs -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib~$rm $lib.exp' # Both c and cxx compiler support -rpath directly hardcode_libdir_flag_spec_F77='-rpath $libdir' fi hardcode_libdir_separator_F77=: ;; solaris*) no_undefined_flag_F77=' -z text' if test "$GCC" = yes; then wlarc='${wl}' archive_cmds_F77='$CC -shared ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds_F77='$echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~$echo "local: *; };" >> $lib.exp~ $CC -shared ${wl}-M ${wl}$lib.exp ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags~$rm $lib.exp' else wlarc='' archive_cmds_F77='$LD -G${allow_undefined_flag} -h $soname -o $lib $libobjs $deplibs $linker_flags' archive_expsym_cmds_F77='$echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~$echo "local: *; };" >> $lib.exp~ $LD -G${allow_undefined_flag} -M $lib.exp -h $soname -o $lib $libobjs $deplibs $linker_flags~$rm $lib.exp' fi hardcode_libdir_flag_spec_F77='-R$libdir' hardcode_shlibpath_var_F77=no case $host_os in solaris2.[0-5] | solaris2.[0-5].*) ;; *) # The compiler driver will combine linker options so we # cannot just pass the convience library names through # without $wl, iff we do not link with $LD. # Luckily, gcc supports the same syntax we need for Sun Studio. # Supported since Solaris 2.6 (maybe 2.5.1?) case $wlarc in '') whole_archive_flag_spec_F77='-z allextract$convenience -z defaultextract' ;; *) whole_archive_flag_spec_F77='${wl}-z ${wl}allextract`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}-z ${wl}defaultextract' ;; esac ;; esac link_all_deplibs_F77=yes ;; sunos4*) if test "x$host_vendor" = xsequent; then # Use $CC to link under sequent, because it throws in some extra .o # files that make .init and .fini sections work. archive_cmds_F77='$CC -G ${wl}-h $soname -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds_F77='$LD -assert pure-text -Bstatic -o $lib $libobjs $deplibs $linker_flags' fi hardcode_libdir_flag_spec_F77='-L$libdir' hardcode_direct_F77=yes hardcode_minus_L_F77=yes hardcode_shlibpath_var_F77=no ;; sysv4) case $host_vendor in sni) archive_cmds_F77='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct_F77=yes # is this really true??? ;; siemens) ## LD is ld it makes a PLAMLIB ## CC just makes a GrossModule. archive_cmds_F77='$LD -G -o $lib $libobjs $deplibs $linker_flags' reload_cmds_F77='$CC -r -o $output$reload_objs' hardcode_direct_F77=no ;; motorola) archive_cmds_F77='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct_F77=no #Motorola manual says yes, but my tests say they lie ;; esac runpath_var='LD_RUN_PATH' hardcode_shlibpath_var_F77=no ;; sysv4.3*) archive_cmds_F77='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_shlibpath_var_F77=no export_dynamic_flag_spec_F77='-Bexport' ;; sysv4*MP*) if test -d /usr/nec; then archive_cmds_F77='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_shlibpath_var_F77=no runpath_var=LD_RUN_PATH hardcode_runpath_var=yes ld_shlibs_F77=yes fi ;; sysv4*uw2* | sysv5OpenUNIX* | sysv5UnixWare7.[01].[10]* | unixware7*) no_undefined_flag_F77='${wl}-z,text' archive_cmds_need_lc_F77=no hardcode_shlibpath_var_F77=no runpath_var='LD_RUN_PATH' if test "$GCC" = yes; then archive_cmds_F77='$CC -shared ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds_F77='$CC -shared ${wl}-Bexport:$export_symbols ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds_F77='$CC -G ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds_F77='$CC -G ${wl}-Bexport:$export_symbols ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' fi ;; sysv5* | sco3.2v5* | sco5v6*) # Note: We can NOT use -z defs as we might desire, because we do not # link with -lc, and that would cause any symbols used from libc to # always be unresolved, which means just about no library would # ever link correctly. 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esac ;; gnu*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}${major} ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH hardcode_into_libs=yes ;; hpux9* | hpux10* | hpux11*) # Give a soname corresponding to the major version so that dld.sl refuses to # link against other versions. version_type=sunos need_lib_prefix=no need_version=no case $host_cpu in ia64*) shrext_cmds='.so' hardcode_into_libs=yes dynamic_linker="$host_os dld.so" shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes # Unless +noenvvar is specified. library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' if test "X$HPUX_IA64_MODE" = X32; then sys_lib_search_path_spec="/usr/lib/hpux32 /usr/local/lib/hpux32 /usr/local/lib" else sys_lib_search_path_spec="/usr/lib/hpux64 /usr/local/lib/hpux64" fi sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; hppa*64*) shrext_cmds='.sl' hardcode_into_libs=yes dynamic_linker="$host_os dld.sl" shlibpath_var=LD_LIBRARY_PATH # How should we handle SHLIB_PATH shlibpath_overrides_runpath=yes # Unless +noenvvar is specified. library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' sys_lib_search_path_spec="/usr/lib/pa20_64 /usr/ccs/lib/pa20_64" sys_lib_dlsearch_path_spec=$sys_lib_search_path_spec ;; *) shrext_cmds='.sl' dynamic_linker="$host_os dld.sl" shlibpath_var=SHLIB_PATH shlibpath_overrides_runpath=no # +s is required to enable SHLIB_PATH library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' ;; esac # HP-UX runs *really* slowly unless shared libraries are mode 555. postinstall_cmds='chmod 555 $lib' ;; interix3*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='Interix 3.x ld.so.1 (PE, like ELF)' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no hardcode_into_libs=yes ;; irix5* | irix6* | nonstopux*) case $host_os in nonstopux*) version_type=nonstopux ;; *) if test "$lt_cv_prog_gnu_ld" = yes; then version_type=linux else version_type=irix fi ;; esac need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${release}${shared_ext} $libname${shared_ext}' case $host_os in irix5* | nonstopux*) libsuff= shlibsuff= ;; *) case $LD in # libtool.m4 will add one of these switches to LD *-32|*"-32 "|*-melf32bsmip|*"-melf32bsmip ") libsuff= shlibsuff= libmagic=32-bit;; *-n32|*"-n32 "|*-melf32bmipn32|*"-melf32bmipn32 ") libsuff=32 shlibsuff=N32 libmagic=N32;; *-64|*"-64 "|*-melf64bmip|*"-melf64bmip ") libsuff=64 shlibsuff=64 libmagic=64-bit;; *) libsuff= shlibsuff= libmagic=never-match;; esac ;; esac shlibpath_var=LD_LIBRARY${shlibsuff}_PATH shlibpath_overrides_runpath=no sys_lib_search_path_spec="/usr/lib${libsuff} /lib${libsuff} /usr/local/lib${libsuff}" sys_lib_dlsearch_path_spec="/usr/lib${libsuff} /lib${libsuff}" hardcode_into_libs=yes ;; # No shared lib support for Linux oldld, aout, or coff. linux*oldld* | linux*aout* | linux*coff*) dynamic_linker=no ;; # This must be Linux ELF. linux*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' finish_cmds='PATH="\$PATH:/sbin" ldconfig -n $libdir' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no # This implies no fast_install, which is unacceptable. # Some rework will be needed to allow for fast_install # before this can be enabled. hardcode_into_libs=yes # Append ld.so.conf contents to the search path if test -f /etc/ld.so.conf; then lt_ld_extra=`awk '/^include / { system(sprintf("cd /etc; cat %s", \$2)); skip = 1; } { if (!skip) print \$0; skip = 0; }' < /etc/ld.so.conf | $SED -e 's/#.*//;s/[:, ]/ /g;s/=[^=]*$//;s/=[^= ]* / /g;/^$/d' | tr '\n' ' '` sys_lib_dlsearch_path_spec="/lib /usr/lib $lt_ld_extra" fi # We used to test for /lib/ld.so.1 and disable shared libraries on # powerpc, because MkLinux only supported shared libraries with the # GNU dynamic linker. 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netbsd*) version_type=sunos need_lib_prefix=no need_version=no if echo __ELF__ | $CC -E - | grep __ELF__ >/dev/null; then library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${shared_ext}$versuffix' finish_cmds='PATH="\$PATH:/sbin" ldconfig -m $libdir' dynamic_linker='NetBSD (a.out) ld.so' else library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='NetBSD ld.elf_so' fi shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes hardcode_into_libs=yes ;; newsos6) version_type=linux library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes ;; nto-qnx*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major $libname${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=yes ;; 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lt_prog_compiler_static_GCJ='${wl}-a ${wl}archive' ;; irix5* | irix6* | nonstopux*) lt_prog_compiler_wl_GCJ='-Wl,' # PIC (with -KPIC) is the default. lt_prog_compiler_static_GCJ='-non_shared' ;; newsos6) lt_prog_compiler_pic_GCJ='-KPIC' lt_prog_compiler_static_GCJ='-Bstatic' ;; linux*) case $cc_basename in icc* | ecc*) lt_prog_compiler_wl_GCJ='-Wl,' lt_prog_compiler_pic_GCJ='-KPIC' lt_prog_compiler_static_GCJ='-static' ;; pgcc* | pgf77* | pgf90* | pgf95*) # Portland Group compilers (*not* the Pentium gcc compiler, # which looks to be a dead project) lt_prog_compiler_wl_GCJ='-Wl,' lt_prog_compiler_pic_GCJ='-fpic' lt_prog_compiler_static_GCJ='-Bstatic' ;; ccc*) lt_prog_compiler_wl_GCJ='-Wl,' # All Alpha code is PIC. lt_prog_compiler_static_GCJ='-non_shared' ;; esac ;; osf3* | osf4* | osf5*) lt_prog_compiler_wl_GCJ='-Wl,' # All OSF/1 code is PIC. lt_prog_compiler_static_GCJ='-non_shared' ;; solaris*) lt_prog_compiler_pic_GCJ='-KPIC' lt_prog_compiler_static_GCJ='-Bstatic' case $cc_basename in f77* | f90* | f95*) lt_prog_compiler_wl_GCJ='-Qoption ld ';; *) lt_prog_compiler_wl_GCJ='-Wl,';; esac ;; sunos4*) lt_prog_compiler_wl_GCJ='-Qoption ld ' lt_prog_compiler_pic_GCJ='-PIC' lt_prog_compiler_static_GCJ='-Bstatic' ;; sysv4 | sysv4.2uw2* | sysv4.3*) lt_prog_compiler_wl_GCJ='-Wl,' lt_prog_compiler_pic_GCJ='-KPIC' lt_prog_compiler_static_GCJ='-Bstatic' ;; sysv4*MP*) if test -d /usr/nec ;then lt_prog_compiler_pic_GCJ='-Kconform_pic' lt_prog_compiler_static_GCJ='-Bstatic' fi ;; sysv5* | unixware* | sco3.2v5* | sco5v6* | OpenUNIX*) lt_prog_compiler_wl_GCJ='-Wl,' lt_prog_compiler_pic_GCJ='-KPIC' lt_prog_compiler_static_GCJ='-Bstatic' ;; unicos*) lt_prog_compiler_wl_GCJ='-Wl,' lt_prog_compiler_can_build_shared_GCJ=no ;; uts4*) lt_prog_compiler_pic_GCJ='-pic' lt_prog_compiler_static_GCJ='-Bstatic' ;; *) lt_prog_compiler_can_build_shared_GCJ=no ;; esac fi echo "$as_me:$LINENO: result: $lt_prog_compiler_pic_GCJ" >&5 echo "${ECHO_T}$lt_prog_compiler_pic_GCJ" >&6 # # Check to make sure the PIC flag actually works. # if test -n "$lt_prog_compiler_pic_GCJ"; then echo "$as_me:$LINENO: checking if $compiler PIC flag $lt_prog_compiler_pic_GCJ works" >&5 echo $ECHO_N "checking if $compiler PIC flag $lt_prog_compiler_pic_GCJ works... $ECHO_C" >&6 if test "${lt_prog_compiler_pic_works_GCJ+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else lt_prog_compiler_pic_works_GCJ=no ac_outfile=conftest.$ac_objext printf "$lt_simple_compile_test_code" > conftest.$ac_ext lt_compiler_flag="$lt_prog_compiler_pic_GCJ" # Insert the option either (1) after the last *FLAGS variable, or # (2) before a word containing "conftest.", or (3) at the end. # Note that $ac_compile itself does not contain backslashes and begins # with a dollar sign (not a hyphen), so the echo should work correctly. # The option is referenced via a variable to avoid confusing sed. lt_compile=`echo "$ac_compile" | $SED \ -e 's:.*FLAGS}\{0,1\} :&$lt_compiler_flag :; t' \ -e 's: [^ ]*conftest\.: $lt_compiler_flag&:; t' \ -e 's:$: $lt_compiler_flag:'` (eval echo "\"\$as_me:15761: $lt_compile\"" >&5) (eval "$lt_compile" 2>conftest.err) ac_status=$? cat conftest.err >&5 echo "$as_me:15765: \$? = $ac_status" >&5 if (exit $ac_status) && test -s "$ac_outfile"; then # The compiler can only warn and ignore the option if not recognized # So say no if there are warnings other than the usual output. $echo "X$_lt_compiler_boilerplate" | $Xsed -e '/^$/d' >conftest.exp $SED '/^$/d; /^ *+/d' conftest.err >conftest.er2 if test ! -s conftest.er2 || diff conftest.exp conftest.er2 >/dev/null; then lt_prog_compiler_pic_works_GCJ=yes fi fi $rm conftest* fi echo "$as_me:$LINENO: result: $lt_prog_compiler_pic_works_GCJ" >&5 echo "${ECHO_T}$lt_prog_compiler_pic_works_GCJ" >&6 if test x"$lt_prog_compiler_pic_works_GCJ" = xyes; then case $lt_prog_compiler_pic_GCJ in "" | " "*) ;; *) lt_prog_compiler_pic_GCJ=" $lt_prog_compiler_pic_GCJ" ;; esac else lt_prog_compiler_pic_GCJ= lt_prog_compiler_can_build_shared_GCJ=no fi fi case $host_os in # For platforms which do not support PIC, -DPIC is meaningless: *djgpp*) lt_prog_compiler_pic_GCJ= ;; *) lt_prog_compiler_pic_GCJ="$lt_prog_compiler_pic_GCJ" ;; esac # # Check to make sure the static flag actually works. # wl=$lt_prog_compiler_wl_GCJ eval lt_tmp_static_flag=\"$lt_prog_compiler_static_GCJ\" echo "$as_me:$LINENO: checking if $compiler static flag $lt_tmp_static_flag works" >&5 echo $ECHO_N "checking if $compiler static flag $lt_tmp_static_flag works... $ECHO_C" >&6 if test "${lt_prog_compiler_static_works_GCJ+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else lt_prog_compiler_static_works_GCJ=no save_LDFLAGS="$LDFLAGS" LDFLAGS="$LDFLAGS $lt_tmp_static_flag" printf "$lt_simple_link_test_code" > conftest.$ac_ext if (eval $ac_link 2>conftest.err) && test -s conftest$ac_exeext; then # The linker can only warn and ignore the option if not recognized # So say no if there are warnings if test -s conftest.err; then # Append any errors to the config.log. cat conftest.err 1>&5 $echo "X$_lt_linker_boilerplate" | $Xsed -e '/^$/d' > conftest.exp $SED '/^$/d; /^ *+/d' conftest.err >conftest.er2 if diff conftest.exp conftest.er2 >/dev/null; then lt_prog_compiler_static_works_GCJ=yes fi else lt_prog_compiler_static_works_GCJ=yes fi fi $rm conftest* LDFLAGS="$save_LDFLAGS" fi echo "$as_me:$LINENO: result: $lt_prog_compiler_static_works_GCJ" >&5 echo "${ECHO_T}$lt_prog_compiler_static_works_GCJ" >&6 if test x"$lt_prog_compiler_static_works_GCJ" = xyes; then : else lt_prog_compiler_static_GCJ= fi echo "$as_me:$LINENO: checking if $compiler supports -c -o file.$ac_objext" >&5 echo $ECHO_N "checking if $compiler supports -c -o file.$ac_objext... $ECHO_C" >&6 if test "${lt_cv_prog_compiler_c_o_GCJ+set}" = set; then echo $ECHO_N "(cached) $ECHO_C" >&6 else lt_cv_prog_compiler_c_o_GCJ=no $rm -r conftest 2>/dev/null mkdir conftest cd conftest mkdir out printf "$lt_simple_compile_test_code" > conftest.$ac_ext lt_compiler_flag="-o out/conftest2.$ac_objext" # Insert the option either (1) after the last *FLAGS variable, or # (2) before a word containing "conftest.", or (3) at the end. # Note that $ac_compile itself does not contain backslashes and begins # with a dollar sign (not a hyphen), so the echo should work correctly. lt_compile=`echo "$ac_compile" | $SED \ -e 's:.*FLAGS}\{0,1\} :&$lt_compiler_flag :; t' \ -e 's: [^ ]*conftest\.: $lt_compiler_flag&:; t' \ -e 's:$: $lt_compiler_flag:'` (eval echo "\"\$as_me:15865: $lt_compile\"" >&5) (eval "$lt_compile" 2>out/conftest.err) ac_status=$? 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FIXME archive_cmds_GCJ='$CC -nostart $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' else ld_shlibs_GCJ=no fi ;; cygwin* | mingw* | pw32*) # _LT_AC_TAGVAR(hardcode_libdir_flag_spec, GCJ) is actually meaningless, # as there is no search path for DLLs. hardcode_libdir_flag_spec_GCJ='-L$libdir' allow_undefined_flag_GCJ=unsupported always_export_symbols_GCJ=no enable_shared_with_static_runtimes_GCJ=yes export_symbols_cmds_GCJ='$NM $libobjs $convenience | $global_symbol_pipe | $SED -e '\''/^[BCDGRS] /s/.* \([^ ]*\)/\1 DATA/'\'' | $SED -e '\''/^[AITW] /s/.* //'\'' | sort | uniq > $export_symbols' if $LD --help 2>&1 | grep 'auto-import' > /dev/null; then archive_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' # If the export-symbols file already is a .def file (1st line # is EXPORTS), use it as is; otherwise, prepend... archive_expsym_cmds_GCJ='if test "x`$SED 1q $export_symbols`" = xEXPORTS; then cp $export_symbols $output_objdir/$soname.def; else echo EXPORTS > $output_objdir/$soname.def; cat $export_symbols >> $output_objdir/$soname.def; fi~ $CC -shared $output_objdir/$soname.def $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' else ld_shlibs_GCJ=no fi ;; interix3*) hardcode_direct_GCJ=no hardcode_shlibpath_var_GCJ=no hardcode_libdir_flag_spec_GCJ='${wl}-rpath,$libdir' export_dynamic_flag_spec_GCJ='${wl}-E' # Hack: On Interix 3.x, we cannot compile PIC because of a broken gcc. # Instead, shared libraries are loaded at an image base (0x10000000 by # default) and relocated if they conflict, which is a slow very memory # consuming and fragmenting process. To avoid this, we pick a random, # 256 KiB-aligned image base between 0x50000000 and 0x6FFC0000 at link # time. Moving up from 0x10000000 also allows more sbrk(2) space. archive_cmds_GCJ='$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-h,$soname ${wl}--image-base,`expr ${RANDOM-$$} % 4096 / 2 \* 262144 + 1342177280` -o $lib' archive_expsym_cmds_GCJ='sed "s,^,_," $export_symbols >$output_objdir/$soname.expsym~$CC -shared $pic_flag $libobjs $deplibs $compiler_flags ${wl}-h,$soname ${wl}--retain-symbols-file,$output_objdir/$soname.expsym ${wl}--image-base,`expr ${RANDOM-$$} % 4096 / 2 \* 262144 + 1342177280` -o $lib' ;; linux*) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then tmp_addflag= case $cc_basename,$host_cpu in pgcc*) # Portland Group C compiler whole_archive_flag_spec_GCJ='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}--no-whole-archive' tmp_addflag=' $pic_flag' ;; pgf77* | pgf90* | pgf95*) # Portland Group f77 and f90 compilers whole_archive_flag_spec_GCJ='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}--no-whole-archive' tmp_addflag=' $pic_flag -Mnomain' ;; ecc*,ia64* | icc*,ia64*) # Intel C compiler on ia64 tmp_addflag=' -i_dynamic' ;; efc*,ia64* | ifort*,ia64*) # Intel Fortran compiler on ia64 tmp_addflag=' -i_dynamic -nofor_main' ;; ifc* | ifort*) # Intel Fortran compiler tmp_addflag=' -nofor_main' ;; esac archive_cmds_GCJ='$CC -shared'"$tmp_addflag"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' if test $supports_anon_versioning = yes; then archive_expsym_cmds_GCJ='$echo "{ global:" > $output_objdir/$libname.ver~ cat $export_symbols | sed -e "s/\(.*\)/\1;/" >> $output_objdir/$libname.ver~ $echo "local: *; };" >> $output_objdir/$libname.ver~ $CC -shared'"$tmp_addflag"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-version-script ${wl}$output_objdir/$libname.ver -o $lib' fi link_all_deplibs_GCJ=no else ld_shlibs_GCJ=no fi ;; netbsd* | netbsdelf*-gnu | knetbsd*-gnu) if echo __ELF__ | $CC -E - | grep __ELF__ >/dev/null; then archive_cmds_GCJ='$LD -Bshareable $libobjs $deplibs $linker_flags -o $lib' wlarc= else archive_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' fi ;; solaris*) if $LD -v 2>&1 | grep 'BFD 2\.8' > /dev/null; then ld_shlibs_GCJ=no cat <&2 *** Warning: The releases 2.8.* of the GNU linker cannot reliably *** create shared libraries on Solaris systems. Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.9.1 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. EOF elif $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then archive_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else ld_shlibs_GCJ=no fi ;; sysv5* | sco3.2v5* | sco5v6* | unixware* | OpenUNIX*) case `$LD -v 2>&1` in *\ [01].* | *\ 2.[0-9].* | *\ 2.1[0-5].*) ld_shlibs_GCJ=no cat <<_LT_EOF 1>&2 *** Warning: Releases of the GNU linker prior to 2.16.91.0.3 can not *** reliably create shared libraries on SCO systems. Therefore, libtool *** is disabling shared libraries support. We urge you to upgrade GNU *** binutils to release 2.16.91.0.3 or newer. Another option is to modify *** your PATH or compiler configuration so that the native linker is *** used, and then restart. _LT_EOF ;; *) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then hardcode_libdir_flag_spec_GCJ='`test -z "$SCOABSPATH" && echo ${wl}-rpath,$libdir`' archive_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname,\${SCOABSPATH:+${install_libdir}/}$soname -o $lib' archive_expsym_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname,\${SCOABSPATH:+${install_libdir}/}$soname,-retain-symbols-file,$export_symbols -o $lib' else ld_shlibs_GCJ=no fi ;; esac ;; sunos4*) archive_cmds_GCJ='$LD -assert pure-text -Bshareable -o $lib $libobjs $deplibs $linker_flags' wlarc= hardcode_direct_GCJ=yes hardcode_shlibpath_var_GCJ=no ;; *) if $LD --help 2>&1 | grep ': supported targets:.* elf' > /dev/null; then archive_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' archive_expsym_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' else ld_shlibs_GCJ=no fi ;; esac if test "$ld_shlibs_GCJ" = no; then runpath_var= hardcode_libdir_flag_spec_GCJ= export_dynamic_flag_spec_GCJ= whole_archive_flag_spec_GCJ= fi else # PORTME fill in a description of your system's linker (not GNU ld) case $host_os in aix3*) allow_undefined_flag_GCJ=unsupported always_export_symbols_GCJ=yes archive_expsym_cmds_GCJ='$LD -o $output_objdir/$soname $libobjs $deplibs $linker_flags -bE:$export_symbols -T512 -H512 -bM:SRE~$AR $AR_FLAGS $lib $output_objdir/$soname' # Note: this linker hardcodes the directories in LIBPATH if there # are no directories specified by -L. hardcode_minus_L_GCJ=yes if test "$GCC" = yes && test -z "$lt_prog_compiler_static"; then # Neither direct hardcoding nor static linking is supported with a # broken collect2. hardcode_direct_GCJ=unsupported fi ;; aix4* | aix5*) if test "$host_cpu" = ia64; then # On IA64, the linker does run time linking by default, so we don't # have to do anything special. aix_use_runtimelinking=no exp_sym_flag='-Bexport' no_entry_flag="" else # If we're using GNU nm, then we don't want the "-C" option. # -C means demangle to AIX nm, but means don't demangle with GNU nm if $NM -V 2>&1 | grep 'GNU' > /dev/null; then export_symbols_cmds_GCJ='$NM -Bpg $libobjs $convenience | awk '\''{ if (((\$2 == "T") || (\$2 == "D") || (\$2 == "B")) && (substr(\$3,1,1) != ".")) { print \$3 } }'\'' | sort -u > $export_symbols' else export_symbols_cmds_GCJ='$NM -BCpg $libobjs $convenience | awk '\''{ if (((\$2 == "T") || (\$2 == "D") || (\$2 == "B")) && (substr(\$3,1,1) != ".")) { print \$3 } }'\'' | sort -u > $export_symbols' fi aix_use_runtimelinking=no # Test if we are trying to use run time linking or normal # AIX style linking. If -brtl is somewhere in LDFLAGS, we # need to do runtime linking. case $host_os in aix4.[23]|aix4.[23].*|aix5*) for ld_flag in $LDFLAGS; do if (test $ld_flag = "-brtl" || test $ld_flag = "-Wl,-brtl"); then aix_use_runtimelinking=yes break fi done ;; esac exp_sym_flag='-bexport' no_entry_flag='-bnoentry' fi # When large executables or shared objects are built, AIX ld can # have problems creating the table of contents. If linking a library # or program results in "error TOC overflow" add -mminimal-toc to # CXXFLAGS/CFLAGS for g++/gcc. In the cases where that is not # enough to fix the problem, add -Wl,-bbigtoc to LDFLAGS. archive_cmds_GCJ='' hardcode_direct_GCJ=yes hardcode_libdir_separator_GCJ=':' link_all_deplibs_GCJ=yes if test "$GCC" = yes; then case $host_os in aix4.[012]|aix4.[012].*) # We only want to do this on AIX 4.2 and lower, the check # below for broken collect2 doesn't work under 4.3+ collect2name=`${CC} -print-prog-name=collect2` if test -f "$collect2name" && \ strings "$collect2name" | grep resolve_lib_name >/dev/null then # We have reworked collect2 hardcode_direct_GCJ=yes else # We have old collect2 hardcode_direct_GCJ=unsupported # It fails to find uninstalled libraries when the uninstalled # path is not listed in the libpath. Setting hardcode_minus_L # to unsupported forces relinking hardcode_minus_L_GCJ=yes hardcode_libdir_flag_spec_GCJ='-L$libdir' hardcode_libdir_separator_GCJ= fi ;; esac shared_flag='-shared' if test "$aix_use_runtimelinking" = yes; then shared_flag="$shared_flag "'${wl}-G' fi else # not using gcc if test "$host_cpu" = ia64; then # VisualAge C++, Version 5.5 for AIX 5L for IA-64, Beta 3 Release # chokes on -Wl,-G. The following line is correct: shared_flag='-G' else if test "$aix_use_runtimelinking" = yes; then shared_flag='${wl}-G' else shared_flag='${wl}-bM:SRE' fi fi fi # It seems that -bexpall does not export symbols beginning with # underscore (_), so it is better to generate a list of symbols to export. always_export_symbols_GCJ=yes if test "$aix_use_runtimelinking" = yes; then # Warning - without using the other runtime loading flags (-brtl), # -berok will link without error, but may produce a broken library. allow_undefined_flag_GCJ='-berok' # Determine the default libpath from the value encoded in an empty executable. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ int main () { ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then aix_libpath=`dump -H conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'` # Check for a 64-bit object if we didn't find anything. if test -z "$aix_libpath"; then aix_libpath=`dump -HX64 conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'`; fi else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext if test -z "$aix_libpath"; then aix_libpath="/usr/lib:/lib"; fi hardcode_libdir_flag_spec_GCJ='${wl}-blibpath:$libdir:'"$aix_libpath" archive_expsym_cmds_GCJ="\$CC"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags `if test "x${allow_undefined_flag}" != "x"; then echo "${wl}${allow_undefined_flag}"; else :; fi` '"\${wl}$exp_sym_flag:\$export_symbols $shared_flag" else if test "$host_cpu" = ia64; then hardcode_libdir_flag_spec_GCJ='${wl}-R $libdir:/usr/lib:/lib' allow_undefined_flag_GCJ="-z nodefs" archive_expsym_cmds_GCJ="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs '"\${wl}$no_entry_flag"' $compiler_flags ${wl}${allow_undefined_flag} '"\${wl}$exp_sym_flag:\$export_symbols" else # Determine the default libpath from the value encoded in an empty executable. cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ int main () { ; return 0; } _ACEOF rm -f conftest.$ac_objext conftest$ac_exeext if { (eval echo "$as_me:$LINENO: \"$ac_link\"") >&5 (eval $ac_link) 2>conftest.er1 ac_status=$? grep -v '^ *+' conftest.er1 >conftest.err rm -f conftest.er1 cat conftest.err >&5 echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } && { ac_try='test -z "$ac_c_werror_flag" || test ! -s conftest.err' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; } && { ac_try='test -s conftest$ac_exeext' { (eval echo "$as_me:$LINENO: \"$ac_try\"") >&5 (eval $ac_try) 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); }; }; then aix_libpath=`dump -H conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'` # Check for a 64-bit object if we didn't find anything. if test -z "$aix_libpath"; then aix_libpath=`dump -HX64 conftest$ac_exeext 2>/dev/null | $SED -n -e '/Import File Strings/,/^$/ { /^0/ { s/^0 *\(.*\)$/\1/; p; } }'`; fi else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 fi rm -f conftest.err conftest.$ac_objext \ conftest$ac_exeext conftest.$ac_ext if test -z "$aix_libpath"; then aix_libpath="/usr/lib:/lib"; fi hardcode_libdir_flag_spec_GCJ='${wl}-blibpath:$libdir:'"$aix_libpath" # Warning - without using the other run time loading flags, # -berok will link without error, but may produce a broken library. no_undefined_flag_GCJ=' ${wl}-bernotok' allow_undefined_flag_GCJ=' ${wl}-berok' # Exported symbols can be pulled into shared objects from archives whole_archive_flag_spec_GCJ='$convenience' archive_cmds_need_lc_GCJ=yes # This is similar to how AIX traditionally builds its shared libraries. archive_expsym_cmds_GCJ="\$CC $shared_flag"' -o $output_objdir/$soname $libobjs $deplibs ${wl}-bnoentry $compiler_flags ${wl}-bE:$export_symbols${allow_undefined_flag}~$AR $AR_FLAGS $output_objdir/$libname$release.a $output_objdir/$soname' fi fi ;; amigaos*) archive_cmds_GCJ='$rm $output_objdir/a2ixlibrary.data~$echo "#define NAME $libname" > $output_objdir/a2ixlibrary.data~$echo "#define LIBRARY_ID 1" >> $output_objdir/a2ixlibrary.data~$echo "#define VERSION $major" >> $output_objdir/a2ixlibrary.data~$echo "#define REVISION $revision" >> $output_objdir/a2ixlibrary.data~$AR $AR_FLAGS $lib $libobjs~$RANLIB $lib~(cd $output_objdir && a2ixlibrary -32)' hardcode_libdir_flag_spec_GCJ='-L$libdir' hardcode_minus_L_GCJ=yes # see comment about different semantics on the GNU ld section ld_shlibs_GCJ=no ;; bsdi[45]*) export_dynamic_flag_spec_GCJ=-rdynamic ;; cygwin* | mingw* | pw32*) # When not using gcc, we currently assume that we are using # Microsoft Visual C++. # hardcode_libdir_flag_spec is actually meaningless, as there is # no search path for DLLs. hardcode_libdir_flag_spec_GCJ=' ' allow_undefined_flag_GCJ=unsupported # Tell ltmain to make .lib files, not .a files. libext=lib # Tell ltmain to make .dll files, not .so files. shrext_cmds=".dll" # FIXME: Setting linknames here is a bad hack. archive_cmds_GCJ='$CC -o $lib $libobjs $compiler_flags `echo "$deplibs" | $SED -e '\''s/ -lc$//'\''` -link -dll~linknames=' # The linker will automatically build a .lib file if we build a DLL. old_archive_From_new_cmds_GCJ='true' # FIXME: Should let the user specify the lib program. old_archive_cmds_GCJ='lib /OUT:$oldlib$oldobjs$old_deplibs' fix_srcfile_path_GCJ='`cygpath -w "$srcfile"`' enable_shared_with_static_runtimes_GCJ=yes ;; darwin* | rhapsody*) case $host_os in rhapsody* | darwin1.[012]) allow_undefined_flag_GCJ='${wl}-undefined ${wl}suppress' ;; *) # Darwin 1.3 on if test -z ${MACOSX_DEPLOYMENT_TARGET} ; then allow_undefined_flag_GCJ='${wl}-flat_namespace ${wl}-undefined ${wl}suppress' else case ${MACOSX_DEPLOYMENT_TARGET} in 10.[012]) allow_undefined_flag_GCJ='${wl}-flat_namespace ${wl}-undefined ${wl}suppress' ;; 10.*) allow_undefined_flag_GCJ='${wl}-undefined ${wl}dynamic_lookup' ;; esac fi ;; esac archive_cmds_need_lc_GCJ=no hardcode_direct_GCJ=no hardcode_automatic_GCJ=yes hardcode_shlibpath_var_GCJ=unsupported whole_archive_flag_spec_GCJ='' link_all_deplibs_GCJ=yes if test "$GCC" = yes ; then output_verbose_link_cmd='echo' archive_cmds_GCJ='$CC -dynamiclib $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags -install_name $rpath/$soname $verstring' module_cmds_GCJ='$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags' # Don't fix this by using the ld -exported_symbols_list flag, it doesn't exist in older darwin lds archive_expsym_cmds_GCJ='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC -dynamiclib $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags -install_name $rpath/$soname $verstring~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' module_expsym_cmds_GCJ='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' else case $cc_basename in xlc*) output_verbose_link_cmd='echo' archive_cmds_GCJ='$CC -qmkshrobj $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-install_name ${wl}`echo $rpath/$soname` $verstring' module_cmds_GCJ='$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags' # Don't fix this by using the ld -exported_symbols_list flag, it doesn't exist in older darwin lds archive_expsym_cmds_GCJ='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC -qmkshrobj $allow_undefined_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-install_name ${wl}$rpath/$soname $verstring~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' module_expsym_cmds_GCJ='sed -e "s,#.*,," -e "s,^[ ]*,," -e "s,^\(..*\),_&," < $export_symbols > $output_objdir/${libname}-symbols.expsym~$CC $allow_undefined_flag -o $lib -bundle $libobjs $deplibs$compiler_flags~nmedit -s $output_objdir/${libname}-symbols.expsym ${lib}' ;; *) ld_shlibs_GCJ=no ;; esac fi ;; dgux*) archive_cmds_GCJ='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_libdir_flag_spec_GCJ='-L$libdir' hardcode_shlibpath_var_GCJ=no ;; freebsd1*) ld_shlibs_GCJ=no ;; # FreeBSD 2.2.[012] allows us to include c++rt0.o to get C++ constructor # support. Future versions do this automatically, but an explicit c++rt0.o # does not break anything, and helps significantly (at the cost of a little # extra space). freebsd2.2*) archive_cmds_GCJ='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags /usr/lib/c++rt0.o' hardcode_libdir_flag_spec_GCJ='-R$libdir' hardcode_direct_GCJ=yes hardcode_shlibpath_var_GCJ=no ;; # Unfortunately, older versions of FreeBSD 2 do not have this feature. freebsd2*) archive_cmds_GCJ='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' hardcode_direct_GCJ=yes hardcode_minus_L_GCJ=yes hardcode_shlibpath_var_GCJ=no ;; # FreeBSD 3 and greater uses gcc -shared to do shared libraries. freebsd* | dragonfly*) archive_cmds_GCJ='$CC -shared -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec_GCJ='-R$libdir' hardcode_direct_GCJ=yes hardcode_shlibpath_var_GCJ=no ;; # GNU/kFreeBSD uses gcc -shared to do shared libraries. kfreebsd*-gnu) archive_cmds_GCJ='$CC -shared -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec_GCJ='-R$libdir' hardcode_direct_GCJ=yes hardcode_shlibpath_var_GCJ=no link_all_deplibs_GCJ=no ;; hpux9*) if test "$GCC" = yes; then archive_cmds_GCJ='$rm $output_objdir/$soname~$CC -shared -fPIC ${wl}+b ${wl}$install_libdir -o $output_objdir/$soname $libobjs $deplibs $compiler_flags~test $output_objdir/$soname = $lib || mv $output_objdir/$soname $lib' else archive_cmds_GCJ='$rm $output_objdir/$soname~$LD -b +b $install_libdir -o $output_objdir/$soname $libobjs $deplibs $linker_flags~test $output_objdir/$soname = $lib || mv $output_objdir/$soname $lib' fi hardcode_libdir_flag_spec_GCJ='${wl}+b ${wl}$libdir' hardcode_libdir_separator_GCJ=: hardcode_direct_GCJ=yes # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L_GCJ=yes export_dynamic_flag_spec_GCJ='${wl}-E' ;; hpux10*) if test "$GCC" = yes -a "$with_gnu_ld" = no; then archive_cmds_GCJ='$CC -shared -fPIC ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds_GCJ='$LD -b +h $soname +b $install_libdir -o $lib $libobjs $deplibs $linker_flags' fi if test "$with_gnu_ld" = no; then hardcode_libdir_flag_spec_GCJ='${wl}+b ${wl}$libdir' hardcode_libdir_separator_GCJ=: hardcode_direct_GCJ=yes export_dynamic_flag_spec_GCJ='${wl}-E' # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L_GCJ=yes fi ;; hpux11*) if test "$GCC" = yes -a "$with_gnu_ld" = no; then case $host_cpu in hppa*64*) archive_cmds_GCJ='$CC -shared ${wl}+h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' ;; ia64*) archive_cmds_GCJ='$CC -shared ${wl}+h ${wl}$soname ${wl}+nodefaultrpath -o $lib $libobjs $deplibs $compiler_flags' ;; *) archive_cmds_GCJ='$CC -shared -fPIC ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' ;; esac else case $host_cpu in hppa*64*) archive_cmds_GCJ='$CC -b ${wl}+h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' ;; ia64*) archive_cmds_GCJ='$CC -b ${wl}+h ${wl}$soname ${wl}+nodefaultrpath -o $lib $libobjs $deplibs $compiler_flags' ;; *) archive_cmds_GCJ='$CC -b ${wl}+h ${wl}$soname ${wl}+b ${wl}$install_libdir -o $lib $libobjs $deplibs $compiler_flags' ;; esac fi if test "$with_gnu_ld" = no; then hardcode_libdir_flag_spec_GCJ='${wl}+b ${wl}$libdir' hardcode_libdir_separator_GCJ=: case $host_cpu in hppa*64*|ia64*) hardcode_libdir_flag_spec_ld_GCJ='+b $libdir' hardcode_direct_GCJ=no hardcode_shlibpath_var_GCJ=no ;; *) hardcode_direct_GCJ=yes export_dynamic_flag_spec_GCJ='${wl}-E' # hardcode_minus_L: Not really in the search PATH, # but as the default location of the library. hardcode_minus_L_GCJ=yes ;; esac fi ;; irix5* | irix6* | nonstopux*) if test "$GCC" = yes; then archive_cmds_GCJ='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' else archive_cmds_GCJ='$LD -shared $libobjs $deplibs $linker_flags -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' hardcode_libdir_flag_spec_ld_GCJ='-rpath $libdir' fi hardcode_libdir_flag_spec_GCJ='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator_GCJ=: link_all_deplibs_GCJ=yes ;; netbsd* | netbsdelf*-gnu | knetbsd*-gnu) if echo __ELF__ | $CC -E - | grep __ELF__ >/dev/null; then archive_cmds_GCJ='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' # a.out else archive_cmds_GCJ='$LD -shared -o $lib $libobjs $deplibs $linker_flags' # ELF fi hardcode_libdir_flag_spec_GCJ='-R$libdir' hardcode_direct_GCJ=yes hardcode_shlibpath_var_GCJ=no ;; newsos6) archive_cmds_GCJ='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct_GCJ=yes hardcode_libdir_flag_spec_GCJ='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator_GCJ=: hardcode_shlibpath_var_GCJ=no ;; openbsd*) hardcode_direct_GCJ=yes hardcode_shlibpath_var_GCJ=no if test -z "`echo __ELF__ | $CC -E - | grep __ELF__`" || test "$host_os-$host_cpu" = "openbsd2.8-powerpc"; then archive_cmds_GCJ='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds_GCJ='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags ${wl}-retain-symbols-file,$export_symbols' hardcode_libdir_flag_spec_GCJ='${wl}-rpath,$libdir' export_dynamic_flag_spec_GCJ='${wl}-E' else case $host_os in openbsd[01].* | openbsd2.[0-7] | openbsd2.[0-7].*) archive_cmds_GCJ='$LD -Bshareable -o $lib $libobjs $deplibs $linker_flags' hardcode_libdir_flag_spec_GCJ='-R$libdir' ;; *) archive_cmds_GCJ='$CC -shared $pic_flag -o $lib $libobjs $deplibs $compiler_flags' hardcode_libdir_flag_spec_GCJ='${wl}-rpath,$libdir' ;; esac fi ;; os2*) hardcode_libdir_flag_spec_GCJ='-L$libdir' hardcode_minus_L_GCJ=yes allow_undefined_flag_GCJ=unsupported archive_cmds_GCJ='$echo "LIBRARY $libname INITINSTANCE" > $output_objdir/$libname.def~$echo "DESCRIPTION \"$libname\"" >> $output_objdir/$libname.def~$echo DATA >> $output_objdir/$libname.def~$echo " SINGLE NONSHARED" >> $output_objdir/$libname.def~$echo EXPORTS >> $output_objdir/$libname.def~emxexp $libobjs >> $output_objdir/$libname.def~$CC -Zdll -Zcrtdll -o $lib $libobjs $deplibs $compiler_flags $output_objdir/$libname.def' old_archive_From_new_cmds_GCJ='emximp -o $output_objdir/$libname.a $output_objdir/$libname.def' ;; osf3*) if test "$GCC" = yes; then allow_undefined_flag_GCJ=' ${wl}-expect_unresolved ${wl}\*' archive_cmds_GCJ='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' else allow_undefined_flag_GCJ=' -expect_unresolved \*' archive_cmds_GCJ='$LD -shared${allow_undefined_flag} $libobjs $deplibs $linker_flags -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' fi hardcode_libdir_flag_spec_GCJ='${wl}-rpath ${wl}$libdir' hardcode_libdir_separator_GCJ=: ;; osf4* | osf5*) # as osf3* with the addition of -msym flag if test "$GCC" = yes; then allow_undefined_flag_GCJ=' ${wl}-expect_unresolved ${wl}\*' archive_cmds_GCJ='$CC -shared${allow_undefined_flag} $libobjs $deplibs $compiler_flags ${wl}-msym ${wl}-soname ${wl}$soname `test -n "$verstring" && echo ${wl}-set_version ${wl}$verstring` ${wl}-update_registry ${wl}${output_objdir}/so_locations -o $lib' hardcode_libdir_flag_spec_GCJ='${wl}-rpath ${wl}$libdir' else allow_undefined_flag_GCJ=' -expect_unresolved \*' archive_cmds_GCJ='$LD -shared${allow_undefined_flag} $libobjs $deplibs $linker_flags -msym -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib' archive_expsym_cmds_GCJ='for i in `cat $export_symbols`; do printf "%s %s\\n" -exported_symbol "\$i" >> $lib.exp; done; echo "-hidden">> $lib.exp~ $LD -shared${allow_undefined_flag} -input $lib.exp $linker_flags $libobjs $deplibs -soname $soname `test -n "$verstring" && echo -set_version $verstring` -update_registry ${output_objdir}/so_locations -o $lib~$rm $lib.exp' # Both c and cxx compiler support -rpath directly hardcode_libdir_flag_spec_GCJ='-rpath $libdir' fi hardcode_libdir_separator_GCJ=: ;; solaris*) no_undefined_flag_GCJ=' -z text' if test "$GCC" = yes; then wlarc='${wl}' archive_cmds_GCJ='$CC -shared ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds_GCJ='$echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~$echo "local: *; };" >> $lib.exp~ $CC -shared ${wl}-M ${wl}$lib.exp ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags~$rm $lib.exp' else wlarc='' archive_cmds_GCJ='$LD -G${allow_undefined_flag} -h $soname -o $lib $libobjs $deplibs $linker_flags' archive_expsym_cmds_GCJ='$echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~$echo "local: *; };" >> $lib.exp~ $LD -G${allow_undefined_flag} -M $lib.exp -h $soname -o $lib $libobjs $deplibs $linker_flags~$rm $lib.exp' fi hardcode_libdir_flag_spec_GCJ='-R$libdir' hardcode_shlibpath_var_GCJ=no case $host_os in solaris2.[0-5] | solaris2.[0-5].*) ;; *) # The compiler driver will combine linker options so we # cannot just pass the convience library names through # without $wl, iff we do not link with $LD. # Luckily, gcc supports the same syntax we need for Sun Studio. # Supported since Solaris 2.6 (maybe 2.5.1?) case $wlarc in '') whole_archive_flag_spec_GCJ='-z allextract$convenience -z defaultextract' ;; *) whole_archive_flag_spec_GCJ='${wl}-z ${wl}allextract`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; $echo \"$new_convenience\"` ${wl}-z ${wl}defaultextract' ;; esac ;; esac link_all_deplibs_GCJ=yes ;; sunos4*) if test "x$host_vendor" = xsequent; then # Use $CC to link under sequent, because it throws in some extra .o # files that make .init and .fini sections work. archive_cmds_GCJ='$CC -G ${wl}-h $soname -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds_GCJ='$LD -assert pure-text -Bstatic -o $lib $libobjs $deplibs $linker_flags' fi hardcode_libdir_flag_spec_GCJ='-L$libdir' hardcode_direct_GCJ=yes hardcode_minus_L_GCJ=yes hardcode_shlibpath_var_GCJ=no ;; sysv4) case $host_vendor in sni) archive_cmds_GCJ='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct_GCJ=yes # is this really true??? ;; siemens) ## LD is ld it makes a PLAMLIB ## CC just makes a GrossModule. archive_cmds_GCJ='$LD -G -o $lib $libobjs $deplibs $linker_flags' reload_cmds_GCJ='$CC -r -o $output$reload_objs' hardcode_direct_GCJ=no ;; motorola) archive_cmds_GCJ='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_direct_GCJ=no #Motorola manual says yes, but my tests say they lie ;; esac runpath_var='LD_RUN_PATH' hardcode_shlibpath_var_GCJ=no ;; sysv4.3*) archive_cmds_GCJ='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_shlibpath_var_GCJ=no export_dynamic_flag_spec_GCJ='-Bexport' ;; sysv4*MP*) if test -d /usr/nec; then archive_cmds_GCJ='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' hardcode_shlibpath_var_GCJ=no runpath_var=LD_RUN_PATH hardcode_runpath_var=yes ld_shlibs_GCJ=yes fi ;; sysv4*uw2* | sysv5OpenUNIX* | sysv5UnixWare7.[01].[10]* | unixware7*) no_undefined_flag_GCJ='${wl}-z,text' archive_cmds_need_lc_GCJ=no hardcode_shlibpath_var_GCJ=no runpath_var='LD_RUN_PATH' if test "$GCC" = yes; then archive_cmds_GCJ='$CC -shared ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds_GCJ='$CC -shared ${wl}-Bexport:$export_symbols ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' else archive_cmds_GCJ='$CC -G ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' archive_expsym_cmds_GCJ='$CC -G ${wl}-Bexport:$export_symbols ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' fi ;; sysv5* | sco3.2v5* | sco5v6*) # Note: We can NOT use -z defs as we might desire, because we do not # link with -lc, and that would cause any symbols used from libc to # always be unresolved, which means just about no library would # ever link correctly. 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All issues with debian/ dir in cvs that caused a changing orig.gz should be sorted out now. -- Steffen Nissen Mon, 17 May 2004 09:44:38 +0200 libfann1 (1.1.0-3) unstable; urgency=low * fixed bug with two build-depend lines -- Steffen Nissen Mon, 10 May 2004 16:43:24 +0200 libfann1 (1.1.0-2) unstable; urgency=low * added docbook-utils to build-depends -- Steffen Nissen Thu, 6 May 2004 16:22:42 +0200 libfann1 (1.1.0-1) unstable; urgency=low * My first try at a debian package, please report any errors. * Initial Release. -- Steffen Nissen Wed, 31 Mar 2004 09:54:39 +0200 libfann-2.1.0~beta~dfsg/debian/compat000066400000000000000000000000021140315177100176370ustar00rootroot000000000000004 libfann-2.1.0~beta~dfsg/debian/control000066400000000000000000000055731140315177100200560ustar00rootroot00000000000000Source: libfann2 Section: libdevel Priority: optional Maintainer: Steffen Nissen Standards-Version: 3.6.0 Build-Depends: docbook-utils, debhelper (>= 4.1.67), python-dev, swig Package: libfann2-dev Section: libdevel Architecture: any Depends: libfann2 (= ${Source-Version}) Conflicts: libfann1-dev Description: Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, C++, .NET, Python, Delphi, Octave, Ruby, Pure Data and Mathematica bindings are available. A reference manual accompanies the library with examples and recommendations on how to use the library. A graphical user interface is also available for the library. . This package contains the header files and static libraries which is needed for developing the libfann applications. Package: libfann2 Section: libs Architecture: any Depends: ${shlibs:Depends} Suggests: libfann2-dev (= ${Source-Version}) Description: Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, C++, .NET, Python, Delphi, Octave, Ruby, Pure Data and Mathematica bindings are available. A reference manual accompanies the library with examples and recommendations on how to use the library. A graphical user interface is also available for the library. . This package contains the shared libraries. Package: libfann2-py Section: python Architecture: any Depends: ${shlibs:Depends}, libfann2, ${python:Depends} Suggests: libfann2-dev (= ${Source-Version}) Description: Python FANN Bindings (pyfann) Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, C++, .NET, Python, Delphi, Octave, Ruby, Pure Data and Mathematica bindings are available. A reference manual accompanies the library with examples and recommendations on how to use the library. A graphical user interface is also available for the library. . This package contains the python wrapper. libfann-2.1.0~beta~dfsg/debian/copyright000066400000000000000000000007321140315177100203760ustar00rootroot00000000000000This package was debianized by Steffen Nissen on Sun, 18 Apr 2004 18:04:22 +0200 It was downloaded from http://sourceforge.net/projects/fann/ Upstream Author: Steffen Nissen Copyright: You are free to distribute this software under the terms of the GNU Lesser General Public License Version 2.1. On Debian systems, the complete text of the GNU Lesser General Public License can be found in the file `/usr/share/common-licenses/LGPL'. libfann-2.1.0~beta~dfsg/debian/docs000066400000000000000000000000621140315177100173120ustar00rootroot00000000000000NEWS README TODO AUTHORS doc/fann_en.pdf doc/*txt libfann-2.1.0~beta~dfsg/debian/libfann2-dev.dirs000066400000000000000000000000241140315177100215670ustar00rootroot00000000000000usr/lib usr/include libfann-2.1.0~beta~dfsg/debian/libfann2-dev.examples000066400000000000000000000000601140315177100224440ustar00rootroot00000000000000examples/*c examples/xor.data examples/Makefile libfann-2.1.0~beta~dfsg/debian/libfann2-dev.files000066400000000000000000000001171140315177100217330ustar00rootroot00000000000000usr/include/* usr/lib/lib*.a usr/lib/lib*.so usr/lib/pkgconfig/* /usr/lib/*.la libfann-2.1.0~beta~dfsg/debian/libfann2-dev.install000066400000000000000000000001161140315177100222760ustar00rootroot00000000000000usr/include/* usr/lib/lib*.a usr/lib/lib*.so usr/lib/pkgconfig/* usr/lib/*.la libfann-2.1.0~beta~dfsg/debian/libfann2-py.dirs000066400000000000000000000000001140315177100214330ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/debian/libfann2-py.files000066400000000000000000000000471140315177100216070ustar00rootroot00000000000000usr/lib/python*/site-packages/pyfann/* libfann-2.1.0~beta~dfsg/debian/libfann2-py.install000066400000000000000000000000471140315177100221530ustar00rootroot00000000000000usr/lib/python*/site-packages/pyfann/* libfann-2.1.0~beta~dfsg/debian/libfann2.dirs000066400000000000000000000000101140315177100210060ustar00rootroot00000000000000usr/lib libfann-2.1.0~beta~dfsg/debian/libfann2.files000066400000000000000000000000221140315177100211520ustar00rootroot00000000000000usr/lib/lib*.so.* libfann-2.1.0~beta~dfsg/debian/libfann2.install000066400000000000000000000000221140315177100215160ustar00rootroot00000000000000usr/lib/lib*.so.* libfann-2.1.0~beta~dfsg/debian/rules000077500000000000000000000053161140315177100175260ustar00rootroot00000000000000#!/usr/bin/make -f # -*- makefile -*- # Sample debian/rules that uses debhelper. # GNU copyright 1997 to 1999 by Joey Hess. # Uncomment this to turn on verbose mode. #export DH_VERBOSE=1 # These are used for cross-compiling and for saving the configure script # from having to guess our platform (since we know it already) DEB_HOST_GNU_TYPE ?= $(shell dpkg-architecture -qDEB_HOST_GNU_TYPE) DEB_BUILD_GNU_TYPE ?= $(shell dpkg-architecture -qDEB_BUILD_GNU_TYPE) CFLAGS = -Wall -g ifneq (,$(findstring noopt,$(DEB_BUILD_OPTIONS))) CFLAGS += -O0 else CFLAGS += -O2 endif ifeq (,$(findstring nostrip,$(DEB_BUILD_OPTIONS))) INSTALL_PROGRAM += -s endif # shared library versions, option 1 version=2.0.0 major=2 # option 2, assuming the library is created as src/.libs/libfoo.so.2.0.5 or so #version=`ls src/.libs/lib*.so.* | \ # awk '{if (match($$0,/[0-9]+\.[0-9]+\.[0-9]+$$/)) print substr($$0,RSTART)}'` #major=`ls src/.libs/lib*.so.* | \ # awk '{if (match($$0,/\.so\.[0-9]+$$/)) print substr($$0,RSTART+4)}'` config.status: configure dh_testdir CFLAGS='$(CFLAGS)' ./configure --host=$(DEB_HOST_GNU_TYPE) --build=$(DEB_BUILD_GNU_TYPE) --prefix=/usr --mandir=\$${prefix}/share/man --infodir=\$${prefix}/share/info build: build-stamp build-stamp: config.status dh_testdir # Add here commands to compile the package. $(MAKE) cd python && make build touch build-stamp clean: dh_testdir dh_testroot rm -f build-stamp # Add here commands to clean up after the build process. -$(MAKE) distclean -(cd python; $(MAKE) clean) ifneq "$(wildcard /usr/share/misc/config.sub)" "" cp -f /usr/share/misc/config.sub config.sub endif ifneq "$(wildcard /usr/share/misc/config.guess)" "" cp -f /usr/share/misc/config.guess config.guess endif dh_clean install: build dh_testdir dh_testroot dh_clean -k dh_installdirs # Add here commands to install the package into debian/tmp $(MAKE) install DESTDIR=$(CURDIR)/debian/tmp ( cd python && make install ROOT=$(CURDIR)/debian/tmp ) # Build architecture-independent files here. binary-indep: build install # We have nothing to do by default. # Build architecture-dependent files here. binary-arch: build install dh_testdir dh_testroot dh_installchangelogs ChangeLog dh_installdocs dh_installexamples # dh_installmenu # dh_installdebconf # dh_installlogrotate # dh_installemacsen # dh_installpam # dh_installmime # dh_installinit # dh_installcron # dh_installinfo dh_installman dh_install --sourcedir=debian/tmp --list-missing dh_link dh_strip dh_compress dh_fixperms # dh_perl dh_makeshlibs -V dh_python dh_installdeb dh_shlibdeps -L libfann dh_gencontrol dh_md5sums dh_builddeb binary: binary-indep binary-arch .PHONY: build clean binary-indep binary-arch binary install libfann-2.1.0~beta~dfsg/depcomp000077500000000000000000000330521140315177100165770ustar00rootroot00000000000000#! 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However on # icc -MD -MF foo.d -c -o sub/foo.o sub/foo.c # ICC 7.0 will fill foo.d with something like # foo.o: sub/foo.c # foo.o: sub/foo.h # which is wrong. We want: # sub/foo.o: sub/foo.c # sub/foo.o: sub/foo.h # sub/foo.c: # sub/foo.h: # ICC 7.1 will output # foo.o: sub/foo.c sub/foo.h # and will wrap long lines using \ : # foo.o: sub/foo.c ... \ # sub/foo.h ... \ # ... "$@" -MD -MF "$tmpdepfile" stat=$? if test $stat -eq 0; then : else rm -f "$tmpdepfile" exit $stat fi rm -f "$depfile" # Each line is of the form `foo.o: dependent.h', # or `foo.o: dep1.h dep2.h \', or ` dep3.h dep4.h \'. # Do two passes, one to just change these to # `$object: dependent.h' and one to simply `dependent.h:'. sed "s,^[^:]*:,$object :," < "$tmpdepfile" > "$depfile" # Some versions of the HPUX 10.20 sed can't process this invocation # correctly. 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IFS=" " for arg do case $arg in -o) shift ;; $object) shift ;; *) set fnord "$@" "$arg" shift # fnord shift # $arg ;; esac done "$@" -E | sed -n '/^# [0-9][0-9]* "\([^"]*\)".*/ s:: \1 \\:p' | sed '$ s: \\$::' > "$tmpdepfile" rm -f "$depfile" echo "$object : \\" > "$depfile" cat < "$tmpdepfile" >> "$depfile" sed < "$tmpdepfile" '/^$/d;s/^ //;s/ \\$//;s/$/ :/' >> "$depfile" rm -f "$tmpdepfile" ;; msvisualcpp) # Important note: in order to support this mode, a compiler *must* # always write the preprocessed file to stdout, regardless of -o, # because we must use -o when running libtool. "$@" || exit $? IFS=" " for arg do case "$arg" in "-Gm"|"/Gm"|"-Gi"|"/Gi"|"-ZI"|"/ZI") set fnord "$@" shift shift ;; *) set fnord "$@" "$arg" shift shift ;; esac done "$@" -E | sed -n '/^#line [0-9][0-9]* "\([^"]*\)"/ s::echo "`cygpath -u \\"\1\\"`":p' | sort | uniq > "$tmpdepfile" rm -f "$depfile" echo "$object : \\" > "$depfile" . "$tmpdepfile" | sed 's% %\\ %g' | sed -n '/^\(.*\)$/ s:: \1 \\:p' >> "$depfile" echo " " >> "$depfile" . "$tmpdepfile" | sed 's% %\\ %g' | sed -n '/^\(.*\)$/ s::\1\::p' >> "$depfile" rm -f "$tmpdepfile" ;; none) exec "$@" ;; *) echo "Unknown depmode $depmode" 1>&2 exit 1 ;; esac exit 0 libfann-2.1.0~beta~dfsg/doc/000077500000000000000000000000001140315177100157645ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/doc/advancedusage.txt000066400000000000000000000215051140315177100213220ustar00rootroot00000000000000Section: Advanced Usage This section describes some of the low-level functions and how they can be used to obtain more control of the fann library. For a full list of functions, please see the Reference Manual, which has an explanation of all the fann library functions. Also feel free to take a look at the source code. This section describes different procedures, which can help to get more power out of the fann library. Topic: Adjusting Parameters Several different parameters exists in an ANN, these parameters are given defaults in the fann library, but they can be adjusted at runtime. There is no sense in adjusting most of these parameters after the training, since it would invalidate the training, but it does make sense to adjust some of the parameters during training, as will be described in Training and Testing. Generally speaking, these are parameters that should be adjusted before training. The training algorithm is one of the most important parameters. The default training algorithm is FANN_TRAIN_RPROP, but this may not always be the best choice. See for more information about the different training algorithms. The training algorithms have several different parameters which can be set. For FANN_TRAIN_INCREMENTAL, FANN_TRAIN_BATCH, FANN_TRAIN_QUICKPROP the most important parameter is the learning rate, but unfortunately this is also a parameter which is hard to find a reasonable default for. I (SN) have several times ended up using 0.7, but it is a good idea to test several different learning rates when training a network. It is also worth noting that the activation function has a profound effect on the optimal learning rate [Thimm and Fiesler, 1997]. The learning rate can be set by using the function. The initial weights are random values between -0.1 and 0.1, if other weights are preferred, the weights can be altered by the or function. In [Thimm and Fiesler, High-Order and Multilayer Perceptron Initialization, 1997], Thimm and Fiesler state that, "An (sic) fixed weight variance of 0.2, which corresponds to a weight range of [-0.77, 0.77], gave the best mean performance for all the applications tested in this study. This performance is similar or better as compared to those of the other weight initialization methods." The standard activation function is the sigmoid activation function, but it is also possible to use other functions. A list of the currently available activation functions is available in the section. The activation function can be set for a single neuron using the function and for a group of neurons by the and the functions. Likewise the steepness parameter used in the activation function can be adjusted with the function. FANN distinguishes between the hidden layers and the output layer, to allow more flexibility. This is especially a good idea for users wanting discrete output from the network, since they can set the activation function for the output to threshold. Please note, that it is not possible to train a network when using the threshold activation function, due to the fact, that it is not differentiable. Topic: Network Design When creating a network it is necessary to define how many layers, neurons and connections it should have. If the network become too large, the ANN will have difficulties learning and when it does learn it will tend to over-fit resulting in poor generalization. If the network becomes too small, it will not be able to represent the rules needed to learn the problem and it will never gain a sufficiently low error rate. The number of hidden layers is also important. Generally speaking, if the problem is simple it is often enough to have one or two hidden layers, but as the problems get more complex, so does the need for more layers. One way of getting a large network which is not too complex, is to adjust the connection_rate parameter given to . If this parameter is 0.5, the constructed network will have the same amount of neurons, but only half as many connections. It is difficult to say which problems this approach is useful for, but if you have a problem which can be solved by a fully connected network, then it would be a good idea to see if it still works after removing half the connections. Version 2.0 of the FANN library introduces a new way of creating ANNs, where the neurons are added one by one to the ANN, until it has build an optimal ANN. This approach uses the Cascade2 algorithm and is described in detail in the section. Topic: Understanding the Error Value The mean square error value is calculated while the ANN is being trained. Some functions are implemented, to use and manipulate this error value. The function returns the error value and the resets the error value. The following explains how the mean square error value is calculated, to give an idea of the value's ability to reveal the quality of the training. If d is the desired output of an output neuron and y is the actual output of the neuron, the square error is (d *-* y) squared. If two output neurons exists, then the mean square error for these two neurons is the average of the two square errors. When training with the function, an error value is printed. This error value is the mean square error for all the training data. Meaning that it is the average of all the square errors in each of the training pairs. Topic: Training and Testing Normally it will be sufficient to use the training function, but sometimes you want to have more control and you will have to write a custom training loop. This could be because you would like another stop criteria, or because you would like to adjust some of the parameters during training. Another stop criteria than the value of the combined mean square error could be that each of the training pairs should have a mean square error lower than a given value. A highly simplified version of the function: (code) struct fann_train_data *data = fann_read_train_from_file(filename); for(i = 1 ; i <= max_epochs ; i++) { error = fann_train_epoch(ann, data); if ( error < desired_error ) { break; } } fann_destroy_train(data); (end) This piece of code introduces the function, which trains the ANN for one epoch and also returns the mean square error. The structure is also introduced, this structure is a container for the training data. The structure can be used to train the ANN, but it can also be used to test the ANN with data which it has not been trained with. Test all of the data in a file and calculates the mean square error: (code) struct fann_train_data *data = fann_read_train_from_file(filename); fann_reset_MSE(ann); fann_test_data(ann, data); printf("Mean Square Error: %f\n", fann_get_MSE(ann)); fann_destroy_train(data); (end) This piece of code introduces another useful function: function, which updates the mean square error. Topic: Avoid Over-Fitting With the knowledge of how to train and test an ANN, a new approach to training can be introduced. If too much training is applied to a set of data, the ANN will eventually over-fit, meaning that it will be fitted precisely to this set of training data and thereby loosing generalization. It is often a good idea to test, how good an ANN performs on data that it has not seen before. Testing with data not seen before, can be done while training, to see how much training is required in order to perform well without over-fitting. The testing can either be done by hand, or an automatic test can be applied, which stops the training when the mean square error of the test data is not improving anymore. Topic: Adjusting Parameters During Training If a very low mean square error is required it can sometimes be a good idea to gradually decrease the learning rate during training, in order to make the adjusting of weights more subtle. If more precision is required, it might also be a good idea to use double precision floats instead of standard floats. The threshold activation function is faster than the sigmoid function, but since it is not possible to train with this function, you may wish to consider an alternate approach. While training the ANN you could slightly increase the steepness parameter of the sigmoid function. This would make the sigmoid function more steep and make it look more like the threshold function. After this training session you could set the activation function to the threshold function and the ANN would work with this activation function. This approach will not work on all kinds of problems, but has been successfully tested on the XOR function. libfann-2.1.0~beta~dfsg/doc/fixedpointusage.txt000066400000000000000000000135261140315177100217320ustar00rootroot00000000000000Section: Fixed Point Usage It is possible to run the ANN with fixed point numbers (internally represented as integers). This option is only intended for use on computers with no floating point processor, for example, the iPAQ or other embedded devices. Topic: Training a Fixed Point ANN The ANN cannot be trained in fixed point, which is why the training part is basically the same as for floating point numbers. The only difference is that you should save the ANN as fixed point. This is done by the function. This function saves a fixed point version of the ANN, but it also does some analysis, in order to find out where the decimal point should be. The result of this analysis is returned from the function. The decimal point returned from the function is an indicator of, how many bits is used for the fractional part of the fixed point numbers. If this number is negative, there will most likely be integer overflow when running the library with fixed point numbers and this should be avoided. Furthermore, if the decimal point is too low (e.g. lower than 5), it is probably not a good idea to use the fixed point version. Please note, that the inputs to networks that should be used in fixed point should be between -1 and 1. An example of a program written to support training in both fixed point and floating point numbers: (code) #include #include #include #include "fann.h" int main() { fann_type *calc_out; unsigned int i; int ret = 0; struct fann *ann; struct fann_train_data *data; printf("Creating network.\n"); #ifdef FIXEDFANN ann = fann_create_from_file("xor_fixed.net"); #else ann = fann_create_from_file("xor_float.net"); #endif if(!ann) { printf("Error creating ann --- ABORTING.\n"); return 0; } printf("Testing network.\n"); #ifdef FIXEDFANN data = fann_read_train_from_file("xor_fixed.data"); #else data = fann_read_train_from_file("xor.data"); #endif for(i = 0; i < data->num_data; i++) { fann_reset_MSE(ann); calc_out = fann_test(ann, data->input[i], data->output[i]); #ifdef FIXEDFANN printf("XOR test (%d, %d) -> %d, should be %d, difference=%f\n", data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0], (float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann)); if((float) fann_abs(*calc_out - data->output[i][0]) / fann_get_multiplier(ann) > 0.1) { printf("Test failed\n"); ret = -1; } #else printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n", data->input[i][0], data->input[i][1], *calc_out, data->output[i][0], (float) fann_abs(*calc_out - data->output[i][0])); #endif } printf("Cleaning up.\n"); fann_destroy_train(data); fann_destroy(ann); return ret; } (end) Topic: Running a Fixed Point ANN Running a fixed point ANN is done much like running an ordinary ANN. The difference is that the inputs and outputs should be in fixed point representation. Furthermore the inputs should be restricted to be between -multiplier and multiplier to avoid integer overflow, where the multiplier is the value returned from . This multiplier is the value that a floating point number should be multiplied with, in order to be a fixed point number, likewise the output of the ANN should be divided by this multiplier in order to be between zero and one. To help using fixed point numbers, another function is provided. which returns the decimal point. The decimal point is the position dividing the integer and fractional part of the fixed point number and is useful for doing operations on the fixed point inputs and outputs. An example of a program written to support both fixed point and floating point numbers: (code) #include #include #include #include "fann.h" int main() { fann_type *calc_out; unsigned int i; int ret = 0; struct fann *ann; struct fann_train_data *data; printf("Creating network.\n"); #ifdef FIXEDFANN ann = fann_create_from_file("xor_fixed.net"); #else ann = fann_create_from_file("xor_float.net"); #endif if(!ann){ printf("Error creating ann --- ABORTING.\n"); return 0; } printf("Testing network.\n"); #ifdef FIXEDFANN data = fann_read_train_from_file("xor_fixed.data"); #else data = fann_read_train_from_file("xor.data"); #endif for(i = 0; i < data->num_data; i++){ fann_reset_MSE(ann); calc_out = fann_test(ann, data->input[i], data->output[i]); #ifdef FIXEDFANN printf("XOR test (%d, %d) -> %d, should be %d, difference=%f\n", data->input[i][0], data->input[i][1], *calc_out, data->output[i][0], (float)fann_abs(*calc_out - data->output[i][0])/fann_get_multiplier(ann)); if((float)fann_abs(*calc_out - data->output[i][0])/fann_get_multiplier(ann) > 0.1){ printf("Test failed\n"); ret = -1; } #else printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n", data->input[i][0], data->input[i][1], *calc_out, data->output[i][0], (float)fann_abs(*calc_out - data->output[i][0])); #endif } printf("Cleaning up.\n"); fann_destroy_train(data); fann_destroy(ann); return ret; } (end) Topic: Precision of a Fixed Point ANN The fixed point ANN is not as precise as a floating point ANN, furthermore it approximates the sigmoid function by a stepwise linear function. Therefore, it is always a good idea to test the fixed point ANN after loading it from a file. This can be done by calculating the mean square error as described earlier. There is, however, one problem with this approach: The training data stored in the file is in floating point format. Therefore, it is possible to save this data in a fixed point format from within the floating point program. This is done by the function . Please note that this function takes the decimal point as an argument, meaning that the decimal point should be calculated first by using the function. libfann-2.1.0~beta~dfsg/doc/gettingstarted.txt000066400000000000000000000054141140315177100215610ustar00rootroot00000000000000Section: Getting Started An ANN is normally run in two different modes, a training mode and an execution mode. Although it is possible to do this in the same program, using different programs is recommended. There are several reasons to why it is usually a good idea to write the training and execution in two different programs, but the most obvious is the fact that a typical ANN system is only trained once, while it is executed many times. Topic: Training The following is a simple program which trains an ANN with a data set and then saves the ANN to a file. Simple training example: (code) #include "fann.h" int main() { const unsigned int num_input = 2; const unsigned int num_output = 1; const unsigned int num_layers = 3; const unsigned int num_neurons_hidden = 3; const float desired_error = (const float) 0.001; const unsigned int max_epochs = 500000; const unsigned int epochs_between_reports = 1000; struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output); fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC); fann_train_on_file(ann, "xor.data", max_epochs, epochs_between_reports, desired_error); fann_save(ann, "xor_float.net"); fann_destroy(ann); return 0; } (end) The file xor.data, used to train the xor function: (code) 4 2 1 -1 -1 -1 -1 1 1 1 -1 1 1 1 -1 (end) The first line consists of three numbers: The first is the number of training pairs in the file, the second is the number of inputs and the third is the number of outputs. The rest of the file is the actual training data, consisting of one line with inputs, one with outputs etc. This example introduces several fundamental functions, namely , , , and . Topic: Execution The following example shows a simple program which executes a single input on the ANN. The program introduces two new functions ( and ) which were not used in the training procedure, as well as the type. Simple execution example: (code) #include #include "floatfann.h" int main() { fann_type *calc_out; fann_type input[2]; struct fann *ann = fann_create_from_file("xor_float.net"); input[0] = -1; input[1] = 1; calc_out = fann_run(ann, input); printf("xor test (%f,%f) -> %f\n", input[0], input[1], calc_out[0]); fann_destroy(ann); return 0; } (end) Topic: Getting Help If after reading the documentation you are still having problems, or have a question that is not covered in the documentation, please consult the fann-general mailing list. Archives and subscription information are available at http://lists.sourceforge.net/mailman/listinfo/fann-general. libfann-2.1.0~beta~dfsg/doc/installation.txt000066400000000000000000000164531140315177100212370ustar00rootroot00000000000000Section: Installing FANN Copies of FANN can be obtained from our SourceForge page, located at http://fann.sf.net You can currently get FANN as source code (fann-*.tar.bz2), Debian packages (fann-*.deb), or RPM's (fann-*.rpm). FANN is available under the terms of the GNU Lesser General Public License. FANN bindings to several other programming languages is also available from: http://fann.sf.net Group: C Library Installation Topic: Windows Installation FANN >= 1.1.0 includes a Microsoft Visual C++ 6.0 project file, which can be used to compile FANN for Windows. To build the library and examples with MSVC++ 6.0. First, navigate to the MicrosoftVisualC++6.0 directory in the FANN distribution and open the all.dsw workspace. In the Visual Studio menu bar, choose "Build" -> "Batch build...", select the project configurations that you would like to build (by default, all are selected), and press "rebuild all" When the build process is complete, the library and examples can be found in the MicrosoftVisualC++6.0\Debug directorie and the examples are automatically copied into the examples where they are supposed to be run. If all of the examples are not copied to the correct location, please compile them separately. Topic: RPM Installation RPMs are a simple way to manage packages, and is used on many common Linux distributions such as Red Hat, Mandrake, and SuSE. Two separate packages exist; fann, the runtime library, and fann-devel, the development library and header files. After downloading FANN, simply run (as root) the following command: rpm -ivh $PATH_TO_RPM Topic: DEB Installation DEBs are packages for the Debian Linux distribution. Two separate packages exists libfann1 and libfann1-dev, where libfann1 is the runtime library and libfann1-dev is the development library. Fann is included in the stable distribution of Debian, so Debian users can simply run (as root) the following command: apt-get install libfann1 libfann1-dev. After downloading the FANN DEB package, simply run (as root) the following command: dpkg -i $PATH_TO_DEB Topic: Compiling from source Compiling FANN from source code entails the standard GNU autotools technique. First, configure the package as you want it by typing (in the FANN directory), ./configure If you need help choosing the options you would like to use, try ./configure --help Next, you have to actually compile the library. To do this, simply type make Finally, to install the library, type make install. Odds are you will have to be root to install, so you may need to su to root before installing. Please remember to log out of the root account immediately after make install finishes. Some people have experienced problems with compiling the library with some compilers, especially windows compilers which can not use GNU autotools. Please look through the help forum and the mailing list archives for info on how these problems was solved. If you do not find any information here, feel free to ask questions. Group: Python Bindings These functions allow you to interact with the FANN library from Python. This extension requires the FANN library, version 1.1.0 or later. This python binding is provided by Vincenzo Di Massa (hawk.it@tiscalinet.it) and updated by Gil Megidish (gil@megidish.net) Topic: Python Install Make sure to make and install the fann library first. Make sure that you have swig and python development files installed. Perhaps change the include directory of python. Then run 'make' to compile in the python directory. Copy the generated _fann.so and fann.py files to python modules or into working directory. After the install, just import fann and all the C functions will be available to your python code. Group: PHP Extension These functions allow you to interact with the FANN library from PHP. This extension requires the FANN library, version 1.1.0 or later. This extension supports the same activation functions as the library, a list of which can be found in the reference manual for the C library. Topic: Installation Using PEAR The easiest way to install FANN-PHP is to use PEAR- if you have a fairly recent version of PHP installed, simply run pear install fann. Note that if there are no stable releases of FANN-PHP, you may have to specify the URI for the package, which can be obtained from http://pecl.php.net/fann. If you cannot install FANN-PHP using PEAR, you can try following the (obsolete) instructions at http://www.cs.utexas.edu/users/UTCS/online-docs/php/pear/faq.install-pecl.html. If you use one of these methods, you'll need to either dl('fann.so') or add it to your php.ini If you use either of the above methods, you will probably need to be root. Topic: Compiling into PHP Please only use this method if using the methods outlined in Using PEAR have failed. If you wish to compile FANN-PHP into PHP itself, you can. First, uncompress the package into the ext subdirectory of your copy of the PHP source code, and rename the directory to ext/fann (from fann-x.x.x). Next, you must rebuild the configure script- to do so, run ./buildconf from the PHP source directory. From here on, the procedure is similar to when you built PHP originally- run ./configure with your desired options, plus --with-fann. Finally, run make and make install. Note that you will probably need to be root for make install to work. This method may require flex and bison to work- more information can be obtained at http://www.php.net/anoncvs.php Group: Delphi Bindings These functions allow you to interact with the FANN library from Delphi. This extension requires the FANN library, version 1.2.0 or later. This extension can be downloaded from the FANN library download section. This Delphi binding is provided by Maurício Pereira Maia (mauricio@uaisol.com.br) Topic: Delphi Install Make sure to make and install the fann library first. Put the file fannfloat.dll in your PATH. (If you want to use the fixed version you should define FIXEDFANN on fann.pas). Include fann.pas in your project and in your unit uses clause, and have fun! See the XorConsole sample for more details. Topic: TFannNetwork TFannNetwork is a Delphi component that encapsulates the Fann Library. You do not have to install TFannNetwork to use Fann on Delphi, but it will make the library more Delphi friendly. Currently it has only a small subset of all the library functions, but I hope that will change in the near future. To install TFannNetwork you should follow all the previous steps and copy the FannNetwork.pas and Fann.dcr to your Delphi Library PATH. Choose Component/Install Component. In the Unit file name field, click on Browse and point to the fannnetwork.pas file. By default Delphi will install in the Borland User Components package, it might be changed using Package file name field or Into new package page. Click on Ok. A confirmation dialog will be shown asking if you want to build the package. Click on Yes. You have just installed TFannNetwork, now close the package window (Don't forget to put Yes when it ask if you want to save the package). See the FannNetwork.pas file or the Xor Sample. Topic: Known Problems If you are getting in trouble to use your own compiled FANN DLL with Delphi that might be because of the C++ naming mangle that changes between C++ compilers. You will need to make a TDUMP on your dll and changes all the name directives on fann.pas to the correct function names on your dll. libfann-2.1.0~beta~dfsg/doc/theory.txt000066400000000000000000000106041140315177100200400ustar00rootroot00000000000000Section: Neural Network Theory This section will briefly explain the theory of neural networks (hereafter known as NN) and artificial neural networks (hereafter known as ANN). For a more in depth explanation of these concepts please consult the literature; [Hassoun, 1995] has good coverage of most concepts of ANN and [Hertz et al., 1991] describes the mathematics of ANN very thoroughly, while [Anderson, 1995] has a more psychological and physiological approach to NN and ANN. For the pragmatic I (SN) could recommend [Tettamanzi and Tomassini, 2001], which has a short and easily understandable introduction to NN and ANN. Topic: Neural Networks The human brain is a highly complicated machine capable of solving very complex problems. Although we have a good understanding of some of the basic operations that drive the brain, we are still far from understanding everything there is to know about the brain. In order to understand ANN, you will need to have a basic knowledge of how the internals of the brain work. The brain is part of the central nervous system and consists of a very large NN. The NN is actually quite complicated, so the following discussion shall be relegated to the details needed to understand ANN, in order to simplify the explanation. The NN is a network consisting of connected neurons. The center of the neuron is called the nucleus. The nucleus is connected to other nucleuses by means of the dendrites and the axon. This connection is called a synaptic connection. The neuron can fire electric pulses through its synaptic connections, which is received at the dendrites of other neurons. When a neuron receives enough electric pulses through its dendrites, it activates and fires a pulse through its axon, which is then received by other neurons. In this way information can propagate through the NN. The synaptic connections change throughout the lifetime of a neuron and the amount of incoming pulses needed to activate a neuron (the threshold) also change. This behavior allows the NN to learn. The human brain consists of around 10^11 neurons which are highly interconnected with around 10^15 connections [Tettamanzi and Tomassini, 2001]. These neurons activates in parallel as an effect to internal and external sources. The brain is connected to the rest of the nervous system, which allows it to receive information by means of the five senses and also allows it to control the muscles. Topic: Artificial Neural Networks It is not possible (at the moment) to make an artificial brain, but it is possible to make simplified artificial neurons and artificial neural networks. These ANNs can be made in many different ways and can try to mimic the brain in many different ways. ANNs are not intelligent, but they are good for recognizing patterns and making simple rules for complex problems. They also have excellent training capabilities which is why they are often used in artificial intelligence research. ANNs are good at generalizing from a set of training data. E.g. this means an ANN given data about a set of animals connected to a fact telling if they are mammals or not, is able to predict whether an animal outside the original set is a mammal from its data. This is a very desirable feature of ANNs, because you do not need to know the characteristics defining a mammal, the ANN will find out by itself. Topic: Training an ANN When training an ANN with a set of input and output data, we wish to adjust the weights in the ANN, to make the ANN give the same outputs as seen in the training data. On the other hand, we do not want to make the ANN too specific, making it give precise results for the training data, but incorrect results for all other data. When this happens, we say that the ANN has been over-fitted. The training process can be seen as an optimization problem, where we wish to minimize the mean square error of the entire set of training data. This problem can be solved in many different ways, ranging from standard optimization heuristics like simulated annealing, through more special optimization techniques like genetic algorithms to specialized gradient descent algorithms like backpropagation. The most used algorithm is the backpropagation algorithm, but this algorithm has some limitations concerning, the extent of adjustment to the weights in each iteration. This problem has been solved in more advanced algorithms like RPROP [Riedmiller and Braun, 1993] and quickprop [Fahlman, 1988]. libfann-2.1.0~beta~dfsg/examples/000077500000000000000000000000001140315177100170355ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/examples/Makefile000066400000000000000000000070221140315177100204760ustar00rootroot00000000000000# This makefile is on purpose not made with configure, to show how to use the library # The make file requires that the fann library is installed (see ../README) GCC=gcc TARGETS = xor_train xor_test xor_test_fixed simple_train steepness_train simple_test robot mushroom cascade_train scaling_test scaling_train DEBUG_TARGETS = xor_train_debug xor_test_debug xor_test_fixed_debug cascade_train_debug all: $(TARGETS) %: %.c Makefile $(GCC) -O3 $< -o $@ -lm -lfann %_fixed: %.c Makefile $(GCC) -O3 -DFIXEDFANN $< -o $@ -lm -lfixedfann clean: rm -f $(TARGETS) $(DEBUG_TARGETS) xor_fixed.data *.net *~ *.obj *.exe *.tds noscale.txt withscale.txt scale_test_results.txt runtest: $(TARGETS) @echo @echo Training network ./xor_train @echo @echo Testing network with floats ./xor_test @echo @echo Testing network with fixed points ./xor_test_fixed #below this line is only for debugging the fann library rundebugtest: $(DEBUG_TARGETS) @echo @echo Training network ./xor_train_debug @echo @echo Testing network with floats ./xor_test_debug @echo @echo Testing network with fixed points ./xor_test_fixed_debug runcompliancetest: $(DEBUG_TARGETS) ./xor_train_debug @echo @echo Testing network with floats @cp xor_float.net xor_float_new.net @cp xor_float_backup.net xor_float.net @echo RUN @./xor_test_debug | grep XOR @echo CAT @cat xor_float_backup.out #used to compare result when scale used and not used runscaletest: scaling_train scaling_test echo Without scaling > noscale.txt ./scaling_train >> noscale.txt ./scaling_test >> noscale.txt echo With scaling > withscale.txt ./scaling_train scale >> withscale.txt ./scaling_test scale >> withscale.txt pr -m -t -T -w 150 noscale.txt withscale.txt > scale_test_results.txt @echo See scale_test_results.txt for results of scaling test #compiletest is used to test whether the library will compile easily in other compilers compiletest: gcc -O3 -ggdb -DDEBUG -Wall -Wformat-security -Wfloat-equal -Wshadow -Wpointer-arith -Wcast-qual -Wsign-compare -pedantic -ansi -I../src/ -I../src/include/ ../src/floatfann.c xor_train.c -o xor_train -lm gcc -O3 -ggdb -DDEBUG -Wall -Wformat-security -Wfloat-equal -Wshadow -Wpointer-arith -Wcast-qual -Wsign-compare -pedantic -ansi -DFIXEDFANN -I../src/ -I../src/include/ ../src/fixedfann.c xor_test.c -o xor_test -lm g++ -O3 -ggdb -DDEBUG -Wall -Wformat-security -Wfloat-equal -Wpointer-arith -Wcast-qual -Wsign-compare -pedantic -ansi -I../src/ -I../src/include/ ../src/floatfann.c xor_train.c -o xor_train -lm quickcompiletest: gcc -O -ggdb -DDEBUG -Wall -Wformat-security -Wfloat-equal -Wshadow -Wpointer-arith -Wcast-qual -Wsign-compare -pedantic -ansi -I../src/ -I../src/include/ ../src/floatfann.c ../examples/xor_train.c -o ../examples/xor_train -lm debug: $(DEBUG_TARGETS) %_debug: %.c Makefile ../src/*c ../src/include/*h $(GCC) -ggdb -DDEBUG -Wall -ansi -I../src/ -I../src/include/ ../src/floatfann.c $< -o $@ -lm %_fixed_debug: %.c Makefile ../src/*c ../src/include/*h $(GCC) -ggdb -DDEBUG -Wall -ansi -DFIXEDFANN -I../src/ -I../src/include/ ../src/fixedfann.c $< -o $@ -lm rundebug: $(DEBUG_TARGETS) @echo @echo Training network valgrind --leak-check=yes --show-reachable=yes --leak-resolution=high --db-attach=yes ./xor_train_debug @echo @echo Testing network with floats valgrind --leak-check=yes --show-reachable=yes --leak-resolution=high --db-attach=yes ./xor_test_debug @echo @echo Testing network with fixed points valgrind --leak-check=yes --show-reachable=yes --leak-resolution=high --db-attach=yes ./xor_test_fixed_debug libfann-2.1.0~beta~dfsg/examples/cascade_train.c000066400000000000000000000070671140315177100217730ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include "fann.h" int main() { struct fann *ann; struct fann_train_data *train_data, *test_data; const float desired_error = (const float) 0.001; unsigned int max_neurons = 30; unsigned int neurons_between_reports = 1; fann_type *steepnesses = NULL; printf("Reading data.\n"); train_data = fann_read_train_from_file("../benchmarks/datasets/two-spiral.train"); test_data = fann_read_train_from_file("../benchmarks/datasets/two-spiral.test"); train_data = fann_read_train_from_file("../benchmarks/datasets/parity13.train"); test_data = fann_read_train_from_file("../benchmarks/datasets/parity13.test"); train_data = fann_read_train_from_file("../benchmarks/datasets/building.train"); test_data = fann_read_train_from_file("../benchmarks/datasets/building.test"); fann_scale_train_data(train_data, -1, 1); fann_scale_train_data(test_data, -1, 1); printf("Creating network.\n"); ann = fann_create_shortcut(2, fann_num_input_train_data(train_data), fann_num_output_train_data(train_data)); fann_set_learning_rate(ann, 0.1); fann_set_quickprop_decay(ann, 0.0); fann_set_quickprop_mu(ann, 2.0); fann_set_cascade_weight_multiplier(ann, 1); fann_set_cascade_max_out_epochs(ann, 150); fann_set_bit_fail_limit(ann, 0.35); fann_set_activation_steepness_output(ann, 1); fann_set_training_algorithm(ann, FANN_TRAIN_RPROP); fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(ann, FANN_LINEAR_PIECE); fann_set_activation_function_output(ann, FANN_LINEAR_PIECE_SYMMETRIC); fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(ann, FANN_LINEAR); fann_set_train_error_function(ann, FANN_ERRORFUNC_TANH); fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR); fann_randomize_weights(ann, 0.1, 0.1); /* fann_set_cascade_weight_multiplier(ann, 0.4); fann_set_cascade_candidate_limit(ann, 1000.0); */ fann_set_cascade_output_change_fraction(ann, 0.01); fann_set_cascade_candidate_change_fraction(ann, 0.01); /* steepnesses = (fann_type *)calloc(1, sizeof(fann_type)); steepnesses[0] = (fann_type)1; fann_set_cascade_activation_steepnesses(ann, steepnesses, 1); */ fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT); fann_print_parameters(ann); printf("Training network.\n"); fann_cascadetrain_on_data(ann, train_data, max_neurons, neurons_between_reports, desired_error); fann_print_connections(ann); printf("\nTrain error: %f, Test error: %f\n\n", fann_test_data(ann, train_data), fann_test_data(ann, test_data)); printf("Saving network.\n"); fann_save(ann, "two_spiral.net"); printf("Cleaning up.\n"); fann_destroy_train(train_data); fann_destroy_train(test_data); fann_destroy(ann); return 0; } libfann-2.1.0~beta~dfsg/examples/momentums.c000066400000000000000000000035521140315177100212320ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA02111-1307USA */ #include #include "fann.h" int main() { const unsigned int num_layers = 3; const unsigned int num_neurons_hidden = 96; const float desired_error = (const float) 0.001; struct fann *ann; struct fann_train_data *train_data, *test_data; float momentum; train_data = fann_read_train_from_file("../benchmarks/datasets/robot.train"); test_data = fann_read_train_from_file("../benchmarks/datasets/robot.test"); for ( momentum = 0.0; momentum < 0.7; momentum += 0.1 ) { printf("============= momentum = %f =============\n", momentum); ann = fann_create_standard(num_layers, train_data->num_input, num_neurons_hidden, train_data->num_output); fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL); fann_set_learning_momentum(ann, momentum); fann_train_on_data(ann, train_data, 2000, 500, desired_error); printf("MSE error on train data: %f\n", fann_test_data(ann, train_data)); printf("MSE error on test data : %f\n", fann_test_data(ann, test_data)); fann_destroy(ann); } fann_destroy_train(train_data); fann_destroy_train(test_data); return 0; } libfann-2.1.0~beta~dfsg/examples/mushroom.c000066400000000000000000000043101140315177100210500ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include "fann.h" int main() { const unsigned int num_layers = 3; const unsigned int num_neurons_hidden = 32; const float desired_error = (const float) 0.0001; const unsigned int max_epochs = 300; const unsigned int epochs_between_reports = 10; struct fann *ann; struct fann_train_data *train_data, *test_data; unsigned int i = 0; printf("Creating network.\n"); train_data = fann_read_train_from_file("../benchmarks/datasets/mushroom.train"); ann = fann_create_standard(num_layers, train_data->num_input, num_neurons_hidden, train_data->num_output); printf("Training network.\n"); fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC_STEPWISE); fann_set_activation_function_output(ann, FANN_SIGMOID_STEPWISE); /*fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL); */ fann_train_on_data(ann, train_data, max_epochs, epochs_between_reports, desired_error); printf("Testing network.\n"); test_data = fann_read_train_from_file("../benchmarks/datasets/mushroom.test"); fann_reset_MSE(ann); for(i = 0; i < fann_length_train_data(test_data); i++) { fann_test(ann, test_data->input[i], test_data->output[i]); } printf("MSE error on test data: %f\n", fann_get_MSE(ann)); printf("Saving network.\n"); fann_save(ann, "mushroom_float.net"); printf("Cleaning up.\n"); fann_destroy_train(train_data); fann_destroy_train(test_data); fann_destroy(ann); return 0; } libfann-2.1.0~beta~dfsg/examples/robot.c000066400000000000000000000037361140315177100203370ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include "fann.h" int main() { const unsigned int num_layers = 3; const unsigned int num_neurons_hidden = 96; const float desired_error = (const float) 0.001; struct fann *ann; struct fann_train_data *train_data, *test_data; unsigned int i = 0; printf("Creating network.\n"); train_data = fann_read_train_from_file("../benchmarks/datasets/robot.train"); ann = fann_create_standard(num_layers, train_data->num_input, num_neurons_hidden, train_data->num_output); printf("Training network.\n"); fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL); fann_set_learning_momentum(ann, 0.4); fann_train_on_data(ann, train_data, 3000, 10, desired_error); printf("Testing network.\n"); test_data = fann_read_train_from_file("../benchmarks/datasets/robot.test"); fann_reset_MSE(ann); for(i = 0; i < fann_length_train_data(test_data); i++) { fann_test(ann, test_data->input[i], test_data->output[i]); } printf("MSE error on test data: %f\n", fann_get_MSE(ann)); printf("Saving network.\n"); fann_save(ann, "robot_float.net"); printf("Cleaning up.\n"); fann_destroy_train(train_data); fann_destroy_train(test_data); fann_destroy(ann); return 0; } libfann-2.1.0~beta~dfsg/examples/scaling.data000066400000000000000000000515251140315177100213200ustar00rootroot000000000000001000 3 1 1000 1 0 1001 1000 1 1 1000.54 1000 1 2 999.584 1000 1 3 999.01 1000 1 4 999.346 1000 1 5 1000.28 1000 1 6 1000.96 1000 1 7 1000.75 1000 1 8 999.854 1000 1 9 999.089 1000 1.01 0 1001.01 1000 1.01 1 1000.55 1000 1.01 2 999.58 1000 1.01 3 999 1000 1.01 4 999.34 1000 1.01 5 1000.29 1000 1.01 6 1000.97 1000 1.01 7 1000.76 1000 1.01 8 999.853 1000 1.01 9 999.08 1000 1.02 0 1001.02 1000 1.02 1 1000.55 1000 1.02 2 999.576 1000 1.02 3 998.99 1000 1.02 4 999.333 1000 1.02 5 1000.29 1000 1.02 6 1000.98 1000 1.02 7 1000.77 1000 1.02 8 999.852 1000 1.02 9 999.071 1000 1.03 0 1001.03 1000 1.03 1 1000.56 1000 1.03 2 999.571 1000 1.03 3 998.98 1000 1.03 4 999.327 1000 1.03 5 1000.29 1000 1.03 6 1000.99 1000 1.03 7 1000.78 1000 1.03 8 999.85 1000 1.03 9 999.062 1000 1.04 0 1001.04 1000 1.04 1 1000.56 1000 1.04 2 999.567 1000 1.04 3 998.97 1000 1.04 4 999.32 1000 1.04 5 1000.29 1000 1.04 6 1001 1000 1.04 7 1000.78 1000 1.04 8 999.849 1000 1.04 9 999.052 1000 1.05 0 1001.05 1000 1.05 1 1000.57 1000 1.05 2 999.563 1000 1.05 3 998.961 1000 1.05 4 999.314 1000 1.05 5 1000.3 1000 1.05 6 1001.01 1000 1.05 7 1000.79 1000 1.05 8 999.847 1000 1.05 9 999.043 1000 1.06 0 1001.06 1000 1.06 1 1000.57 1000 1.06 2 999.559 1000 1.06 3 998.951 1000 1.06 4 999.307 1000 1.06 5 1000.3 1000 1.06 6 1001.02 1000 1.06 7 1000.8 1000 1.06 8 999.846 1000 1.06 9 999.034 1000 1.07 0 1001.07 1000 1.07 1 1000.58 1000 1.07 2 999.555 1000 1.07 3 998.941 1000 1.07 4 999.301 1000 1.07 5 1000.3 1000 1.07 6 1001.03 1000 1.07 7 1000.81 1000 1.07 8 999.844 1000 1.07 9 999.025 1000 1.08 0 1001.08 1000 1.08 1 1000.58 1000 1.08 2 999.551 1000 1.08 3 998.931 1000 1.08 4 999.294 1000 1.08 5 1000.31 1000 1.08 6 1001.04 1000 1.08 7 1000.81 1000 1.08 8 999.843 1000 1.08 9 999.016 1000 1.09 0 1001.09 1000 1.09 1 1000.59 1000 1.09 2 999.546 1000 1.09 3 998.921 1000 1.09 4 999.288 1000 1.09 5 1000.31 1000 1.09 6 1001.05 1000 1.09 7 1000.82 1000 1.09 8 999.841 1000 1.09 9 999.007 1000.1 1 0 1001.1 1000.1 1 1 1000.64 1000.1 1 2 999.684 1000.1 1 3 999.11 1000.1 1 4 999.446 1000.1 1 5 1000.38 1000.1 1 6 1001.06 1000.1 1 7 1000.85 1000.1 1 8 999.954 1000.1 1 9 999.189 1000.1 1.01 0 1001.11 1000.1 1.01 1 1000.65 1000.1 1.01 2 999.68 1000.1 1.01 3 999.1 1000.1 1.01 4 999.44 1000.1 1.01 5 1000.39 1000.1 1.01 6 1001.07 1000.1 1.01 7 1000.86 1000.1 1.01 8 999.953 1000.1 1.01 9 999.18 1000.1 1.02 0 1001.12 1000.1 1.02 1 1000.65 1000.1 1.02 2 999.675 1000.1 1.02 3 999.09 1000.1 1.02 4 999.433 1000.1 1.02 5 1000.39 1000.1 1.02 6 1001.08 1000.1 1.02 7 1000.87 1000.1 1.02 8 999.952 1000.1 1.02 9 999.171 1000.1 1.03 0 1001.13 1000.1 1.03 1 1000.66 1000.1 1.03 2 999.671 1000.1 1.03 3 999.08 1000.1 1.03 4 999.427 1000.1 1.03 5 1000.39 1000.1 1.03 6 1001.09 1000.1 1.03 7 1000.88 1000.1 1.03 8 999.95 1000.1 1.03 9 999.161 1000.1 1.04 0 1001.14 1000.1 1.04 1 1000.66 1000.1 1.04 2 999.667 1000.1 1.04 3 999.07 1000.1 1.04 4 999.42 1000.1 1.04 5 1000.39 1000.1 1.04 6 1001.1 1000.1 1.04 7 1000.88 1000.1 1.04 8 999.949 1000.1 1.04 9 999.152 1000.1 1.05 0 1001.15 1000.1 1.05 1 1000.67 1000.1 1.05 2 999.663 1000.1 1.05 3 999.06 1000.1 1.05 4 999.414 1000.1 1.05 5 1000.4 1000.1 1.05 6 1001.11 1000.1 1.05 7 1000.89 1000.1 1.05 8 999.947 1000.1 1.05 9 999.143 1000.1 1.06 0 1001.16 1000.1 1.06 1 1000.67 1000.1 1.06 2 999.659 1000.1 1.06 3 999.051 1000.1 1.06 4 999.407 1000.1 1.06 5 1000.4 1000.1 1.06 6 1001.12 1000.1 1.06 7 1000.9 1000.1 1.06 8 999.946 1000.1 1.06 9 999.134 1000.1 1.07 0 1001.17 1000.1 1.07 1 1000.68 1000.1 1.07 2 999.655 1000.1 1.07 3 999.041 1000.1 1.07 4 999.401 1000.1 1.07 5 1000.4 1000.1 1.07 6 1001.13 1000.1 1.07 7 1000.91 1000.1 1.07 8 999.944 1000.1 1.07 9 999.125 1000.1 1.08 0 1001.18 1000.1 1.08 1 1000.68 1000.1 1.08 2 999.651 1000.1 1.08 3 999.031 1000.1 1.08 4 999.394 1000.1 1.08 5 1000.41 1000.1 1.08 6 1001.14 1000.1 1.08 7 1000.91 1000.1 1.08 8 999.943 1000.1 1.08 9 999.116 1000.1 1.09 0 1001.19 1000.1 1.09 1 1000.69 1000.1 1.09 2 999.646 1000.1 1.09 3 999.021 1000.1 1.09 4 999.388 1000.1 1.09 5 1000.41 1000.1 1.09 6 1001.15 1000.1 1.09 7 1000.92 1000.1 1.09 8 999.941 1000.1 1.09 9 999.107 1000.2 1 0 1001.2 1000.2 1 1 1000.74 1000.2 1 2 999.784 1000.2 1 3 999.21 1000.2 1 4 999.546 1000.2 1 5 1000.48 1000.2 1 6 1001.16 1000.2 1 7 1000.95 1000.2 1 8 1000.05 1000.2 1 9 999.289 1000.2 1.01 0 1001.21 1000.2 1.01 1 1000.75 1000.2 1.01 2 999.78 1000.2 1.01 3 999.2 1000.2 1.01 4 999.54 1000.2 1.01 5 1000.49 1000.2 1.01 6 1001.17 1000.2 1.01 7 1000.96 1000.2 1.01 8 1000.05 1000.2 1.01 9 999.28 1000.2 1.02 0 1001.22 1000.2 1.02 1 1000.75 1000.2 1.02 2 999.776 1000.2 1.02 3 999.19 1000.2 1.02 4 999.533 1000.2 1.02 5 1000.49 1000.2 1.02 6 1001.18 1000.2 1.02 7 1000.97 1000.2 1.02 8 1000.05 1000.2 1.02 9 999.271 1000.2 1.03 0 1001.23 1000.2 1.03 1 1000.76 1000.2 1.03 2 999.771 1000.2 1.03 3 999.18 1000.2 1.03 4 999.527 1000.2 1.03 5 1000.49 1000.2 1.03 6 1001.19 1000.2 1.03 7 1000.98 1000.2 1.03 8 1000.05 1000.2 1.03 9 999.262 1000.2 1.04 0 1001.24 1000.2 1.04 1 1000.76 1000.2 1.04 2 999.767 1000.2 1.04 3 999.17 1000.2 1.04 4 999.52 1000.2 1.04 5 1000.49 1000.2 1.04 6 1001.2 1000.2 1.04 7 1000.98 1000.2 1.04 8 1000.05 1000.2 1.04 9 999.252 1000.2 1.05 0 1001.25 1000.2 1.05 1 1000.77 1000.2 1.05 2 999.763 1000.2 1.05 3 999.161 1000.2 1.05 4 999.514 1000.2 1.05 5 1000.5 1000.2 1.05 6 1001.21 1000.2 1.05 7 1000.99 1000.2 1.05 8 1000.05 1000.2 1.05 9 999.243 1000.2 1.06 0 1001.26 1000.2 1.06 1 1000.77 1000.2 1.06 2 999.759 1000.2 1.06 3 999.151 1000.2 1.06 4 999.507 1000.2 1.06 5 1000.5 1000.2 1.06 6 1001.22 1000.2 1.06 7 1001 1000.2 1.06 8 1000.05 1000.2 1.06 9 999.234 1000.2 1.07 0 1001.27 1000.2 1.07 1 1000.78 1000.2 1.07 2 999.755 1000.2 1.07 3 999.141 1000.2 1.07 4 999.501 1000.2 1.07 5 1000.5 1000.2 1.07 6 1001.23 1000.2 1.07 7 1001.01 1000.2 1.07 8 1000.04 1000.2 1.07 9 999.225 1000.2 1.08 0 1001.28 1000.2 1.08 1 1000.78 1000.2 1.08 2 999.751 1000.2 1.08 3 999.131 1000.2 1.08 4 999.494 1000.2 1.08 5 1000.51 1000.2 1.08 6 1001.24 1000.2 1.08 7 1001.01 1000.2 1.08 8 1000.04 1000.2 1.08 9 999.216 1000.2 1.09 0 1001.29 1000.2 1.09 1 1000.79 1000.2 1.09 2 999.746 1000.2 1.09 3 999.121 1000.2 1.09 4 999.488 1000.2 1.09 5 1000.51 1000.2 1.09 6 1001.25 1000.2 1.09 7 1001.02 1000.2 1.09 8 1000.04 1000.2 1.09 9 999.207 1000.3 1 0 1001.3 1000.3 1 1 1000.84 1000.3 1 2 999.884 1000.3 1 3 999.31 1000.3 1 4 999.646 1000.3 1 5 1000.58 1000.3 1 6 1001.26 1000.3 1 7 1001.05 1000.3 1 8 1000.15 1000.3 1 9 999.389 1000.3 1.01 0 1001.31 1000.3 1.01 1 1000.85 1000.3 1.01 2 999.88 1000.3 1.01 3 999.3 1000.3 1.01 4 999.64 1000.3 1.01 5 1000.59 1000.3 1.01 6 1001.27 1000.3 1.01 7 1001.06 1000.3 1.01 8 1000.15 1000.3 1.01 9 999.38 1000.3 1.02 0 1001.32 1000.3 1.02 1 1000.85 1000.3 1.02 2 999.875 1000.3 1.02 3 999.29 1000.3 1.02 4 999.633 1000.3 1.02 5 1000.59 1000.3 1.02 6 1001.28 1000.3 1.02 7 1001.07 1000.3 1.02 8 1000.15 1000.3 1.02 9 999.371 1000.3 1.03 0 1001.33 1000.3 1.03 1 1000.86 1000.3 1.03 2 999.871 1000.3 1.03 3 999.28 1000.3 1.03 4 999.627 1000.3 1.03 5 1000.59 1000.3 1.03 6 1001.29 1000.3 1.03 7 1001.08 1000.3 1.03 8 1000.15 1000.3 1.03 9 999.362 1000.3 1.04 0 1001.34 1000.3 1.04 1 1000.86 1000.3 1.04 2 999.867 1000.3 1.04 3 999.27 1000.3 1.04 4 999.62 1000.3 1.04 5 1000.59 1000.3 1.04 6 1001.3 1000.3 1.04 7 1001.08 1000.3 1.04 8 1000.15 1000.3 1.04 9 999.352 1000.3 1.05 0 1001.35 1000.3 1.05 1 1000.87 1000.3 1.05 2 999.863 1000.3 1.05 3 999.26 1000.3 1.05 4 999.614 1000.3 1.05 5 1000.6 1000.3 1.05 6 1001.31 1000.3 1.05 7 1001.09 1000.3 1.05 8 1000.15 1000.3 1.05 9 999.343 1000.3 1.06 0 1001.36 1000.3 1.06 1 1000.87 1000.3 1.06 2 999.859 1000.3 1.06 3 999.251 1000.3 1.06 4 999.607 1000.3 1.06 5 1000.6 1000.3 1.06 6 1001.32 1000.3 1.06 7 1001.1 1000.3 1.06 8 1000.15 1000.3 1.06 9 999.334 1000.3 1.07 0 1001.37 1000.3 1.07 1 1000.88 1000.3 1.07 2 999.855 1000.3 1.07 3 999.241 1000.3 1.07 4 999.601 1000.3 1.07 5 1000.6 1000.3 1.07 6 1001.33 1000.3 1.07 7 1001.11 1000.3 1.07 8 1000.14 1000.3 1.07 9 999.325 1000.3 1.08 0 1001.38 1000.3 1.08 1 1000.88 1000.3 1.08 2 999.851 1000.3 1.08 3 999.231 1000.3 1.08 4 999.594 1000.3 1.08 5 1000.61 1000.3 1.08 6 1001.34 1000.3 1.08 7 1001.11 1000.3 1.08 8 1000.14 1000.3 1.08 9 999.316 1000.3 1.09 0 1001.39 1000.3 1.09 1 1000.89 1000.3 1.09 2 999.846 1000.3 1.09 3 999.221 1000.3 1.09 4 999.588 1000.3 1.09 5 1000.61 1000.3 1.09 6 1001.35 1000.3 1.09 7 1001.12 1000.3 1.09 8 1000.14 1000.3 1.09 9 999.307 1000.4 1 0 1001.4 1000.4 1 1 1000.94 1000.4 1 2 999.984 1000.4 1 3 999.41 1000.4 1 4 999.746 1000.4 1 5 1000.68 1000.4 1 6 1001.36 1000.4 1 7 1001.15 1000.4 1 8 1000.25 1000.4 1 9 999.489 1000.4 1.01 0 1001.41 1000.4 1.01 1 1000.95 1000.4 1.01 2 999.98 1000.4 1.01 3 999.4 1000.4 1.01 4 999.74 1000.4 1.01 5 1000.69 1000.4 1.01 6 1001.37 1000.4 1.01 7 1001.16 1000.4 1.01 8 1000.25 1000.4 1.01 9 999.48 1000.4 1.02 0 1001.42 1000.4 1.02 1 1000.95 1000.4 1.02 2 999.976 1000.4 1.02 3 999.39 1000.4 1.02 4 999.733 1000.4 1.02 5 1000.69 1000.4 1.02 6 1001.38 1000.4 1.02 7 1001.17 1000.4 1.02 8 1000.25 1000.4 1.02 9 999.471 1000.4 1.03 0 1001.43 1000.4 1.03 1 1000.96 1000.4 1.03 2 999.971 1000.4 1.03 3 999.38 1000.4 1.03 4 999.727 1000.4 1.03 5 1000.69 1000.4 1.03 6 1001.39 1000.4 1.03 7 1001.18 1000.4 1.03 8 1000.25 1000.4 1.03 9 999.462 1000.4 1.04 0 1001.44 1000.4 1.04 1 1000.96 1000.4 1.04 2 999.967 1000.4 1.04 3 999.37 1000.4 1.04 4 999.72 1000.4 1.04 5 1000.7 1000.4 1.04 6 1001.4 1000.4 1.04 7 1001.18 1000.4 1.04 8 1000.25 1000.4 1.04 9 999.452 1000.4 1.05 0 1001.45 1000.4 1.05 1 1000.97 1000.4 1.05 2 999.963 1000.4 1.05 3 999.361 1000.4 1.05 4 999.714 1000.4 1.05 5 1000.7 1000.4 1.05 6 1001.41 1000.4 1.05 7 1001.19 1000.4 1.05 8 1000.25 1000.4 1.05 9 999.443 1000.4 1.06 0 1001.46 1000.4 1.06 1 1000.97 1000.4 1.06 2 999.959 1000.4 1.06 3 999.351 1000.4 1.06 4 999.707 1000.4 1.06 5 1000.7 1000.4 1.06 6 1001.42 1000.4 1.06 7 1001.2 1000.4 1.06 8 1000.25 1000.4 1.06 9 999.434 1000.4 1.07 0 1001.47 1000.4 1.07 1 1000.98 1000.4 1.07 2 999.955 1000.4 1.07 3 999.341 1000.4 1.07 4 999.701 1000.4 1.07 5 1000.7 1000.4 1.07 6 1001.43 1000.4 1.07 7 1001.21 1000.4 1.07 8 1000.24 1000.4 1.07 9 999.425 1000.4 1.08 0 1001.48 1000.4 1.08 1 1000.98 1000.4 1.08 2 999.951 1000.4 1.08 3 999.331 1000.4 1.08 4 999.694 1000.4 1.08 5 1000.71 1000.4 1.08 6 1001.44 1000.4 1.08 7 1001.21 1000.4 1.08 8 1000.24 1000.4 1.08 9 999.416 1000.4 1.09 0 1001.49 1000.4 1.09 1 1000.99 1000.4 1.09 2 999.946 1000.4 1.09 3 999.321 1000.4 1.09 4 999.688 1000.4 1.09 5 1000.71 1000.4 1.09 6 1001.45 1000.4 1.09 7 1001.22 1000.4 1.09 8 1000.24 1000.4 1.09 9 999.407 1000.5 1 0 1001.5 1000.5 1 1 1001.04 1000.5 1 2 1000.08 1000.5 1 3 999.51 1000.5 1 4 999.846 1000.5 1 5 1000.78 1000.5 1 6 1001.46 1000.5 1 7 1001.25 1000.5 1 8 1000.35 1000.5 1 9 999.589 1000.5 1.01 0 1001.51 1000.5 1.01 1 1001.05 1000.5 1.01 2 1000.08 1000.5 1.01 3 999.5 1000.5 1.01 4 999.84 1000.5 1.01 5 1000.79 1000.5 1.01 6 1001.47 1000.5 1.01 7 1001.26 1000.5 1.01 8 1000.35 1000.5 1.01 9 999.58 1000.5 1.02 0 1001.52 1000.5 1.02 1 1001.05 1000.5 1.02 2 1000.08 1000.5 1.02 3 999.49 1000.5 1.02 4 999.833 1000.5 1.02 5 1000.79 1000.5 1.02 6 1001.48 1000.5 1.02 7 1001.27 1000.5 1.02 8 1000.35 1000.5 1.02 9 999.571 1000.5 1.03 0 1001.53 1000.5 1.03 1 1001.06 1000.5 1.03 2 1000.07 1000.5 1.03 3 999.48 1000.5 1.03 4 999.827 1000.5 1.03 5 1000.79 1000.5 1.03 6 1001.49 1000.5 1.03 7 1001.28 1000.5 1.03 8 1000.35 1000.5 1.03 9 999.562 1000.5 1.04 0 1001.54 1000.5 1.04 1 1001.06 1000.5 1.04 2 1000.07 1000.5 1.04 3 999.47 1000.5 1.04 4 999.82 1000.5 1.04 5 1000.79 1000.5 1.04 6 1001.5 1000.5 1.04 7 1001.28 1000.5 1.04 8 1000.35 1000.5 1.04 9 999.552 1000.5 1.05 0 1001.55 1000.5 1.05 1 1001.07 1000.5 1.05 2 1000.06 1000.5 1.05 3 999.461 1000.5 1.05 4 999.814 1000.5 1.05 5 1000.8 1000.5 1.05 6 1001.51 1000.5 1.05 7 1001.29 1000.5 1.05 8 1000.35 1000.5 1.05 9 999.543 1000.5 1.06 0 1001.56 1000.5 1.06 1 1001.07 1000.5 1.06 2 1000.06 1000.5 1.06 3 999.451 1000.5 1.06 4 999.807 1000.5 1.06 5 1000.8 1000.5 1.06 6 1001.52 1000.5 1.06 7 1001.3 1000.5 1.06 8 1000.35 1000.5 1.06 9 999.534 1000.5 1.07 0 1001.57 1000.5 1.07 1 1001.08 1000.5 1.07 2 1000.05 1000.5 1.07 3 999.441 1000.5 1.07 4 999.801 1000.5 1.07 5 1000.8 1000.5 1.07 6 1001.53 1000.5 1.07 7 1001.31 1000.5 1.07 8 1000.34 1000.5 1.07 9 999.525 1000.5 1.08 0 1001.58 1000.5 1.08 1 1001.08 1000.5 1.08 2 1000.05 1000.5 1.08 3 999.431 1000.5 1.08 4 999.794 1000.5 1.08 5 1000.81 1000.5 1.08 6 1001.54 1000.5 1.08 7 1001.31 1000.5 1.08 8 1000.34 1000.5 1.08 9 999.516 1000.5 1.09 0 1001.59 1000.5 1.09 1 1001.09 1000.5 1.09 2 1000.05 1000.5 1.09 3 999.421 1000.5 1.09 4 999.788 1000.5 1.09 5 1000.81 1000.5 1.09 6 1001.55 1000.5 1.09 7 1001.32 1000.5 1.09 8 1000.34 1000.5 1.09 9 999.507 1000.6 1 0 1001.6 1000.6 1 1 1001.14 1000.6 1 2 1000.18 1000.6 1 3 999.61 1000.6 1 4 999.946 1000.6 1 5 1000.88 1000.6 1 6 1001.56 1000.6 1 7 1001.35 1000.6 1 8 1000.45 1000.6 1 9 999.689 1000.6 1.01 0 1001.61 1000.6 1.01 1 1001.15 1000.6 1.01 2 1000.18 1000.6 1.01 3 999.6 1000.6 1.01 4 999.94 1000.6 1.01 5 1000.89 1000.6 1.01 6 1001.57 1000.6 1.01 7 1001.36 1000.6 1.01 8 1000.45 1000.6 1.01 9 999.68 1000.6 1.02 0 1001.62 1000.6 1.02 1 1001.15 1000.6 1.02 2 1000.18 1000.6 1.02 3 999.59 1000.6 1.02 4 999.933 1000.6 1.02 5 1000.89 1000.6 1.02 6 1001.58 1000.6 1.02 7 1001.37 1000.6 1.02 8 1000.45 1000.6 1.02 9 999.671 1000.6 1.03 0 1001.63 1000.6 1.03 1 1001.16 1000.6 1.03 2 1000.17 1000.6 1.03 3 999.58 1000.6 1.03 4 999.927 1000.6 1.03 5 1000.89 1000.6 1.03 6 1001.59 1000.6 1.03 7 1001.38 1000.6 1.03 8 1000.45 1000.6 1.03 9 999.661 1000.6 1.04 0 1001.64 1000.6 1.04 1 1001.16 1000.6 1.04 2 1000.17 1000.6 1.04 3 999.57 1000.6 1.04 4 999.92 1000.6 1.04 5 1000.89 1000.6 1.04 6 1001.6 1000.6 1.04 7 1001.38 1000.6 1.04 8 1000.45 1000.6 1.04 9 999.652 1000.6 1.05 0 1001.65 1000.6 1.05 1 1001.17 1000.6 1.05 2 1000.16 1000.6 1.05 3 999.56 1000.6 1.05 4 999.914 1000.6 1.05 5 1000.9 1000.6 1.05 6 1001.61 1000.6 1.05 7 1001.39 1000.6 1.05 8 1000.45 1000.6 1.05 9 999.643 1000.6 1.06 0 1001.66 1000.6 1.06 1 1001.17 1000.6 1.06 2 1000.16 1000.6 1.06 3 999.551 1000.6 1.06 4 999.907 1000.6 1.06 5 1000.9 1000.6 1.06 6 1001.62 1000.6 1.06 7 1001.4 1000.6 1.06 8 1000.45 1000.6 1.06 9 999.634 1000.6 1.07 0 1001.67 1000.6 1.07 1 1001.18 1000.6 1.07 2 1000.15 1000.6 1.07 3 999.541 1000.6 1.07 4 999.901 1000.6 1.07 5 1000.9 1000.6 1.07 6 1001.63 1000.6 1.07 7 1001.41 1000.6 1.07 8 1000.44 1000.6 1.07 9 999.625 1000.6 1.08 0 1001.68 1000.6 1.08 1 1001.18 1000.6 1.08 2 1000.15 1000.6 1.08 3 999.531 1000.6 1.08 4 999.894 1000.6 1.08 5 1000.91 1000.6 1.08 6 1001.64 1000.6 1.08 7 1001.41 1000.6 1.08 8 1000.44 1000.6 1.08 9 999.616 1000.6 1.09 0 1001.69 1000.6 1.09 1 1001.19 1000.6 1.09 2 1000.15 1000.6 1.09 3 999.521 1000.6 1.09 4 999.888 1000.6 1.09 5 1000.91 1000.6 1.09 6 1001.65 1000.6 1.09 7 1001.42 1000.6 1.09 8 1000.44 1000.6 1.09 9 999.607 1000.7 1 0 1001.7 1000.7 1 1 1001.24 1000.7 1 2 1000.28 1000.7 1 3 999.71 1000.7 1 4 1000.05 1000.7 1 5 1000.98 1000.7 1 6 1001.66 1000.7 1 7 1001.45 1000.7 1 8 1000.55 1000.7 1 9 999.789 1000.7 1.01 0 1001.71 1000.7 1.01 1 1001.25 1000.7 1.01 2 1000.28 1000.7 1.01 3 999.7 1000.7 1.01 4 1000.04 1000.7 1.01 5 1000.99 1000.7 1.01 6 1001.67 1000.7 1.01 7 1001.46 1000.7 1.01 8 1000.55 1000.7 1.01 9 999.78 1000.7 1.02 0 1001.72 1000.7 1.02 1 1001.25 1000.7 1.02 2 1000.28 1000.7 1.02 3 999.69 1000.7 1.02 4 1000.03 1000.7 1.02 5 1000.99 1000.7 1.02 6 1001.68 1000.7 1.02 7 1001.47 1000.7 1.02 8 1000.55 1000.7 1.02 9 999.771 1000.7 1.03 0 1001.73 1000.7 1.03 1 1001.26 1000.7 1.03 2 1000.27 1000.7 1.03 3 999.68 1000.7 1.03 4 1000.03 1000.7 1.03 5 1000.99 1000.7 1.03 6 1001.69 1000.7 1.03 7 1001.48 1000.7 1.03 8 1000.55 1000.7 1.03 9 999.762 1000.7 1.04 0 1001.74 1000.7 1.04 1 1001.26 1000.7 1.04 2 1000.27 1000.7 1.04 3 999.67 1000.7 1.04 4 1000.02 1000.7 1.04 5 1000.99 1000.7 1.04 6 1001.7 1000.7 1.04 7 1001.48 1000.7 1.04 8 1000.55 1000.7 1.04 9 999.752 1000.7 1.05 0 1001.75 1000.7 1.05 1 1001.27 1000.7 1.05 2 1000.26 1000.7 1.05 3 999.661 1000.7 1.05 4 1000.01 1000.7 1.05 5 1001 1000.7 1.05 6 1001.71 1000.7 1.05 7 1001.49 1000.7 1.05 8 1000.55 1000.7 1.05 9 999.743 1000.7 1.06 0 1001.76 1000.7 1.06 1 1001.27 1000.7 1.06 2 1000.26 1000.7 1.06 3 999.651 1000.7 1.06 4 1000.01 1000.7 1.06 5 1001 1000.7 1.06 6 1001.72 1000.7 1.06 7 1001.5 1000.7 1.06 8 1000.55 1000.7 1.06 9 999.734 1000.7 1.07 0 1001.77 1000.7 1.07 1 1001.28 1000.7 1.07 2 1000.25 1000.7 1.07 3 999.641 1000.7 1.07 4 1000 1000.7 1.07 5 1001 1000.7 1.07 6 1001.73 1000.7 1.07 7 1001.51 1000.7 1.07 8 1000.54 1000.7 1.07 9 999.725 1000.7 1.08 0 1001.78 1000.7 1.08 1 1001.28 1000.7 1.08 2 1000.25 1000.7 1.08 3 999.631 1000.7 1.08 4 999.994 1000.7 1.08 5 1001.01 1000.7 1.08 6 1001.74 1000.7 1.08 7 1001.51 1000.7 1.08 8 1000.54 1000.7 1.08 9 999.716 1000.7 1.09 0 1001.79 1000.7 1.09 1 1001.29 1000.7 1.09 2 1000.25 1000.7 1.09 3 999.621 1000.7 1.09 4 999.988 1000.7 1.09 5 1001.01 1000.7 1.09 6 1001.75 1000.7 1.09 7 1001.52 1000.7 1.09 8 1000.54 1000.7 1.09 9 999.707 1000.8 1 0 1001.8 1000.8 1 1 1001.34 1000.8 1 2 1000.38 1000.8 1 3 999.81 1000.8 1 4 1000.15 1000.8 1 5 1001.08 1000.8 1 6 1001.76 1000.8 1 7 1001.55 1000.8 1 8 1000.65 1000.8 1 9 999.889 1000.8 1.01 0 1001.81 1000.8 1.01 1 1001.35 1000.8 1.01 2 1000.38 1000.8 1.01 3 999.8 1000.8 1.01 4 1000.14 1000.8 1.01 5 1001.09 1000.8 1.01 6 1001.77 1000.8 1.01 7 1001.56 1000.8 1.01 8 1000.65 1000.8 1.01 9 999.88 1000.8 1.02 0 1001.82 1000.8 1.02 1 1001.35 1000.8 1.02 2 1000.38 1000.8 1.02 3 999.79 1000.8 1.02 4 1000.13 1000.8 1.02 5 1001.09 1000.8 1.02 6 1001.78 1000.8 1.02 7 1001.57 1000.8 1.02 8 1000.65 1000.8 1.02 9 999.871 1000.8 1.03 0 1001.83 1000.8 1.03 1 1001.36 1000.8 1.03 2 1000.37 1000.8 1.03 3 999.78 1000.8 1.03 4 1000.13 1000.8 1.03 5 1001.09 1000.8 1.03 6 1001.79 1000.8 1.03 7 1001.58 1000.8 1.03 8 1000.65 1000.8 1.03 9 999.862 1000.8 1.04 0 1001.84 1000.8 1.04 1 1001.36 1000.8 1.04 2 1000.37 1000.8 1.04 3 999.77 1000.8 1.04 4 1000.12 1000.8 1.04 5 1001.09 1000.8 1.04 6 1001.8 1000.8 1.04 7 1001.58 1000.8 1.04 8 1000.65 1000.8 1.04 9 999.852 1000.8 1.05 0 1001.85 1000.8 1.05 1 1001.37 1000.8 1.05 2 1000.36 1000.8 1.05 3 999.76 1000.8 1.05 4 1000.11 1000.8 1.05 5 1001.1 1000.8 1.05 6 1001.81 1000.8 1.05 7 1001.59 1000.8 1.05 8 1000.65 1000.8 1.05 9 999.843 1000.8 1.06 0 1001.86 1000.8 1.06 1 1001.37 1000.8 1.06 2 1000.36 1000.8 1.06 3 999.751 1000.8 1.06 4 1000.11 1000.8 1.06 5 1001.1 1000.8 1.06 6 1001.82 1000.8 1.06 7 1001.6 1000.8 1.06 8 1000.65 1000.8 1.06 9 999.834 1000.8 1.07 0 1001.87 1000.8 1.07 1 1001.38 1000.8 1.07 2 1000.35 1000.8 1.07 3 999.741 1000.8 1.07 4 1000.1 1000.8 1.07 5 1001.1 1000.8 1.07 6 1001.83 1000.8 1.07 7 1001.61 1000.8 1.07 8 1000.64 1000.8 1.07 9 999.825 1000.8 1.08 0 1001.88 1000.8 1.08 1 1001.38 1000.8 1.08 2 1000.35 1000.8 1.08 3 999.731 1000.8 1.08 4 1000.09 1000.8 1.08 5 1001.11 1000.8 1.08 6 1001.84 1000.8 1.08 7 1001.61 1000.8 1.08 8 1000.64 1000.8 1.08 9 999.816 1000.8 1.09 0 1001.89 1000.8 1.09 1 1001.39 1000.8 1.09 2 1000.35 1000.8 1.09 3 999.721 1000.8 1.09 4 1000.09 1000.8 1.09 5 1001.11 1000.8 1.09 6 1001.85 1000.8 1.09 7 1001.62 1000.8 1.09 8 1000.64 1000.8 1.09 9 999.807 1000.9 1 0 1001.9 1000.9 1 1 1001.44 1000.9 1 2 1000.48 1000.9 1 3 999.91 1000.9 1 4 1000.25 1000.9 1 5 1001.18 1000.9 1 6 1001.86 1000.9 1 7 1001.65 1000.9 1 8 1000.75 1000.9 1 9 999.989 1000.9 1.01 0 1001.91 1000.9 1.01 1 1001.45 1000.9 1.01 2 1000.48 1000.9 1.01 3 999.9 1000.9 1.01 4 1000.24 1000.9 1.01 5 1001.19 1000.9 1.01 6 1001.87 1000.9 1.01 7 1001.66 1000.9 1.01 8 1000.75 1000.9 1.01 9 999.98 1000.9 1.02 0 1001.92 1000.9 1.02 1 1001.45 1000.9 1.02 2 1000.48 1000.9 1.02 3 999.89 1000.9 1.02 4 1000.23 1000.9 1.02 5 1001.19 1000.9 1.02 6 1001.88 1000.9 1.02 7 1001.67 1000.9 1.02 8 1000.75 1000.9 1.02 9 999.971 1000.9 1.03 0 1001.93 1000.9 1.03 1 1001.46 1000.9 1.03 2 1000.47 1000.9 1.03 3 999.88 1000.9 1.03 4 1000.23 1000.9 1.03 5 1001.19 1000.9 1.03 6 1001.89 1000.9 1.03 7 1001.68 1000.9 1.03 8 1000.75 1000.9 1.03 9 999.962 1000.9 1.04 0 1001.94 1000.9 1.04 1 1001.46 1000.9 1.04 2 1000.47 1000.9 1.04 3 999.87 1000.9 1.04 4 1000.22 1000.9 1.04 5 1001.2 1000.9 1.04 6 1001.9 1000.9 1.04 7 1001.68 1000.9 1.04 8 1000.75 1000.9 1.04 9 999.952 1000.9 1.05 0 1001.95 1000.9 1.05 1 1001.47 1000.9 1.05 2 1000.46 1000.9 1.05 3 999.861 1000.9 1.05 4 1000.21 1000.9 1.05 5 1001.2 1000.9 1.05 6 1001.91 1000.9 1.05 7 1001.69 1000.9 1.05 8 1000.75 1000.9 1.05 9 999.943 1000.9 1.06 0 1001.96 1000.9 1.06 1 1001.47 1000.9 1.06 2 1000.46 1000.9 1.06 3 999.851 1000.9 1.06 4 1000.21 1000.9 1.06 5 1001.2 1000.9 1.06 6 1001.92 1000.9 1.06 7 1001.7 1000.9 1.06 8 1000.75 1000.9 1.06 9 999.934 1000.9 1.07 0 1001.97 1000.9 1.07 1 1001.48 1000.9 1.07 2 1000.45 1000.9 1.07 3 999.841 1000.9 1.07 4 1000.2 1000.9 1.07 5 1001.2 1000.9 1.07 6 1001.93 1000.9 1.07 7 1001.71 1000.9 1.07 8 1000.74 1000.9 1.07 9 999.925 1000.9 1.08 0 1001.98 1000.9 1.08 1 1001.48 1000.9 1.08 2 1000.45 1000.9 1.08 3 999.831 1000.9 1.08 4 1000.19 1000.9 1.08 5 1001.21 1000.9 1.08 6 1001.94 1000.9 1.08 7 1001.71 1000.9 1.08 8 1000.74 1000.9 1.08 9 999.916 1000.9 1.09 0 1001.99 1000.9 1.09 1 1001.49 1000.9 1.09 2 1000.45 1000.9 1.09 3 999.821 1000.9 1.09 4 1000.19 1000.9 1.09 5 1001.21 1000.9 1.09 6 1001.95 1000.9 1.09 7 1001.72 1000.9 1.09 8 1000.74 1000.9 1.09 9 999.907 libfann-2.1.0~beta~dfsg/examples/scaling_test.c000066400000000000000000000020431140315177100216570ustar00rootroot00000000000000#include #include "fann.h" int main( int argc, char** argv ) { fann_type *calc_out; unsigned int i; int ret = 0; struct fann *ann; struct fann_train_data *data; printf("Creating network.\n"); ann = fann_create_from_file("scaling.net"); if(!ann) { printf("Error creating ann --- ABORTING.\n"); return 0; } fann_print_connections(ann); fann_print_parameters(ann); printf("Testing network.\n"); data = fann_read_train_from_file("scaling.data"); for(i = 0; i < fann_length_train_data(data); i++) { fann_reset_MSE(ann); /* Just pass any param to perform scaling */ if( argc > 1 ) { fann_scale_input( ann, data->input[i] ); calc_out = fann_run( ann, data->input[i] ); fann_descale_output( ann, calc_out ); } else { calc_out = fann_test(ann, data->input[i], data->output[i]); } printf("Result %f original %f error %f\n", calc_out[0], data->output[i][0], (float) fann_abs(calc_out[0] - data->output[i][0])); } printf("Cleaning up.\n"); fann_destroy_train(data); fann_destroy(ann); return ret; } libfann-2.1.0~beta~dfsg/examples/scaling_train.c000066400000000000000000000024431140315177100220210ustar00rootroot00000000000000#include "fann.h" int main( int argc, char** argv ) { const unsigned int num_input = 3; const unsigned int num_output = 1; const unsigned int num_layers = 4; const unsigned int num_neurons_hidden = 5; const float desired_error = (const float) 0.0001; const unsigned int max_epochs = 5000; const unsigned int epochs_between_reports = 1000; struct fann_train_data * data = NULL; struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden, num_output); fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(ann, FANN_LINEAR); fann_set_training_algorithm(ann, FANN_TRAIN_RPROP); data = fann_read_train_from_file( "scaling.data" ); /* Just pass any param to perform scaling */ if( argc > 1 ) { fann_set_scaling_params( ann, data, -1, /* New input minimum */ 1, /* New input maximum */ -1, /* New output minimum */ 1); /* New output maximum */ fann_scale_train( ann, data ); /* fann_save_train(data, "scaled_scaling.data"); fann_descale_train( ann, data ); fann_save_train(data, "descaled_scaling.data"); */ } fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error); free( data ); fann_save(ann, "scaling.net"); fann_destroy(ann); return 0; } libfann-2.1.0~beta~dfsg/examples/simple_test.c000066400000000000000000000021601140315177100215300ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include "floatfann.h" int main() { fann_type *calc_out; fann_type input[2]; struct fann *ann = fann_create_from_file("xor_float.net"); input[0] = -1; input[1] = 1; calc_out = fann_run(ann, input); printf("xor test (%f,%f) -> %f\n", input[0], input[1], calc_out[0]); fann_destroy(ann); return 0; } libfann-2.1.0~beta~dfsg/examples/simple_train.c000066400000000000000000000027631140315177100216770ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include "fann.h" int main() { const unsigned int num_input = 2; const unsigned int num_output = 1; const unsigned int num_layers = 3; const unsigned int num_neurons_hidden = 3; const float desired_error = (const float) 0.001; const unsigned int max_epochs = 500000; const unsigned int epochs_between_reports = 1000; struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output); fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC); fann_train_on_file(ann, "xor.data", max_epochs, epochs_between_reports, desired_error); fann_save(ann, "xor_float.net"); fann_destroy(ann); return 0; } libfann-2.1.0~beta~dfsg/examples/steepness_train.c000066400000000000000000000066751140315177100224250ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include "fann.h" #include void train_on_steepness_file(struct fann *ann, char *filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error, float steepness_start, float steepness_step, float steepness_end) { float error; unsigned int i; struct fann_train_data *data = fann_read_train_from_file(filename); if(epochs_between_reports) { printf("Max epochs %8d. Desired error: %.10f\n", max_epochs, desired_error); } fann_set_activation_steepness_hidden(ann, steepness_start); fann_set_activation_steepness_output(ann, steepness_start); for(i = 1; i <= max_epochs; i++) { /* train */ error = fann_train_epoch(ann, data); /* print current output */ if(epochs_between_reports && (i % epochs_between_reports == 0 || i == max_epochs || i == 1 || error < desired_error)) { printf("Epochs %8d. Current error: %.10f\n", i, error); } if(error < desired_error) { steepness_start += steepness_step; if(steepness_start <= steepness_end) { printf("Steepness: %f\n", steepness_start); fann_set_activation_steepness_hidden(ann, steepness_start); fann_set_activation_steepness_output(ann, steepness_start); } else { break; } } } fann_destroy_train(data); } int main() { const unsigned int num_input = 2; const unsigned int num_output = 1; const unsigned int num_layers = 3; const unsigned int num_neurons_hidden = 3; const float desired_error = (const float) 0.001; const unsigned int max_epochs = 500000; const unsigned int epochs_between_reports = 1000; unsigned int i; fann_type *calc_out; struct fann_train_data *data; struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output); data = fann_read_train_from_file("xor.data"); fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC); fann_set_training_algorithm(ann, FANN_TRAIN_QUICKPROP); train_on_steepness_file(ann, "xor.data", max_epochs, epochs_between_reports, desired_error, (float) 1.0, (float) 0.1, (float) 20.0); fann_set_activation_function_hidden(ann, FANN_THRESHOLD_SYMMETRIC); fann_set_activation_function_output(ann, FANN_THRESHOLD_SYMMETRIC); for(i = 0; i != fann_length_train_data(data); i++) { calc_out = fann_run(ann, data->input[i]); printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n", data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0], (float) fann_abs(calc_out[0] - data->output[i][0])); } fann_save(ann, "xor_float.net"); fann_destroy(ann); fann_destroy_train(data); return 0; } libfann-2.1.0~beta~dfsg/examples/xor.data000066400000000000000000000000441140315177100204760ustar00rootroot000000000000004 2 1 -1 -1 -1 -1 1 1 1 -1 1 1 1 -1 libfann-2.1.0~beta~dfsg/examples/xor_sample.cpp000066400000000000000000000115461140315177100217210ustar00rootroot00000000000000/* * * Fast Artificial Neural Network (fann) C++ Wrapper Sample * * C++ wrapper XOR sample with functionality similar to xor_train.c * * Copyright (C) 2004-2006 created by freegoldbar (at) yahoo dot com * * This wrapper is free software; you can redistribute it and/or * modify it under the terms of the GNU Lesser General Public * License as published by the Free Software Foundation; either * version 2.1 of the License, or (at your option) any later version. * * This wrapper is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU * Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public * License along with this library; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA * */ #include "floatfann.h" #include "fann_cpp.h" #include #include #include using std::cout; using std::cerr; using std::endl; using std::setw; using std::left; using std::right; using std::showpos; using std::noshowpos; // Callback function that simply prints the information to cout int print_callback(FANN::neural_net &net, FANN::training_data &train, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error, unsigned int epochs, void *user_data) { cout << "Epochs " << setw(8) << epochs << ". " << "Current Error: " << left << net.get_MSE() << right << endl; return 0; } // Test function that demonstrates usage of the fann C++ wrapper void xor_test() { cout << endl << "XOR test started." << endl; const float learning_rate = 0.7f; const unsigned int num_layers = 3; const unsigned int num_input = 2; const unsigned int num_hidden = 3; const unsigned int num_output = 1; const float desired_error = 0.001f; const unsigned int max_iterations = 300000; const unsigned int iterations_between_reports = 1000; cout << endl << "Creating network." << endl; FANN::neural_net net; net.create_standard(num_layers, num_input, num_hidden, num_output); net.set_learning_rate(learning_rate); net.set_activation_steepness_hidden(1.0); net.set_activation_steepness_output(1.0); net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE); net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE); // Set additional properties such as the training algorithm //net.set_training_algorithm(FANN::TRAIN_QUICKPROP); // Output network type and parameters cout << endl << "Network Type : "; switch (net.get_network_type()) { case FANN::LAYER: cout << "LAYER" << endl; break; case FANN::SHORTCUT: cout << "SHORTCUT" << endl; break; default: cout << "UNKNOWN" << endl; break; } net.print_parameters(); cout << endl << "Training network." << endl; FANN::training_data data; if (data.read_train_from_file("xor.data")) { // Initialize and train the network with the data net.init_weights(data); cout << "Max Epochs " << setw(8) << max_iterations << ". " << "Desired Error: " << left << desired_error << right << endl; net.set_callback(print_callback, NULL); net.train_on_data(data, max_iterations, iterations_between_reports, desired_error); cout << endl << "Testing network." << endl; for (unsigned int i = 0; i < data.length_train_data(); ++i) { // Run the network on the test data fann_type *calc_out = net.run(data.get_input()[i]); cout << "XOR test (" << showpos << data.get_input()[i][0] << ", " << data.get_input()[i][1] << ") -> " << *calc_out << ", should be " << data.get_output()[i][0] << ", " << "difference = " << noshowpos << fann_abs(*calc_out - data.get_output()[i][0]) << endl; } cout << endl << "Saving network." << endl; // Save the network in floating point and fixed point net.save("xor_float.net"); unsigned int decimal_point = net.save_to_fixed("xor_fixed.net"); data.save_train_to_fixed("xor_fixed.data", decimal_point); cout << endl << "XOR test completed." << endl; } } /* Startup function. Syncronizes C and C++ output, calls the test function and reports any exceptions */ int main(int argc, char **argv) { try { std::ios::sync_with_stdio(); // Syncronize cout and printf output xor_test(); } catch (...) { cerr << endl << "Abnormal exception." << endl; } return 0; } /******************************************************************************/ libfann-2.1.0~beta~dfsg/examples/xor_test.c000066400000000000000000000043201140315177100210470ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include "fann.h" int main() { fann_type *calc_out; unsigned int i; int ret = 0; struct fann *ann; struct fann_train_data *data; printf("Creating network.\n"); #ifdef FIXEDFANN ann = fann_create_from_file("xor_fixed.net"); #else ann = fann_create_from_file("xor_float.net"); #endif if(!ann) { printf("Error creating ann --- ABORTING.\n"); return 0; } fann_print_connections(ann); fann_print_parameters(ann); printf("Testing network.\n"); #ifdef FIXEDFANN data = fann_read_train_from_file("xor_fixed.data"); #else data = fann_read_train_from_file("xor.data"); #endif for(i = 0; i < fann_length_train_data(data); i++) { fann_reset_MSE(ann); calc_out = fann_test(ann, data->input[i], data->output[i]); #ifdef FIXEDFANN printf("XOR test (%d, %d) -> %d, should be %d, difference=%f\n", data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0], (float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann)); if((float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann) > 0.2) { printf("Test failed\n"); ret = -1; } #else printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n", data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0], (float) fann_abs(calc_out[0] - data->output[i][0])); #endif } printf("Cleaning up.\n"); fann_destroy_train(data); fann_destroy(ann); return ret; } libfann-2.1.0~beta~dfsg/examples/xor_train.c000066400000000000000000000054321140315177100212120ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include "fann.h" int FANN_API test_callback(struct fann *ann, struct fann_train_data *train, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error, unsigned int epochs) { printf("Epochs %8d. MSE: %.5f. Desired-MSE: %.5f\n", epochs, fann_get_MSE(ann), desired_error); return 0; } int main() { fann_type *calc_out; const unsigned int num_input = 2; const unsigned int num_output = 1; const unsigned int num_layers = 3; const unsigned int num_neurons_hidden = 3; const float desired_error = (const float) 0; const unsigned int max_epochs = 1000; const unsigned int epochs_between_reports = 10; struct fann *ann; struct fann_train_data *data; unsigned int i = 0; unsigned int decimal_point; printf("Creating network.\n"); ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output); data = fann_read_train_from_file("xor.data"); fann_set_activation_steepness_hidden(ann, 1); fann_set_activation_steepness_output(ann, 1); fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC); fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT); fann_set_bit_fail_limit(ann, 0.01f); fann_init_weights(ann, data); printf("Training network.\n"); fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error); printf("Testing network. %f\n", fann_test_data(ann, data)); for(i = 0; i < fann_length_train_data(data); i++) { calc_out = fann_run(ann, data->input[i]); printf("XOR test (%f,%f) -> %f, should be %f, difference=%f\n", data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0], fann_abs(calc_out[0] - data->output[i][0])); } printf("Saving network.\n"); fann_save(ann, "xor_float.net"); decimal_point = fann_save_to_fixed(ann, "xor_fixed.net"); fann_save_train_to_fixed(data, "xor_fixed.data", decimal_point); printf("Cleaning up.\n"); fann_destroy_train(data); fann_destroy(ann); return 0; } libfann-2.1.0~beta~dfsg/fann.pc.in000066400000000000000000000003331140315177100170710ustar00rootroot00000000000000prefix=@prefix@ exec_prefix=@exec_prefix@ libdir=@libdir@ includedir=@includedir@ Name: fann Description: Fast Artificial Neural Network Library Version: @VERSION@ Libs: -L${libdir} -lm -lfann Cflags: -I${includedir} libfann-2.1.0~beta~dfsg/fann.spec.in000066400000000000000000000043021140315177100174210ustar00rootroot00000000000000Summary: A fast artificial neural network library. Name: @PACKAGE@ Version: @VERSION@ Release: 1 License: LGPL Group: Development/Libraries URL: http://fann.sf.net/ Source: http://prdownloads.sourceforge.net/fann/%{name}-%{version}.tar.bz2 BuildRoot: %{_tmppath}/%{name}-%{version}-root %package devel Summary: Development libraries for FANN Requires: fann = @VERSION@-%{release} Group: Development/Libraries %description Fast Artificial Neural Network (FANN) Library is written in ANSI C. The library implements multilayer feedforward ANNs, up to 150 times faster than other libraries. FANN supports execution in fixed point, for fast execution on systems like the iPAQ. %description devel This package is only needed if you intend to develop and/or compile programs based on the FANN library. %prep %setup -q ./configure --prefix=@prefix@ %build make (cd doc && make html-single) %install rm -rf $RPM_BUILD_ROOT make DESTDIR=$RPM_BUILD_ROOT install %clean rm -rf $RPM_BUILD_ROOT %files %defattr(-,root,root) %{_libdir}/libdoublefann.so %{_libdir}/libdoublefann.so.1 %{_libdir}/libdoublefann.so.@VERSION@ %{_libdir}/libfloatfann.so %{_libdir}/libfloatfann.so.1 %{_libdir}/libfloatfann.so.@VERSION@ %{_libdir}/libfixedfann.so %{_libdir}/libfixedfann.so.1 %{_libdir}/libfixedfann.so.@VERSION@ %{_libdir}/libfann.so %{_libdir}/libfann.so.1 %{_libdir}/libfann.so.@VERSION@ %files devel %doc AUTHORS ChangeLog COPYING INSTALL NEWS README TODO doc/fann.html %{_libdir}/pkgconfig/fann.pc %{_libdir}/libdoublefann.a %{_libdir}/libdoublefann.la %{_libdir}/libfloatfann.a %{_libdir}/libfloatfann.la %{_libdir}/libfixedfann.a %{_libdir}/libfixedfann.la %{_libdir}/libfann.a %{_libdir}/libfann.la %{_includedir}/fann.h %{_includedir}/doublefann.h %{_includedir}/fann_internal.h %{_includedir}/floatfann.h %{_includedir}/fann_data.h %{_includedir}/fixedfann.h %{_includedir}/compat_time.h %{_includedir}/fann_errno.h %{_includedir}/fann_activation.h %changelog * Fri Mar 30 2004 Evan Nemerson - Build and install HTML documentation. * Fri Jan 16 2004 Evan Nemerson - Added activation header. * Mon Jan 10 2004 Evan Nemerson - RPM created libfann-2.1.0~beta~dfsg/install-sh000077500000000000000000000144061140315177100172300ustar00rootroot00000000000000#!/bin/sh # # install - install a program, script, or datafile # # This originates from X11R5 (mit/util/scripts/install.sh), which was # later released in X11R6 (xc/config/util/install.sh) with the # following copyright and license. # # Copyright (C) 1994 X Consortium # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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It can only install one file at a time, a restriction # shared with many OS's install programs. # set DOITPROG to echo to test this script # Don't use :- since 4.3BSD and earlier shells don't like it. doit="${DOITPROG-}" # put in absolute paths if you don't have them in your path; or use env. vars. mvprog="${MVPROG-mv}" cpprog="${CPPROG-cp}" chmodprog="${CHMODPROG-chmod}" chownprog="${CHOWNPROG-chown}" chgrpprog="${CHGRPPROG-chgrp}" stripprog="${STRIPPROG-strip}" rmprog="${RMPROG-rm}" mkdirprog="${MKDIRPROG-mkdir}" transformbasename="" transform_arg="" instcmd="$mvprog" chmodcmd="$chmodprog 0755" chowncmd="" chgrpcmd="" stripcmd="" rmcmd="$rmprog -f" mvcmd="$mvprog" src="" dst="" dir_arg="" while [ x"$1" != x ]; do case $1 in -c) instcmd="$cpprog" shift continue;; -d) dir_arg=true shift continue;; -m) chmodcmd="$chmodprog $2" shift shift continue;; -o) chowncmd="$chownprog $2" shift shift continue;; -g) chgrpcmd="$chgrpprog $2" shift shift continue;; -s) stripcmd="$stripprog" shift continue;; -t=*) transformarg=`echo $1 | sed 's/-t=//'` shift continue;; -b=*) transformbasename=`echo $1 | sed 's/-b=//'` shift continue;; *) if [ x"$src" = x ] then src=$1 else # this colon is to work around a 386BSD /bin/sh bug : dst=$1 fi shift continue;; esac done if [ x"$src" = x ] then echo "install: no input file specified" exit 1 else true fi if [ x"$dir_arg" != x ]; then dst=$src src="" if [ -d $dst ]; then instcmd=: chmodcmd="" else instcmd=mkdir fi else # Waiting for this to be detected by the "$instcmd $src $dsttmp" command # might cause directories to be created, which would be especially bad # if $src (and thus $dsttmp) contains '*'. if [ -f $src -o -d $src ] then true else echo "install: $src does not exist" exit 1 fi if [ x"$dst" = x ] then echo "install: no destination specified" exit 1 else true fi # If destination is a directory, append the input filename; if your system # does not like double slashes in filenames, you may need to add some logic if [ -d $dst ] then dst="$dst"/`basename $src` else true fi fi ## this sed command emulates the dirname command dstdir=`echo $dst | sed -e 's,[^/]*$,,;s,/$,,;s,^$,.,'` # Make sure that the destination directory exists. # this part is taken from Noah Friedman's mkinstalldirs script # Skip lots of stat calls in the usual case. if [ ! -d "$dstdir" ]; then defaultIFS=' ' IFS="${IFS-${defaultIFS}}" oIFS="${IFS}" # Some sh's can't handle IFS=/ for some reason. IFS='%' set - `echo ${dstdir} | sed -e 's@/@%@g' -e 's@^%@/@'` IFS="${oIFS}" pathcomp='' while [ $# -ne 0 ] ; do pathcomp="${pathcomp}${1}" shift if [ ! -d "${pathcomp}" ] ; then $mkdirprog "${pathcomp}" else true fi pathcomp="${pathcomp}/" done fi if [ x"$dir_arg" != x ] then $doit $instcmd $dst && if [ x"$chowncmd" != x ]; then $doit $chowncmd $dst; else true ; fi && if [ x"$chgrpcmd" != x ]; then $doit $chgrpcmd $dst; else true ; fi && if [ x"$stripcmd" != x ]; then $doit $stripcmd $dst; else true ; fi && if [ x"$chmodcmd" != x ]; then $doit $chmodcmd $dst; else true ; fi else # If we're going to rename the final executable, determine the name now. if [ x"$transformarg" = x ] then dstfile=`basename $dst` else dstfile=`basename $dst $transformbasename | sed $transformarg`$transformbasename fi # don't allow the sed command to completely eliminate the filename if [ x"$dstfile" = x ] then dstfile=`basename $dst` else true fi # Make a temp file name in the proper directory. dsttmp=$dstdir/#inst.$$# # Move or copy the file name to the temp name $doit $instcmd $src $dsttmp && trap "rm -f ${dsttmp}" 0 && # and set any options; do chmod last to preserve setuid bits # If any of these fail, we abort the whole thing. If we want to # ignore errors from any of these, just make sure not to ignore # errors from the above "$doit $instcmd $src $dsttmp" command. if [ x"$chowncmd" != x ]; then $doit $chowncmd $dsttmp; else true;fi && if [ x"$chgrpcmd" != x ]; then $doit $chgrpcmd $dsttmp; else true;fi && if [ x"$stripcmd" != x ]; then $doit $stripcmd $dsttmp; else true;fi && if [ x"$chmodcmd" != x ]; then $doit $chmodcmd $dsttmp; else true;fi && # Now rename the file to the real destination. $doit $rmcmd -f $dstdir/$dstfile && $doit $mvcmd $dsttmp $dstdir/$dstfile fi && exit 0 libfann-2.1.0~beta~dfsg/ltmain.sh000066400000000000000000006001571140315177100170500ustar00rootroot00000000000000# ltmain.sh - Provide generalized library-building support services. # NOTE: Changing this file will not affect anything until you rerun configure. # # Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005 # Free Software Foundation, Inc. # Originally by Gordon Matzigkeit , 1996 # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. basename="s,^.*/,,g" # Work around backward compatibility issue on IRIX 6.5. On IRIX 6.4+, sh # is ksh but when the shell is invoked as "sh" and the current value of # the _XPG environment variable is not equal to 1 (one), the special # positional parameter $0, within a function call, is the name of the # function. progpath="$0" # The name of this program: progname=`echo "$progpath" | $SED $basename` modename="$progname" # Global variables: EXIT_SUCCESS=0 EXIT_FAILURE=1 PROGRAM=ltmain.sh PACKAGE=libtool VERSION="1.5.22 Debian 1.5.22-2" TIMESTAMP=" (1.1220.2.365 2005/12/18 22:14:06)" # See if we are running on zsh, and set the options which allow our # commands through without removal of \ escapes. if test -n "${ZSH_VERSION+set}" ; then setopt NO_GLOB_SUBST fi # Check that we have a working $echo. if test "X$1" = X--no-reexec; then # Discard the --no-reexec flag, and continue. shift elif test "X$1" = X--fallback-echo; then # Avoid inline document here, it may be left over : elif test "X`($echo '\t') 2>/dev/null`" = 'X\t'; then # Yippee, $echo works! : else # Restart under the correct shell, and then maybe $echo will work. exec $SHELL "$progpath" --no-reexec ${1+"$@"} fi if test "X$1" = X--fallback-echo; then # used as fallback echo shift cat <&2 $echo "Fatal configuration error. See the $PACKAGE docs for more information." 1>&2 exit $EXIT_FAILURE fi # Global variables. mode=$default_mode nonopt= prev= prevopt= run= show="$echo" show_help= execute_dlfiles= duplicate_deps=no preserve_args= lo2o="s/\\.lo\$/.${objext}/" o2lo="s/\\.${objext}\$/.lo/" ##################################### # Shell function definitions: # This seems to be the best place for them # func_mktempdir [string] # Make a temporary directory that won't clash with other running # libtool processes, and avoids race conditions if possible. If # given, STRING is the basename for that directory. func_mktempdir () { my_template="${TMPDIR-/tmp}/${1-$progname}" if test "$run" = ":"; then # Return a directory name, but don't create it in dry-run mode my_tmpdir="${my_template}-$$" else # If mktemp works, use that first and foremost my_tmpdir=`mktemp -d "${my_template}-XXXXXXXX" 2>/dev/null` if test ! -d "$my_tmpdir"; then # Failing that, at least try and use $RANDOM to avoid a race my_tmpdir="${my_template}-${RANDOM-0}$$" save_mktempdir_umask=`umask` umask 0077 $mkdir "$my_tmpdir" umask $save_mktempdir_umask fi # If we're not in dry-run mode, bomb out on failure test -d "$my_tmpdir" || { $echo "cannot create temporary directory \`$my_tmpdir'" 1>&2 exit $EXIT_FAILURE } fi $echo "X$my_tmpdir" | $Xsed } # func_win32_libid arg # return the library type of file 'arg' # # Need a lot of goo to handle *both* DLLs and import libs # Has to be a shell function in order to 'eat' the argument # that is supplied when $file_magic_command is called. func_win32_libid () { win32_libid_type="unknown" win32_fileres=`file -L $1 2>/dev/null` case $win32_fileres in *ar\ archive\ import\ library*) # definitely import win32_libid_type="x86 archive import" ;; *ar\ archive*) # could be an import, or static if eval $OBJDUMP -f $1 | $SED -e '10q' 2>/dev/null | \ $EGREP -e 'file format pe-i386(.*architecture: i386)?' >/dev/null ; then win32_nmres=`eval $NM -f posix -A $1 | \ $SED -n -e '1,100{/ I /{s,.*,import,;p;q;};}'` case $win32_nmres in import*) win32_libid_type="x86 archive import";; *) win32_libid_type="x86 archive static";; esac fi ;; *DLL*) win32_libid_type="x86 DLL" ;; *executable*) # but shell scripts are "executable" too... case $win32_fileres in *MS\ Windows\ PE\ Intel*) win32_libid_type="x86 DLL" ;; esac ;; esac $echo $win32_libid_type } # func_infer_tag arg # Infer tagged configuration to use if any are available and # if one wasn't chosen via the "--tag" command line option. # Only attempt this if the compiler in the base compile # command doesn't match the default compiler. # arg is usually of the form 'gcc ...' func_infer_tag () { if test -n "$available_tags" && test -z "$tagname"; then CC_quoted= for arg in $CC; do case $arg in *[\[\~\#\^\&\*\(\)\{\}\|\;\<\>\?\'\ \ ]*|*]*|"") arg="\"$arg\"" ;; esac CC_quoted="$CC_quoted $arg" done case $@ in # Blanks in the command may have been stripped by the calling shell, # but not from the CC environment variable when configure was run. " $CC "* | "$CC "* | " `$echo $CC` "* | "`$echo $CC` "* | " $CC_quoted"* | "$CC_quoted "* | " `$echo $CC_quoted` "* | "`$echo $CC_quoted` "*) ;; # Blanks at the start of $base_compile will cause this to fail # if we don't check for them as well. *) for z in $available_tags; do if grep "^# ### BEGIN LIBTOOL TAG CONFIG: $z$" < "$progpath" > /dev/null; then # Evaluate the configuration. eval "`${SED} -n -e '/^# ### BEGIN LIBTOOL TAG CONFIG: '$z'$/,/^# ### END LIBTOOL TAG CONFIG: '$z'$/p' < $progpath`" CC_quoted= for arg in $CC; do # Double-quote args containing other shell metacharacters. case $arg in *[\[\~\#\^\&\*\(\)\{\}\|\;\<\>\?\'\ \ ]*|*]*|"") arg="\"$arg\"" ;; esac CC_quoted="$CC_quoted $arg" done case "$@ " in " $CC "* | "$CC "* | " `$echo $CC` "* | "`$echo $CC` "* | " $CC_quoted"* | "$CC_quoted "* | " `$echo $CC_quoted` "* | "`$echo $CC_quoted` "*) # The compiler in the base compile command matches # the one in the tagged configuration. # Assume this is the tagged configuration we want. tagname=$z break ;; esac fi done # If $tagname still isn't set, then no tagged configuration # was found and let the user know that the "--tag" command # line option must be used. if test -z "$tagname"; then $echo "$modename: unable to infer tagged configuration" $echo "$modename: specify a tag with \`--tag'" 1>&2 exit $EXIT_FAILURE # else # $echo "$modename: using $tagname tagged configuration" fi ;; esac fi } # func_extract_an_archive dir oldlib func_extract_an_archive () { f_ex_an_ar_dir="$1"; shift f_ex_an_ar_oldlib="$1" $show "(cd $f_ex_an_ar_dir && $AR x $f_ex_an_ar_oldlib)" $run eval "(cd \$f_ex_an_ar_dir && $AR x \$f_ex_an_ar_oldlib)" || exit $? if ($AR t "$f_ex_an_ar_oldlib" | sort | sort -uc >/dev/null 2>&1); then : else $echo "$modename: ERROR: object name conflicts: $f_ex_an_ar_dir/$f_ex_an_ar_oldlib" 1>&2 exit $EXIT_FAILURE fi } # func_extract_archives gentop oldlib ... func_extract_archives () { my_gentop="$1"; shift my_oldlibs=${1+"$@"} my_oldobjs="" my_xlib="" my_xabs="" my_xdir="" my_status="" $show "${rm}r $my_gentop" $run ${rm}r "$my_gentop" $show "$mkdir $my_gentop" $run $mkdir "$my_gentop" my_status=$? if test "$my_status" -ne 0 && test ! -d "$my_gentop"; then exit $my_status fi for my_xlib in $my_oldlibs; do # Extract the objects. case $my_xlib in [\\/]* | [A-Za-z]:[\\/]*) my_xabs="$my_xlib" ;; *) my_xabs=`pwd`"/$my_xlib" ;; esac my_xlib=`$echo "X$my_xlib" | $Xsed -e 's%^.*/%%'` my_xdir="$my_gentop/$my_xlib" $show "${rm}r $my_xdir" $run ${rm}r "$my_xdir" $show "$mkdir $my_xdir" $run $mkdir "$my_xdir" exit_status=$? if test "$exit_status" -ne 0 && test ! -d "$my_xdir"; then exit $exit_status fi case $host in *-darwin*) $show "Extracting $my_xabs" # Do not bother doing anything if just a dry run if test -z "$run"; then darwin_orig_dir=`pwd` cd $my_xdir || exit $? darwin_archive=$my_xabs darwin_curdir=`pwd` darwin_base_archive=`$echo "X$darwin_archive" | $Xsed -e 's%^.*/%%'` darwin_arches=`lipo -info "$darwin_archive" 2>/dev/null | $EGREP Architectures 2>/dev/null` if test -n "$darwin_arches"; then darwin_arches=`echo "$darwin_arches" | $SED -e 's/.*are://'` darwin_arch= $show "$darwin_base_archive has multiple architectures $darwin_arches" for darwin_arch in $darwin_arches ; do mkdir -p "unfat-$$/${darwin_base_archive}-${darwin_arch}" lipo -thin $darwin_arch -output "unfat-$$/${darwin_base_archive}-${darwin_arch}/${darwin_base_archive}" "${darwin_archive}" cd "unfat-$$/${darwin_base_archive}-${darwin_arch}" func_extract_an_archive "`pwd`" "${darwin_base_archive}" cd "$darwin_curdir" $rm "unfat-$$/${darwin_base_archive}-${darwin_arch}/${darwin_base_archive}" done # $darwin_arches ## Okay now we have a bunch of thin objects, gotta fatten them up :) darwin_filelist=`find unfat-$$ -type f -name \*.o -print -o -name \*.lo -print| xargs basename | sort -u | $NL2SP` darwin_file= darwin_files= for darwin_file in $darwin_filelist; do darwin_files=`find unfat-$$ -name $darwin_file -print | $NL2SP` lipo -create -output "$darwin_file" $darwin_files done # $darwin_filelist ${rm}r unfat-$$ cd "$darwin_orig_dir" else cd "$darwin_orig_dir" func_extract_an_archive "$my_xdir" "$my_xabs" fi # $darwin_arches fi # $run ;; *) func_extract_an_archive "$my_xdir" "$my_xabs" ;; esac my_oldobjs="$my_oldobjs "`find $my_xdir -name \*.$objext -print -o -name \*.lo -print | $NL2SP` done func_extract_archives_result="$my_oldobjs" } # End of Shell function definitions ##################################### # Darwin sucks eval std_shrext=\"$shrext_cmds\" disable_libs=no # Parse our command line options once, thoroughly. while test "$#" -gt 0 do arg="$1" shift case $arg in -*=*) optarg=`$echo "X$arg" | $Xsed -e 's/[-_a-zA-Z0-9]*=//'` ;; *) optarg= ;; esac # If the previous option needs an argument, assign it. if test -n "$prev"; then case $prev in execute_dlfiles) execute_dlfiles="$execute_dlfiles $arg" ;; tag) tagname="$arg" preserve_args="${preserve_args}=$arg" # Check whether tagname contains only valid characters case $tagname in *[!-_A-Za-z0-9,/]*) $echo "$progname: invalid tag name: $tagname" 1>&2 exit $EXIT_FAILURE ;; esac case $tagname in CC) # Don't test for the "default" C tag, as we know, it's there, but # not specially marked. ;; *) if grep "^# ### BEGIN LIBTOOL TAG CONFIG: $tagname$" < "$progpath" > /dev/null; then taglist="$taglist $tagname" # Evaluate the configuration. eval "`${SED} -n -e '/^# ### BEGIN LIBTOOL TAG CONFIG: '$tagname'$/,/^# ### END LIBTOOL TAG CONFIG: '$tagname'$/p' < $progpath`" else $echo "$progname: ignoring unknown tag $tagname" 1>&2 fi ;; esac ;; *) eval "$prev=\$arg" ;; esac prev= prevopt= continue fi # Have we seen a non-optional argument yet? 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then $run eval '$EGREP -v " ($exclude_expsyms)$" "$nlist" > "$nlist"T' $run eval '$mv "$nlist"T "$nlist"' fi if test -n "$export_symbols_regex"; then $run eval '$EGREP -e "$export_symbols_regex" "$nlist" > "$nlist"T' $run eval '$mv "$nlist"T "$nlist"' fi # Prepare the list of exported symbols if test -z "$export_symbols"; then export_symbols="$output_objdir/$outputname.exp" $run $rm $export_symbols $run eval "${SED} -n -e '/^: @PROGRAM@ $/d' -e 's/^.* \(.*\)$/\1/p' "'< "$nlist" > "$export_symbols"' case $host in *cygwin* | *mingw* ) $run eval "echo EXPORTS "'> "$output_objdir/$outputname.def"' $run eval 'cat "$export_symbols" >> "$output_objdir/$outputname.def"' ;; esac else $run eval "${SED} -e 's/\([].[*^$]\)/\\\\\1/g' -e 's/^/ /' -e 's/$/$/'"' < "$export_symbols" > "$output_objdir/$outputname.exp"' $run eval 'grep -f "$output_objdir/$outputname.exp" < "$nlist" > "$nlist"T' $run eval 'mv "$nlist"T "$nlist"' case $host in *cygwin* | *mingw* ) $run eval "echo EXPORTS "'> "$output_objdir/$outputname.def"' $run eval 'cat "$nlist" >> "$output_objdir/$outputname.def"' ;; 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* ) $echo >> "$output_objdir/$dlsyms" "\ const struct { " ;; esac $echo >> "$output_objdir/$dlsyms" "\ const char *name; lt_ptr address; } lt_preloaded_symbols[] = {\ " eval "$global_symbol_to_c_name_address" < "$nlist" >> "$output_objdir/$dlsyms" $echo >> "$output_objdir/$dlsyms" "\ {0, (lt_ptr) 0} }; /* This works around a problem in FreeBSD linker */ #ifdef FREEBSD_WORKAROUND static const void *lt_preloaded_setup() { return lt_preloaded_symbols; } #endif #ifdef __cplusplus } #endif\ " fi pic_flag_for_symtable= case $host in # compiling the symbol table file with pic_flag works around # a FreeBSD bug that causes programs to crash when -lm is # linked before any other PIC object. 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exit $EXIT_SUCCESS fi if test "$hardcode_action" = relink; then # Fast installation is not supported link_command="$compile_var$compile_command$compile_rpath" relink_command="$finalize_var$finalize_command$finalize_rpath" $echo "$modename: warning: this platform does not like uninstalled shared libraries" 1>&2 $echo "$modename: \`$output' will be relinked during installation" 1>&2 else if test "$fast_install" != no; then link_command="$finalize_var$compile_command$finalize_rpath" if test "$fast_install" = yes; then relink_command=`$echo "X$compile_var$compile_command$compile_rpath" | $Xsed -e 's%@OUTPUT@%\$progdir/\$file%g'` else # fast_install is set to needless relink_command= fi else link_command="$compile_var$compile_command$compile_rpath" relink_command="$finalize_var$finalize_command$finalize_rpath" fi fi # Replace the output file specification. link_command=`$echo "X$link_command" | $Xsed -e 's%@OUTPUT@%'"$output_objdir/$outputname"'%g'` # Delete the old output files. $run $rm $output $output_objdir/$outputname $output_objdir/lt-$outputname $show "$link_command" $run eval "$link_command" || exit $? # Now create the wrapper script. $show "creating $output" # Quote the relink command for shipping. if test -n "$relink_command"; then # Preserve any variables that may affect compiler behavior for var in $variables_saved_for_relink; do if eval test -z \"\${$var+set}\"; then relink_command="{ test -z \"\${$var+set}\" || unset $var || { $var=; export $var; }; }; $relink_command" elif eval var_value=\$$var; test -z "$var_value"; then relink_command="$var=; export $var; $relink_command" else var_value=`$echo "X$var_value" | $Xsed -e "$sed_quote_subst"` relink_command="$var=\"$var_value\"; export $var; $relink_command" fi done relink_command="(cd `pwd`; $relink_command)" relink_command=`$echo "X$relink_command" | $Xsed -e "$sed_quote_subst"` fi # Quote $echo for shipping. if test "X$echo" = "X$SHELL $progpath --fallback-echo"; then case $progpath in [\\/]* | [A-Za-z]:[\\/]*) qecho="$SHELL $progpath --fallback-echo";; *) qecho="$SHELL `pwd`/$progpath --fallback-echo";; esac qecho=`$echo "X$qecho" | $Xsed -e "$sed_quote_subst"` else qecho=`$echo "X$echo" | $Xsed -e "$sed_quote_subst"` fi # Only actually do things if our run command is non-null. if test -z "$run"; then # win32 will think the script is a binary if it has # a .exe suffix, so we strip it off here. case $output in *.exe) output=`$echo $output|${SED} 's,.exe$,,'` ;; esac # test for cygwin because mv fails w/o .exe extensions case $host in *cygwin*) exeext=.exe outputname=`$echo $outputname|${SED} 's,.exe$,,'` ;; *) exeext= ;; esac case $host in *cygwin* | *mingw* ) output_name=`basename $output` output_path=`dirname $output` cwrappersource="$output_path/$objdir/lt-$output_name.c" cwrapper="$output_path/$output_name.exe" $rm $cwrappersource $cwrapper trap "$rm $cwrappersource $cwrapper; exit $EXIT_FAILURE" 1 2 15 cat > $cwrappersource <> $cwrappersource<<"EOF" #include #include #include #include #include #include #include #include #include #if defined(PATH_MAX) # define LT_PATHMAX PATH_MAX #elif defined(MAXPATHLEN) # define LT_PATHMAX MAXPATHLEN #else # define LT_PATHMAX 1024 #endif #ifndef DIR_SEPARATOR # define DIR_SEPARATOR '/' # define PATH_SEPARATOR ':' #endif #if defined (_WIN32) || defined (__MSDOS__) || defined (__DJGPP__) || \ defined (__OS2__) # define HAVE_DOS_BASED_FILE_SYSTEM # ifndef DIR_SEPARATOR_2 # define DIR_SEPARATOR_2 '\\' # endif # ifndef PATH_SEPARATOR_2 # define PATH_SEPARATOR_2 ';' # endif #endif #ifndef DIR_SEPARATOR_2 # define IS_DIR_SEPARATOR(ch) ((ch) == DIR_SEPARATOR) #else /* DIR_SEPARATOR_2 */ # define IS_DIR_SEPARATOR(ch) \ (((ch) == DIR_SEPARATOR) || ((ch) == DIR_SEPARATOR_2)) #endif /* DIR_SEPARATOR_2 */ #ifndef PATH_SEPARATOR_2 # define IS_PATH_SEPARATOR(ch) ((ch) == PATH_SEPARATOR) #else /* PATH_SEPARATOR_2 */ # define IS_PATH_SEPARATOR(ch) ((ch) == PATH_SEPARATOR_2) #endif /* PATH_SEPARATOR_2 */ #define XMALLOC(type, num) ((type *) xmalloc ((num) * sizeof(type))) #define XFREE(stale) do { \ if (stale) { free ((void *) stale); stale = 0; } \ } while (0) /* -DDEBUG is fairly common in CFLAGS. */ #undef DEBUG #if defined DEBUGWRAPPER # define DEBUG(format, ...) fprintf(stderr, format, __VA_ARGS__) #else # define DEBUG(format, ...) #endif const char *program_name = NULL; void * xmalloc (size_t num); char * xstrdup (const char *string); const char * base_name (const char *name); char * find_executable(const char *wrapper); int check_executable(const char *path); char * strendzap(char *str, const char *pat); void lt_fatal (const char *message, ...); int main (int argc, char *argv[]) { char **newargz; int i; program_name = (char *) xstrdup (base_name (argv[0])); DEBUG("(main) argv[0] : %s\n",argv[0]); DEBUG("(main) program_name : %s\n",program_name); newargz = XMALLOC(char *, argc+2); EOF cat >> $cwrappersource <> $cwrappersource <<"EOF" newargz[1] = find_executable(argv[0]); if (newargz[1] == NULL) lt_fatal("Couldn't find %s", argv[0]); DEBUG("(main) found exe at : %s\n",newargz[1]); /* we know the script has the same name, without the .exe */ /* so make sure newargz[1] doesn't end in .exe */ strendzap(newargz[1],".exe"); for (i = 1; i < argc; i++) newargz[i+1] = xstrdup(argv[i]); newargz[argc+1] = NULL; for (i=0; i> $cwrappersource <> $cwrappersource <> $cwrappersource <<"EOF" return 127; } void * xmalloc (size_t num) { void * p = (void *) malloc (num); if (!p) lt_fatal ("Memory exhausted"); return p; } char * xstrdup (const char *string) { return string ? strcpy ((char *) xmalloc (strlen (string) + 1), string) : NULL ; } const char * base_name (const char *name) { const char *base; #if defined (HAVE_DOS_BASED_FILE_SYSTEM) /* Skip over the disk name in MSDOS pathnames. */ if (isalpha ((unsigned char)name[0]) && name[1] == ':') name += 2; #endif for (base = name; *name; name++) if (IS_DIR_SEPARATOR (*name)) base = name + 1; return base; } int check_executable(const char * path) { struct stat st; DEBUG("(check_executable) : %s\n", path ? (*path ? path : "EMPTY!") : "NULL!"); if ((!path) || (!*path)) return 0; if ((stat (path, &st) >= 0) && ( /* MinGW & native WIN32 do not support S_IXOTH or S_IXGRP */ #if defined (S_IXOTH) ((st.st_mode & S_IXOTH) == S_IXOTH) || #endif #if defined (S_IXGRP) ((st.st_mode & S_IXGRP) == S_IXGRP) || #endif ((st.st_mode & S_IXUSR) == S_IXUSR)) ) return 1; else return 0; } /* Searches for the full path of the wrapper. Returns newly allocated full path name if found, NULL otherwise */ char * find_executable (const char* wrapper) { int has_slash = 0; const char* p; const char* p_next; /* static buffer for getcwd */ char tmp[LT_PATHMAX + 1]; int tmp_len; char* concat_name; DEBUG("(find_executable) : %s\n", wrapper ? (*wrapper ? wrapper : "EMPTY!") : "NULL!"); if ((wrapper == NULL) || (*wrapper == '\0')) return NULL; /* Absolute path? */ #if defined (HAVE_DOS_BASED_FILE_SYSTEM) if (isalpha ((unsigned char)wrapper[0]) && wrapper[1] == ':') { concat_name = xstrdup (wrapper); if (check_executable(concat_name)) return concat_name; XFREE(concat_name); } else { #endif if (IS_DIR_SEPARATOR (wrapper[0])) { concat_name = xstrdup (wrapper); if (check_executable(concat_name)) return concat_name; XFREE(concat_name); } #if defined (HAVE_DOS_BASED_FILE_SYSTEM) } #endif for (p = wrapper; *p; p++) if (*p == '/') { has_slash = 1; break; } if (!has_slash) { /* no slashes; search PATH */ const char* path = getenv ("PATH"); if (path != NULL) { for (p = path; *p; p = p_next) { const char* q; size_t p_len; for (q = p; *q; q++) if (IS_PATH_SEPARATOR(*q)) break; p_len = q - p; p_next = (*q == '\0' ? q : q + 1); if (p_len == 0) { /* empty path: current directory */ if (getcwd (tmp, LT_PATHMAX) == NULL) lt_fatal ("getcwd failed"); tmp_len = strlen(tmp); concat_name = XMALLOC(char, tmp_len + 1 + strlen(wrapper) + 1); memcpy (concat_name, tmp, tmp_len); concat_name[tmp_len] = '/'; strcpy (concat_name + tmp_len + 1, wrapper); } else { concat_name = XMALLOC(char, p_len + 1 + strlen(wrapper) + 1); memcpy (concat_name, p, p_len); concat_name[p_len] = '/'; strcpy (concat_name + p_len + 1, wrapper); } if (check_executable(concat_name)) return concat_name; XFREE(concat_name); } } /* not found in PATH; assume curdir */ } /* Relative path | not found in path: prepend cwd */ if (getcwd (tmp, LT_PATHMAX) == NULL) lt_fatal ("getcwd failed"); tmp_len = strlen(tmp); concat_name = XMALLOC(char, tmp_len + 1 + strlen(wrapper) + 1); memcpy (concat_name, tmp, tmp_len); concat_name[tmp_len] = '/'; strcpy (concat_name + tmp_len + 1, wrapper); if (check_executable(concat_name)) return concat_name; XFREE(concat_name); return NULL; } char * strendzap(char *str, const char *pat) { size_t len, patlen; assert(str != NULL); assert(pat != NULL); len = strlen(str); patlen = strlen(pat); if (patlen <= len) { str += len - patlen; if (strcmp(str, pat) == 0) *str = '\0'; } return str; } static void lt_error_core (int exit_status, const char * mode, const char * message, va_list ap) { fprintf (stderr, "%s: %s: ", program_name, mode); vfprintf (stderr, message, ap); fprintf (stderr, ".\n"); if (exit_status >= 0) exit (exit_status); } void lt_fatal (const char *message, ...) { va_list ap; va_start (ap, message); lt_error_core (EXIT_FAILURE, "FATAL", message, ap); va_end (ap); } EOF # we should really use a build-platform specific compiler # here, but OTOH, the wrappers (shell script and this C one) # are only useful if you want to execute the "real" binary. # Since the "real" binary is built for $host, then this # wrapper might as well be built for $host, too. $run $LTCC $LTCFLAGS -s -o $cwrapper $cwrappersource ;; esac $rm $output trap "$rm $output; exit $EXIT_FAILURE" 1 2 15 $echo > $output "\ #! $SHELL # $output - temporary wrapper script for $objdir/$outputname # Generated by $PROGRAM - GNU $PACKAGE $VERSION$TIMESTAMP # # The $output program cannot be directly executed until all the libtool # libraries that it depends on are installed. # # This wrapper script should never be moved out of the build directory. # If it is, it will not operate correctly. # Sed substitution that helps us do robust quoting. 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You may also peek at any GNU archive site, in case some other package would contain this missing \`$1' program." exit 1 ;; esac exit 0 libfann-2.1.0~beta~dfsg/mkinstalldirs000077500000000000000000000013331140315177100200250ustar00rootroot00000000000000#! /bin/sh # mkinstalldirs --- make directory hierarchy # Author: Noah Friedman # Created: 1993-05-16 # Public domain # $Id: mkinstalldirs,v 1.1 2003/11/12 23:11:12 looksirdroids Exp $ errstatus=0 for file do set fnord `echo ":$file" | sed -ne 's/^:\//#/;s/^://;s/\// /g;s/^#/\//;p'` shift pathcomp= for d do pathcomp="$pathcomp$d" case "$pathcomp" in -* ) pathcomp=./$pathcomp ;; esac if test ! -d "$pathcomp"; then echo "mkdir $pathcomp" mkdir "$pathcomp" || lasterr=$? if test ! -d "$pathcomp"; then errstatus=$lasterr fi fi pathcomp="$pathcomp/" done done exit $errstatus # mkinstalldirs ends here libfann-2.1.0~beta~dfsg/python/000077500000000000000000000000001140315177100165405ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/python/INSTALL000066400000000000000000000016271140315177100175770ustar00rootroot00000000000000INSTRUCTIONS PREREQUISITES ^^^^^^^^^^^^^ Make sure you can make and install the fann library first. You are required to have swig and python development files installed. After you compiled the fann library... BUILDING AND INSTALLING USING MAKE (preferred) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ run the following command: $ make then as root run # make install BUILDING AND INSTALLING USING DISTUTILS (second alternative) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ run the following command as root # python setyp.py install ######### ** ** ** ** ** ** ** ** ** ** ** ** ** ** ########## ##### The wrapper should be installed uing above methods ##### ##### The following instructions are for advanced users ##### BUILD USING LINUX MAKEFILES ^^^^^^^^^^^^^^^^^^^^^^^^^^^ run the following command $ make linux BUILD USING MSVC MAKEFILES ^^^^^^^^^^^^^^^^^^^^^^^^^^ run the following command $ make msvc libfann-2.1.0~beta~dfsg/python/Makefile000066400000000000000000000016031140315177100202000ustar00rootroot00000000000000# This makefile was written to compile a distribution of pyfann for # GNU platforms (cygwin included.) ROOT=/ PYTHON=python SOURCES = pyfann/*.i pyfann/*.h all: build-stamp linux: @(cd pyfann && make) clean: @(cd pyfann && make clean) @rm -fr dist build setup.pyc @rm -fr examples/{nets,pyfann/} @rm -f build-stamp msvc: @(cd pyfann && make -f makefile.msvc) build: build-stamp: ../src/doublefann.o $(SOURCES) @$(PYTHON) setup.py build @touch build-stamp install: build-stamp @$(PYTHON) setup.py install --root $(ROOT) ../src/doublefann.o: ../Makefile @(cd .. && make ) ../Makefile: @(cd .. && ./configure) copy: build-stamp @- [ -d examples/nets ] || mkdir examples/nets @cp build/lib.*/pyfann/ -a examples test: copy @cd examples && \ $(PYTHON) simple_train.py && \ $(PYTHON) mushroom.py &&\ $(PYTHON) cascade_train.py .PHONY: test install msvc clean linux all libfann-2.1.0~beta~dfsg/python/README000066400000000000000000000012561140315177100174240ustar00rootroot00000000000000This python binding is provided by Vincenzo Di Massa and Gil Megidish DESCRIPTION =========== Using this wrapper it is possible to use libfann from python scripts. USAGE ===== Just >> from pyfan import libfann and then create libfann.neural_net and libfann.training_data objects >> ann = libfann.neural_net() >> train_data = libfann.training_data() Look at the examples in examples/ and at the FANN documentation and its C++ bindings for further reference. TESTING ======= To test the python bindings type $ make test to build the library and execute example scripts in the example directory. BUILDING ======== see INSTALL libfann-2.1.0~beta~dfsg/python/build.bat000077500000000000000000000001051140315177100203260ustar00rootroot00000000000000deltree build deltree dist c:\python23\python setup.py bdist_wininst libfann-2.1.0~beta~dfsg/python/examples/000077500000000000000000000000001140315177100203565ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/python/examples/cascade_train.py000077500000000000000000000034071140315177100235170ustar00rootroot00000000000000#!/usr/bin/python from pyfann import libfann num_neurons_hidden = 4 num_output = 1 desired_error = 0.0001 max_neurons = 40 neurons_between_reports = 1 steepnesses = [0.1,0.2,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1] train_data = libfann.training_data() train_data.read_train_from_file("../../benchmarks/datasets/two-spiral.train") test_data = libfann.training_data() test_data.read_train_from_file("../../benchmarks/datasets/two-spiral.test") train_data.scale_train_data(0, 1) test_data.scale_train_data(0, 1) ann = libfann.neural_net() ann.create_shortcut_array([len(train_data.get_input()[0]), len(train_data.get_output()[0])]) ann.set_training_algorithm(libfann.TRAIN_RPROP); ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC); ann.set_activation_function_output(libfann.LINEAR_PIECE); ann.set_activation_steepness_hidden(0.5); ann.set_activation_steepness_output(0.5); ann.set_train_error_function(libfann.ERRORFUNC_LINEAR); ann.set_rprop_increase_factor(1.2); ann.set_rprop_decrease_factor(0.5); ann.set_rprop_delta_min(0.0); ann.set_rprop_delta_max(50.0); ann.set_cascade_output_change_fraction(0.01); ann.set_cascade_output_stagnation_epochs(12); ann.set_cascade_candidate_change_fraction(0.01); ann.set_cascade_candidate_stagnation_epochs(12); ann.set_cascade_weight_multiplier(0.4); ann.set_cascade_candidate_limit(1000.0); ann.set_cascade_max_out_epochs(150); ann.set_cascade_max_cand_epochs(150); ann.set_cascade_activation_steepnesses(steepnesses); ann.set_cascade_num_candidate_groups(1); ann.print_parameters(); ann.cascadetrain_on_data(train_data, max_neurons, neurons_between_reports, desired_error); ann.print_connections(); print "\nTrain error: %f, Test error: %f\n\n" %( ann.test_data(train_data),ann.test_data(test_data)) ann.save("nets/xor_float.net"); libfann-2.1.0~beta~dfsg/python/examples/mushroom.py000077500000000000000000000032051140315177100226040ustar00rootroot00000000000000#!/usr/bin/python import os import sys from pyfann import libfann def print_callback(epochs, error): print "Epochs %8d. Current MSE-Error: %.10f\n" % (epochs, error) return 0 # initialize network parameters connection_rate = 1 learning_rate = 0.7 num_neurons_hidden = 32 desired_error = 0.000001 max_iterations = 300 iterations_between_reports = 1 # create training data, and ann object print "Creating network." train_data = libfann.training_data() train_data.read_train_from_file(os.path.join("..","..","benchmarks","datasets","mushroom.train")) ann = libfann.neural_net() ann.create_sparse_array(connection_rate, (len(train_data.get_input()[0]), num_neurons_hidden, len(train_data.get_output()[0]))) ann.set_learning_rate(learning_rate) # start training the network print "Training network" ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC_STEPWISE) ann.set_activation_function_output(libfann.SIGMOID_STEPWISE) ann.set_training_algorithm(libfann.TRAIN_INCREMENTAL) ann.train_on_data(train_data, max_iterations, iterations_between_reports, desired_error) # test outcome print "Testing network" test_data = libfann.training_data() test_data.read_train_from_file(os.path.join("..","..","benchmarks","datasets","mushroom.test")) ann.reset_MSE() ann.test_data(test_data) print "MSE error on test data: %f" % ann.get_MSE() print "Testing network again" ann.reset_MSE() input=test_data.get_input() output=test_data.get_output() for i in range(len(input)): ann.test(input[i], output[i]) print "MSE error on test data: %f" % ann.get_MSE() # save network to disk print "Saving network" ann.save(os.path.join("nets","mushroom_float.net")) libfann-2.1.0~beta~dfsg/python/examples/simple_train.py000077500000000000000000000011041140315177100234150ustar00rootroot00000000000000#!/usr/bin/python from pyfann import libfann connection_rate = 1 learning_rate = 0.7 num_input = 2 num_neurons_hidden = 4 num_output = 1 desired_error = 0.0001 max_iterations = 100000 iterations_between_reports = 1000 ann = libfann.neural_net() ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden, num_output)) ann.set_learning_rate(learning_rate) ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE) ann.train_on_file("../../examples/xor.data", max_iterations, iterations_between_reports, desired_error) ann.save("nets/xor_float.net") libfann-2.1.0~beta~dfsg/python/pyfann/000077500000000000000000000000001140315177100200335ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/python/pyfann/Makefile000066400000000000000000000010201140315177100214640ustar00rootroot00000000000000# This makefile was written to compile a distribution of pyfann for # GNU platforms (cygwin included.) TARGETS = _libfann.so PYTHON=python2.3 FANN_DIR="../.." LIBS=-L. -L/usr/local/lib -L/usr/bin -l$(PYTHON) $(FANN_DIR)/src/fann*.o all: $(TARGETS) _%.so: pyfann_wrap.o fann_helper.o gcc $(LIBS) -fPIC -shared -dll $^ -o $@ %.o: %.c gcc -fPIC -shared -c $< -I/usr/include/$(PYTHON)/ -I$(FANN_DIR)/src/include/ pyfann_wrap.cpp: pyfann.i swig -c++ -python $< clean: rm -f $(TARGETS) *_wrap.* *.py{c,o} *.so libfann.* libfann-2.1.0~beta~dfsg/python/pyfann/__init__.py000066400000000000000000000001521140315177100221420ustar00rootroot00000000000000# # Fast Artificial Neural Network library for Python # import libfann __all__ = [ 'libfann' ] libfann-2.1.0~beta~dfsg/python/pyfann/fann_cpp_subclass.h000066400000000000000000000435171140315177100237010ustar00rootroot00000000000000#ifndef FANN_CPP_SUBCLASS_H_INCLUDED #define FANN_CPP_SUBCLASS_H_INCLUDED #include #include #include #include /* Namespace: FANN The FANN namespace groups the C++ wrapper definitions */ namespace FANN { template class helper_array { public: helper_array() { array=0; array_len=0; can_delete=true; } void set (T * array, unsigned int len) { this->array=array; this->array_len=array_len; } T* array; unsigned int array_len; bool can_delete; }; template class helper_array_array { public: helper_array_array() { arrays=0; array_len=0; array_num=0; can_delete=false; } void set (T ** arrays, unsigned int len, unsigned int nun) { this->arrays=arrays; this->array_len=array_len; this->array_num=array_num; } T** arrays; unsigned int array_len; unsigned int array_num; bool can_delete; }; /* Forward declaration of class neural_net and training_data */ class Neural_net; class Training_data; /*************************************************************************/ /* Class: training_data Encapsulation of a training data set and associated C API functions. */ class Training_data : public training_data { public: /* Constructor: training_data Default constructor creates an empty neural net. Use , or to initialize. */ Training_data() : training_data() { } /* Constructor: training_data Copy constructor constructs a copy of the training data. Corresponds to the C API function. */ Training_data(const Training_data &data) { destroy_train(); if (data.train_data != NULL) { train_data = fann_duplicate_train_data(data.train_data); } } /* Destructor: ~training_data Provides automatic cleanup of data. Define USE_VIRTUAL_DESTRUCTOR if you need the destructor to be virtual. See also: */ #ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~Training_data() { destroy_train(); } /* Grant access to the encapsulated data since many situations and applications creates the data from sources other than files or uses the training data for testing and related functions */ /* Method: get_input Returns: A pointer to the array of input training data See also: , */ helper_array_array* get_input() { if (train_data == NULL) { return NULL; } else { helper_array_array* ret = new helper_array_array; ret->arrays=train_data->input; ret->array_num=train_data->num_data; ret->array_len=train_data->num_input; ret->can_delete=false; return ret; } } /* Method: get_output Returns: A pointer to the array of output training data See also: , */ helper_array_array* get_output() { if (train_data == NULL) { return NULL; } else { helper_array_array* ret = new helper_array_array; ret->arrays=train_data->output; ret->array_num=train_data->num_data; ret->array_len=train_data->num_output; ret->can_delete=false; return ret; } } /* Method: set_train_data Set the training data to the input and output data provided. A copy of the data is made so there are no restrictions on the allocation of the input/output data and the caller is responsible for the deallocation of the data pointed to by input and output. See also: , */ void set_train_data(helper_array_array< fann_type >* input, helper_array_array< fann_type >* output) { if (input->array_num!=output->array_num) { std::cerr<<"Error: input and output must have the same dimension!"<can_delete=true; output->can_delete=true; training_data::set_train_data(input->array_num, input->array_len, input->arrays, output->array_len, output->arrays); } }; /*************************************************************************/ /* Class: Neural_net Encapsulation of a neural network and associated C API functions. */ class Neural_net : public neural_net { public: /* Constructor: neural_net Default constructor creates an empty neural net. Use one of the create functions to create the neural network. See also: , , , , , */ Neural_net() : neural_net() { } /* Destructor: ~neural_net Provides automatic cleanup of data. Define USE_VIRTUAL_DESTRUCTOR if you need the destructor to be virtual. See also: */ #ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~Neural_net() { destroy(); } /* Method: create_standard_array Just like , but with an array of layer sizes instead of individual parameters. See also: , , , This function appears in FANN >= 2.0.0. */ bool create_standard_array( helper_array* layers) { return neural_net::create_standard_array( layers->array_len, layers->array); } /* Method: create_sparse_array Just like , but with an array of layer sizes instead of individual parameters. See for a description of the parameters. See also: , , , This function appears in FANN >= 2.0.0. */ bool create_sparse_array(float connection_rate, helper_array* layers) { return neural_net::create_sparse_array( connection_rate, layers->array_len, layers->array); } /* Method: create_shortcut_array Just like , but with an array of layer sizes instead of individual parameters. See for a description of the parameters. See also: , , , This function appears in FANN >= 2.0.0. */ bool create_shortcut_array( helper_array* layers) { return neural_net::create_shortcut_array( layers->array_len, layers->array); } /* Method: run Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer. See also: , This function appears in FANN >= 1.0.0. */ helper_array* run(helper_array *input) { if (ann == NULL && input->array_len!=ann->num_input) { return NULL; } helper_array* res= new helper_array; res->array=fann_run(ann, input->array); res->array_len=ann->num_output; res->can_delete=false; return res; } #ifndef FIXEDFANN /* Method: train Train one iteration with a set of inputs, and a set of desired outputs. This training is always incremental training (see ), since only one pattern is presented. Parameters: ann - The neural network structure input - an array of inputs. This array must be exactly long. desired_output - an array of desired outputs. This array must be exactly long. See also: , , This function appears in FANN >= 1.0.0. */ void train(helper_array *input, helper_array *desired_output) { if (ann != NULL && input->array_len==ann->num_input && desired_output->array_len==ann->num_output) { fann_train(ann, input->array, desired_output->array); } } #endif /* NOT FIXEDFANN */ /* Method: test Test with a set of inputs, and a set of desired outputs. This operation updates the mean square error, but does not change the network in any way. See also: , , This function appears in FANN >= 1.0.0. */ helper_array* test(helper_array *input, helper_array* desired_output) { if (ann == NULL) { return NULL; } helper_array* res= new helper_array; res->array=fann_test(ann, input->array, desired_output->array); res->array_len=ann->num_output; res->can_delete=false; return res; } /*************************************************************************************************************/ /* Method: get_layer_array Get the number of neurons in each layer in the network. Bias is not included so the layers match the create methods. The layers array must be preallocated to at least sizeof(unsigned int) * get_num_layers() long. See also: This function appears in FANN >= 2.1.0 */ void get_layer_array(helper_array* ARGOUT) { if (ann != NULL) { ARGOUT->array_len = fann_get_num_layers(ann); ARGOUT->array = (unsigned int*) malloc(sizeof(unsigned int)* ARGOUT->array_len); fann_get_layer_array(ann, ARGOUT->array); } } /* Method: get_bias_array Get the number of bias in each layer in the network. The bias array must be preallocated to at least sizeof(unsigned int) * get_num_layers() long. See also: This function appears in FANN >= 2.1.0 */ void get_bias_array(helper_array* ARGOUT) { if (ann != NULL) { ARGOUT->array_len = fann_get_num_layers(ann); ARGOUT->array = (unsigned int*) malloc(sizeof(unsigned int)* ARGOUT->array_len); fann_get_bias_array(ann, ARGOUT->array); } } /* Method: get_connection_array Get the connections in the network. The connections array must be preallocated to at least sizeof(struct fann_connection) * get_total_connections() long. See also: This function appears in FANN >= 2.1.0 */ void get_connection_array(helper_array *ARGOUT) { if (ann != NULL) { ARGOUT->array_len = fann_get_total_connections(ann); ARGOUT->array = (connection*) malloc(sizeof(connection)* ARGOUT->array_len); fann_get_connection_array(ann, ARGOUT->array); } } /* Method: set_weight_array Set connections in the network. Only the weights can be changed, connections and weights are ignored if they do not already exist in the network. The array must have sizeof(struct fann_connection) * num_connections size. See also: This function appears in FANN >= 2.1.0 */ void set_weight_array(helper_array *connections) { if (ann != NULL) { fann_set_weight_array(ann, connections->array, connections->array_len); } } /*********************************************************************/ #ifdef TODO /* Method: get_cascade_activation_functions The cascade activation functions array is an array of the different activation functions used by the candidates. See for a description of which candidate neurons will be generated by this array. See also: , , This function appears in FANN >= 2.0.0. */ activation_function_enum * get_cascade_activation_functions() { enum fann_activationfunc_enum *activation_functions = NULL; if (ann != NULL) { activation_functions = fann_get_cascade_activation_functions(ann); } return reinterpret_cast(activation_functions); } /* Method: set_cascade_activation_functions Sets the array of cascade candidate activation functions. The array must be just as long as defined by the count. See for a description of which candidate neurons will be generated by this array. See also: , , This function appears in FANN >= 2.0.0. */ void set_cascade_activation_functions(activation_function_enum *cascade_activation_functions, unsigned int cascade_activation_functions_count) { if (ann != NULL) { fann_set_cascade_activation_functions(ann, reinterpret_cast(cascade_activation_functions), cascade_activation_functions_count); } } #endif /* Method: get_cascade_activation_steepnesses The cascade activation steepnesses array is an array of the different activation functions used by the candidates. See for a description of which candidate neurons will be generated by this array. The default activation steepnesses is {0.25, 0.50, 0.75, 1.00} See also: , , This function appears in FANN >= 2.0.0. */ helper_array *get_cascade_activation_steepnesses() { helper_array *activation_steepnesses = NULL; if (ann != NULL) { activation_steepnesses->array_len = fann_get_cascade_activation_steepnesses_count(ann); activation_steepnesses->array = fann_get_cascade_activation_steepnesses(ann); } return activation_steepnesses; } /* Method: set_cascade_activation_steepnesses Sets the array of cascade candidate activation steepnesses. The array must be just as long as defined by the count. See for a description of which candidate neurons will be generated by this array. See also: , , This function appears in FANN >= 2.0.0. */ void set_cascade_activation_steepnesses(helper_array *cascade_activation_steepnesses) { if (ann != NULL) { fann_set_cascade_activation_steepnesses(ann, cascade_activation_steepnesses->array, cascade_activation_steepnesses->array_len); } } }; /*************************************************************************/ }; #endif /* FANN_CPP_SUBCLASS_H_INCLUDED */ libfann-2.1.0~beta~dfsg/python/pyfann/makefile.msvc000066400000000000000000000010241140315177100224770ustar00rootroot00000000000000# This makefile was written to compile a distribution of pyfann for # Microsoft Visual C++. TARGETS = _libfann.pyd PYTHON=python2.3 PYDIR=C:/Python23 OBJS=libfann_wrap.obj fann_helper.obj all: $(TARGETS) _libfann.pyd: $(OBJS) link /LIBPATH:$(PYDIR)/libs /DLL /OUT:$@ $(OBJS) ../../src/fann*.obj python23.lib .c.obj: cl -c $< -I $(PYDIR)/include -I ../../src/include libfann_wrap.c: libfann.i swig -python libfann.i clean: del -f $(TARGETS) *_wrap.* fann_helper.obj fann.pyc libfann.py _libfann.* libfann-2.1.0~beta~dfsg/python/pyfann/pyfann.i000066400000000000000000000132011140315177100214750ustar00rootroot00000000000000/* File : fann.i */ %module libfann %include "typemaps.i" %include "stl.i" %{ #include "doublefann.h" #include "fann_io.h" #include "fann_train.h" #include "fann_data.h" #include "fann_cascade.h" #include "fann_error.h" #include "fann_activation.h" #include "fann_cpp_subclass.h" %} %define HELPER_ARRAY_TEMPLATE( templ , T, GetFunc, SetFunc, cast) %typemap(in) templ * (templ temp){ // templ* type_map in int i; if (!PySequence_Check($input)) { PyErr_SetString(PyExc_ValueError,"Expected a sequence"); SWIG_fail; } if (PySequence_Length($input) == 0) { PyErr_SetString(PyExc_ValueError,"Size mismatch. Expected some elements"); SWIG_fail; } $1=&temp; $1->array_len=PySequence_Length($input); $1->array = (T *) malloc($1->array_len*sizeof(T)); for (i = 0; i < PySequence_Length($input); i++) { PyObject *o = PySequence_GetItem($input,i); if (PyNumber_Check(o)) { $1->array[i] = (T) GetFunc(o); } else { PyErr_SetString(PyExc_ValueError,"Sequence elements must be numbers"); Py_DECREF(o); SWIG_fail; } Py_DECREF(o); } } %typemap(freearg) templ* { // templ* type_map freearg if ($1 && $1->array && $1->can_delete) { free($1->array); } } %typemap(out) templ* { // templ* type_map out $result= PyList_New( $1->array_len ); for (unsigned int i = 0; i < $1->array_len; i++) { PyObject *o = SetFunc( (cast) $1->array[i]); PyList_SetItem($result,i,o); } if ($1 && $1->array && $1->can_delete) { free($1->array); } if ($1) delete $1; } %typemap(argout) templ* ARGOUT{ // templ* type_map out $result= PyList_New( $1->array_len ); for (unsigned int i = 0; i < $1->array_len; i++) { PyObject *o = SetFunc( (cast) $1->array[i]); PyList_SetItem($result,i,o); } if ($1 && $1->array && $1->can_delete) { free($1->array); } if ($1) delete $1; } %enddef %define HELPER_ARRAY_ARRAY_TEMPLATE(templ, T, GetFunc, SetFunc, cast) %typemap(in) templ< T >* ( templ temp) { // templ* type_map unsigned int i; unsigned int j; unsigned int dim; unsigned int num; if (!PySequence_Check($input)) { PyErr_SetString(PyExc_ValueError,"Expected a sequence"); SWIG_fail; } if (PySequence_Length($input) == 0) { PyErr_SetString(PyExc_ValueError,"Size mismatch. Expected some elements"); SWIG_fail; } $1=&temp; num=PySequence_Length($input); $1->array_num=num; dim=PySequence_Length(PySequence_GetItem($input,0)); $1->array_len=dim; $1->arrays = (T **) calloc(num,sizeof(T*)); for (j = 0; j< num; j++) { PyObject* o1=PySequence_GetItem($input,j); if ((unsigned int)PySequence_Length(o1) == dim) { PyErr_SetString(PyExc_ValueError,"Size mismatch. All items must be of the same size"); SWIG_fail; } $1->arrays[j] = (T*) malloc(dim*sizeof(T)); for (i = 0; i < dim; i++) { PyObject *o = PySequence_GetItem(o1,i); if (PyNumber_Check(o)) { $1->arrays[j][i] = (T) GetFunc(o); } else { PyErr_SetString(PyExc_ValueError,"Sequence elements must be numbers"); Py_DECREF(o); SWIG_fail; } Py_DECREF(o); } } } %typemap(freearg) templ< T >* { // templ* type_map freearg unsigned int i; if ($1 && $1->arrays && $1->can_delete) { for (i=0; i < $1->array_num;++i) if ($1->arrays[i]) free($1->arrays[i]); free($1->arrays); } } %typemap(out) templ* { // templ* type_map out $result= PyList_New( $1->array_num ); for (unsigned int j = 0; j < $1->array_num; ++j) { PyObject *l= PyList_New( $1->array_len ); PyList_SetItem($result,j,l); for (unsigned int i = 0; i < $1->array_len; i++) { PyObject *o = SetFunc($1->arrays[j][i] ); //PyObject *o = SetFunc($1->arrays[i][j] ); PyList_SetItem(l,i,o); } } unsigned int i; if ($1 && $1->arrays && $1->can_delete) { for (i=0; i < $1->array_num;++i) if ($1->arrays[i]) free($1->arrays[i]); free($1->arrays); } if ($1) delete $1; } %enddef %import "../../src/include/doublefann.h" %import "../../src/include/fann.h" %import "../../src/include/fann_io.h" %import "../../src/include/fann_train.h" %import "../../src/include/fann_data.h" %import "../../src/include/fann_cascade.h" %import "../../src/include/fann_error.h" %import "../../src/include/fann_activation.h" HELPER_ARRAY_TEMPLATE( FANN::helper_array, unsigned int, PyInt_AsLong , PyInt_FromLong , long ); HELPER_ARRAY_TEMPLATE( FANN::helper_array, fann_type , PyFloat_AsDouble, PyFloat_FromDouble, double ); HELPER_ARRAY_ARRAY_TEMPLATE( FANN::helper_array_array, fann_type , PyFloat_AsDouble, PyFloat_FromDouble, double ); %rename(neural_net_parent) FANN::neural_net; %rename(neural_net) FANN::Neural_net; %rename(training_data_parent) FANN::training_data; %rename(training_data) FANN::Training_data; %include "../../src/include/fann_cpp.h" %include "fann_cpp_subclass.h" /* ex: set ts=4: set sw=4: set cin */ libfann-2.1.0~beta~dfsg/python/setup.py000077500000000000000000000031751140315177100202630ustar00rootroot00000000000000#!/usr/bin/python from distutils.core import setup, Extension import glob #from compiler.pycodegen import compileFile #import distutils #import distutils.sysconfig #import distutils.core import os NAME='pyfann' VERSION='2.0.0' LONG_DESCRIPTION="""\ Fast Artificial Neural Network Library implements multilayer artificial neural networks with support for both fully connected and sparsely connected networks. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. """ #These lines are needed to circumvent a bug in distutils swig_cmd = 'swig -c++ -python pyfann/pyfann.i' print 'Running SWIG before:', swig_cmd os.system(swig_cmd) #This utility function searches for files def hunt_files(root, which): return glob.glob(os.path.join(root, which)) setup( name=NAME, description='Fast Artificial Neural Network Library (fann)', long_description=LONG_DESCRIPTION, version=VERSION, author='Steffen Nissen', author_email='lukesky@diku.dk', maintainer='Gil Megidish & Vincenzo Di Massa', maintainer_email='gil@megidish.net & hawk.it@tiscali,it', url='http://sourceforge.net/projects/fann/', license='GNU LESSER GENERAL PUBLIC LICENSE (LGPL)', py_modules=['pyfann.libfann'], ext_modules=[Extension('pyfann._libfann',['pyfann/pyfann_wrap.cxx'], include_dirs=['../src/include'], extra_objects=['../src/doublefann.o'], define_macros=[("SWIG_COMPILE",None)] ), ] ) libfann-2.1.0~beta~dfsg/src/000077500000000000000000000000001140315177100160065ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/src/COPYING000066400000000000000000000634761140315177100170610ustar00rootroot00000000000000 GNU LESSER GENERAL PUBLIC LICENSE Version 2.1, February 1999 Copyright (C) 1991, 1999 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the library `Frob' (a library for tweaking knobs) written by James Random Hacker. , 1 April 1990 Ty Coon, President of Vice That's all there is to it! libfann-2.1.0~beta~dfsg/src/Makefile.am000066400000000000000000000006141140315177100200430ustar00rootroot00000000000000SUBDIRS = include lib_LTLIBRARIES = libfloatfann.la libdoublefann.la libfixedfann.la libfann.la AM_LDFLAGS = -version-info 2:1:0 AM_CFLAGS = -D_REENTRANT libfloatfann_la_SOURCES = floatfann.c libdoublefann_la_SOURCES = doublefann.c libfixedfann_la_SOURCES = fixedfann.c libfann_la_SOURCES = floatfann.c EXTRA_DIST = fann.c fann_io.c fann_train.c fann_train_data.c fann_error.c fann_cascade.c libfann-2.1.0~beta~dfsg/src/Makefile.in000066400000000000000000000462021140315177100200570ustar00rootroot00000000000000# Makefile.in generated by automake 1.9.6 from Makefile.am. # @configure_input@ # Copyright (C) 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, # 2003, 2004, 2005 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; without # even the implied warranty of MERCHANTABILITY or FITNESS FOR A # PARTICULAR PURPOSE. @SET_MAKE@ srcdir = @srcdir@ top_srcdir = @top_srcdir@ VPATH = @srcdir@ pkgdatadir = $(datadir)/@PACKAGE@ pkglibdir = $(libdir)/@PACKAGE@ pkgincludedir = $(includedir)/@PACKAGE@ top_builddir = .. am__cd = CDPATH="$${ZSH_VERSION+.}$(PATH_SEPARATOR)" && cd INSTALL = @INSTALL@ install_sh_DATA = $(install_sh) -c -m 644 install_sh_PROGRAM = $(install_sh) -c install_sh_SCRIPT = $(install_sh) -c INSTALL_HEADER = $(INSTALL_DATA) transform = $(program_transform_name) NORMAL_INSTALL = : PRE_INSTALL = : POST_INSTALL = : NORMAL_UNINSTALL = : PRE_UNINSTALL = : POST_UNINSTALL = : build_triplet = @build@ host_triplet = @host@ subdir = src DIST_COMMON = $(srcdir)/Makefile.am $(srcdir)/Makefile.in COPYING ACLOCAL_M4 = $(top_srcdir)/aclocal.m4 am__aclocal_m4_deps = $(top_srcdir)/configure.in am__configure_deps = $(am__aclocal_m4_deps) $(CONFIGURE_DEPENDENCIES) \ $(ACLOCAL_M4) mkinstalldirs = $(SHELL) $(top_srcdir)/mkinstalldirs CONFIG_HEADER = $(top_builddir)/src/include/config.h CONFIG_CLEAN_FILES = am__vpath_adj_setup = srcdirstrip=`echo "$(srcdir)" | sed 's|.|.|g'`; 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you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ /* Easy way to allow for build of multiple binaries */ #include "config.h" #include "doublefann.h" #include "fann.c" #include "fann_io.c" #include "fann_train.c" #include "fann_train_data.c" #include "fann_error.c" #include "fann_cascade.c" libfann-2.1.0~beta~dfsg/src/fann.c000066400000000000000000001364241140315177100171060ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include #include #include #include #include #include "config.h" #include "fann.h" FANN_EXTERNAL struct fann *FANN_API fann_create_standard(unsigned int num_layers, ...) { struct fann *ann; va_list layer_sizes; int i; unsigned int *layers = (unsigned int *) calloc(num_layers, sizeof(unsigned int)); if(layers == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } va_start(layer_sizes, num_layers); for(i = 0; i < (int) num_layers; i++) { layers[i] = va_arg(layer_sizes, unsigned int); } va_end(layer_sizes); ann = fann_create_standard_array(num_layers, layers); free(layers); return ann; } FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array(unsigned int num_layers, const unsigned int *layers) { return fann_create_sparse_array(1, num_layers, layers); } FANN_EXTERNAL struct fann *FANN_API fann_create_sparse(float connection_rate, unsigned int num_layers, ...) { struct fann *ann; va_list layer_sizes; int i; unsigned int *layers = (unsigned int *) calloc(num_layers, sizeof(unsigned int)); if(layers == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } va_start(layer_sizes, num_layers); for(i = 0; i < (int) num_layers; i++) { layers[i] = va_arg(layer_sizes, unsigned int); } va_end(layer_sizes); ann = fann_create_sparse_array(connection_rate, num_layers, layers); free(layers); return ann; } FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array(float connection_rate, unsigned int num_layers, const unsigned int *layers) { struct fann_layer *layer_it, *last_layer, *prev_layer; struct fann *ann; struct fann_neuron *neuron_it, *last_neuron, *random_neuron, *bias_neuron; #ifdef DEBUG unsigned int prev_layer_size; #endif unsigned int num_neurons_in, num_neurons_out, i, j; unsigned int min_connections, max_connections, num_connections; unsigned int connections_per_neuron, allocated_connections; unsigned int random_number, found_connection, tmp_con; #ifdef FIXEDFANN unsigned int decimal_point; unsigned int multiplier; #endif if(connection_rate > 1) { connection_rate = 1; } /* seed random */ #ifndef FANN_NO_SEED fann_seed_rand(); #endif /* allocate the general structure */ ann = fann_allocate_structure(num_layers); if(ann == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } ann->connection_rate = connection_rate; #ifdef FIXEDFANN decimal_point = ann->decimal_point; multiplier = ann->multiplier; fann_update_stepwise(ann); #endif /* determine how many neurons there should be in each layer */ i = 0; for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { /* we do not allocate room here, but we make sure that * last_neuron - first_neuron is the number of neurons */ layer_it->first_neuron = NULL; layer_it->last_neuron = layer_it->first_neuron + layers[i++] + 1; /* +1 for bias */ ann->total_neurons += layer_it->last_neuron - layer_it->first_neuron; } ann->num_output = (ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron - 1; ann->num_input = ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1; /* allocate room for the actual neurons */ fann_allocate_neurons(ann); if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM) { fann_destroy(ann); return NULL; } #ifdef DEBUG printf("creating network with connection rate %f\n", connection_rate); printf("input\n"); printf(" layer : %d neurons, 1 bias\n", ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1); #endif num_neurons_in = ann->num_input; for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++) { num_neurons_out = layer_it->last_neuron - layer_it->first_neuron - 1; /*�if all neurons in each layer should be connected to at least one neuron * in the previous layer, and one neuron in the next layer. * and the bias node should be connected to the all neurons in the next layer. * Then this is the minimum amount of neurons */ min_connections = fann_max(num_neurons_in, num_neurons_out) + num_neurons_out; max_connections = num_neurons_in * num_neurons_out; /* not calculating bias */ num_connections = fann_max(min_connections, (unsigned int) (0.5 + (connection_rate * max_connections)) + num_neurons_out); connections_per_neuron = num_connections / num_neurons_out; allocated_connections = 0; /* Now split out the connections on the different neurons */ for(i = 0; i != num_neurons_out; i++) { layer_it->first_neuron[i].first_con = ann->total_connections + allocated_connections; allocated_connections += connections_per_neuron; layer_it->first_neuron[i].last_con = ann->total_connections + allocated_connections; layer_it->first_neuron[i].activation_function = FANN_SIGMOID_STEPWISE; #ifdef FIXEDFANN layer_it->first_neuron[i].activation_steepness = ann->multiplier / 2; #else layer_it->first_neuron[i].activation_steepness = 0.5; #endif if(allocated_connections < (num_connections * (i + 1)) / num_neurons_out) { layer_it->first_neuron[i].last_con++; allocated_connections++; } } /* bias neuron also gets stuff */ layer_it->first_neuron[i].first_con = ann->total_connections + allocated_connections; layer_it->first_neuron[i].last_con = ann->total_connections + allocated_connections; ann->total_connections += num_connections; /* used in the next run of the loop */ num_neurons_in = num_neurons_out; } fann_allocate_connections(ann); if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM) { fann_destroy(ann); return NULL; } if(connection_rate >= 1) { #ifdef DEBUG prev_layer_size = ann->num_input + 1; #endif prev_layer = ann->first_layer; last_layer = ann->last_layer; for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++) { last_neuron = layer_it->last_neuron - 1; for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { tmp_con = neuron_it->last_con - 1; for(i = neuron_it->first_con; i != tmp_con; i++) { ann->weights[i] = (fann_type) fann_random_weight(); /* these connections are still initialized for fully connected networks, to allow * operations to work, that are not optimized for fully connected networks. */ ann->connections[i] = prev_layer->first_neuron + (i - neuron_it->first_con); } /* bias weight */ ann->weights[tmp_con] = (fann_type) fann_random_bias_weight(); ann->connections[tmp_con] = prev_layer->first_neuron + (tmp_con - neuron_it->first_con); } #ifdef DEBUG prev_layer_size = layer_it->last_neuron - layer_it->first_neuron; #endif prev_layer = layer_it; #ifdef DEBUG printf(" layer : %d neurons, 1 bias\n", prev_layer_size - 1); #endif } } else { /* make connections for a network, that are not fully connected */ /* generally, what we do is first to connect all the input * neurons to a output neuron, respecting the number of * available input neurons for each output neuron. Then * we go through all the output neurons, and connect the * rest of the connections to input neurons, that they are * not allready connected to. */ /* All the connections are cleared by calloc, because we want to * be able to see which connections are allready connected */ for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++) { num_neurons_out = layer_it->last_neuron - layer_it->first_neuron - 1; num_neurons_in = (layer_it - 1)->last_neuron - (layer_it - 1)->first_neuron - 1; /* first connect the bias neuron */ bias_neuron = (layer_it - 1)->last_neuron - 1; last_neuron = layer_it->last_neuron - 1; for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { ann->connections[neuron_it->first_con] = bias_neuron; ann->weights[neuron_it->first_con] = (fann_type) fann_random_bias_weight(); } /* then connect all neurons in the input layer */ last_neuron = (layer_it - 1)->last_neuron - 1; for(neuron_it = (layer_it - 1)->first_neuron; neuron_it != last_neuron; neuron_it++) { /* random neuron in the output layer that has space * for more connections */ do { random_number = (int) (0.5 + fann_rand(0, num_neurons_out - 1)); random_neuron = layer_it->first_neuron + random_number; /* checks the last space in the connections array for room */ } while(ann->connections[random_neuron->last_con - 1]); /* find an empty space in the connection array and connect */ for(i = random_neuron->first_con; i < random_neuron->last_con; i++) { if(ann->connections[i] == NULL) { ann->connections[i] = neuron_it; ann->weights[i] = (fann_type) fann_random_weight(); break; } } } /* then connect the rest of the unconnected neurons */ last_neuron = layer_it->last_neuron - 1; for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { /* find empty space in the connection array and connect */ for(i = neuron_it->first_con; i < neuron_it->last_con; i++) { /* continue if allready connected */ if(ann->connections[i] != NULL) continue; do { found_connection = 0; random_number = (int) (0.5 + fann_rand(0, num_neurons_in - 1)); random_neuron = (layer_it - 1)->first_neuron + random_number; /* check to see if this connection is allready there */ for(j = neuron_it->first_con; j < i; j++) { if(random_neuron == ann->connections[j]) { found_connection = 1; break; } } } while(found_connection); /* we have found a neuron that is not allready * connected to us, connect it */ ann->connections[i] = random_neuron; ann->weights[i] = (fann_type) fann_random_weight(); } } #ifdef DEBUG printf(" layer : %d neurons, 1 bias\n", num_neurons_out); #endif } /* TODO it would be nice to have the randomly created * connections sorted for smoother memory access. */ } #ifdef DEBUG printf("output\n"); #endif return ann; } FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut(unsigned int num_layers, ...) { struct fann *ann; int i; va_list layer_sizes; unsigned int *layers = (unsigned int *) calloc(num_layers, sizeof(unsigned int)); if(layers == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } va_start(layer_sizes, num_layers); for(i = 0; i < (int) num_layers; i++) { layers[i] = va_arg(layer_sizes, unsigned int); } va_end(layer_sizes); ann = fann_create_shortcut_array(num_layers, layers); free(layers); return ann; } FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array(unsigned int num_layers, const unsigned int *layers) { struct fann_layer *layer_it, *layer_it2, *last_layer; struct fann *ann; struct fann_neuron *neuron_it, *neuron_it2 = 0; unsigned int i; unsigned int num_neurons_in, num_neurons_out; #ifdef FIXEDFANN unsigned int decimal_point; unsigned int multiplier; #endif /* seed random */ #ifndef FANN_NO_SEED fann_seed_rand(); #endif /* allocate the general structure */ ann = fann_allocate_structure(num_layers); if(ann == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } ann->connection_rate = 1; ann->network_type = FANN_NETTYPE_SHORTCUT; #ifdef FIXEDFANN decimal_point = ann->decimal_point; multiplier = ann->multiplier; fann_update_stepwise(ann); #endif /* determine how many neurons there should be in each layer */ i = 0; for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { /* we do not allocate room here, but we make sure that * last_neuron - first_neuron is the number of neurons */ layer_it->first_neuron = NULL; layer_it->last_neuron = layer_it->first_neuron + layers[i++]; if(layer_it == ann->first_layer) { /* there is a bias neuron in the first layer */ layer_it->last_neuron++; } ann->total_neurons += layer_it->last_neuron - layer_it->first_neuron; } ann->num_output = (ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron; ann->num_input = ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1; /* allocate room for the actual neurons */ fann_allocate_neurons(ann); if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM) { fann_destroy(ann); return NULL; } #ifdef DEBUG printf("creating fully shortcut connected network.\n"); printf("input\n"); printf(" layer : %d neurons, 1 bias\n", ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1); #endif num_neurons_in = ann->num_input; last_layer = ann->last_layer; for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++) { num_neurons_out = layer_it->last_neuron - layer_it->first_neuron; /* Now split out the connections on the different neurons */ for(i = 0; i != num_neurons_out; i++) { layer_it->first_neuron[i].first_con = ann->total_connections; ann->total_connections += num_neurons_in + 1; layer_it->first_neuron[i].last_con = ann->total_connections; layer_it->first_neuron[i].activation_function = FANN_SIGMOID_STEPWISE; #ifdef FIXEDFANN layer_it->first_neuron[i].activation_steepness = ann->multiplier / 2; #else layer_it->first_neuron[i].activation_steepness = 0.5; #endif } #ifdef DEBUG printf(" layer : %d neurons, 0 bias\n", num_neurons_out); #endif /* used in the next run of the loop */ num_neurons_in += num_neurons_out; } fann_allocate_connections(ann); if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM) { fann_destroy(ann); return NULL; } /* Connections are created from all neurons to all neurons in later layers */ num_neurons_in = ann->num_input + 1; for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++) { for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++) { i = neuron_it->first_con; for(layer_it2 = ann->first_layer; layer_it2 != layer_it; layer_it2++) { for(neuron_it2 = layer_it2->first_neuron; neuron_it2 != layer_it2->last_neuron; neuron_it2++) { ann->weights[i] = (fann_type) fann_random_weight(); ann->connections[i] = neuron_it2; i++; } } } num_neurons_in += layer_it->last_neuron - layer_it->first_neuron; } #ifdef DEBUG printf("output\n"); #endif return ann; } FANN_EXTERNAL fann_type *FANN_API fann_run(struct fann * ann, fann_type * input) { struct fann_neuron *neuron_it, *last_neuron, *neurons, **neuron_pointers; unsigned int i, num_connections, num_input, num_output; fann_type neuron_sum, *output; fann_type *weights; struct fann_layer *layer_it, *last_layer; unsigned int activation_function; fann_type steepness; /* store some variabels local for fast access */ struct fann_neuron *first_neuron = ann->first_layer->first_neuron; #ifdef FIXEDFANN int multiplier = ann->multiplier; unsigned int decimal_point = ann->decimal_point; /* values used for the stepwise linear sigmoid function */ fann_type r1 = 0, r2 = 0, r3 = 0, r4 = 0, r5 = 0, r6 = 0; fann_type v1 = 0, v2 = 0, v3 = 0, v4 = 0, v5 = 0, v6 = 0; fann_type last_steepness = 0; unsigned int last_activation_function = 0; #else fann_type max_sum; #endif /* first set the input */ num_input = ann->num_input; for(i = 0; i != num_input; i++) { #ifdef FIXEDFANN if(fann_abs(input[i]) > multiplier) { printf ("Warning input number %d is out of range -%d - %d with value %d, integer overflow may occur.\n", i, multiplier, multiplier, input[i]); } #endif first_neuron[i].value = input[i]; } /* Set the bias neuron in the input layer */ #ifdef FIXEDFANN (ann->first_layer->last_neuron - 1)->value = multiplier; #else (ann->first_layer->last_neuron - 1)->value = 1; #endif last_layer = ann->last_layer; for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++) { last_neuron = layer_it->last_neuron; for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { if(neuron_it->first_con == neuron_it->last_con) { /* bias neurons */ #ifdef FIXEDFANN neuron_it->value = multiplier; #else neuron_it->value = 1; #endif continue; } activation_function = neuron_it->activation_function; steepness = neuron_it->activation_steepness; neuron_sum = 0; num_connections = neuron_it->last_con - neuron_it->first_con; weights = ann->weights + neuron_it->first_con; if(ann->connection_rate >= 1) { if(ann->network_type == FANN_NETTYPE_SHORTCUT) { neurons = ann->first_layer->first_neuron; } else { neurons = (layer_it - 1)->first_neuron; } /* unrolled loop start */ i = num_connections & 3; /* same as modulo 4 */ switch (i) { case 3: neuron_sum += fann_mult(weights[2], neurons[2].value); case 2: neuron_sum += fann_mult(weights[1], neurons[1].value); case 1: neuron_sum += fann_mult(weights[0], neurons[0].value); case 0: break; } for(; i != num_connections; i += 4) { neuron_sum += fann_mult(weights[i], neurons[i].value) + fann_mult(weights[i + 1], neurons[i + 1].value) + fann_mult(weights[i + 2], neurons[i + 2].value) + fann_mult(weights[i + 3], neurons[i + 3].value); } /* unrolled loop end */ /* * for(i = 0;i != num_connections; i++){ * printf("%f += %f*%f, ", neuron_sum, weights[i], neurons[i].value); * neuron_sum += fann_mult(weights[i], neurons[i].value); * } */ } else { neuron_pointers = ann->connections + neuron_it->first_con; i = num_connections & 3; /* same as modulo 4 */ switch (i) { case 3: neuron_sum += fann_mult(weights[2], neuron_pointers[2]->value); case 2: neuron_sum += fann_mult(weights[1], neuron_pointers[1]->value); case 1: neuron_sum += fann_mult(weights[0], neuron_pointers[0]->value); case 0: break; } for(; i != num_connections; i += 4) { neuron_sum += fann_mult(weights[i], neuron_pointers[i]->value) + fann_mult(weights[i + 1], neuron_pointers[i + 1]->value) + fann_mult(weights[i + 2], neuron_pointers[i + 2]->value) + fann_mult(weights[i + 3], neuron_pointers[i + 3]->value); } } #ifdef FIXEDFANN neuron_it->sum = fann_mult(steepness, neuron_sum); if(activation_function != last_activation_function || steepness != last_steepness) { switch (activation_function) { case FANN_SIGMOID: case FANN_SIGMOID_STEPWISE: r1 = ann->sigmoid_results[0]; r2 = ann->sigmoid_results[1]; r3 = ann->sigmoid_results[2]; r4 = ann->sigmoid_results[3]; r5 = ann->sigmoid_results[4]; r6 = ann->sigmoid_results[5]; v1 = ann->sigmoid_values[0] / steepness; v2 = ann->sigmoid_values[1] / steepness; v3 = ann->sigmoid_values[2] / steepness; v4 = ann->sigmoid_values[3] / steepness; v5 = ann->sigmoid_values[4] / steepness; v6 = ann->sigmoid_values[5] / steepness; break; case FANN_SIGMOID_SYMMETRIC: case FANN_SIGMOID_SYMMETRIC_STEPWISE: r1 = ann->sigmoid_symmetric_results[0]; r2 = ann->sigmoid_symmetric_results[1]; r3 = ann->sigmoid_symmetric_results[2]; r4 = ann->sigmoid_symmetric_results[3]; r5 = ann->sigmoid_symmetric_results[4]; r6 = ann->sigmoid_symmetric_results[5]; v1 = ann->sigmoid_symmetric_values[0] / steepness; v2 = ann->sigmoid_symmetric_values[1] / steepness; v3 = ann->sigmoid_symmetric_values[2] / steepness; v4 = ann->sigmoid_symmetric_values[3] / steepness; v5 = ann->sigmoid_symmetric_values[4] / steepness; v6 = ann->sigmoid_symmetric_values[5] / steepness; break; case FANN_THRESHOLD: break; } } switch (activation_function) { case FANN_SIGMOID: case FANN_SIGMOID_STEPWISE: neuron_it->value = (fann_type) fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6, 0, multiplier, neuron_sum); break; case FANN_SIGMOID_SYMMETRIC: case FANN_SIGMOID_SYMMETRIC_STEPWISE: neuron_it->value = (fann_type) fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6, -multiplier, multiplier, neuron_sum); break; case FANN_THRESHOLD: neuron_it->value = (fann_type) ((neuron_sum < 0) ? 0 : multiplier); break; case FANN_THRESHOLD_SYMMETRIC: neuron_it->value = (fann_type) ((neuron_sum < 0) ? -multiplier : multiplier); break; case FANN_LINEAR: neuron_it->value = neuron_sum; break; case FANN_LINEAR_PIECE: neuron_it->value = (fann_type)((neuron_sum < 0) ? 0 : (neuron_sum > multiplier) ? multiplier : neuron_sum); break; case FANN_LINEAR_PIECE_SYMMETRIC: neuron_it->value = (fann_type)((neuron_sum < -multiplier) ? -multiplier : (neuron_sum > multiplier) ? multiplier : neuron_sum); break; case FANN_ELLIOT: case FANN_ELLIOT_SYMMETRIC: case FANN_GAUSSIAN: case FANN_GAUSSIAN_SYMMETRIC: case FANN_GAUSSIAN_STEPWISE: case FANN_SIN_SYMMETRIC: case FANN_COS_SYMMETRIC: fann_error((struct fann_error *) ann, FANN_E_CANT_USE_ACTIVATION); break; } last_steepness = steepness; last_activation_function = activation_function; #else neuron_sum = fann_mult(steepness, neuron_sum); max_sum = 150/steepness; if(neuron_sum > max_sum) neuron_sum = max_sum; else if(neuron_sum < -max_sum) neuron_sum = -max_sum; neuron_it->sum = neuron_sum; fann_activation_switch(activation_function, neuron_sum, neuron_it->value); #endif } } /* set the output */ output = ann->output; num_output = ann->num_output; neurons = (ann->last_layer - 1)->first_neuron; for(i = 0; i != num_output; i++) { output[i] = neurons[i].value; } return ann->output; } FANN_EXTERNAL void FANN_API fann_destroy(struct fann *ann) { if(ann == NULL) return; fann_safe_free(ann->weights); fann_safe_free(ann->connections); fann_safe_free(ann->first_layer->first_neuron); fann_safe_free(ann->first_layer); fann_safe_free(ann->output); fann_safe_free(ann->train_errors); fann_safe_free(ann->train_slopes); fann_safe_free(ann->prev_train_slopes); fann_safe_free(ann->prev_steps); fann_safe_free(ann->prev_weights_deltas); fann_safe_free(ann->errstr); fann_safe_free(ann->cascade_activation_functions); fann_safe_free(ann->cascade_activation_steepnesses); #ifndef FIXEDFANN fann_safe_free( ann->scale_mean_in ); fann_safe_free( ann->scale_deviation_in ); fann_safe_free( ann->scale_new_min_in ); fann_safe_free( ann->scale_factor_in ); fann_safe_free( ann->scale_mean_out ); fann_safe_free( ann->scale_deviation_out ); fann_safe_free( ann->scale_new_min_out ); fann_safe_free( ann->scale_factor_out ); #endif fann_safe_free(ann); } FANN_EXTERNAL void FANN_API fann_randomize_weights(struct fann *ann, fann_type min_weight, fann_type max_weight) { fann_type *last_weight; fann_type *weights = ann->weights; last_weight = weights + ann->total_connections; for(; weights != last_weight; weights++) { *weights = (fann_type) (fann_rand(min_weight, max_weight)); } #ifndef FIXEDFANN if(ann->prev_train_slopes != NULL) { fann_clear_train_arrays(ann); } #endif } FANN_EXTERNAL void FANN_API fann_print_connections(struct fann *ann) { struct fann_layer *layer_it; struct fann_neuron *neuron_it; unsigned int i; int value; char *neurons; unsigned int num_neurons = fann_get_total_neurons(ann) - fann_get_num_output(ann); neurons = (char *) malloc(num_neurons + 1); if(neurons == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return; } neurons[num_neurons] = 0; printf("Layer / Neuron "); for(i = 0; i < num_neurons; i++) { printf("%d", i % 10); } printf("\n"); for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++) { for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++) { memset(neurons, (int) '.', num_neurons); for(i = neuron_it->first_con; i < neuron_it->last_con; i++) { if(ann->weights[i] < 0) { #ifdef FIXEDFANN value = (int) ((ann->weights[i] / (double) ann->multiplier) - 0.5); #else value = (int) ((ann->weights[i]) - 0.5); #endif if(value < -25) value = -25; neurons[ann->connections[i] - ann->first_layer->first_neuron] = (char)('a' - value); } else { #ifdef FIXEDFANN value = (int) ((ann->weights[i] / (double) ann->multiplier) + 0.5); #else value = (int) ((ann->weights[i]) + 0.5); #endif if(value > 25) value = 25; neurons[ann->connections[i] - ann->first_layer->first_neuron] = (char)('A' + value); } } printf("L %3d / N %4d %s\n", layer_it - ann->first_layer, neuron_it - ann->first_layer->first_neuron, neurons); } } free(neurons); } /* Initialize the weights using Widrow + Nguyen's algorithm. */ FANN_EXTERNAL void FANN_API fann_init_weights(struct fann *ann, struct fann_train_data *train_data) { fann_type smallest_inp, largest_inp; unsigned int dat = 0, elem, num_connect, num_hidden_neurons; struct fann_layer *layer_it; struct fann_neuron *neuron_it, *last_neuron, *bias_neuron; #ifdef FIXEDFANN unsigned int multiplier = ann->multiplier; #endif float scale_factor; for(smallest_inp = largest_inp = train_data->input[0][0]; dat < train_data->num_data; dat++) { for(elem = 0; elem < train_data->num_input; elem++) { if(train_data->input[dat][elem] < smallest_inp) smallest_inp = train_data->input[dat][elem]; if(train_data->input[dat][elem] > largest_inp) largest_inp = train_data->input[dat][elem]; } } num_hidden_neurons = ann->total_neurons - (ann->num_input + ann->num_output + (ann->last_layer - ann->first_layer)); scale_factor = (float) (pow ((double) (0.7f * (double) num_hidden_neurons), (double) (1.0f / (double) ann->num_input)) / (double) (largest_inp - smallest_inp)); #ifdef DEBUG printf("Initializing weights with scale factor %f\n", scale_factor); #endif bias_neuron = ann->first_layer->last_neuron - 1; for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++) { last_neuron = layer_it->last_neuron; if(ann->network_type == FANN_NETTYPE_LAYER) { bias_neuron = (layer_it - 1)->last_neuron - 1; } for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { for(num_connect = neuron_it->first_con; num_connect < neuron_it->last_con; num_connect++) { if(bias_neuron == ann->connections[num_connect]) { #ifdef FIXEDFANN ann->weights[num_connect] = (fann_type) fann_rand(-scale_factor, scale_factor * multiplier); #else ann->weights[num_connect] = (fann_type) fann_rand(-scale_factor, scale_factor); #endif } else { #ifdef FIXEDFANN ann->weights[num_connect] = (fann_type) fann_rand(0, scale_factor * multiplier); #else ann->weights[num_connect] = (fann_type) fann_rand(0, scale_factor); #endif } } } } #ifndef FIXEDFANN if(ann->prev_train_slopes != NULL) { fann_clear_train_arrays(ann); } #endif } FANN_EXTERNAL void FANN_API fann_print_parameters(struct fann *ann) { struct fann_layer *layer_it; #ifndef FIXEDFANN unsigned int i; #endif printf("Input layer :%4d neurons, 1 bias\n", ann->num_input); for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer - 1; layer_it++) { if(ann->network_type == FANN_NETTYPE_SHORTCUT) { printf(" Hidden layer :%4d neurons, 0 bias\n", layer_it->last_neuron - layer_it->first_neuron); } else { printf(" Hidden layer :%4d neurons, 1 bias\n", layer_it->last_neuron - layer_it->first_neuron - 1); } } printf("Output layer :%4d neurons\n", ann->num_output); printf("Total neurons and biases :%4d\n", fann_get_total_neurons(ann)); printf("Total connections :%4d\n", ann->total_connections); printf("Connection rate :%8.3f\n", ann->connection_rate); printf("Network type : %s\n", FANN_NETTYPE_NAMES[ann->network_type]); #ifdef FIXEDFANN printf("Decimal point :%4d\n", ann->decimal_point); printf("Multiplier :%4d\n", ann->multiplier); #else printf("Training algorithm : %s\n", FANN_TRAIN_NAMES[ann->training_algorithm]); printf("Training error function : %s\n", FANN_ERRORFUNC_NAMES[ann->train_error_function]); printf("Training stop function : %s\n", FANN_STOPFUNC_NAMES[ann->train_stop_function]); #endif #ifdef FIXEDFANN printf("Bit fail limit :%4d\n", ann->bit_fail_limit); #else printf("Bit fail limit :%8.3f\n", ann->bit_fail_limit); printf("Learning rate :%8.3f\n", ann->learning_rate); printf("Learning momentum :%8.3f\n", ann->learning_momentum); printf("Quickprop decay :%11.6f\n", ann->quickprop_decay); printf("Quickprop mu :%8.3f\n", ann->quickprop_mu); printf("RPROP increase factor :%8.3f\n", ann->rprop_increase_factor); printf("RPROP decrease factor :%8.3f\n", ann->rprop_decrease_factor); printf("RPROP delta min :%8.3f\n", ann->rprop_delta_min); printf("RPROP delta max :%8.3f\n", ann->rprop_delta_max); printf("Cascade output change fraction :%11.6f\n", ann->cascade_output_change_fraction); printf("Cascade candidate change fraction :%11.6f\n", ann->cascade_candidate_change_fraction); printf("Cascade output stagnation epochs :%4d\n", ann->cascade_output_stagnation_epochs); printf("Cascade candidate stagnation epochs :%4d\n", ann->cascade_candidate_stagnation_epochs); printf("Cascade max output epochs :%4d\n", ann->cascade_max_out_epochs); printf("Cascade max candidate epochs :%4d\n", ann->cascade_max_cand_epochs); printf("Cascade weight multiplier :%8.3f\n", ann->cascade_weight_multiplier); printf("Cascade candidate limit :%8.3f\n", ann->cascade_candidate_limit); for(i = 0; i < ann->cascade_activation_functions_count; i++) printf("Cascade activation functions[%d] : %s\n", i, FANN_ACTIVATIONFUNC_NAMES[ann->cascade_activation_functions[i]]); for(i = 0; i < ann->cascade_activation_steepnesses_count; i++) printf("Cascade activation steepnesses[%d] :%8.3f\n", i, ann->cascade_activation_steepnesses[i]); printf("Cascade candidate groups :%4d\n", ann->cascade_num_candidate_groups); printf("Cascade no. of candidates :%4d\n", fann_get_cascade_num_candidates(ann)); /* TODO: dump scale parameters */ #endif } FANN_GET(unsigned int, num_input) FANN_GET(unsigned int, num_output) FANN_EXTERNAL unsigned int FANN_API fann_get_total_neurons(struct fann *ann) { if(ann->network_type) { return ann->total_neurons; } else { /* -1, because there is always an unused bias neuron in the last layer */ return ann->total_neurons - 1; } } FANN_GET(unsigned int, total_connections) FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type(struct fann *ann) { /* Currently two types: LAYER = 0, SHORTCUT = 1 */ /* Enum network_types must be set to match the return values */ return ann->network_type; } FANN_EXTERNAL float FANN_API fann_get_connection_rate(struct fann *ann) { return ann->connection_rate; } FANN_EXTERNAL unsigned int FANN_API fann_get_num_layers(struct fann *ann) { return ann->last_layer - ann->first_layer; } FANN_EXTERNAL void FANN_API fann_get_layer_array(struct fann *ann, unsigned int *layers) { struct fann_layer *layer_it; for (layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { unsigned int count = layer_it->last_neuron - layer_it->first_neuron; /* Remove the bias from the count of neurons. */ switch (fann_get_network_type(ann)) { case FANN_NETTYPE_LAYER: { --count; break; } case FANN_NETTYPE_SHORTCUT: { /* The bias in the first layer is reused for all layers */ if (layer_it == ann->first_layer) --count; break; } default: { /* Unknown network type, assume no bias present */ break; } } *layers++ = count; } } FANN_EXTERNAL void FANN_API fann_get_bias_array(struct fann *ann, unsigned int *bias) { struct fann_layer *layer_it; for (layer_it = ann->first_layer; layer_it != ann->last_layer; ++layer_it, ++bias) { switch (fann_get_network_type(ann)) { case FANN_NETTYPE_LAYER: { /* Report one bias in each layer except the last */ if (layer_it != ann->last_layer-1) *bias = 1; else *bias = 0; break; } case FANN_NETTYPE_SHORTCUT: { /* The bias in the first layer is reused for all layers */ if (layer_it == ann->first_layer) *bias = 1; else *bias = 0; break; } default: { /* Unknown network type, assume no bias present */ *bias = 0; break; } } } } FANN_EXTERNAL void FANN_API fann_get_connection_array(struct fann *ann, struct fann_connection *connections) { struct fann_neuron *first_neuron; struct fann_layer *layer_it; struct fann_neuron *neuron_it; unsigned int index; unsigned int source_index; unsigned int destination_index; first_neuron = ann->first_layer->first_neuron; source_index = 0; destination_index = 0; /* The following assumes that the last unused bias has no connections */ /* for each layer */ for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++){ /* for each neuron */ for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++){ /* for each connection */ for (index = neuron_it->first_con; index < neuron_it->last_con; index++){ /* Assign the source, destination and weight */ connections->from_neuron = ann->connections[source_index] - first_neuron; connections->to_neuron = destination_index; connections->weight = ann->weights[source_index]; connections++; source_index++; } destination_index++; } } } FANN_EXTERNAL void FANN_API fann_set_weight_array(struct fann *ann, struct fann_connection *connections, unsigned int num_connections) { unsigned int index; for (index = 0; index < num_connections; index++) { fann_set_weight(ann, connections[index].from_neuron, connections[index].to_neuron, connections[index].weight); } } FANN_EXTERNAL void FANN_API fann_set_weight(struct fann *ann, unsigned int from_neuron, unsigned int to_neuron, fann_type weight) { struct fann_neuron *first_neuron; struct fann_layer *layer_it; struct fann_neuron *neuron_it; unsigned int index; unsigned int source_index; unsigned int destination_index; first_neuron = ann->first_layer->first_neuron; source_index = 0; destination_index = 0; /* Find the connection, simple brute force search through the network for one or more connections that match to minimize datastructure dependencies. Nothing is done if the connection does not already exist in the network. */ /* for each layer */ for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++){ /* for each neuron */ for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++){ /* for each connection */ for (index = neuron_it->first_con; index < neuron_it->last_con; index++){ /* If the source and destination neurons match, assign the weight */ if (((int)from_neuron == ann->connections[source_index] - first_neuron) && (to_neuron == destination_index)) { ann->weights[source_index] = weight; } source_index++; } destination_index++; } } } FANN_GET_SET(void *, user_data) #ifdef FIXEDFANN FANN_GET(unsigned int, decimal_point) FANN_GET(unsigned int, multiplier) /* INTERNAL FUNCTION Adjust the steepwise functions (if used) */ void fann_update_stepwise(struct fann *ann) { unsigned int i = 0; /* Calculate the parameters for the stepwise linear * sigmoid function fixed point. * Using a rewritten sigmoid function. * results 0.005, 0.05, 0.25, 0.75, 0.95, 0.995 */ ann->sigmoid_results[0] = fann_max((fann_type) (ann->multiplier / 200.0 + 0.5), 1); ann->sigmoid_results[1] = fann_max((fann_type) (ann->multiplier / 20.0 + 0.5), 1); ann->sigmoid_results[2] = fann_max((fann_type) (ann->multiplier / 4.0 + 0.5), 1); ann->sigmoid_results[3] = fann_min(ann->multiplier - (fann_type) (ann->multiplier / 4.0 + 0.5), ann->multiplier - 1); ann->sigmoid_results[4] = fann_min(ann->multiplier - (fann_type) (ann->multiplier / 20.0 + 0.5), ann->multiplier - 1); ann->sigmoid_results[5] = fann_min(ann->multiplier - (fann_type) (ann->multiplier / 200.0 + 0.5), ann->multiplier - 1); ann->sigmoid_symmetric_results[0] = fann_max((fann_type) ((ann->multiplier / 100.0) - ann->multiplier - 0.5), (fann_type) (1 - (fann_type) ann->multiplier)); ann->sigmoid_symmetric_results[1] = fann_max((fann_type) ((ann->multiplier / 10.0) - ann->multiplier - 0.5), (fann_type) (1 - (fann_type) ann->multiplier)); ann->sigmoid_symmetric_results[2] = fann_max((fann_type) ((ann->multiplier / 2.0) - ann->multiplier - 0.5), (fann_type) (1 - (fann_type) ann->multiplier)); ann->sigmoid_symmetric_results[3] = fann_min(ann->multiplier - (fann_type) (ann->multiplier / 2.0 + 0.5), ann->multiplier - 1); ann->sigmoid_symmetric_results[4] = fann_min(ann->multiplier - (fann_type) (ann->multiplier / 10.0 + 0.5), ann->multiplier - 1); ann->sigmoid_symmetric_results[5] = fann_min(ann->multiplier - (fann_type) (ann->multiplier / 100.0 + 1.0), ann->multiplier - 1); for(i = 0; i < 6; i++) { ann->sigmoid_values[i] = (fann_type) (((log(ann->multiplier / (float) ann->sigmoid_results[i] - 1) * (float) ann->multiplier) / -2.0) * (float) ann->multiplier); ann->sigmoid_symmetric_values[i] = (fann_type) (((log ((ann->multiplier - (float) ann->sigmoid_symmetric_results[i]) / ((float) ann->sigmoid_symmetric_results[i] + ann->multiplier)) * (float) ann->multiplier) / -2.0) * (float) ann->multiplier); } } #endif /* INTERNAL FUNCTION Allocates the main structure and sets some default values. */ struct fann *fann_allocate_structure(unsigned int num_layers) { struct fann *ann; if(num_layers < 2) { #ifdef DEBUG printf("less than 2 layers - ABORTING.\n"); #endif return NULL; } /* allocate and initialize the main network structure */ ann = (struct fann *) malloc(sizeof(struct fann)); if(ann == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } ann->errno_f = FANN_E_NO_ERROR; ann->error_log = fann_default_error_log; ann->errstr = NULL; ann->learning_rate = 0.7f; ann->learning_momentum = 0.0; ann->total_neurons = 0; ann->total_connections = 0; ann->num_input = 0; ann->num_output = 0; ann->train_errors = NULL; ann->train_slopes = NULL; ann->prev_steps = NULL; ann->prev_train_slopes = NULL; ann->prev_weights_deltas = NULL; ann->training_algorithm = FANN_TRAIN_RPROP; ann->num_MSE = 0; ann->MSE_value = 0; ann->num_bit_fail = 0; ann->bit_fail_limit = (fann_type)0.35; ann->network_type = FANN_NETTYPE_LAYER; ann->train_error_function = FANN_ERRORFUNC_TANH; ann->train_stop_function = FANN_STOPFUNC_MSE; ann->callback = NULL; ann->user_data = NULL; /* User is responsible for deallocation */ ann->weights = NULL; ann->connections = NULL; ann->output = NULL; #ifndef FIXEDFANN ann->scale_mean_in = NULL; ann->scale_deviation_in = NULL; ann->scale_new_min_in = NULL; ann->scale_factor_in = NULL; ann->scale_mean_out = NULL; ann->scale_deviation_out = NULL; ann->scale_new_min_out = NULL; ann->scale_factor_out = NULL; #endif /* variables used for cascade correlation (reasonable defaults) */ ann->cascade_output_change_fraction = 0.01f; ann->cascade_candidate_change_fraction = 0.01f; ann->cascade_output_stagnation_epochs = 12; ann->cascade_candidate_stagnation_epochs = 12; ann->cascade_num_candidate_groups = 2; ann->cascade_weight_multiplier = (fann_type)0.4; ann->cascade_candidate_limit = (fann_type)1000.0; ann->cascade_max_out_epochs = 150; ann->cascade_max_cand_epochs = 150; ann->cascade_candidate_scores = NULL; ann->cascade_activation_functions_count = 10; ann->cascade_activation_functions = (enum fann_activationfunc_enum *)calloc(ann->cascade_activation_functions_count, sizeof(enum fann_activationfunc_enum)); if(ann->cascade_activation_functions == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); free(ann); return NULL; } ann->cascade_activation_functions[0] = FANN_SIGMOID; ann->cascade_activation_functions[1] = FANN_SIGMOID_SYMMETRIC; ann->cascade_activation_functions[2] = FANN_GAUSSIAN; ann->cascade_activation_functions[3] = FANN_GAUSSIAN_SYMMETRIC; ann->cascade_activation_functions[4] = FANN_ELLIOT; ann->cascade_activation_functions[5] = FANN_ELLIOT_SYMMETRIC; ann->cascade_activation_functions[6] = FANN_SIN_SYMMETRIC; ann->cascade_activation_functions[7] = FANN_COS_SYMMETRIC; ann->cascade_activation_functions[8] = FANN_SIN; ann->cascade_activation_functions[9] = FANN_COS; ann->cascade_activation_steepnesses_count = 4; ann->cascade_activation_steepnesses = (fann_type *)calloc(ann->cascade_activation_steepnesses_count, sizeof(fann_type)); if(ann->cascade_activation_steepnesses == NULL) { fann_safe_free(ann->cascade_activation_functions); fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); free(ann); return NULL; } ann->cascade_activation_steepnesses[0] = (fann_type)0.25; ann->cascade_activation_steepnesses[1] = (fann_type)0.5; ann->cascade_activation_steepnesses[2] = (fann_type)0.75; ann->cascade_activation_steepnesses[3] = (fann_type)1.0; /* Variables for use with with Quickprop training (reasonable defaults) */ ann->quickprop_decay = (float) -0.0001; ann->quickprop_mu = 1.75; /* Variables for use with with RPROP training (reasonable defaults) */ ann->rprop_increase_factor = (float) 1.2; ann->rprop_decrease_factor = 0.5; ann->rprop_delta_min = 0.0; ann->rprop_delta_max = 50.0; ann->rprop_delta_zero = 0.1; fann_init_error_data((struct fann_error *) ann); #ifdef FIXEDFANN /* these values are only boring defaults, and should really * never be used, since the real values are always loaded from a file. */ ann->decimal_point = 8; ann->multiplier = 256; #endif /* allocate room for the layers */ ann->first_layer = (struct fann_layer *) calloc(num_layers, sizeof(struct fann_layer)); if(ann->first_layer == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); free(ann); return NULL; } ann->last_layer = ann->first_layer + num_layers; return ann; } /* INTERNAL FUNCTION Allocates room for the scaling parameters. */ int fann_allocate_scale(struct fann *ann) { /* todo this should only be allocated when needed */ #ifndef FIXEDFANN unsigned int i = 0; #define SCALE_ALLOCATE( what, where, default_value ) \ ann->what##_##where = (float *)calloc( \ ann->num_##where##put, \ sizeof( float ) \ ); \ if( ann->what##_##where == NULL ) \ { \ fann_error( NULL, FANN_E_CANT_ALLOCATE_MEM ); \ fann_destroy( ann ); \ return 1; \ } \ for( i = 0; i < ann->num_##where##put; i++ ) \ ann->what##_##where[ i ] = ( default_value ); SCALE_ALLOCATE( scale_mean, in, 0.0 ) SCALE_ALLOCATE( scale_deviation, in, 1.0 ) SCALE_ALLOCATE( scale_new_min, in, -1.0 ) SCALE_ALLOCATE( scale_factor, in, 1.0 ) SCALE_ALLOCATE( scale_mean, out, 0.0 ) SCALE_ALLOCATE( scale_deviation, out, 1.0 ) SCALE_ALLOCATE( scale_new_min, out, -1.0 ) SCALE_ALLOCATE( scale_factor, out, 1.0 ) #undef SCALE_ALLOCATE #endif return 0; } /* INTERNAL FUNCTION Allocates room for the neurons. */ void fann_allocate_neurons(struct fann *ann) { struct fann_layer *layer_it; struct fann_neuron *neurons; unsigned int num_neurons_so_far = 0; unsigned int num_neurons = 0; /* all the neurons is allocated in one long array (calloc clears mem) */ neurons = (struct fann_neuron *) calloc(ann->total_neurons, sizeof(struct fann_neuron)); ann->total_neurons_allocated = ann->total_neurons; if(neurons == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { num_neurons = layer_it->last_neuron - layer_it->first_neuron; layer_it->first_neuron = neurons + num_neurons_so_far; layer_it->last_neuron = layer_it->first_neuron + num_neurons; num_neurons_so_far += num_neurons; } ann->output = (fann_type *) calloc(num_neurons, sizeof(fann_type)); if(ann->output == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } } /* INTERNAL FUNCTION Allocate room for the connections. */ void fann_allocate_connections(struct fann *ann) { ann->weights = (fann_type *) calloc(ann->total_connections, sizeof(fann_type)); if(ann->weights == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } ann->total_connections_allocated = ann->total_connections; /* TODO make special cases for all places where the connections * is used, so that it is not needed for fully connected networks. */ ann->connections = (struct fann_neuron **) calloc(ann->total_connections_allocated, sizeof(struct fann_neuron *)); if(ann->connections == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } } /* INTERNAL FUNCTION Seed the random function. */ void fann_seed_rand() { #ifndef _WIN32 FILE *fp = fopen("/dev/urandom", "r"); unsigned int foo; struct timeval t; if(!fp) { gettimeofday(&t, NULL); foo = t.tv_usec; #ifdef DEBUG printf("unable to open /dev/urandom\n"); #endif } else { fread(&foo, sizeof(foo), 1, fp); fclose(fp); } srand(foo); #else /* COMPAT_TIME REPLACEMENT */ srand(GetTickCount()); #endif } libfann-2.1.0~beta~dfsg/src/fann_cascade.c000066400000000000000000001010231140315177100205340ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include "config.h" #include "fann.h" #include "string.h" #ifndef FIXEDFANN /* #define CASCADE_DEBUG */ /* #define CASCADE_DEBUG_FULL */ void fann_print_connections_raw(struct fann *ann) { unsigned int i; for(i = 0; i < ann->total_connections_allocated; i++) { if(i == ann->total_connections) { printf("* "); } printf("%f ", ann->weights[i]); } printf("\n\n"); } /* Cascade training directly on the training data. The connected_neurons pointers are not valid during training, but they will be again after training. */ FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data(struct fann *ann, struct fann_train_data *data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error) { float error; unsigned int i; unsigned int total_epochs = 0; int desired_error_reached; if(neurons_between_reports && ann->callback == NULL) { printf("Max neurons %3d. Desired error: %.6f\n", max_neurons, desired_error); } for(i = 1; i <= max_neurons; i++) { /* train output neurons */ total_epochs += fann_train_outputs(ann, data, desired_error); error = fann_get_MSE(ann); desired_error_reached = fann_desired_error_reached(ann, desired_error); /* print current error */ if(neurons_between_reports && (i % neurons_between_reports == 0 || i == max_neurons || i == 1 || desired_error_reached == 0)) { if(ann->callback == NULL) { printf ("Neurons %3d. Current error: %.6f. Total error:%8.4f. Epochs %5d. Bit fail %3d", i, error, ann->MSE_value, total_epochs, ann->num_bit_fail); if((ann->last_layer-2) != ann->first_layer) { printf(". candidate steepness %.2f. function %s", (ann->last_layer-2)->first_neuron->activation_steepness, FANN_ACTIVATIONFUNC_NAMES[(ann->last_layer-2)->first_neuron->activation_function]); } printf("\n"); } else if((*ann->callback) (ann, data, max_neurons, neurons_between_reports, desired_error, total_epochs) == -1) { /* you can break the training by returning -1 */ break; } } if(desired_error_reached == 0) break; if(fann_initialize_candidates(ann) == -1) { /* Unable to initialize room for candidates */ break; } /* train new candidates */ total_epochs += fann_train_candidates(ann, data); /* this installs the best candidate */ fann_install_candidate(ann); } /* Train outputs one last time but without any desired error */ total_epochs += fann_train_outputs(ann, data, 0.0); if(neurons_between_reports && ann->callback == NULL) { printf("Train outputs Current error: %.6f. Epochs %6d\n", fann_get_MSE(ann), total_epochs); } /* Set pointers in connected_neurons * This is ONLY done in the end of cascade training, * since there is no need for them during training. */ fann_set_shortcut_connections(ann); } FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file(struct fann *ann, const char *filename, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error) { struct fann_train_data *data = fann_read_train_from_file(filename); if(data == NULL) { return; } fann_cascadetrain_on_data(ann, data, max_neurons, neurons_between_reports, desired_error); fann_destroy_train(data); } int fann_train_outputs(struct fann *ann, struct fann_train_data *data, float desired_error) { float error, initial_error, error_improvement; float target_improvement = 0.0; float backslide_improvement = -1.0e20f; unsigned int i; unsigned int max_epochs = ann->cascade_max_out_epochs; unsigned int stagnation = max_epochs; /* TODO should perhaps not clear all arrays */ fann_clear_train_arrays(ann); /* run an initial epoch to set the initital error */ initial_error = fann_train_outputs_epoch(ann, data); if(fann_desired_error_reached(ann, desired_error) == 0) return 1; for(i = 1; i < max_epochs; i++) { error = fann_train_outputs_epoch(ann, data); /*printf("Epoch %6d. Current error: %.6f. Bit fail %d.\n", i, error, ann->num_bit_fail); */ if(fann_desired_error_reached(ann, desired_error) == 0) { #ifdef CASCADE_DEBUG printf("Error %f < %f\n", error, desired_error); #endif return i + 1; } /* Improvement since start of train */ error_improvement = initial_error - error; /* After any significant change, set a new goal and * allow a new quota of epochs to reach it */ if((error_improvement > target_improvement) || (error_improvement < backslide_improvement)) { /*printf("error_improvement=%f, target_improvement=%f, backslide_improvement=%f, stagnation=%d\n", error_improvement, target_improvement, backslide_improvement, stagnation); */ target_improvement = error_improvement * (1.0f + ann->cascade_output_change_fraction); backslide_improvement = error_improvement * (1.0f - ann->cascade_output_change_fraction); stagnation = i + ann->cascade_output_stagnation_epochs; } /* No improvement in allotted period, so quit */ if(i >= stagnation) { return i + 1; } } return max_epochs; } float fann_train_outputs_epoch(struct fann *ann, struct fann_train_data *data) { unsigned int i; fann_reset_MSE(ann); for(i = 0; i < data->num_data; i++) { fann_run(ann, data->input[i]); fann_compute_MSE(ann, data->output[i]); fann_update_slopes_batch(ann, ann->last_layer - 1, ann->last_layer - 1); } switch (ann->training_algorithm) { case FANN_TRAIN_RPROP: fann_update_weights_irpropm(ann, (ann->last_layer - 1)->first_neuron->first_con, ann->total_connections); break; case FANN_TRAIN_QUICKPROP: fann_update_weights_quickprop(ann, data->num_data, (ann->last_layer - 1)->first_neuron->first_con, ann->total_connections); break; case FANN_TRAIN_BATCH: case FANN_TRAIN_INCREMENTAL: fann_error((struct fann_error *) ann, FANN_E_CANT_USE_TRAIN_ALG); } return fann_get_MSE(ann); } int fann_reallocate_connections(struct fann *ann, unsigned int total_connections) { /* The connections are allocated, but the pointers inside are * first moved in the end of the cascade training session. */ #ifdef CASCADE_DEBUG printf("realloc from %d to %d\n", ann->total_connections_allocated, total_connections); #endif ann->connections = (struct fann_neuron **) realloc(ann->connections, total_connections * sizeof(struct fann_neuron *)); if(ann->connections == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return -1; } ann->weights = (fann_type *) realloc(ann->weights, total_connections * sizeof(fann_type)); if(ann->weights == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return -1; } ann->train_slopes = (fann_type *) realloc(ann->train_slopes, total_connections * sizeof(fann_type)); if(ann->train_slopes == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return -1; } ann->prev_steps = (fann_type *) realloc(ann->prev_steps, total_connections * sizeof(fann_type)); if(ann->prev_steps == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return -1; } ann->prev_train_slopes = (fann_type *) realloc(ann->prev_train_slopes, total_connections * sizeof(fann_type)); if(ann->prev_train_slopes == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return -1; } ann->total_connections_allocated = total_connections; return 0; } int fann_reallocate_neurons(struct fann *ann, unsigned int total_neurons) { struct fann_layer *layer_it; struct fann_neuron *neurons; unsigned int num_neurons = 0; unsigned int num_neurons_so_far = 0; neurons = (struct fann_neuron *) realloc(ann->first_layer->first_neuron, total_neurons * sizeof(struct fann_neuron)); ann->total_neurons_allocated = total_neurons; if(neurons == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return -1; } /* Also allocate room for more train_errors */ ann->train_errors = (fann_type *) realloc(ann->train_errors, total_neurons * sizeof(fann_type)); if(ann->train_errors == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return -1; } if(neurons != ann->first_layer->first_neuron) { /* Then the memory has moved, also move the pointers */ #ifdef CASCADE_DEBUG_FULL printf("Moving neuron pointers\n"); #endif /* Move pointers from layers to neurons */ for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { num_neurons = layer_it->last_neuron - layer_it->first_neuron; layer_it->first_neuron = neurons + num_neurons_so_far; layer_it->last_neuron = layer_it->first_neuron + num_neurons; num_neurons_so_far += num_neurons; } } return 0; } void initialize_candidate_weights(struct fann *ann, unsigned int first_con, unsigned int last_con, float scale_factor) { fann_type prev_step; unsigned int i = 0; unsigned int bias_weight = first_con + (ann->first_layer->last_neuron - ann->first_layer->first_neuron) - 1; if(ann->training_algorithm == FANN_TRAIN_RPROP) prev_step = ann->rprop_delta_zero; else prev_step = 0; for(i = first_con; i < last_con; i++) { if(i == bias_weight) ann->weights[i] = fann_rand(-scale_factor, scale_factor); else ann->weights[i] = fann_rand(0,scale_factor); ann->train_slopes[i] = 0; ann->prev_steps[i] = prev_step; ann->prev_train_slopes[i] = 0; } } int fann_initialize_candidates(struct fann *ann) { /* The candidates are allocated after the normal neurons and connections, * but there is an empty place between the real neurons and the candidate neurons, * so that it will be possible to make room when the chosen candidate are copied in * on the desired place. */ unsigned int neurons_to_allocate, connections_to_allocate; unsigned int num_candidates = fann_get_cascade_num_candidates(ann); unsigned int num_neurons = ann->total_neurons + num_candidates + 1; unsigned int num_hidden_neurons = ann->total_neurons - ann->num_input - ann->num_output; unsigned int candidate_connections_in = ann->total_neurons - ann->num_output; unsigned int candidate_connections_out = ann->num_output; /* the number of connections going into a and out of a candidate is * ann->total_neurons */ unsigned int num_connections = ann->total_connections + (ann->total_neurons * (num_candidates + 1)); unsigned int first_candidate_connection = ann->total_connections + ann->total_neurons; unsigned int first_candidate_neuron = ann->total_neurons + 1; unsigned int connection_it, i, j, k, candidate_index; struct fann_neuron *neurons; float scale_factor; /* First make sure that there is enough room, and if not then allocate a * bit more so that we do not need to allocate more room each time. */ if(num_neurons > ann->total_neurons_allocated) { /* Then we need to allocate more neurons * Allocate half as many neurons as already exist (at least ten) */ neurons_to_allocate = num_neurons + num_neurons / 2; if(neurons_to_allocate < num_neurons + 10) { neurons_to_allocate = num_neurons + 10; } if(fann_reallocate_neurons(ann, neurons_to_allocate) == -1) { return -1; } } if(num_connections > ann->total_connections_allocated) { /* Then we need to allocate more connections * Allocate half as many connections as already exist * (at least enough for ten neurons) */ connections_to_allocate = num_connections + num_connections / 2; if(connections_to_allocate < num_connections + ann->total_neurons * 10) { connections_to_allocate = num_connections + ann->total_neurons * 10; } if(fann_reallocate_connections(ann, connections_to_allocate) == -1) { return -1; } } /* Some test code to do semi Widrow + Nguyen initialization */ scale_factor = (float) 2.0f*(pow((double) (0.7f * (double) num_hidden_neurons), (double) (1.0f / (double) ann->num_input))); if(scale_factor > 8) scale_factor = 8; else if(scale_factor < 0.5) scale_factor = 0.5; /* Set the neurons. */ connection_it = first_candidate_connection; neurons = ann->first_layer->first_neuron; candidate_index = first_candidate_neuron; for(i = 0; i < ann->cascade_activation_functions_count; i++) { for(j = 0; j < ann->cascade_activation_steepnesses_count; j++) { for(k = 0; k < ann->cascade_num_candidate_groups; k++) { /* TODO candidates should actually be created both in * the last layer before the output layer, and in a new layer. */ neurons[candidate_index].value = 0; neurons[candidate_index].sum = 0; neurons[candidate_index].activation_function = ann->cascade_activation_functions[i]; neurons[candidate_index].activation_steepness = ann->cascade_activation_steepnesses[j]; neurons[candidate_index].first_con = connection_it; connection_it += candidate_connections_in; neurons[candidate_index].last_con = connection_it; /* We have no specific pointers to the output weights, but they are * available after last_con */ connection_it += candidate_connections_out; ann->train_errors[candidate_index] = 0; initialize_candidate_weights(ann, neurons[candidate_index].first_con, neurons[candidate_index].last_con+candidate_connections_out, scale_factor); candidate_index++; } } } /* Now randomize the weights and zero out the arrays that needs zeroing out. */ /* #ifdef CASCADE_DEBUG_FULL printf("random cand weight [%d ... %d]\n", first_candidate_connection, num_connections - 1); #endif for(i = first_candidate_connection; i < num_connections; i++) { //ann->weights[i] = fann_random_weight(); ann->weights[i] = fann_rand(-2.0,2.0); ann->train_slopes[i] = 0; ann->prev_steps[i] = 0; ann->prev_train_slopes[i] = initial_slope; } */ return 0; } int fann_train_candidates(struct fann *ann, struct fann_train_data *data) { fann_type best_cand_score = 0.0; fann_type target_cand_score = 0.0; fann_type backslide_cand_score = -1.0e20f; unsigned int i; unsigned int max_epochs = ann->cascade_max_cand_epochs; unsigned int stagnation = max_epochs; if(ann->cascade_candidate_scores == NULL) { ann->cascade_candidate_scores = (fann_type *) malloc(fann_get_cascade_num_candidates(ann) * sizeof(fann_type)); if(ann->cascade_candidate_scores == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return 0; } } for(i = 0; i < max_epochs; i++) { best_cand_score = fann_train_candidates_epoch(ann, data); if(best_cand_score / ann->MSE_value > ann->cascade_candidate_limit) { #ifdef CASCADE_DEBUG printf("above candidate limit %f/%f > %f", best_cand_score, ann->MSE_value, ann->cascade_candidate_limit); #endif return i + 1; } if((best_cand_score > target_cand_score) || (best_cand_score < backslide_cand_score)) { #ifdef CASCADE_DEBUG_FULL printf("Best candidate score %f, real score: %f\n", ann->MSE_value - best_cand_score, best_cand_score); /* printf("best_cand_score=%f, target_cand_score=%f, backslide_cand_score=%f, stagnation=%d\n", best_cand_score, target_cand_score, backslide_cand_score, stagnation); */ #endif target_cand_score = best_cand_score * (1.0f + ann->cascade_candidate_change_fraction); backslide_cand_score = best_cand_score * (1.0f - ann->cascade_candidate_change_fraction); stagnation = i + ann->cascade_candidate_stagnation_epochs; } /* No improvement in allotted period, so quit */ if(i >= stagnation) { #ifdef CASCADE_DEBUG printf("Stagnation with %d epochs, best candidate score %f, real score: %f\n", i + 1, ann->MSE_value - best_cand_score, best_cand_score); #endif return i + 1; } } #ifdef CASCADE_DEBUG printf("Max epochs %d reached, best candidate score %f, real score: %f\n", max_epochs, ann->MSE_value - best_cand_score, best_cand_score); #endif return max_epochs; } void fann_update_candidate_slopes(struct fann *ann) { struct fann_neuron *neurons = ann->first_layer->first_neuron; struct fann_neuron *first_cand = neurons + ann->total_neurons + 1; struct fann_neuron *last_cand = first_cand + fann_get_cascade_num_candidates(ann); struct fann_neuron *cand_it; unsigned int i, j, num_connections; unsigned int num_output = ann->num_output; fann_type max_sum, cand_sum, activation, derived, error_value, diff, cand_score; fann_type *weights, *cand_out_weights, *cand_slopes, *cand_out_slopes; fann_type *output_train_errors = ann->train_errors + (ann->total_neurons - ann->num_output); for(cand_it = first_cand; cand_it < last_cand; cand_it++) { cand_score = ann->cascade_candidate_scores[cand_it - first_cand]; error_value = 0.0; /* code more or less stolen from fann_run to fast forward pass */ cand_sum = 0.0; num_connections = cand_it->last_con - cand_it->first_con; weights = ann->weights + cand_it->first_con; /* unrolled loop start */ i = num_connections & 3; /* same as modulo 4 */ switch (i) { case 3: cand_sum += weights[2] * neurons[2].value; case 2: cand_sum += weights[1] * neurons[1].value; case 1: cand_sum += weights[0] * neurons[0].value; case 0: break; } for(; i != num_connections; i += 4) { cand_sum += weights[i] * neurons[i].value + weights[i + 1] * neurons[i + 1].value + weights[i + 2] * neurons[i + 2].value + weights[i + 3] * neurons[i + 3].value; } /* * for(i = 0; i < num_connections; i++){ * cand_sum += weights[i] * neurons[i].value; * } */ /* unrolled loop end */ max_sum = 150/cand_it->activation_steepness; if(cand_sum > max_sum) cand_sum = max_sum; else if(cand_sum < -max_sum) cand_sum = -max_sum; activation = fann_activation(ann, cand_it->activation_function, cand_it->activation_steepness, cand_sum); /* printf("%f = sigmoid(%f);\n", activation, cand_sum); */ cand_it->sum = cand_sum; cand_it->value = activation; derived = fann_activation_derived(cand_it->activation_function, cand_it->activation_steepness, activation, cand_sum); /* The output weights is located right after the input weights in * the weight array. */ cand_out_weights = weights + num_connections; cand_out_slopes = ann->train_slopes + cand_it->first_con + num_connections; for(j = 0; j < num_output; j++) { diff = (activation * cand_out_weights[j]) - output_train_errors[j]; #ifdef CASCADE_DEBUG_FULL /* printf("diff = %f = (%f * %f) - %f;\n", diff, activation, cand_out_weights[j], output_train_errors[j]); */ #endif cand_out_slopes[j] -= 2.0f * diff * activation; #ifdef CASCADE_DEBUG_FULL /* printf("cand_out_slopes[%d] <= %f += %f * %f;\n", j, cand_out_slopes[j], diff, activation); */ #endif error_value += diff * cand_out_weights[j]; cand_score -= (diff * diff); #ifdef CASCADE_DEBUG_FULL /* printf("cand_score[%d][%d] = %f -= (%f * %f)\n", cand_it - first_cand, j, cand_score, diff, diff); */ printf("cand[%d]: error=%f, activation=%f, diff=%f, slope=%f\n", cand_it - first_cand, output_train_errors[j], (activation * cand_out_weights[j]), diff, -2.0 * diff * activation); #endif } ann->cascade_candidate_scores[cand_it - first_cand] = cand_score; error_value *= derived; cand_slopes = ann->train_slopes + cand_it->first_con; for(i = 0; i < num_connections; i++) { cand_slopes[i] -= error_value * neurons[i].value; } } } void fann_update_candidate_weights(struct fann *ann, unsigned int num_data) { struct fann_neuron *first_cand = (ann->last_layer - 1)->last_neuron + 1; /* there is an empty neuron between the actual neurons and the candidate neuron */ struct fann_neuron *last_cand = first_cand + fann_get_cascade_num_candidates(ann) - 1; switch (ann->training_algorithm) { case FANN_TRAIN_RPROP: fann_update_weights_irpropm(ann, first_cand->first_con, last_cand->last_con + ann->num_output); break; case FANN_TRAIN_QUICKPROP: fann_update_weights_quickprop(ann, num_data, first_cand->first_con, last_cand->last_con + ann->num_output); break; case FANN_TRAIN_BATCH: case FANN_TRAIN_INCREMENTAL: fann_error((struct fann_error *) ann, FANN_E_CANT_USE_TRAIN_ALG); break; } } fann_type fann_train_candidates_epoch(struct fann *ann, struct fann_train_data *data) { unsigned int i, j; unsigned int best_candidate; fann_type best_score; unsigned int num_cand = fann_get_cascade_num_candidates(ann); fann_type *output_train_errors = ann->train_errors + (ann->total_neurons - ann->num_output); struct fann_neuron *output_neurons = (ann->last_layer - 1)->first_neuron; for(i = 0; i < num_cand; i++) { /* The ann->MSE_value is actually the sum squared error */ ann->cascade_candidate_scores[i] = ann->MSE_value; } /*printf("start score: %f\n", ann->MSE_value); */ for(i = 0; i < data->num_data; i++) { fann_run(ann, data->input[i]); for(j = 0; j < ann->num_output; j++) { /* TODO only debug, but the error is in opposite direction, this might be usefull info */ /* if(output_train_errors[j] != (ann->output[j] - data->output[i][j])){ * printf("difference in calculated error at %f != %f; %f = %f - %f;\n", output_train_errors[j], (ann->output[j] - data->output[i][j]), output_train_errors[j], ann->output[j], data->output[i][j]); * } */ /* * output_train_errors[j] = (data->output[i][j] - ann->output[j])/2; * output_train_errors[j] = ann->output[j] - data->output[i][j]; */ output_train_errors[j] = (data->output[i][j] - ann->output[j]); switch (output_neurons[j].activation_function) { case FANN_LINEAR_PIECE_SYMMETRIC: case FANN_SIGMOID_SYMMETRIC: case FANN_SIGMOID_SYMMETRIC_STEPWISE: case FANN_THRESHOLD_SYMMETRIC: case FANN_ELLIOT_SYMMETRIC: case FANN_GAUSSIAN_SYMMETRIC: case FANN_SIN_SYMMETRIC: case FANN_COS_SYMMETRIC: output_train_errors[j] /= 2.0; break; case FANN_LINEAR: case FANN_THRESHOLD: case FANN_SIGMOID: case FANN_SIGMOID_STEPWISE: case FANN_GAUSSIAN: case FANN_GAUSSIAN_STEPWISE: case FANN_ELLIOT: case FANN_LINEAR_PIECE: case FANN_SIN: case FANN_COS: break; } } fann_update_candidate_slopes(ann); } fann_update_candidate_weights(ann, data->num_data); /* find the best candidate score */ best_candidate = 0; best_score = ann->cascade_candidate_scores[best_candidate]; for(i = 1; i < num_cand; i++) { /*struct fann_neuron *cand = ann->first_layer->first_neuron + ann->total_neurons + 1 + i; * printf("candidate[%d] = activation: %s, steepness: %f, score: %f\n", * i, FANN_ACTIVATIONFUNC_NAMES[cand->activation_function], * cand->activation_steepness, ann->cascade_candidate_scores[i]); */ if(ann->cascade_candidate_scores[i] > best_score) { best_candidate = i; best_score = ann->cascade_candidate_scores[best_candidate]; } } ann->cascade_best_candidate = ann->total_neurons + best_candidate + 1; #ifdef CASCADE_DEBUG_FULL printf("Best candidate[%d]: with score %f, real score: %f\n", best_candidate, ann->MSE_value - best_score, best_score); #endif return best_score; } /* add a layer ad the position pointed to by *layer */ struct fann_layer *fann_add_layer(struct fann *ann, struct fann_layer *layer) { int layer_pos = layer - ann->first_layer; int num_layers = ann->last_layer - ann->first_layer + 1; int i; /* allocate the layer */ struct fann_layer *layers = (struct fann_layer *) realloc(ann->first_layer, num_layers * sizeof(struct fann_layer)); if(layers == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return NULL; } /* copy layers so that the free space is at the right location */ for(i = num_layers - 1; i >= layer_pos; i--) { layers[i] = layers[i - 1]; } /* the newly allocated layer is empty */ layers[layer_pos].first_neuron = layers[layer_pos + 1].first_neuron; layers[layer_pos].last_neuron = layers[layer_pos + 1].first_neuron; /* Set the ann pointers correctly */ ann->first_layer = layers; ann->last_layer = layers + num_layers; #ifdef CASCADE_DEBUG_FULL printf("add layer at pos %d\n", layer_pos); #endif return layers + layer_pos; } void fann_set_shortcut_connections(struct fann *ann) { struct fann_layer *layer_it; struct fann_neuron *neuron_it, **neuron_pointers, *neurons; unsigned int num_connections = 0, i; neuron_pointers = ann->connections; neurons = ann->first_layer->first_neuron; for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++) { for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++) { neuron_pointers += num_connections; num_connections = neuron_it->last_con - neuron_it->first_con; for(i = 0; i != num_connections; i++) { neuron_pointers[i] = neurons + i; } } } } void fann_add_candidate_neuron(struct fann *ann, struct fann_layer *layer) { unsigned int num_connections_in = layer->first_neuron - ann->first_layer->first_neuron; unsigned int num_connections_out = (ann->last_layer - 1)->last_neuron - (layer + 1)->first_neuron; unsigned int num_connections_move = num_connections_out + num_connections_in; unsigned int candidate_con, candidate_output_weight; int i; struct fann_layer *layer_it; struct fann_neuron *neuron_it, *neuron_place, *candidate; /* We know that there is enough room for the new neuron * (the candidates are in the same arrays), so move * the last neurons to make room for this neuron. */ /* first move the pointers to neurons in the layer structs */ for(layer_it = ann->last_layer - 1; layer_it != layer; layer_it--) { #ifdef CASCADE_DEBUG_FULL printf("move neuron pointers in layer %d, first(%d -> %d), last(%d -> %d)\n", layer_it - ann->first_layer, layer_it->first_neuron - ann->first_layer->first_neuron, layer_it->first_neuron - ann->first_layer->first_neuron + 1, layer_it->last_neuron - ann->first_layer->first_neuron, layer_it->last_neuron - ann->first_layer->first_neuron + 1); #endif layer_it->first_neuron++; layer_it->last_neuron++; } /* also move the last neuron in the layer that needs the neuron added */ layer->last_neuron++; /* this is the place that should hold the new neuron */ neuron_place = layer->last_neuron - 1; #ifdef CASCADE_DEBUG_FULL printf("num_connections_in=%d, num_connections_out=%d\n", num_connections_in, num_connections_out); #endif candidate = ann->first_layer->first_neuron + ann->cascade_best_candidate; /* the output weights for the candidates are located after the input weights */ candidate_output_weight = candidate->last_con; /* move the actual output neurons and the indexes to the connection arrays */ for(neuron_it = (ann->last_layer - 1)->last_neuron - 1; neuron_it != neuron_place; neuron_it--) { #ifdef CASCADE_DEBUG_FULL printf("move neuron %d -> %d\n", neuron_it - ann->first_layer->first_neuron - 1, neuron_it - ann->first_layer->first_neuron); #endif *neuron_it = *(neuron_it - 1); /* move the weights */ #ifdef CASCADE_DEBUG_FULL printf("move weight[%d ... %d] -> weight[%d ... %d]\n", neuron_it->first_con, neuron_it->last_con - 1, neuron_it->first_con + num_connections_move - 1, neuron_it->last_con + num_connections_move - 2); #endif for(i = neuron_it->last_con - 1; i >= (int)neuron_it->first_con; i--) { #ifdef CASCADE_DEBUG_FULL printf("move weight[%d] = weight[%d]\n", i + num_connections_move - 1, i); #endif ann->weights[i + num_connections_move - 1] = ann->weights[i]; } /* move the indexes to weights */ neuron_it->last_con += num_connections_move; num_connections_move--; neuron_it->first_con += num_connections_move; /* set the new weight to the newly allocated neuron */ ann->weights[neuron_it->last_con - 1] = (ann->weights[candidate_output_weight]) * ann->cascade_weight_multiplier; candidate_output_weight++; } /* Now inititalize the actual neuron */ neuron_place->value = 0; neuron_place->sum = 0; neuron_place->activation_function = candidate->activation_function; neuron_place->activation_steepness = candidate->activation_steepness; neuron_place->last_con = (neuron_place + 1)->first_con; neuron_place->first_con = neuron_place->last_con - num_connections_in; #ifdef CASCADE_DEBUG_FULL printf("neuron[%d] = weights[%d ... %d] activation: %s, steepness: %f\n", neuron_place - ann->first_layer->first_neuron, neuron_place->first_con, neuron_place->last_con - 1, FANN_ACTIVATIONFUNC_NAMES[neuron_place->activation_function], neuron_place->activation_steepness);/* TODO remove */ #endif candidate_con = candidate->first_con; /* initialize the input weights at random */ #ifdef CASCADE_DEBUG_FULL printf("move cand weights[%d ... %d] -> [%d ... %d]\n", candidate_con, candidate_con + num_connections_in - 1, neuron_place->first_con, neuron_place->last_con - 1); #endif for(i = 0; i < (int)num_connections_in; i++) { ann->weights[i + neuron_place->first_con] = ann->weights[i + candidate_con]; #ifdef CASCADE_DEBUG_FULL printf("move weights[%d] -> weights[%d] (%f)\n", i + candidate_con, i + neuron_place->first_con, ann->weights[i + neuron_place->first_con]); #endif } /* Change some of main variables */ ann->total_neurons++; ann->total_connections += num_connections_in + num_connections_out; return; } void fann_install_candidate(struct fann *ann) { struct fann_layer *layer; layer = fann_add_layer(ann, ann->last_layer - 1); fann_add_candidate_neuron(ann, layer); return; } #endif /* FIXEDFANN */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidates(struct fann *ann) { return ann->cascade_activation_functions_count * ann->cascade_activation_steepnesses_count * ann->cascade_num_candidate_groups; } FANN_GET_SET(float, cascade_output_change_fraction) FANN_GET_SET(unsigned int, cascade_output_stagnation_epochs) FANN_GET_SET(float, cascade_candidate_change_fraction) FANN_GET_SET(unsigned int, cascade_candidate_stagnation_epochs) FANN_GET_SET(unsigned int, cascade_num_candidate_groups) FANN_GET_SET(fann_type, cascade_weight_multiplier) FANN_GET_SET(fann_type, cascade_candidate_limit) FANN_GET_SET(unsigned int, cascade_max_out_epochs) FANN_GET_SET(unsigned int, cascade_max_cand_epochs) FANN_GET(unsigned int, cascade_activation_functions_count) FANN_GET(enum fann_activationfunc_enum *, cascade_activation_functions) FANN_EXTERNAL void FANN_API fann_set_cascade_activation_functions(struct fann *ann, enum fann_activationfunc_enum * cascade_activation_functions, unsigned int cascade_activation_functions_count) { if(ann->cascade_activation_functions_count != cascade_activation_functions_count) { ann->cascade_activation_functions_count = cascade_activation_functions_count; /* reallocate mem */ ann->cascade_activation_functions = (enum fann_activationfunc_enum *)realloc(ann->cascade_activation_functions, ann->cascade_activation_functions_count * sizeof(enum fann_activationfunc_enum)); if(ann->cascade_activation_functions == NULL) { fann_error((struct fann_error*)ann, FANN_E_CANT_ALLOCATE_MEM); return; } } memmove(ann->cascade_activation_functions, cascade_activation_functions, ann->cascade_activation_functions_count * sizeof(enum fann_activationfunc_enum)); } FANN_GET(unsigned int, cascade_activation_steepnesses_count) FANN_GET(fann_type *, cascade_activation_steepnesses) FANN_EXTERNAL void FANN_API fann_set_cascade_activation_steepnesses(struct fann *ann, fann_type * cascade_activation_steepnesses, unsigned int cascade_activation_steepnesses_count) { if(ann->cascade_activation_steepnesses_count != cascade_activation_steepnesses_count) { ann->cascade_activation_steepnesses_count = cascade_activation_steepnesses_count; /* reallocate mem */ ann->cascade_activation_steepnesses = (fann_type *)realloc(ann->cascade_activation_steepnesses, ann->cascade_activation_steepnesses_count * sizeof(fann_type)); if(ann->cascade_activation_steepnesses == NULL) { fann_error((struct fann_error*)ann, FANN_E_CANT_ALLOCATE_MEM); return; } } memmove(ann->cascade_activation_steepnesses, cascade_activation_steepnesses, ann->cascade_activation_steepnesses_count * sizeof(fann_type)); } libfann-2.1.0~beta~dfsg/src/fann_error.c000066400000000000000000000127261140315177100203150ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include #include #include #include "config.h" #include "fann.h" #ifdef _MSC_VER #define vsnprintf _vsnprintf #define snprintf _snprintf #endif FILE * fann_default_error_log = (FILE *)-1; /* resets the last error number */ FANN_EXTERNAL void FANN_API fann_reset_errno(struct fann_error *errdat) { errdat->errno_f = FANN_E_NO_ERROR; } /* resets the last errstr */ FANN_EXTERNAL void FANN_API fann_reset_errstr(struct fann_error *errdat) { if(errdat->errstr != NULL) free(errdat->errstr); errdat->errstr = NULL; } /* returns the last error number */ FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno(struct fann_error *errdat) { return errdat->errno_f; } /* returns the last errstr */ FANN_EXTERNAL char *FANN_API fann_get_errstr(struct fann_error *errdat) { char *errstr = errdat->errstr; fann_reset_errno(errdat); fann_reset_errstr(errdat); return errstr; } /* change where errors are logged to */ FANN_EXTERNAL void FANN_API fann_set_error_log(struct fann_error *errdat, FILE * log_file) { if(errdat == NULL) fann_default_error_log = log_file; else errdat->error_log = log_file; } /* prints the last error to stderr */ FANN_EXTERNAL void FANN_API fann_print_error(struct fann_error *errdat) { if(errdat->errno_f != FANN_E_NO_ERROR && errdat->errstr != NULL) { fprintf(stderr, "FANN Error %d: %s", errdat->errno_f, errdat->errstr); } } /* INTERNAL FUNCTION Populate the error information */ void fann_error(struct fann_error *errdat, const enum fann_errno_enum errno_f, ...) { va_list ap; char *errstr; FILE * error_log = fann_default_error_log; if(errdat != NULL) errdat->errno_f = errno_f; if(errdat != NULL && errdat->errstr != NULL) { errstr = errdat->errstr; } else { errstr = (char *) malloc(FANN_ERRSTR_MAX); if(errstr == NULL) { fprintf(stderr, "Unable to allocate memory.\n"); return; } } va_start(ap, errno_f); switch (errno_f) { case FANN_E_NO_ERROR: break; case FANN_E_CANT_OPEN_CONFIG_R: vsprintf(errstr, "Unable to open configuration file \"%s\" for reading.\n", ap); break; case FANN_E_CANT_OPEN_CONFIG_W: vsprintf(errstr, "Unable to open configuration file \"%s\" for writing.\n", ap); break; case FANN_E_WRONG_CONFIG_VERSION: vsprintf(errstr, "Wrong version of configuration file, aborting read of configuration file \"%s\".\n", ap); break; case FANN_E_CANT_READ_CONFIG: vsprintf(errstr, "Error reading \"%s\" from configuration file \"%s\".\n", ap); break; case FANN_E_CANT_READ_NEURON: vsprintf(errstr, "Error reading neuron info from configuration file \"%s\".\n", ap); break; case FANN_E_CANT_READ_CONNECTIONS: vsprintf(errstr, "Error reading connections from configuration file \"%s\".\n", ap); break; case FANN_E_WRONG_NUM_CONNECTIONS: vsprintf(errstr, "ERROR connections_so_far=%d, total_connections=%d\n", ap); break; case FANN_E_CANT_OPEN_TD_W: vsprintf(errstr, "Unable to open train data file \"%s\" for writing.\n", ap); break; case FANN_E_CANT_OPEN_TD_R: vsprintf(errstr, "Unable to open train data file \"%s\" for writing.\n", ap); break; case FANN_E_CANT_READ_TD: vsprintf(errstr, "Error reading info from train data file \"%s\", line: %d.\n", ap); break; case FANN_E_CANT_ALLOCATE_MEM: sprintf(errstr, "Unable to allocate memory.\n"); break; case FANN_E_CANT_TRAIN_ACTIVATION: sprintf(errstr, "Unable to train with the selected activation function.\n"); break; case FANN_E_CANT_USE_ACTIVATION: sprintf(errstr, "Unable to use the selected activation function.\n"); break; case FANN_E_TRAIN_DATA_MISMATCH: sprintf(errstr, "Training data must be of equivalent structure.\n"); break; case FANN_E_CANT_USE_TRAIN_ALG: sprintf(errstr, "Unable to use the selected training algorithm.\n"); break; case FANN_E_TRAIN_DATA_SUBSET: vsprintf(errstr, "Subset from %d of length %d not valid in training set of length %d.\n", ap); break; case FANN_E_INDEX_OUT_OF_BOUND: vsprintf(errstr, "Index %d is out of bound.\n", ap); break; case FANN_E_SCALE_NOT_PRESENT: sprintf(errstr, "Scaling parameters not present.\n"); break; } va_end(ap); if(errdat != NULL) { errdat->errstr = errstr; error_log = errdat->error_log; } if(error_log == (FILE *)-1) /* This is the default behavior and will give stderr */ { fprintf(stderr, "FANN Error %d: %s", errno_f, errstr); } else if(error_log != NULL) { fprintf(error_log, "FANN Error %d: %s", errno_f, errstr); } } /* INTERNAL FUNCTION Initialize an error data strcuture */ void fann_init_error_data(struct fann_error *errdat) { errdat->errstr = NULL; errdat->errno_f = FANN_E_NO_ERROR; errdat->error_log = fann_default_error_log; } libfann-2.1.0~beta~dfsg/src/fann_io.c000066400000000000000000000603041140315177100175660ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include #include #include #include "config.h" #include "fann.h" /* Create a network from a configuration file. */ FANN_EXTERNAL struct fann *FANN_API fann_create_from_file(const char *configuration_file) { struct fann *ann; FILE *conf = fopen(configuration_file, "r"); if(!conf) { fann_error(NULL, FANN_E_CANT_OPEN_CONFIG_R, configuration_file); return NULL; } ann = fann_create_from_fd(conf, configuration_file); fclose(conf); return ann; } /* Save the network. */ FANN_EXTERNAL int FANN_API fann_save(struct fann *ann, const char *configuration_file) { return fann_save_internal(ann, configuration_file, 0); } /* Save the network as fixed point data. */ FANN_EXTERNAL int FANN_API fann_save_to_fixed(struct fann *ann, const char *configuration_file) { return fann_save_internal(ann, configuration_file, 1); } /* INTERNAL FUNCTION Used to save the network to a file. */ int fann_save_internal(struct fann *ann, const char *configuration_file, unsigned int save_as_fixed) { int retval; FILE *conf = fopen(configuration_file, "w+"); if(!conf) { fann_error((struct fann_error *) ann, FANN_E_CANT_OPEN_CONFIG_W, configuration_file); return -1; } retval = fann_save_internal_fd(ann, conf, configuration_file, save_as_fixed); fclose(conf); return retval; } /* INTERNAL FUNCTION Used to save the network to a file descriptor. */ int fann_save_internal_fd(struct fann *ann, FILE * conf, const char *configuration_file, unsigned int save_as_fixed) { struct fann_layer *layer_it; int calculated_decimal_point = 0; struct fann_neuron *neuron_it, *first_neuron; fann_type *weights; struct fann_neuron **connected_neurons; unsigned int i = 0; #ifndef FIXEDFANN /* variabels for use when saving floats as fixed point variabels */ unsigned int decimal_point = 0; unsigned int fixed_multiplier = 0; fann_type max_possible_value = 0; unsigned int bits_used_for_max = 0; fann_type current_max_value = 0; #endif #ifndef FIXEDFANN if(save_as_fixed) { /* save the version information */ fprintf(conf, FANN_FIX_VERSION "\n"); } else { /* save the version information */ fprintf(conf, FANN_FLO_VERSION "\n"); } #else /* save the version information */ fprintf(conf, FANN_FIX_VERSION "\n"); #endif #ifndef FIXEDFANN if(save_as_fixed) { /* calculate the maximal possible shift value */ for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++) { for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++) { /* look at all connections to each neurons, and see how high a value we can get */ current_max_value = 0; for(i = neuron_it->first_con; i != neuron_it->last_con; i++) { current_max_value += fann_abs(ann->weights[i]); } if(current_max_value > max_possible_value) { max_possible_value = current_max_value; } } } for(bits_used_for_max = 0; max_possible_value >= 1; bits_used_for_max++) { max_possible_value /= 2.0; } /* The maximum number of bits we shift the fix point, is the number * of bits in a integer, minus one for the sign, one for the minus * in stepwise, and minus the bits used for the maximum. * This is devided by two, to allow multiplication of two fixed * point numbers. */ calculated_decimal_point = (sizeof(int) * 8 - 2 - bits_used_for_max) / 2; if(calculated_decimal_point < 0) { decimal_point = 0; } else { decimal_point = calculated_decimal_point; } fixed_multiplier = 1 << decimal_point; #ifdef DEBUG printf("calculated_decimal_point=%d, decimal_point=%u, bits_used_for_max=%u\n", calculated_decimal_point, decimal_point, bits_used_for_max); #endif /* save the decimal_point on a seperate line */ fprintf(conf, "decimal_point=%u\n", decimal_point); } #else /* save the decimal_point on a seperate line */ fprintf(conf, "decimal_point=%u\n", ann->decimal_point); #endif /* Save network parameters */ fprintf(conf, "num_layers=%u\n", ann->last_layer - ann->first_layer); fprintf(conf, "learning_rate=%f\n", ann->learning_rate); fprintf(conf, "connection_rate=%f\n", ann->connection_rate); fprintf(conf, "network_type=%u\n", ann->network_type); fprintf(conf, "learning_momentum=%f\n", ann->learning_momentum); fprintf(conf, "training_algorithm=%u\n", ann->training_algorithm); fprintf(conf, "train_error_function=%u\n", ann->train_error_function); fprintf(conf, "train_stop_function=%u\n", ann->train_stop_function); fprintf(conf, "cascade_output_change_fraction=%f\n", ann->cascade_output_change_fraction); fprintf(conf, "quickprop_decay=%f\n", ann->quickprop_decay); fprintf(conf, "quickprop_mu=%f\n", ann->quickprop_mu); fprintf(conf, "rprop_increase_factor=%f\n", ann->rprop_increase_factor); fprintf(conf, "rprop_decrease_factor=%f\n", ann->rprop_decrease_factor); fprintf(conf, "rprop_delta_min=%f\n", ann->rprop_delta_min); fprintf(conf, "rprop_delta_max=%f\n", ann->rprop_delta_max); fprintf(conf, "rprop_delta_zero=%f\n", ann->rprop_delta_zero); fprintf(conf, "cascade_output_stagnation_epochs=%u\n", ann->cascade_output_stagnation_epochs); fprintf(conf, "cascade_candidate_change_fraction=%f\n", ann->cascade_candidate_change_fraction); fprintf(conf, "cascade_candidate_stagnation_epochs=%u\n", ann->cascade_candidate_stagnation_epochs); fprintf(conf, "cascade_max_out_epochs=%u\n", ann->cascade_max_out_epochs); fprintf(conf, "cascade_max_cand_epochs=%u\n", ann->cascade_max_cand_epochs); fprintf(conf, "cascade_num_candidate_groups=%u\n", ann->cascade_num_candidate_groups); #ifndef FIXEDFANN if(save_as_fixed) { fprintf(conf, "bit_fail_limit=%u\n", (int) floor((ann->bit_fail_limit * fixed_multiplier) + 0.5)); fprintf(conf, "cascade_candidate_limit=%u\n", (int) floor((ann->cascade_candidate_limit * fixed_multiplier) + 0.5)); fprintf(conf, "cascade_weight_multiplier=%u\n", (int) floor((ann->cascade_weight_multiplier * fixed_multiplier) + 0.5)); } else #endif { fprintf(conf, "bit_fail_limit="FANNPRINTF"\n", ann->bit_fail_limit); fprintf(conf, "cascade_candidate_limit="FANNPRINTF"\n", ann->cascade_candidate_limit); fprintf(conf, "cascade_weight_multiplier="FANNPRINTF"\n", ann->cascade_weight_multiplier); } fprintf(conf, "cascade_activation_functions_count=%u\n", ann->cascade_activation_functions_count); fprintf(conf, "cascade_activation_functions="); for(i = 0; i < ann->cascade_activation_functions_count; i++) fprintf(conf, "%u ", ann->cascade_activation_functions[i]); fprintf(conf, "\n"); fprintf(conf, "cascade_activation_steepnesses_count=%u\n", ann->cascade_activation_steepnesses_count); fprintf(conf, "cascade_activation_steepnesses="); for(i = 0; i < ann->cascade_activation_steepnesses_count; i++) { #ifndef FIXEDFANN if(save_as_fixed) fprintf(conf, "%u ", (int) floor((ann->cascade_activation_steepnesses[i] * fixed_multiplier) + 0.5)); else #endif fprintf(conf, FANNPRINTF" ", ann->cascade_activation_steepnesses[i]); } fprintf(conf, "\n"); fprintf(conf, "layer_sizes="); for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { /* the number of neurons in the layers (in the last layer, there is always one too many neurons, because of an unused bias) */ fprintf(conf, "%u ", layer_it->last_neuron - layer_it->first_neuron); } fprintf(conf, "\n"); #ifndef FIXEDFANN /* 2.1 */ #define SCALE_SAVE( what, where ) \ fprintf( conf, #what "_" #where "=" ); \ for( i = 0; i < ann->num_##where##put; i++ ) \ fprintf( conf, "%f ", ann->what##_##where[ i ] ); \ fprintf( conf, "\n" ); if(!save_as_fixed) { if(ann->scale_mean_in != NULL) { fprintf(conf, "scale_included=1\n"); SCALE_SAVE( scale_mean, in ) SCALE_SAVE( scale_deviation, in ) SCALE_SAVE( scale_new_min, in ) SCALE_SAVE( scale_factor, in ) SCALE_SAVE( scale_mean, out ) SCALE_SAVE( scale_deviation, out ) SCALE_SAVE( scale_new_min, out ) SCALE_SAVE( scale_factor, out ) } else fprintf(conf, "scale_included=0\n"); } #undef SCALE_SAVE #endif /* 2.0 */ fprintf(conf, "neurons (num_inputs, activation_function, activation_steepness)="); for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { /* the neurons */ for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++) { #ifndef FIXEDFANN if(save_as_fixed) { fprintf(conf, "(%u, %u, %u) ", neuron_it->last_con - neuron_it->first_con, neuron_it->activation_function, (int) floor((neuron_it->activation_steepness * fixed_multiplier) + 0.5)); } else { fprintf(conf, "(%u, %u, " FANNPRINTF ") ", neuron_it->last_con - neuron_it->first_con, neuron_it->activation_function, neuron_it->activation_steepness); } #else fprintf(conf, "(%u, %u, " FANNPRINTF ") ", neuron_it->last_con - neuron_it->first_con, neuron_it->activation_function, neuron_it->activation_steepness); #endif } } fprintf(conf, "\n"); connected_neurons = ann->connections; weights = ann->weights; first_neuron = ann->first_layer->first_neuron; /* Now save all the connections. * We only need to save the source and the weight, * since the destination is given by the order. * * The weight is not saved binary due to differences * in binary definition of floating point numbers. * Especially an iPAQ does not use the same binary * representation as an i386 machine. */ fprintf(conf, "connections (connected_to_neuron, weight)="); for(i = 0; i < ann->total_connections; i++) { #ifndef FIXEDFANN if(save_as_fixed) { /* save the connection "(source weight) " */ fprintf(conf, "(%u, %d) ", connected_neurons[i] - first_neuron, (int) floor((weights[i] * fixed_multiplier) + 0.5)); } else { /* save the connection "(source weight) " */ fprintf(conf, "(%u, " FANNPRINTF ") ", connected_neurons[i] - first_neuron, weights[i]); } #else /* save the connection "(source weight) " */ fprintf(conf, "(%u, " FANNPRINTF ") ", connected_neurons[i] - first_neuron, weights[i]); #endif } fprintf(conf, "\n"); return calculated_decimal_point; } struct fann *fann_create_from_fd_1_1(FILE * conf, const char *configuration_file); #define fann_scanf(type, name, val) \ { \ if(fscanf(conf, name"="type"\n", val) != 1) \ { \ fann_error(NULL, FANN_E_CANT_READ_CONFIG, name, configuration_file); \ fann_destroy(ann); \ return NULL; \ } \ } /* INTERNAL FUNCTION Create a network from a configuration file descriptor. */ struct fann *fann_create_from_fd(FILE * conf, const char *configuration_file) { unsigned int num_layers, layer_size, input_neuron, i, num_connections; #ifdef FIXEDFANN unsigned int decimal_point, multiplier; #else unsigned int scale_included; #endif struct fann_neuron *first_neuron, *neuron_it, *last_neuron, **connected_neurons; fann_type *weights; struct fann_layer *layer_it; struct fann *ann = NULL; char *read_version; read_version = (char *) calloc(strlen(FANN_CONF_VERSION "\n"), 1); if(read_version == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } fread(read_version, 1, strlen(FANN_CONF_VERSION "\n"), conf); /* reads version */ /* compares the version information */ if(strncmp(read_version, FANN_CONF_VERSION "\n", strlen(FANN_CONF_VERSION "\n")) != 0) { #ifdef FIXEDFANN if(strncmp(read_version, "FANN_FIX_1.1\n", strlen("FANN_FIX_1.1\n")) == 0) { #else if(strncmp(read_version, "FANN_FLO_1.1\n", strlen("FANN_FLO_1.1\n")) == 0) { #endif free(read_version); return fann_create_from_fd_1_1(conf, configuration_file); } #ifndef FIXEDFANN /* Maintain compatibility with 2.0 version that doesnt have scale parameters. */ if(strncmp(read_version, "FANN_FLO_2.0\n", strlen("FANN_FLO_2.0\n")) != 0 && strncmp(read_version, "FANN_FLO_2.1\n", strlen("FANN_FLO_2.1\n")) != 0) #else if(strncmp(read_version, "FANN_FIX_2.0\n", strlen("FANN_FIX_2.0\n")) != 0 && strncmp(read_version, "FANN_FIX_2.1\n", strlen("FANN_FIX_2.1\n")) != 0) #endif { free(read_version); fann_error(NULL, FANN_E_WRONG_CONFIG_VERSION, configuration_file); return NULL; } } free(read_version); #ifdef FIXEDFANN fann_scanf("%u", "decimal_point", &decimal_point); multiplier = 1 << decimal_point; #endif fann_scanf("%u", "num_layers", &num_layers); ann = fann_allocate_structure(num_layers); if(ann == NULL) { return NULL; } fann_scanf("%f", "learning_rate", &ann->learning_rate); fann_scanf("%f", "connection_rate", &ann->connection_rate); fann_scanf("%u", "network_type", (unsigned int *)&ann->network_type); fann_scanf("%f", "learning_momentum", &ann->learning_momentum); fann_scanf("%u", "training_algorithm", (unsigned int *)&ann->training_algorithm); fann_scanf("%u", "train_error_function", (unsigned int *)&ann->train_error_function); fann_scanf("%u", "train_stop_function", (unsigned int *)&ann->train_stop_function); fann_scanf("%f", "cascade_output_change_fraction", &ann->cascade_output_change_fraction); fann_scanf("%f", "quickprop_decay", &ann->quickprop_decay); fann_scanf("%f", "quickprop_mu", &ann->quickprop_mu); fann_scanf("%f", "rprop_increase_factor", &ann->rprop_increase_factor); fann_scanf("%f", "rprop_decrease_factor", &ann->rprop_decrease_factor); fann_scanf("%f", "rprop_delta_min", &ann->rprop_delta_min); fann_scanf("%f", "rprop_delta_max", &ann->rprop_delta_max); fann_scanf("%f", "rprop_delta_zero", &ann->rprop_delta_zero); fann_scanf("%u", "cascade_output_stagnation_epochs", &ann->cascade_output_stagnation_epochs); fann_scanf("%f", "cascade_candidate_change_fraction", &ann->cascade_candidate_change_fraction); fann_scanf("%u", "cascade_candidate_stagnation_epochs", &ann->cascade_candidate_stagnation_epochs); fann_scanf("%u", "cascade_max_out_epochs", &ann->cascade_max_out_epochs); fann_scanf("%u", "cascade_max_cand_epochs", &ann->cascade_max_cand_epochs); fann_scanf("%u", "cascade_num_candidate_groups", &ann->cascade_num_candidate_groups); fann_scanf(FANNSCANF, "bit_fail_limit", &ann->bit_fail_limit); fann_scanf(FANNSCANF, "cascade_candidate_limit", &ann->cascade_candidate_limit); fann_scanf(FANNSCANF, "cascade_weight_multiplier", &ann->cascade_weight_multiplier); fann_scanf("%u", "cascade_activation_functions_count", &ann->cascade_activation_functions_count); /* reallocate mem */ ann->cascade_activation_functions = (enum fann_activationfunc_enum *)realloc(ann->cascade_activation_functions, ann->cascade_activation_functions_count * sizeof(enum fann_activationfunc_enum)); if(ann->cascade_activation_functions == NULL) { fann_error((struct fann_error*)ann, FANN_E_CANT_ALLOCATE_MEM); fann_destroy(ann); return NULL; } fscanf(conf, "cascade_activation_functions="); for(i = 0; i < ann->cascade_activation_functions_count; i++) fscanf(conf, "%u ", (unsigned int *)&ann->cascade_activation_functions[i]); fann_scanf("%u", "cascade_activation_steepnesses_count", &ann->cascade_activation_steepnesses_count); /* reallocate mem */ ann->cascade_activation_steepnesses = (fann_type *)realloc(ann->cascade_activation_steepnesses, ann->cascade_activation_steepnesses_count * sizeof(fann_type)); if(ann->cascade_activation_steepnesses == NULL) { fann_error((struct fann_error*)ann, FANN_E_CANT_ALLOCATE_MEM); fann_destroy(ann); return NULL; } fscanf(conf, "cascade_activation_steepnesses="); for(i = 0; i < ann->cascade_activation_steepnesses_count; i++) fscanf(conf, FANNSCANF" ", &ann->cascade_activation_steepnesses[i]); #ifdef FIXEDFANN ann->decimal_point = decimal_point; ann->multiplier = multiplier; #endif #ifdef FIXEDFANN fann_update_stepwise(ann); #endif #ifdef DEBUG printf("creating network with %d layers\n", num_layers); printf("input\n"); #endif fscanf(conf, "layer_sizes="); /* determine how many neurons there should be in each layer */ for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { if(fscanf(conf, "%u ", &layer_size) != 1) { fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONFIG, "layer_sizes", configuration_file); fann_destroy(ann); return NULL; } /* we do not allocate room here, but we make sure that * last_neuron - first_neuron is the number of neurons */ layer_it->first_neuron = NULL; layer_it->last_neuron = layer_it->first_neuron + layer_size; ann->total_neurons += layer_size; #ifdef DEBUG if(ann->network_type == FANN_NETTYPE_SHORTCUT && layer_it != ann->first_layer) { printf(" layer : %d neurons, 0 bias\n", layer_size); } else { printf(" layer : %d neurons, 1 bias\n", layer_size - 1); } #endif } ann->num_input = ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1; ann->num_output = ((ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron); if(ann->network_type == FANN_NETTYPE_LAYER) { /* one too many (bias) in the output layer */ ann->num_output--; } #ifndef FIXEDFANN #define SCALE_LOAD( what, where ) \ fscanf( conf, #what "_" #where "=" ); \ for(i = 0; i < ann->num_##where##put; i++) \ { \ if(fscanf( conf, "%f ", (float *)&ann->what##_##where[ i ] ) != 1) \ { \ fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONFIG, #what "_" #where, configuration_file); \ fann_destroy(ann); \ return NULL; \ } \ } if(fscanf(conf, "scale_included=%u\n", &scale_included) == 1 && scale_included == 1) { fann_allocate_scale(ann); SCALE_LOAD( scale_mean, in ) SCALE_LOAD( scale_deviation, in ) SCALE_LOAD( scale_new_min, in ) SCALE_LOAD( scale_factor, in ) SCALE_LOAD( scale_mean, out ) SCALE_LOAD( scale_deviation, out ) SCALE_LOAD( scale_new_min, out ) SCALE_LOAD( scale_factor, out ) } #undef SCALE_LOAD #endif /* allocate room for the actual neurons */ fann_allocate_neurons(ann); if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM) { fann_destroy(ann); return NULL; } last_neuron = (ann->last_layer - 1)->last_neuron; fscanf(conf, "neurons (num_inputs, activation_function, activation_steepness)="); for(neuron_it = ann->first_layer->first_neuron; neuron_it != last_neuron; neuron_it++) { if(fscanf (conf, "(%u, %u, " FANNSCANF ") ", &num_connections, (unsigned int *)&neuron_it->activation_function, &neuron_it->activation_steepness) != 3) { fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file); fann_destroy(ann); return NULL; } neuron_it->first_con = ann->total_connections; ann->total_connections += num_connections; neuron_it->last_con = ann->total_connections; } fann_allocate_connections(ann); if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM) { fann_destroy(ann); return NULL; } connected_neurons = ann->connections; weights = ann->weights; first_neuron = ann->first_layer->first_neuron; fscanf(conf, "connections (connected_to_neuron, weight)="); for(i = 0; i < ann->total_connections; i++) { if(fscanf(conf, "(%u, " FANNSCANF ") ", &input_neuron, &weights[i]) != 2) { fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONNECTIONS, configuration_file); fann_destroy(ann); return NULL; } connected_neurons[i] = first_neuron + input_neuron; } #ifdef DEBUG printf("output\n"); #endif return ann; } /* INTERNAL FUNCTION Create a network from a configuration file descriptor. (backward compatible read of version 1.1 files) */ struct fann *fann_create_from_fd_1_1(FILE * conf, const char *configuration_file) { unsigned int num_layers, layer_size, input_neuron, i, network_type, num_connections; unsigned int activation_function_hidden, activation_function_output; #ifdef FIXEDFANN unsigned int decimal_point, multiplier; #endif fann_type activation_steepness_hidden, activation_steepness_output; float learning_rate, connection_rate; struct fann_neuron *first_neuron, *neuron_it, *last_neuron, **connected_neurons; fann_type *weights; struct fann_layer *layer_it; struct fann *ann; #ifdef FIXEDFANN if(fscanf(conf, "%u\n", &decimal_point) != 1) { fann_error(NULL, FANN_E_CANT_READ_CONFIG, "decimal_point", configuration_file); return NULL; } multiplier = 1 << decimal_point; #endif if(fscanf(conf, "%u %f %f %u %u %u " FANNSCANF " " FANNSCANF "\n", &num_layers, &learning_rate, &connection_rate, &network_type, &activation_function_hidden, &activation_function_output, &activation_steepness_hidden, &activation_steepness_output) != 8) { fann_error(NULL, FANN_E_CANT_READ_CONFIG, "parameters", configuration_file); return NULL; } ann = fann_allocate_structure(num_layers); if(ann == NULL) { return NULL; } ann->connection_rate = connection_rate; ann->network_type = (enum fann_nettype_enum)network_type; ann->learning_rate = learning_rate; #ifdef FIXEDFANN ann->decimal_point = decimal_point; ann->multiplier = multiplier; #endif #ifdef FIXEDFANN fann_update_stepwise(ann); #endif #ifdef DEBUG printf("creating network with learning rate %f\n", learning_rate); printf("input\n"); #endif /* determine how many neurons there should be in each layer */ for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) { if(fscanf(conf, "%u ", &layer_size) != 1) { fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file); fann_destroy(ann); return NULL; } /* we do not allocate room here, but we make sure that * last_neuron - first_neuron is the number of neurons */ layer_it->first_neuron = NULL; layer_it->last_neuron = layer_it->first_neuron + layer_size; ann->total_neurons += layer_size; #ifdef DEBUG if(ann->network_type == FANN_NETTYPE_SHORTCUT && layer_it != ann->first_layer) { printf(" layer : %d neurons, 0 bias\n", layer_size); } else { printf(" layer : %d neurons, 1 bias\n", layer_size - 1); } #endif } ann->num_input = ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1; ann->num_output = ((ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron); if(ann->network_type == FANN_NETTYPE_LAYER) { /* one too many (bias) in the output layer */ ann->num_output--; } /* allocate room for the actual neurons */ fann_allocate_neurons(ann); if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM) { fann_destroy(ann); return NULL; } last_neuron = (ann->last_layer - 1)->last_neuron; for(neuron_it = ann->first_layer->first_neuron; neuron_it != last_neuron; neuron_it++) { if(fscanf(conf, "%u ", &num_connections) != 1) { fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file); fann_destroy(ann); return NULL; } neuron_it->first_con = ann->total_connections; ann->total_connections += num_connections; neuron_it->last_con = ann->total_connections; } fann_allocate_connections(ann); if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM) { fann_destroy(ann); return NULL; } connected_neurons = ann->connections; weights = ann->weights; first_neuron = ann->first_layer->first_neuron; for(i = 0; i < ann->total_connections; i++) { if(fscanf(conf, "(%u " FANNSCANF ") ", &input_neuron, &weights[i]) != 2) { fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONNECTIONS, configuration_file); fann_destroy(ann); return NULL; } connected_neurons[i] = first_neuron + input_neuron; } fann_set_activation_steepness_hidden(ann, activation_steepness_hidden); fann_set_activation_steepness_output(ann, activation_steepness_output); fann_set_activation_function_hidden(ann, (enum fann_activationfunc_enum)activation_function_hidden); fann_set_activation_function_output(ann, (enum fann_activationfunc_enum)activation_function_output); #ifdef DEBUG printf("output\n"); #endif return ann; } libfann-2.1.0~beta~dfsg/src/fann_train.c000066400000000000000000000664311140315177100203030ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include #include #include #include "config.h" #include "fann.h" /*#define DEBUGTRAIN*/ #ifndef FIXEDFANN /* INTERNAL FUNCTION Calculates the derived of a value, given an activation function and a steepness */ fann_type fann_activation_derived(unsigned int activation_function, fann_type steepness, fann_type value, fann_type sum) { switch (activation_function) { case FANN_LINEAR: case FANN_LINEAR_PIECE: case FANN_LINEAR_PIECE_SYMMETRIC: return (fann_type) fann_linear_derive(steepness, value); case FANN_SIGMOID: case FANN_SIGMOID_STEPWISE: value = fann_clip(value, 0.01f, 0.99f); return (fann_type) fann_sigmoid_derive(steepness, value); case FANN_SIGMOID_SYMMETRIC: case FANN_SIGMOID_SYMMETRIC_STEPWISE: value = fann_clip(value, -0.98f, 0.98f); return (fann_type) fann_sigmoid_symmetric_derive(steepness, value); case FANN_GAUSSIAN: /* value = fann_clip(value, 0.01f, 0.99f); */ return (fann_type) fann_gaussian_derive(steepness, value, sum); case FANN_GAUSSIAN_SYMMETRIC: /* value = fann_clip(value, -0.98f, 0.98f); */ return (fann_type) fann_gaussian_symmetric_derive(steepness, value, sum); case FANN_ELLIOT: value = fann_clip(value, 0.01f, 0.99f); return (fann_type) fann_elliot_derive(steepness, value, sum); case FANN_ELLIOT_SYMMETRIC: value = fann_clip(value, -0.98f, 0.98f); return (fann_type) fann_elliot_symmetric_derive(steepness, value, sum); case FANN_SIN_SYMMETRIC: return (fann_type) fann_sin_symmetric_derive(steepness, sum); case FANN_COS_SYMMETRIC: return (fann_type) fann_cos_symmetric_derive(steepness, sum); case FANN_SIN: return (fann_type) fann_sin_derive(steepness, sum); case FANN_COS: return (fann_type) fann_cos_derive(steepness, sum); case FANN_THRESHOLD: fann_error(NULL, FANN_E_CANT_TRAIN_ACTIVATION); } return 0; } /* INTERNAL FUNCTION Calculates the activation of a value, given an activation function and a steepness */ fann_type fann_activation(struct fann * ann, unsigned int activation_function, fann_type steepness, fann_type value) { value = fann_mult(steepness, value); fann_activation_switch(activation_function, value, value); return value; } /* Trains the network with the backpropagation algorithm. */ FANN_EXTERNAL void FANN_API fann_train(struct fann *ann, fann_type * input, fann_type * desired_output) { fann_run(ann, input); fann_compute_MSE(ann, desired_output); fann_backpropagate_MSE(ann); fann_update_weights(ann); } #endif /* INTERNAL FUNCTION Helper function to update the MSE value and return a diff which takes symmetric functions into account */ fann_type fann_update_MSE(struct fann *ann, struct fann_neuron* neuron, fann_type neuron_diff) { float neuron_diff2; switch (neuron->activation_function) { case FANN_LINEAR_PIECE_SYMMETRIC: case FANN_THRESHOLD_SYMMETRIC: case FANN_SIGMOID_SYMMETRIC: case FANN_SIGMOID_SYMMETRIC_STEPWISE: case FANN_ELLIOT_SYMMETRIC: case FANN_GAUSSIAN_SYMMETRIC: case FANN_SIN_SYMMETRIC: case FANN_COS_SYMMETRIC: neuron_diff /= (fann_type)2.0; break; case FANN_THRESHOLD: case FANN_LINEAR: case FANN_SIGMOID: case FANN_SIGMOID_STEPWISE: case FANN_GAUSSIAN: case FANN_GAUSSIAN_STEPWISE: case FANN_ELLIOT: case FANN_LINEAR_PIECE: case FANN_SIN: case FANN_COS: break; } #ifdef FIXEDFANN neuron_diff2 = (neuron_diff / (float) ann->multiplier) * (neuron_diff / (float) ann->multiplier); #else neuron_diff2 = (float) (neuron_diff * neuron_diff); #endif ann->MSE_value += neuron_diff2; /*printf("neuron_diff %f = (%f - %f)[/2], neuron_diff2=%f, sum=%f, MSE_value=%f, num_MSE=%d\n", neuron_diff, *desired_output, neuron_value, neuron_diff2, last_layer_begin->sum, ann->MSE_value, ann->num_MSE); */ if(fann_abs(neuron_diff) >= ann->bit_fail_limit) { ann->num_bit_fail++; } return neuron_diff; } /* Tests the network. */ FANN_EXTERNAL fann_type *FANN_API fann_test(struct fann *ann, fann_type * input, fann_type * desired_output) { fann_type neuron_value; fann_type *output_begin = fann_run(ann, input); fann_type *output_it; const fann_type *output_end = output_begin + ann->num_output; fann_type neuron_diff; struct fann_neuron *output_neuron = (ann->last_layer - 1)->first_neuron; /* calculate the error */ for(output_it = output_begin; output_it != output_end; output_it++) { neuron_value = *output_it; neuron_diff = (*desired_output - neuron_value); neuron_diff = fann_update_MSE(ann, output_neuron, neuron_diff); desired_output++; output_neuron++; ann->num_MSE++; } return output_begin; } /* get the mean square error. */ FANN_EXTERNAL float FANN_API fann_get_MSE(struct fann *ann) { if(ann->num_MSE) { return ann->MSE_value / (float) ann->num_MSE; } else { return 0; } } FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail(struct fann *ann) { return ann->num_bit_fail; } /* reset the mean square error. */ FANN_EXTERNAL void FANN_API fann_reset_MSE(struct fann *ann) { ann->num_MSE = 0; ann->MSE_value = 0; ann->num_bit_fail = 0; } #ifndef FIXEDFANN /* INTERNAL FUNCTION compute the error at the network output (usually, after forward propagation of a certain input vector, fann_run) the error is a sum of squares for all the output units also increments a counter because MSE is an average of such errors After this train_errors in the output layer will be set to: neuron_value_derived * (desired_output - neuron_value) */ void fann_compute_MSE(struct fann *ann, fann_type * desired_output) { fann_type neuron_value, neuron_diff, *error_it = 0, *error_begin = 0; struct fann_neuron *last_layer_begin = (ann->last_layer - 1)->first_neuron; const struct fann_neuron *last_layer_end = last_layer_begin + ann->num_output; const struct fann_neuron *first_neuron = ann->first_layer->first_neuron; /* if no room allocated for the error variabels, allocate it now */ if(ann->train_errors == NULL) { ann->train_errors = (fann_type *) calloc(ann->total_neurons, sizeof(fann_type)); if(ann->train_errors == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } } else { /* clear the error variabels */ memset(ann->train_errors, 0, (ann->total_neurons) * sizeof(fann_type)); } error_begin = ann->train_errors; #ifdef DEBUGTRAIN printf("\ncalculate errors\n"); #endif /* calculate the error and place it in the output layer */ error_it = error_begin + (last_layer_begin - first_neuron); for(; last_layer_begin != last_layer_end; last_layer_begin++) { neuron_value = last_layer_begin->value; neuron_diff = *desired_output - neuron_value; neuron_diff = fann_update_MSE(ann, last_layer_begin, neuron_diff); if(ann->train_error_function) { /* TODO make switch when more functions */ if(neuron_diff < -.9999999) neuron_diff = -17.0; else if(neuron_diff > .9999999) neuron_diff = 17.0; else neuron_diff = (fann_type) log((1.0 + neuron_diff) / (1.0 - neuron_diff)); } *error_it = fann_activation_derived(last_layer_begin->activation_function, last_layer_begin->activation_steepness, neuron_value, last_layer_begin->sum) * neuron_diff; desired_output++; error_it++; ann->num_MSE++; } } /* INTERNAL FUNCTION Propagate the error backwards from the output layer. After this the train_errors in the hidden layers will be: neuron_value_derived * sum(outgoing_weights * connected_neuron) */ void fann_backpropagate_MSE(struct fann *ann) { fann_type tmp_error; unsigned int i; struct fann_layer *layer_it; struct fann_neuron *neuron_it, *last_neuron; struct fann_neuron **connections; fann_type *error_begin = ann->train_errors; fann_type *error_prev_layer; fann_type *weights; const struct fann_neuron *first_neuron = ann->first_layer->first_neuron; const struct fann_layer *second_layer = ann->first_layer + 1; struct fann_layer *last_layer = ann->last_layer; /* go through all the layers, from last to first. * And propagate the error backwards */ for(layer_it = last_layer - 1; layer_it > second_layer; --layer_it) { last_neuron = layer_it->last_neuron; /* for each connection in this layer, propagate the error backwards */ if(ann->connection_rate >= 1) { if(ann->network_type == FANN_NETTYPE_LAYER) { error_prev_layer = error_begin + ((layer_it - 1)->first_neuron - first_neuron); } else { error_prev_layer = error_begin; } for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { tmp_error = error_begin[neuron_it - first_neuron]; weights = ann->weights + neuron_it->first_con; for(i = neuron_it->last_con - neuron_it->first_con; i--;) { /*printf("i = %d\n", i); * printf("error_prev_layer[%d] = %f\n", i, error_prev_layer[i]); * printf("weights[%d] = %f\n", i, weights[i]); */ error_prev_layer[i] += tmp_error * weights[i]; } } } else { for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { tmp_error = error_begin[neuron_it - first_neuron]; weights = ann->weights + neuron_it->first_con; connections = ann->connections + neuron_it->first_con; for(i = neuron_it->last_con - neuron_it->first_con; i--;) { error_begin[connections[i] - first_neuron] += tmp_error * weights[i]; } } } /* then calculate the actual errors in the previous layer */ error_prev_layer = error_begin + ((layer_it - 1)->first_neuron - first_neuron); last_neuron = (layer_it - 1)->last_neuron; for(neuron_it = (layer_it - 1)->first_neuron; neuron_it != last_neuron; neuron_it++) { *error_prev_layer *= fann_activation_derived(neuron_it->activation_function, neuron_it->activation_steepness, neuron_it->value, neuron_it->sum); error_prev_layer++; } } } /* INTERNAL FUNCTION Update weights for incremental training */ void fann_update_weights(struct fann *ann) { struct fann_neuron *neuron_it, *last_neuron, *prev_neurons; fann_type tmp_error, delta_w, *weights; struct fann_layer *layer_it; unsigned int i; unsigned int num_connections; /* store some variabels local for fast access */ const float learning_rate = ann->learning_rate; const float learning_momentum = ann->learning_momentum; struct fann_neuron *first_neuron = ann->first_layer->first_neuron; struct fann_layer *first_layer = ann->first_layer; const struct fann_layer *last_layer = ann->last_layer; fann_type *error_begin = ann->train_errors; fann_type *deltas_begin, *weights_deltas; /* if no room allocated for the deltas, allocate it now */ if(ann->prev_weights_deltas == NULL) { ann->prev_weights_deltas = (fann_type *) calloc(ann->total_connections_allocated, sizeof(fann_type)); if(ann->prev_weights_deltas == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } } #ifdef DEBUGTRAIN printf("\nupdate weights\n"); #endif deltas_begin = ann->prev_weights_deltas; prev_neurons = first_neuron; for(layer_it = (first_layer + 1); layer_it != last_layer; layer_it++) { #ifdef DEBUGTRAIN printf("layer[%d]\n", layer_it - first_layer); #endif last_neuron = layer_it->last_neuron; if(ann->connection_rate >= 1) { if(ann->network_type == FANN_NETTYPE_LAYER) { prev_neurons = (layer_it - 1)->first_neuron; } for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { tmp_error = error_begin[neuron_it - first_neuron] * learning_rate; num_connections = neuron_it->last_con - neuron_it->first_con; weights = ann->weights + neuron_it->first_con; weights_deltas = deltas_begin + neuron_it->first_con; for(i = 0; i != num_connections; i++) { delta_w = tmp_error * prev_neurons[i].value + learning_momentum * weights_deltas[i]; weights[i] += delta_w ; weights_deltas[i] = delta_w; } } } else { for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { tmp_error = error_begin[neuron_it - first_neuron] * learning_rate; num_connections = neuron_it->last_con - neuron_it->first_con; weights = ann->weights + neuron_it->first_con; weights_deltas = deltas_begin + neuron_it->first_con; for(i = 0; i != num_connections; i++) { delta_w = tmp_error * prev_neurons[i].value + learning_momentum * weights_deltas[i]; weights[i] += delta_w; weights_deltas[i] = delta_w; } } } } } /* INTERNAL FUNCTION Update slopes for batch training layer_begin = ann->first_layer+1 and layer_end = ann->last_layer-1 will update all slopes. */ void fann_update_slopes_batch(struct fann *ann, struct fann_layer *layer_begin, struct fann_layer *layer_end) { struct fann_neuron *neuron_it, *last_neuron, *prev_neurons, **connections; fann_type tmp_error; unsigned int i, num_connections; /* store some variabels local for fast access */ struct fann_neuron *first_neuron = ann->first_layer->first_neuron; fann_type *error_begin = ann->train_errors; fann_type *slope_begin, *neuron_slope; /* if no room allocated for the slope variabels, allocate it now */ if(ann->train_slopes == NULL) { ann->train_slopes = (fann_type *) calloc(ann->total_connections_allocated, sizeof(fann_type)); if(ann->train_slopes == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } } if(layer_begin == NULL) { layer_begin = ann->first_layer + 1; } if(layer_end == NULL) { layer_end = ann->last_layer - 1; } slope_begin = ann->train_slopes; #ifdef DEBUGTRAIN printf("\nupdate slopes\n"); #endif prev_neurons = first_neuron; for(; layer_begin <= layer_end; layer_begin++) { #ifdef DEBUGTRAIN printf("layer[%d]\n", layer_begin - ann->first_layer); #endif last_neuron = layer_begin->last_neuron; if(ann->connection_rate >= 1) { if(ann->network_type == FANN_NETTYPE_LAYER) { prev_neurons = (layer_begin - 1)->first_neuron; } for(neuron_it = layer_begin->first_neuron; neuron_it != last_neuron; neuron_it++) { tmp_error = error_begin[neuron_it - first_neuron]; neuron_slope = slope_begin + neuron_it->first_con; num_connections = neuron_it->last_con - neuron_it->first_con; for(i = 0; i != num_connections; i++) { neuron_slope[i] += tmp_error * prev_neurons[i].value; } } } else { for(neuron_it = layer_begin->first_neuron; neuron_it != last_neuron; neuron_it++) { tmp_error = error_begin[neuron_it - first_neuron]; neuron_slope = slope_begin + neuron_it->first_con; num_connections = neuron_it->last_con - neuron_it->first_con; connections = ann->connections + neuron_it->first_con; for(i = 0; i != num_connections; i++) { neuron_slope[i] += tmp_error * connections[i]->value; } } } } } /* INTERNAL FUNCTION Clears arrays used for training before a new training session. Also creates the arrays that do not exist yet. */ void fann_clear_train_arrays(struct fann *ann) { unsigned int i; fann_type delta_zero; /* if no room allocated for the slope variabels, allocate it now * (calloc clears mem) */ if(ann->train_slopes == NULL) { ann->train_slopes = (fann_type *) calloc(ann->total_connections_allocated, sizeof(fann_type)); if(ann->train_slopes == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } } else { memset(ann->train_slopes, 0, (ann->total_connections_allocated) * sizeof(fann_type)); } /* if no room allocated for the variabels, allocate it now */ if(ann->prev_steps == NULL) { ann->prev_steps = (fann_type *) malloc(ann->total_connections_allocated * sizeof(fann_type)); if(ann->prev_steps == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } } if(ann->training_algorithm == FANN_TRAIN_RPROP) { delta_zero = ann->rprop_delta_zero; for(i = 0; i < ann->total_connections_allocated; i++) ann->prev_steps[i] = delta_zero; } else { memset(ann->prev_steps, 0, (ann->total_connections_allocated) * sizeof(fann_type)); } /* if no room allocated for the variabels, allocate it now */ if(ann->prev_train_slopes == NULL) { ann->prev_train_slopes = (fann_type *) calloc(ann->total_connections_allocated, sizeof(fann_type)); if(ann->prev_train_slopes == NULL) { fann_error((struct fann_error *) ann, FANN_E_CANT_ALLOCATE_MEM); return; } } else { memset(ann->prev_train_slopes, 0, (ann->total_connections_allocated) * sizeof(fann_type)); } } /* INTERNAL FUNCTION Update weights for batch training */ void fann_update_weights_batch(struct fann *ann, unsigned int num_data, unsigned int first_weight, unsigned int past_end) { fann_type *train_slopes = ann->train_slopes; fann_type *weights = ann->weights; const float epsilon = ann->learning_rate / num_data; unsigned int i = first_weight; for(; i != past_end; i++) { weights[i] += train_slopes[i] * epsilon; train_slopes[i] = 0.0; } } /* INTERNAL FUNCTION The quickprop training algorithm */ void fann_update_weights_quickprop(struct fann *ann, unsigned int num_data, unsigned int first_weight, unsigned int past_end) { fann_type *train_slopes = ann->train_slopes; fann_type *weights = ann->weights; fann_type *prev_steps = ann->prev_steps; fann_type *prev_train_slopes = ann->prev_train_slopes; fann_type w, prev_step, slope, prev_slope, next_step; float epsilon = ann->learning_rate / num_data; float decay = ann->quickprop_decay; /*-0.0001;*/ float mu = ann->quickprop_mu; /*1.75; */ float shrink_factor = (float) (mu / (1.0 + mu)); unsigned int i = first_weight; for(; i != past_end; i++) { w = weights[i]; prev_step = prev_steps[i]; slope = train_slopes[i] + decay * w; prev_slope = prev_train_slopes[i]; next_step = 0.0; /* The step must always be in direction opposite to the slope. */ if(prev_step > 0.001) { /* If last step was positive... */ if(slope > 0.0) /* Add in linear term if current slope is still positive. */ next_step += epsilon * slope; /*If current slope is close to or larger than prev slope... */ if(slope > (shrink_factor * prev_slope)) next_step += mu * prev_step; /* Take maximum size negative step. */ else next_step += prev_step * slope / (prev_slope - slope); /* Else, use quadratic estimate. */ } else if(prev_step < -0.001) { /* If last step was negative... */ if(slope < 0.0) /* Add in linear term if current slope is still negative. */ next_step += epsilon * slope; /* If current slope is close to or more neg than prev slope... */ if(slope < (shrink_factor * prev_slope)) next_step += mu * prev_step; /* Take maximum size negative step. */ else next_step += prev_step * slope / (prev_slope - slope); /* Else, use quadratic estimate. */ } else /* Last step was zero, so use only linear term. */ next_step += epsilon * slope; /* if(next_step > 1000 || next_step < -1000) { printf("quickprop[%d] weight=%f, slope=%f, prev_slope=%f, next_step=%f, prev_step=%f\n", i, weights[i], slope, prev_slope, next_step, prev_step); if(next_step > 1000) next_step = 1000; else next_step = -1000; } */ /* update global data arrays */ prev_steps[i] = next_step; w += next_step; if(w > 1500) weights[i] = 1500; else if(w < -1500) weights[i] = -1500; else weights[i] = w; /*weights[i] = w;*/ prev_train_slopes[i] = slope; train_slopes[i] = 0.0; } } /* INTERNAL FUNCTION The iRprop- algorithm */ void fann_update_weights_irpropm(struct fann *ann, unsigned int first_weight, unsigned int past_end) { fann_type *train_slopes = ann->train_slopes; fann_type *weights = ann->weights; fann_type *prev_steps = ann->prev_steps; fann_type *prev_train_slopes = ann->prev_train_slopes; fann_type prev_step, slope, prev_slope, next_step, same_sign; float increase_factor = ann->rprop_increase_factor; /*1.2; */ float decrease_factor = ann->rprop_decrease_factor; /*0.5; */ float delta_min = ann->rprop_delta_min; /*0.0; */ float delta_max = ann->rprop_delta_max; /*50.0; */ unsigned int i = first_weight; for(; i != past_end; i++) { prev_step = fann_max(prev_steps[i], (fann_type) 0.0001); /* prev_step may not be zero because then the training will stop */ slope = train_slopes[i]; prev_slope = prev_train_slopes[i]; same_sign = prev_slope * slope; if(same_sign >= 0.0) next_step = fann_min(prev_step * increase_factor, delta_max); else { next_step = fann_max(prev_step * decrease_factor, delta_min); slope = 0; } if(slope < 0) { weights[i] -= next_step; if(weights[i] < -1500) weights[i] = -1500; } else { weights[i] += next_step; if(weights[i] > 1500) weights[i] = 1500; } /*if(i == 2){ * printf("weight=%f, slope=%f, next_step=%f, prev_step=%f\n", weights[i], slope, next_step, prev_step); * } */ /* update global data arrays */ prev_steps[i] = next_step; prev_train_slopes[i] = slope; train_slopes[i] = 0.0; } } #endif FANN_GET_SET(enum fann_train_enum, training_algorithm) FANN_GET_SET(float, learning_rate) FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden(struct fann *ann, enum fann_activationfunc_enum activation_function) { struct fann_neuron *last_neuron, *neuron_it; struct fann_layer *layer_it; struct fann_layer *last_layer = ann->last_layer - 1; /* -1 to not update the output layer */ for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++) { last_neuron = layer_it->last_neuron; for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { neuron_it->activation_function = activation_function; } } } FANN_EXTERNAL struct fann_layer* FANN_API fann_get_layer(struct fann *ann, int layer) { if(layer <= 0 || layer >= (ann->last_layer - ann->first_layer)) { fann_error((struct fann_error *) ann, FANN_E_INDEX_OUT_OF_BOUND, layer); return NULL; } return ann->first_layer + layer; } FANN_EXTERNAL struct fann_neuron* FANN_API fann_get_neuron_layer(struct fann *ann, struct fann_layer* layer, int neuron) { if(neuron >= (layer->last_neuron - layer->first_neuron)) { fann_error((struct fann_error *) ann, FANN_E_INDEX_OUT_OF_BOUND, neuron); return NULL; } return layer->first_neuron + neuron; } FANN_EXTERNAL struct fann_neuron* FANN_API fann_get_neuron(struct fann *ann, unsigned int layer, int neuron) { struct fann_layer *layer_it = fann_get_layer(ann, layer); if(layer_it == NULL) return NULL; return fann_get_neuron_layer(ann, layer_it, neuron); } FANN_EXTERNAL enum fann_activationfunc_enum FANN_API fann_get_activation_function(struct fann *ann, int layer, int neuron) { struct fann_neuron* neuron_it = fann_get_neuron(ann, layer, neuron); if (neuron_it == NULL) { return (enum fann_activationfunc_enum)-1; /* layer or neuron out of bounds */ } else { return neuron_it->activation_function; } } FANN_EXTERNAL void FANN_API fann_set_activation_function(struct fann *ann, enum fann_activationfunc_enum activation_function, int layer, int neuron) { struct fann_neuron* neuron_it = fann_get_neuron(ann, layer, neuron); if(neuron_it == NULL) return; neuron_it->activation_function = activation_function; } FANN_EXTERNAL void FANN_API fann_set_activation_function_layer(struct fann *ann, enum fann_activationfunc_enum activation_function, int layer) { struct fann_neuron *last_neuron, *neuron_it; struct fann_layer *layer_it = fann_get_layer(ann, layer); if(layer_it == NULL) return; last_neuron = layer_it->last_neuron; for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { neuron_it->activation_function = activation_function; } } FANN_EXTERNAL void FANN_API fann_set_activation_function_output(struct fann *ann, enum fann_activationfunc_enum activation_function) { struct fann_neuron *last_neuron, *neuron_it; struct fann_layer *last_layer = ann->last_layer - 1; last_neuron = last_layer->last_neuron; for(neuron_it = last_layer->first_neuron; neuron_it != last_neuron; neuron_it++) { neuron_it->activation_function = activation_function; } } FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden(struct fann *ann, fann_type steepness) { struct fann_neuron *last_neuron, *neuron_it; struct fann_layer *layer_it; struct fann_layer *last_layer = ann->last_layer - 1; /* -1 to not update the output layer */ for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++) { last_neuron = layer_it->last_neuron; for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { neuron_it->activation_steepness = steepness; } } } FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness(struct fann *ann, int layer, int neuron) { struct fann_neuron* neuron_it = fann_get_neuron(ann, layer, neuron); if(neuron_it == NULL) { return -1; /* layer or neuron out of bounds */ } else { return neuron_it->activation_steepness; } } FANN_EXTERNAL void FANN_API fann_set_activation_steepness(struct fann *ann, fann_type steepness, int layer, int neuron) { struct fann_neuron* neuron_it = fann_get_neuron(ann, layer, neuron); if(neuron_it == NULL) return; neuron_it->activation_steepness = steepness; } FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer(struct fann *ann, fann_type steepness, int layer) { struct fann_neuron *last_neuron, *neuron_it; struct fann_layer *layer_it = fann_get_layer(ann, layer); if(layer_it == NULL) return; last_neuron = layer_it->last_neuron; for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++) { neuron_it->activation_steepness = steepness; } } FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output(struct fann *ann, fann_type steepness) { struct fann_neuron *last_neuron, *neuron_it; struct fann_layer *last_layer = ann->last_layer - 1; last_neuron = last_layer->last_neuron; for(neuron_it = last_layer->first_neuron; neuron_it != last_neuron; neuron_it++) { neuron_it->activation_steepness = steepness; } } FANN_GET_SET(enum fann_errorfunc_enum, train_error_function) FANN_GET_SET(fann_callback_type, callback) FANN_GET_SET(float, quickprop_decay) FANN_GET_SET(float, quickprop_mu) FANN_GET_SET(float, rprop_increase_factor) FANN_GET_SET(float, rprop_decrease_factor) FANN_GET_SET(float, rprop_delta_min) FANN_GET_SET(float, rprop_delta_max) FANN_GET_SET(float, rprop_delta_zero) FANN_GET_SET(enum fann_stopfunc_enum, train_stop_function) FANN_GET_SET(fann_type, bit_fail_limit) FANN_GET_SET(float, learning_momentum) libfann-2.1.0~beta~dfsg/src/fann_train_data.c000066400000000000000000001011051140315177100212600ustar00rootroot00000000000000/* * Fast Artificial Neural Network Library (fann) Copyright (C) 2003 * Steffen Nissen (lukesky@diku.dk) * * This library is free software; you can redistribute it and/or modify it * under the terms of the GNU Lesser General Public License as published * by the Free Software Foundation; either version 2.1 of the License, or * (at your option) any later version. * * This library is distributed in the hope that it will be useful, but * WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU * Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public * License along with this library; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include #include #include #include #include "config.h" #include "fann.h" /* * Reads training data from a file. */ FANN_EXTERNAL struct fann_train_data *FANN_API fann_read_train_from_file(const char *configuration_file) { struct fann_train_data *data; FILE *file = fopen(configuration_file, "r"); if(!file) { fann_error(NULL, FANN_E_CANT_OPEN_CONFIG_R, configuration_file); return NULL; } data = fann_read_train_from_fd(file, configuration_file); fclose(file); return data; } /* * Save training data to a file */ FANN_EXTERNAL int FANN_API fann_save_train(struct fann_train_data *data, const char *filename) { return fann_save_train_internal(data, filename, 0, 0); } /* * Save training data to a file in fixed point algebra. (Good for testing * a network in fixed point) */ FANN_EXTERNAL int FANN_API fann_save_train_to_fixed(struct fann_train_data *data, const char *filename, unsigned int decimal_point) { return fann_save_train_internal(data, filename, 1, decimal_point); } /* * deallocate the train data structure. */ FANN_EXTERNAL void FANN_API fann_destroy_train(struct fann_train_data *data) { if(data == NULL) return; if(data->input != NULL) fann_safe_free(data->input[0]); if(data->output != NULL) fann_safe_free(data->output[0]); fann_safe_free(data->input); fann_safe_free(data->output); fann_safe_free(data); } /* * Test a set of training data and calculate the MSE */ FANN_EXTERNAL float FANN_API fann_test_data(struct fann *ann, struct fann_train_data *data) { unsigned int i; fann_reset_MSE(ann); for(i = 0; i != data->num_data; i++) { fann_test(ann, data->input[i], data->output[i]); } return fann_get_MSE(ann); } /* * Creates training data from a callback function. */ FANN_EXTERNAL struct fann_train_data * FANN_API fann_create_train_from_callback(unsigned int num_data, unsigned int num_input, unsigned int num_output, void (FANN_API *user_function)( unsigned int, unsigned int, unsigned int, fann_type * , fann_type * )) { unsigned int i; fann_type *data_input, *data_output; struct fann_train_data *data = (struct fann_train_data *) malloc(sizeof(struct fann_train_data)); if(data == NULL){ fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } fann_init_error_data((struct fann_error *) data); data->num_data = num_data; data->num_input = num_input; data->num_output = num_output; data->input = (fann_type **) calloc(num_data, sizeof(fann_type *)); if(data->input == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(data); return NULL; } data->output = (fann_type **) calloc(num_data, sizeof(fann_type *)); if(data->output == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(data); return NULL; } data_input = (fann_type *) calloc(num_input * num_data, sizeof(fann_type)); if(data_input == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(data); return NULL; } data_output = (fann_type *) calloc(num_output * num_data, sizeof(fann_type)); if(data_output == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(data); return NULL; } for( i = 0; i != num_data; i++) { data->input[i] = data_input; data_input += num_input; data->output[i] = data_output; data_output += num_output; (*user_function)(i, num_input, num_output, data->input[i],data->output[i] ); } return data; } #ifndef FIXEDFANN /* * Internal train function */ float fann_train_epoch_quickprop(struct fann *ann, struct fann_train_data *data) { unsigned int i; if(ann->prev_train_slopes == NULL) { fann_clear_train_arrays(ann); } fann_reset_MSE(ann); for(i = 0; i < data->num_data; i++) { fann_run(ann, data->input[i]); fann_compute_MSE(ann, data->output[i]); fann_backpropagate_MSE(ann); fann_update_slopes_batch(ann, ann->first_layer + 1, ann->last_layer - 1); } fann_update_weights_quickprop(ann, data->num_data, 0, ann->total_connections); return fann_get_MSE(ann); } /* * Internal train function */ float fann_train_epoch_irpropm(struct fann *ann, struct fann_train_data *data) { unsigned int i; if(ann->prev_train_slopes == NULL) { fann_clear_train_arrays(ann); } fann_reset_MSE(ann); for(i = 0; i < data->num_data; i++) { fann_run(ann, data->input[i]); fann_compute_MSE(ann, data->output[i]); fann_backpropagate_MSE(ann); fann_update_slopes_batch(ann, ann->first_layer + 1, ann->last_layer - 1); } fann_update_weights_irpropm(ann, 0, ann->total_connections); return fann_get_MSE(ann); } /* * Internal train function */ float fann_train_epoch_batch(struct fann *ann, struct fann_train_data *data) { unsigned int i; fann_reset_MSE(ann); for(i = 0; i < data->num_data; i++) { fann_run(ann, data->input[i]); fann_compute_MSE(ann, data->output[i]); fann_backpropagate_MSE(ann); fann_update_slopes_batch(ann, ann->first_layer + 1, ann->last_layer - 1); } fann_update_weights_batch(ann, data->num_data, 0, ann->total_connections); return fann_get_MSE(ann); } /* * Internal train function */ float fann_train_epoch_incremental(struct fann *ann, struct fann_train_data *data) { unsigned int i; fann_reset_MSE(ann); for(i = 0; i != data->num_data; i++) { fann_train(ann, data->input[i], data->output[i]); } return fann_get_MSE(ann); } /* * Train for one epoch with the selected training algorithm */ FANN_EXTERNAL float FANN_API fann_train_epoch(struct fann *ann, struct fann_train_data *data) { switch (ann->training_algorithm) { case FANN_TRAIN_QUICKPROP: return fann_train_epoch_quickprop(ann, data); case FANN_TRAIN_RPROP: return fann_train_epoch_irpropm(ann, data); case FANN_TRAIN_BATCH: return fann_train_epoch_batch(ann, data); case FANN_TRAIN_INCREMENTAL: return fann_train_epoch_incremental(ann, data); } return 0; } FANN_EXTERNAL void FANN_API fann_train_on_data(struct fann *ann, struct fann_train_data *data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error) { float error; unsigned int i; int desired_error_reached; #ifdef DEBUG printf("Training with %s\n", FANN_TRAIN_NAMES[ann->training_algorithm]); #endif if(epochs_between_reports && ann->callback == NULL) { printf("Max epochs %8d. Desired error: %.10f.\n", max_epochs, desired_error); } for(i = 1; i <= max_epochs; i++) { /* * train */ error = fann_train_epoch(ann, data); desired_error_reached = fann_desired_error_reached(ann, desired_error); /* * print current output */ if(epochs_between_reports && (i % epochs_between_reports == 0 || i == max_epochs || i == 1 || desired_error_reached == 0)) { if(ann->callback == NULL) { printf("Epochs %8d. Current error: %.10f. Bit fail %d.\n", i, error, ann->num_bit_fail); } else if(((*ann->callback)(ann, data, max_epochs, epochs_between_reports, desired_error, i)) == -1) { /* * you can break the training by returning -1 */ break; } } if(desired_error_reached == 0) break; } } FANN_EXTERNAL void FANN_API fann_train_on_file(struct fann *ann, const char *filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error) { struct fann_train_data *data = fann_read_train_from_file(filename); if(data == NULL) { return; } fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error); fann_destroy_train(data); } #endif /* * shuffles training data, randomizing the order */ FANN_EXTERNAL void FANN_API fann_shuffle_train_data(struct fann_train_data *train_data) { unsigned int dat = 0, elem, swap; fann_type temp; for(; dat < train_data->num_data; dat++) { swap = (unsigned int) (rand() % train_data->num_data); if(swap != dat) { for(elem = 0; elem < train_data->num_input; elem++) { temp = train_data->input[dat][elem]; train_data->input[dat][elem] = train_data->input[swap][elem]; train_data->input[swap][elem] = temp; } for(elem = 0; elem < train_data->num_output; elem++) { temp = train_data->output[dat][elem]; train_data->output[dat][elem] = train_data->output[swap][elem]; train_data->output[swap][elem] = temp; } } } } /* * INTERNAL FUNCTION Scales data to a specific range */ void fann_scale_data(fann_type ** data, unsigned int num_data, unsigned int num_elem, fann_type new_min, fann_type new_max) { unsigned int dat, elem; fann_type old_min, old_max, temp, old_span, new_span, factor; old_min = old_max = data[0][0]; /* * first calculate min and max */ for(dat = 0; dat < num_data; dat++) { for(elem = 0; elem < num_elem; elem++) { temp = data[dat][elem]; if(temp < old_min) old_min = temp; else if(temp > old_max) old_max = temp; } } old_span = old_max - old_min; new_span = new_max - new_min; factor = new_span / old_span; /*printf("max %f, min %f, factor %f\n", old_max, old_min, factor);*/ for(dat = 0; dat < num_data; dat++) { for(elem = 0; elem < num_elem; elem++) { temp = (data[dat][elem] - old_min) * factor + new_min; if(temp < new_min) { data[dat][elem] = new_min; /* * printf("error %f < %f\n", temp, new_min); */ } else if(temp > new_max) { data[dat][elem] = new_max; /* * printf("error %f > %f\n", temp, new_max); */ } else { data[dat][elem] = temp; } } } } /* * Scales the inputs in the training data to the specified range */ FANN_EXTERNAL void FANN_API fann_scale_input_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max) { fann_scale_data(train_data->input, train_data->num_data, train_data->num_input, new_min, new_max); } /* * Scales the inputs in the training data to the specified range */ FANN_EXTERNAL void FANN_API fann_scale_output_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max) { fann_scale_data(train_data->output, train_data->num_data, train_data->num_output, new_min, new_max); } /* * Scales the inputs in the training data to the specified range */ FANN_EXTERNAL void FANN_API fann_scale_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max) { fann_scale_data(train_data->input, train_data->num_data, train_data->num_input, new_min, new_max); fann_scale_data(train_data->output, train_data->num_data, train_data->num_output, new_min, new_max); } /* * merges training data into a single struct. */ FANN_EXTERNAL struct fann_train_data *FANN_API fann_merge_train_data(struct fann_train_data *data1, struct fann_train_data *data2) { unsigned int i; fann_type *data_input, *data_output; struct fann_train_data *dest = (struct fann_train_data *) malloc(sizeof(struct fann_train_data)); if(dest == NULL) { fann_error((struct fann_error*)data1, FANN_E_CANT_ALLOCATE_MEM); return NULL; } if((data1->num_input != data2->num_input) || (data1->num_output != data2->num_output)) { fann_error((struct fann_error*)data1, FANN_E_TRAIN_DATA_MISMATCH); return NULL; } fann_init_error_data((struct fann_error *) dest); dest->error_log = data1->error_log; dest->num_data = data1->num_data+data2->num_data; dest->num_input = data1->num_input; dest->num_output = data1->num_output; dest->input = (fann_type **) calloc(dest->num_data, sizeof(fann_type *)); if(dest->input == NULL) { fann_error((struct fann_error*)data1, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } dest->output = (fann_type **) calloc(dest->num_data, sizeof(fann_type *)); if(dest->output == NULL) { fann_error((struct fann_error*)data1, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } data_input = (fann_type *) calloc(dest->num_input * dest->num_data, sizeof(fann_type)); if(data_input == NULL) { fann_error((struct fann_error*)data1, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } memcpy(data_input, data1->input[0], dest->num_input * data1->num_data * sizeof(fann_type)); memcpy(data_input + (dest->num_input*data1->num_data), data2->input[0], dest->num_input * data2->num_data * sizeof(fann_type)); data_output = (fann_type *) calloc(dest->num_output * dest->num_data, sizeof(fann_type)); if(data_output == NULL) { fann_error((struct fann_error*)data1, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } memcpy(data_output, data1->output[0], dest->num_output * data1->num_data * sizeof(fann_type)); memcpy(data_output + (dest->num_output*data1->num_data), data2->output[0], dest->num_output * data2->num_data * sizeof(fann_type)); for(i = 0; i != dest->num_data; i++) { dest->input[i] = data_input; data_input += dest->num_input; dest->output[i] = data_output; data_output += dest->num_output; } return dest; } /* * return a copy of a fann_train_data struct */ FANN_EXTERNAL struct fann_train_data *FANN_API fann_duplicate_train_data(struct fann_train_data *data) { unsigned int i; fann_type *data_input, *data_output; struct fann_train_data *dest = (struct fann_train_data *) malloc(sizeof(struct fann_train_data)); if(dest == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); return NULL; } fann_init_error_data((struct fann_error *) dest); dest->error_log = data->error_log; dest->num_data = data->num_data; dest->num_input = data->num_input; dest->num_output = data->num_output; dest->input = (fann_type **) calloc(dest->num_data, sizeof(fann_type *)); if(dest->input == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } dest->output = (fann_type **) calloc(dest->num_data, sizeof(fann_type *)); if(dest->output == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } data_input = (fann_type *) calloc(dest->num_input * dest->num_data, sizeof(fann_type)); if(data_input == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } memcpy(data_input, data->input[0], dest->num_input * dest->num_data * sizeof(fann_type)); data_output = (fann_type *) calloc(dest->num_output * dest->num_data, sizeof(fann_type)); if(data_output == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } memcpy(data_output, data->output[0], dest->num_output * dest->num_data * sizeof(fann_type)); for(i = 0; i != dest->num_data; i++) { dest->input[i] = data_input; data_input += dest->num_input; dest->output[i] = data_output; data_output += dest->num_output; } return dest; } FANN_EXTERNAL struct fann_train_data *FANN_API fann_subset_train_data(struct fann_train_data *data, unsigned int pos, unsigned int length) { unsigned int i; fann_type *data_input, *data_output; struct fann_train_data *dest = (struct fann_train_data *) malloc(sizeof(struct fann_train_data)); if(dest == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); return NULL; } if(pos > data->num_data || pos+length > data->num_data) { fann_error((struct fann_error*)data, FANN_E_TRAIN_DATA_SUBSET, pos, length, data->num_data); return NULL; } fann_init_error_data((struct fann_error *) dest); dest->error_log = data->error_log; dest->num_data = length; dest->num_input = data->num_input; dest->num_output = data->num_output; dest->input = (fann_type **) calloc(dest->num_data, sizeof(fann_type *)); if(dest->input == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } dest->output = (fann_type **) calloc(dest->num_data, sizeof(fann_type *)); if(dest->output == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } data_input = (fann_type *) calloc(dest->num_input * dest->num_data, sizeof(fann_type)); if(data_input == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } memcpy(data_input, data->input[pos], dest->num_input * dest->num_data * sizeof(fann_type)); data_output = (fann_type *) calloc(dest->num_output * dest->num_data, sizeof(fann_type)); if(data_output == NULL) { fann_error((struct fann_error*)data, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(dest); return NULL; } memcpy(data_output, data->output[pos], dest->num_output * dest->num_data * sizeof(fann_type)); for(i = 0; i != dest->num_data; i++) { dest->input[i] = data_input; data_input += dest->num_input; dest->output[i] = data_output; data_output += dest->num_output; } return dest; } FANN_EXTERNAL unsigned int FANN_API fann_length_train_data(struct fann_train_data *data) { return data->num_data; } FANN_EXTERNAL unsigned int FANN_API fann_num_input_train_data(struct fann_train_data *data) { return data->num_input; } FANN_EXTERNAL unsigned int FANN_API fann_num_output_train_data(struct fann_train_data *data) { return data->num_output; } /* INTERNAL FUNCTION Save the train data structure. */ int fann_save_train_internal(struct fann_train_data *data, const char *filename, unsigned int save_as_fixed, unsigned int decimal_point) { int retval = 0; FILE *file = fopen(filename, "w"); if(!file) { fann_error((struct fann_error *) data, FANN_E_CANT_OPEN_TD_W, filename); return -1; } retval = fann_save_train_internal_fd(data, file, filename, save_as_fixed, decimal_point); fclose(file); return retval; } /* INTERNAL FUNCTION Save the train data structure. */ int fann_save_train_internal_fd(struct fann_train_data *data, FILE * file, const char *filename, unsigned int save_as_fixed, unsigned int decimal_point) { unsigned int num_data = data->num_data; unsigned int num_input = data->num_input; unsigned int num_output = data->num_output; unsigned int i, j; int retval = 0; #ifndef FIXEDFANN unsigned int multiplier = 1 << decimal_point; #endif fprintf(file, "%u %u %u\n", data->num_data, data->num_input, data->num_output); for(i = 0; i < num_data; i++) { for(j = 0; j < num_input; j++) { #ifndef FIXEDFANN if(save_as_fixed) { fprintf(file, "%d ", (int) (data->input[i][j] * multiplier)); } else { if(((int) floor(data->input[i][j] + 0.5) * 1000000) == ((int) floor(data->input[i][j] * 1000000.0 + 0.5))) { fprintf(file, "%d ", (int) data->input[i][j]); } else { fprintf(file, "%f ", data->input[i][j]); } } #else fprintf(file, FANNPRINTF " ", data->input[i][j]); #endif } fprintf(file, "\n"); for(j = 0; j < num_output; j++) { #ifndef FIXEDFANN if(save_as_fixed) { fprintf(file, "%d ", (int) (data->output[i][j] * multiplier)); } else { if(((int) floor(data->output[i][j] + 0.5) * 1000000) == ((int) floor(data->output[i][j] * 1000000.0 + 0.5))) { fprintf(file, "%d ", (int) data->output[i][j]); } else { fprintf(file, "%f ", data->output[i][j]); } } #else fprintf(file, FANNPRINTF " ", data->output[i][j]); #endif } fprintf(file, "\n"); } return retval; } /* * INTERNAL FUNCTION Reads training data from a file descriptor. */ struct fann_train_data *fann_read_train_from_fd(FILE * file, const char *filename) { unsigned int num_input, num_output, num_data, i, j; unsigned int line = 1; fann_type *data_input, *data_output; struct fann_train_data *data = (struct fann_train_data *) malloc(sizeof(struct fann_train_data)); if(data == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); return NULL; } if(fscanf(file, "%u %u %u\n", &num_data, &num_input, &num_output) != 3) { fann_error(NULL, FANN_E_CANT_READ_TD, filename, line); fann_destroy_train(data); return NULL; } line++; fann_init_error_data((struct fann_error *) data); data->num_data = num_data; data->num_input = num_input; data->num_output = num_output; data->input = (fann_type **) calloc(num_data, sizeof(fann_type *)); if(data->input == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(data); return NULL; } data->output = (fann_type **) calloc(num_data, sizeof(fann_type *)); if(data->output == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(data); return NULL; } data_input = (fann_type *) calloc(num_input * num_data, sizeof(fann_type)); if(data_input == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(data); return NULL; } data_output = (fann_type *) calloc(num_output * num_data, sizeof(fann_type)); if(data_output == NULL) { fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM); fann_destroy_train(data); return NULL; } for(i = 0; i != num_data; i++) { data->input[i] = data_input; data_input += num_input; for(j = 0; j != num_input; j++) { if(fscanf(file, FANNSCANF " ", &data->input[i][j]) != 1) { fann_error(NULL, FANN_E_CANT_READ_TD, filename, line); fann_destroy_train(data); return NULL; } } line++; data->output[i] = data_output; data_output += num_output; for(j = 0; j != num_output; j++) { if(fscanf(file, FANNSCANF " ", &data->output[i][j]) != 1) { fann_error(NULL, FANN_E_CANT_READ_TD, filename, line); fann_destroy_train(data); return NULL; } } line++; } return data; } /* * INTERNAL FUNCTION returns 0 if the desired error is reached and -1 if it is not reached */ int fann_desired_error_reached(struct fann *ann, float desired_error) { switch (ann->train_stop_function) { case FANN_STOPFUNC_MSE: if(fann_get_MSE(ann) <= desired_error) return 0; break; case FANN_STOPFUNC_BIT: if(ann->num_bit_fail <= (unsigned int)desired_error) return 0; break; } return -1; } #ifndef FIXEDFANN /* * Scale data in input vector before feed it to ann based on previously calculated parameters. */ FANN_EXTERNAL void FANN_API fann_scale_input( struct fann *ann, fann_type *input_vector ) { unsigned cur_neuron; if(ann->scale_mean_in == NULL) { fann_error( (struct fann_error *) ann, FANN_E_SCALE_NOT_PRESENT ); return; } for( cur_neuron = 0; cur_neuron < ann->num_input; cur_neuron++ ) input_vector[ cur_neuron ] = ( ( input_vector[ cur_neuron ] - ann->scale_mean_in[ cur_neuron ] ) / ann->scale_deviation_in[ cur_neuron ] - ( -1.0 ) /* This is old_min */ ) * ann->scale_factor_in[ cur_neuron ] + ann->scale_new_min_in[ cur_neuron ]; } /* * Scale data in output vector before feed it to ann based on previously calculated parameters. */ FANN_EXTERNAL void FANN_API fann_scale_output( struct fann *ann, fann_type *output_vector ) { unsigned cur_neuron; if(ann->scale_mean_in == NULL) { fann_error( (struct fann_error *) ann, FANN_E_SCALE_NOT_PRESENT ); return; } for( cur_neuron = 0; cur_neuron < ann->num_output; cur_neuron++ ) output_vector[ cur_neuron ] = ( ( output_vector[ cur_neuron ] - ann->scale_mean_out[ cur_neuron ] ) / ann->scale_deviation_out[ cur_neuron ] - ( -1.0 ) /* This is old_min */ ) * ann->scale_factor_out[ cur_neuron ] + ann->scale_new_min_out[ cur_neuron ]; } /* * Descale data in input vector after based on previously calculated parameters. */ FANN_EXTERNAL void FANN_API fann_descale_input( struct fann *ann, fann_type *input_vector ) { unsigned cur_neuron; if(ann->scale_mean_in == NULL) { fann_error( (struct fann_error *) ann, FANN_E_SCALE_NOT_PRESENT ); return; } for( cur_neuron = 0; cur_neuron < ann->num_input; cur_neuron++ ) input_vector[ cur_neuron ] = ( ( input_vector[ cur_neuron ] - ann->scale_new_min_in[ cur_neuron ] ) / ann->scale_factor_in[ cur_neuron ] + ( -1.0 ) /* This is old_min */ ) * ann->scale_deviation_in[ cur_neuron ] + ann->scale_mean_in[ cur_neuron ]; } /* * Descale data in output vector after get it from ann based on previously calculated parameters. */ FANN_EXTERNAL void FANN_API fann_descale_output( struct fann *ann, fann_type *output_vector ) { unsigned cur_neuron; if(ann->scale_mean_in == NULL) { fann_error( (struct fann_error *) ann, FANN_E_SCALE_NOT_PRESENT ); return; } for( cur_neuron = 0; cur_neuron < ann->num_output; cur_neuron++ ) output_vector[ cur_neuron ] = ( ( output_vector[ cur_neuron ] - ann->scale_new_min_out[ cur_neuron ] ) / ann->scale_factor_out[ cur_neuron ] + ( -1.0 ) /* This is old_min */ ) * ann->scale_deviation_out[ cur_neuron ] + ann->scale_mean_out[ cur_neuron ]; } /* * Scale input and output data based on previously calculated parameters. */ FANN_EXTERNAL void FANN_API fann_scale_train( struct fann *ann, struct fann_train_data *data ) { unsigned cur_sample; if(ann->scale_mean_in == NULL) { fann_error( (struct fann_error *) ann, FANN_E_SCALE_NOT_PRESENT ); return; } /* Check that we have good training data. */ /* No need for if( !params || !ann ) */ if( data->num_input != ann->num_input || data->num_output != ann->num_output ) { fann_error( (struct fann_error *) ann, FANN_E_TRAIN_DATA_MISMATCH ); return; } for( cur_sample = 0; cur_sample < data->num_data; cur_sample++ ) { fann_scale_input( ann, data->input[ cur_sample ] ); fann_scale_output( ann, data->output[ cur_sample ] ); } } /* * Scale input and output data based on previously calculated parameters. */ FANN_EXTERNAL void FANN_API fann_descale_train( struct fann *ann, struct fann_train_data *data ) { unsigned cur_sample; if(ann->scale_mean_in == NULL) { fann_error( (struct fann_error *) ann, FANN_E_SCALE_NOT_PRESENT ); return; } /* Check that we have good training data. */ /* No need for if( !params || !ann ) */ if( data->num_input != ann->num_input || data->num_output != ann->num_output ) { fann_error( (struct fann_error *) ann, FANN_E_TRAIN_DATA_MISMATCH ); return; } for( cur_sample = 0; cur_sample < data->num_data; cur_sample++ ) { fann_descale_input( ann, data->input[ cur_sample ] ); fann_descale_output( ann, data->output[ cur_sample ] ); } } #define SCALE_RESET( what, where, default_value ) \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ ann->what##_##where[ cur_neuron ] = ( default_value ); #define SCALE_SET_PARAM( where ) \ /* Calculate mean: sum(x)/length */ \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ ann->scale_mean_##where[ cur_neuron ] = 0.0; \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ for( cur_sample = 0; cur_sample < data->num_data; cur_sample++ ) \ ann->scale_mean_##where[ cur_neuron ] += data->where##put[ cur_sample ][ cur_neuron ]; \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ ann->scale_mean_##where[ cur_neuron ] /= (float)data->num_data; \ /* Calculate deviation: sqrt(sum((x-mean)^2)/length) */ \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ ann->scale_deviation_##where[ cur_neuron ] = 0.0; \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ for( cur_sample = 0; cur_sample < data->num_data; cur_sample++ ) \ ann->scale_deviation_##where[ cur_neuron ] += \ /* Another local variable in macro? Oh no! */ \ ( \ data->where##put[ cur_sample ][ cur_neuron ] \ - ann->scale_mean_##where[ cur_neuron ] \ ) \ * \ ( \ data->where##put[ cur_sample ][ cur_neuron ] \ - ann->scale_mean_##where[ cur_neuron ] \ ); \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ ann->scale_deviation_##where[ cur_neuron ] = \ sqrt( ann->scale_deviation_##where[ cur_neuron ] / (float)data->num_data ); \ /* Calculate factor: (new_max-new_min)/(old_max(1)-old_min(-1)) */ \ /* Looks like we dont need whole array of factors? */ \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ ann->scale_factor_##where[ cur_neuron ] = \ ( new_##where##put_max - new_##where##put_min ) \ / \ ( 1.0 - ( -1.0 ) ); \ /* Copy new minimum. */ \ /* Looks like we dont need whole array of new minimums? */ \ for( cur_neuron = 0; cur_neuron < ann->num_##where##put; cur_neuron++ ) \ ann->scale_new_min_##where[ cur_neuron ] = new_##where##put_min; FANN_EXTERNAL int FANN_API fann_set_input_scaling_params( struct fann *ann, const struct fann_train_data *data, float new_input_min, float new_input_max) { unsigned cur_neuron, cur_sample; /* Check that we have good training data. */ /* No need for if( !params || !ann ) */ if(data->num_input != ann->num_input || data->num_output != ann->num_output) { fann_error( (struct fann_error *) ann, FANN_E_TRAIN_DATA_MISMATCH ); return -1; } if(ann->scale_mean_in == NULL) fann_allocate_scale(ann); if(ann->scale_mean_in == NULL) return -1; if( !data->num_data ) { SCALE_RESET( scale_mean, in, 0.0 ) SCALE_RESET( scale_deviation, in, 1.0 ) SCALE_RESET( scale_new_min, in, -1.0 ) SCALE_RESET( scale_factor, in, 1.0 ) } else { SCALE_SET_PARAM( in ); } return 0; } FANN_EXTERNAL int FANN_API fann_set_output_scaling_params( struct fann *ann, const struct fann_train_data *data, float new_output_min, float new_output_max) { unsigned cur_neuron, cur_sample; /* Check that we have good training data. */ /* No need for if( !params || !ann ) */ if(data->num_input != ann->num_input || data->num_output != ann->num_output) { fann_error( (struct fann_error *) ann, FANN_E_TRAIN_DATA_MISMATCH ); return -1; } if(ann->scale_mean_out == NULL) fann_allocate_scale(ann); if(ann->scale_mean_out == NULL) return -1; if( !data->num_data ) { SCALE_RESET( scale_mean, out, 0.0 ) SCALE_RESET( scale_deviation, out, 1.0 ) SCALE_RESET( scale_new_min, out, -1.0 ) SCALE_RESET( scale_factor, out, 1.0 ) } else { SCALE_SET_PARAM( out ); } return 0; } /* * Calculate scaling parameters for future use based on training data. */ FANN_EXTERNAL int FANN_API fann_set_scaling_params( struct fann *ann, const struct fann_train_data *data, float new_input_min, float new_input_max, float new_output_min, float new_output_max) { if(fann_set_input_scaling_params(ann, data, new_input_min, new_input_max) == 0) return fann_set_output_scaling_params(ann, data, new_output_min, new_output_max); else return -1; } /* * Clears scaling parameters. */ FANN_EXTERNAL int FANN_API fann_clear_scaling_params(struct fann *ann) { unsigned cur_neuron; if(ann->scale_mean_out == NULL) fann_allocate_scale(ann); if(ann->scale_mean_out == NULL) return -1; SCALE_RESET( scale_mean, in, 0.0 ) SCALE_RESET( scale_deviation, in, 1.0 ) SCALE_RESET( scale_new_min, in, -1.0 ) SCALE_RESET( scale_factor, in, 1.0 ) SCALE_RESET( scale_mean, out, 0.0 ) SCALE_RESET( scale_deviation, out, 1.0 ) SCALE_RESET( scale_new_min, out, -1.0 ) SCALE_RESET( scale_factor, out, 1.0 ) return 0; } #endif libfann-2.1.0~beta~dfsg/src/fixedfann.c000066400000000000000000000020271140315177100201150ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ /* Easy way to allow for build of multiple binaries */ #include "config.h" #include "fixedfann.h" #include "fann.c" #include "fann_io.c" #include "fann_train.c" #include "fann_train_data.c" #include "fann_error.c" #include "fann_cascade.c" libfann-2.1.0~beta~dfsg/src/floatfann.c000066400000000000000000000020271140315177100201230ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ /* Easy way to allow for build of multiple binaries */ #include "config.h" #include "floatfann.h" #include "fann.c" #include "fann_io.c" #include "fann_train.c" #include "fann_train_data.c" #include "fann_error.c" #include "fann_cascade.c" libfann-2.1.0~beta~dfsg/src/include/000077500000000000000000000000001140315177100174315ustar00rootroot00000000000000libfann-2.1.0~beta~dfsg/src/include/Makefile.am000066400000000000000000000002551140315177100214670ustar00rootroot00000000000000include_HEADERS = fann.h doublefann.h fann_internal.h floatfann.h fann_data.h fixedfann.h compat_time.h fann_activation.h fann_cascade.h fann_error.h fann_train.h fann_io.h libfann-2.1.0~beta~dfsg/src/include/Makefile.in000066400000000000000000000271761140315177100215130ustar00rootroot00000000000000# Makefile.in generated by automake 1.9.6 from Makefile.am. # @configure_input@ # Copyright (C) 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, # 2003, 2004, 2005 Free Software Foundation, Inc. # This Makefile.in is free software; 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you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ /* * timeval.h 1.0 01/12/19 * * Defines gettimeofday, timeval, etc. for Win32 * * By Wu Yongwei * */ #ifndef _TIMEVAL_H #define _TIMEVAL_H #ifdef _WIN32 /* Modified to compile as ANSI C without include of windows.h If this gives problems with future Windows/MSC versions, then uncomment the USE_WINDOWS_H definition to switch back. */ /* #define USE_WINDOWS_H */ #ifdef USE_WINDOWS_H #define WIN32_LEAN_AND_MEAN #include #else /* */ #ifndef _INC_WINDOWS #define VOID void #define WINAPI __stdcall #define OUT #define WINBASEAPI typedef long LONG; typedef unsigned long DWORD; typedef __int64 LONGLONG; typedef struct _FILETIME { DWORD dwLowDateTime; DWORD dwHighDateTime; } FILETIME, *LPFILETIME; typedef union _LARGE_INTEGER { /* Removed unnamed struct, * it is not ANSI C compatible */ /* struct { * DWORD LowPart; * LONG HighPart; * }; */ struct { DWORD LowPart; LONG HighPart; } u; LONGLONG QuadPart; } LARGE_INTEGER; WINBASEAPI VOID WINAPI GetSystemTimeAsFileTime(OUT LPFILETIME lpSystemTimeAsFileTime); #endif /* _INC_WINDOWS */ #endif /* USE_WINDOWS_H */ #include #ifndef __GNUC__ #define EPOCHFILETIME (116444736000000000i64) #else /* */ #define EPOCHFILETIME (116444736000000000LL) #endif /* */ struct timeval { long tv_sec; /* seconds */ long tv_usec; /* microseconds */ }; struct timezone { int tz_minuteswest; /* minutes W of Greenwich */ int tz_dsttime; /* type of dst correction */ }; __inline int gettimeofday(struct timeval *tv, struct timezone *tz) { FILETIME ft; LARGE_INTEGER li; __int64 t; static int tzflag; if(tv) { GetSystemTimeAsFileTime(&ft); /* The following two lines have been modified to use the named * union member. Unnamed members are not ANSI C compatible. */ li.u.LowPart = ft.dwLowDateTime; li.u.HighPart = ft.dwHighDateTime; t = li.QuadPart; /* In 100-nanosecond intervals */ t -= EPOCHFILETIME; /* Offset to the Epoch time */ t /= 10; /* In microseconds */ tv->tv_sec = (long) (t / 1000000); tv->tv_usec = (long) (t % 1000000); } if(tz) { if(!tzflag) { _tzset(); tzflag++; } tz->tz_minuteswest = _timezone / 60; tz->tz_dsttime = _daylight; } return 0; } #else /* _WIN32 */ #include #endif /* _WIN32 */ #endif /* _TIMEVAL_H */ libfann-2.1.0~beta~dfsg/src/include/config.h000066400000000000000000000034641140315177100210560ustar00rootroot00000000000000/* src/include/config.h. Generated by configure. */ /* config.in. Generated from configure.in by autoheader. */ /* Define to 1 if you have the header file. */ #define HAVE_DLFCN_H 1 /* Define to 1 if you have the `gettimeofday' function. */ #define HAVE_GETTIMEOFDAY 1 /* Define to 1 if you have the header file. */ #define HAVE_INTTYPES_H 1 /* Define to 1 if you have the header file. */ #define HAVE_MEMORY_H 1 /* Define to 1 if you have the header file. */ #define HAVE_STDINT_H 1 /* Define to 1 if you have the header file. */ #define HAVE_STDLIB_H 1 /* Define to 1 if you have the header file. */ #define HAVE_STRINGS_H 1 /* Define to 1 if you have the header file. */ #define HAVE_STRING_H 1 /* Define to 1 if you have the header file. */ #define HAVE_SYS_STAT_H 1 /* Define to 1 if you have the header file. */ #define HAVE_SYS_TYPES_H 1 /* Define to 1 if you have the header file. */ #define HAVE_UNISTD_H 1 /* Name of package */ #define PACKAGE "fann" /* Define to the address where bug reports for this package should be sent. */ #define PACKAGE_BUGREPORT "" /* Define to the full name of this package. */ #define PACKAGE_NAME "" /* Define to the full name and version of this package. */ #define PACKAGE_STRING "" /* Define to the one symbol short name of this package. */ #define PACKAGE_TARNAME "" /* Define to the version of this package. */ #define PACKAGE_VERSION "" /* Define to 1 if you have the ANSI C header files. */ #define STDC_HEADERS 1 /* Define to 1 if you can safely include both and . */ #define TIME_WITH_SYS_TIME 1 /* Version number of package */ #define VERSION "2.1.0" /* Define to empty if `const' does not conform to ANSI C. */ /* #undef const */ libfann-2.1.0~beta~dfsg/src/include/doublefann.h000066400000000000000000000017731140315177100217270ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __doublefann_h__ #define __doublefann_h__ typedef double fann_type; #undef DOUBLEFANN #define DOUBLEFANN #define FANNPRINTF "%.20e" #define FANNSCANF "%le" #define FANN_INCLUDE #include "fann.h" #endif libfann-2.1.0~beta~dfsg/src/include/fann.h000066400000000000000000000470441140315177100205350ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ /* This file defines the user interface to the fann library. It is included from fixedfann.h, floatfann.h and doublefann.h and should NOT be included directly. If included directly it will react as if floatfann.h was included. */ /* Section: FANN Creation/Execution The FANN library is designed to be very easy to use. A feedforward ann can be created by a simple function, while other ANNs can be created just as easily. The ANNs can be trained by and executed by . All of this can be done without much knowledge of the internals of ANNs, although the ANNs created will still be powerfull and effective. If you have more knowledge about ANNs, and desire more control, almost every part of the ANNs can be parametized to create specialized and highly optimal ANNs. */ /* Group: Creation, Destruction & Execution */ #ifndef FANN_INCLUDE /* just to allow for inclusion of fann.h in normal stuations where only floats are needed */ #ifdef FIXEDFANN #include "fixedfann.h" #else #include "floatfann.h" #endif /* FIXEDFANN */ #else /* COMPAT_TIME REPLACEMENT */ #ifndef _WIN32 #include #else /* _WIN32 */ #if !defined(_MSC_EXTENSIONS) && !defined(_INC_WINDOWS) extern unsigned long __stdcall GetTickCount(void); #else /* _MSC_EXTENSIONS */ #define WIN32_LEAN_AND_MEAN #include #endif /* _MSC_EXTENSIONS */ #endif /* _WIN32 */ #ifndef __fann_h__ #define __fann_h__ #ifdef __cplusplus extern "C" { #ifndef __cplusplus } /* to fool automatic indention engines */ #endif #endif /* __cplusplus */ #ifndef NULL #define NULL 0 #endif /* NULL */ /* ----- Macros used to define DLL external entrypoints ----- */ /* DLL Export, import and calling convention for Windows. Only defined for Microsoft VC++ FANN_EXTERNAL indicates that a function will be exported/imported from a dll FANN_API ensures that the DLL calling convention will be used for a function regardless of the calling convention used when compiling. For a function to be exported from a DLL its prototype and declaration must be like this: FANN_EXTERNAL void FANN_API function(char *argument) The following ifdef block is a way of creating macros which make exporting from a DLL simple. All files within a DLL are compiled with the FANN_DLL_EXPORTS symbol defined on the command line. This symbol should not be defined on any project that uses this DLL. This way any other project whose source files include this file see FANN_EXTERNAL functions as being imported from a DLL, whereas a DLL sees symbols defined with this macro as being exported which makes calls more efficient. The __stdcall calling convention is used for functions in a windows DLL. The callback functions for fann_set_callback must be declared as FANN_API so the DLL and the application program both use the same calling convention. */ /* The following sets the default for MSVC++ 2003 or later to use the fann dll's. To use a lib or fixedfann.c, floatfann.c or doublefann.c with those compilers FANN_NO_DLL has to be defined before including the fann headers. The default for previous MSVC compilers such as VC++ 6 is not to use dll's. To use dll's FANN_USE_DLL has to be defined before including the fann headers. */ #if (_MSC_VER > 1300) #ifndef FANN_NO_DLL #define FANN_USE_DLL #endif /* FANN_USE_LIB */ #endif /* _MSC_VER */ #if defined(_MSC_VER) && (defined(FANN_USE_DLL) || defined(FANN_DLL_EXPORTS)) #ifdef FANN_DLL_EXPORTS #define FANN_EXTERNAL __declspec(dllexport) #else /* */ #define FANN_EXTERNAL __declspec(dllimport) #endif /* FANN_DLL_EXPORTS*/ #define FANN_API __stdcall #else /* */ #define FANN_EXTERNAL #define FANN_API #endif /* _MSC_VER */ /* ----- End of macros used to define DLL external entrypoints ----- */ #include "fann_error.h" #include "fann_activation.h" #include "fann_data.h" #include "fann_internal.h" #include "fann_train.h" #include "fann_cascade.h" #include "fann_io.h" /* Function: fann_create_standard Creates a standard fully connected backpropagation neural network. There will be a bias neuron in each layer (except the output layer), and this bias neuron will be connected to all neurons in the next layer. When running the network, the bias nodes always emits 1. To destroy a use the function. Parameters: num_layers - The total number of layers including the input and the output layer. ... - Integer values determining the number of neurons in each layer starting with the input layer and ending with the output layer. Returns: A pointer to the newly created . Example: > // Creating an ANN with 2 input neurons, 1 output neuron, > // and two hidden neurons with 8 and 9 neurons > struct fann *ann = fann_create_standard(4, 2, 8, 9, 1); See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL struct fann *FANN_API fann_create_standard(unsigned int num_layers, ...); /* Function: fann_create_standard_array Just like , but with an array of layer sizes instead of individual parameters. Example: > // Creating an ANN with 2 input neurons, 1 output neuron, > // and two hidden neurons with 8 and 9 neurons > unsigned int layers[4] = {2, 8, 9, 1}; > struct fann *ann = fann_create_standard_array(4, layers); See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array(unsigned int num_layers, const unsigned int *layers); /* Function: fann_create_sparse Creates a standard backpropagation neural network, which is not fully connected. Parameters: connection_rate - The connection rate controls how many connections there will be in the network. If the connection rate is set to 1, the network will be fully connected, but if it is set to 0.5 only half of the connections will be set. A connection rate of 1 will yield the same result as num_layers - The total number of layers including the input and the output layer. ... - Integer values determining the number of neurons in each layer starting with the input layer and ending with the output layer. Returns: A pointer to the newly created . See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL struct fann *FANN_API fann_create_sparse(float connection_rate, unsigned int num_layers, ...); /* Function: fann_create_sparse_array Just like , but with an array of layer sizes instead of individual parameters. See for a description of the parameters. See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array(float connection_rate, unsigned int num_layers, const unsigned int *layers); /* Function: fann_create_shortcut Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections. Shortcut connections are connections that skip layers. A fully connected network with shortcut connections, is a network where all neurons are connected to all neurons in later layers. Including direct connections from the input layer to the output layer. See for a description of the parameters. See also: , , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut(unsigned int num_layers, ...); /* Function: fann_create_shortcut_array Just like , but with an array of layer sizes instead of individual parameters. See for a description of the parameters. See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array(unsigned int num_layers, const unsigned int *layers); /* Function: fann_destroy Destroys the entire network and properly freeing all the associated memmory. This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_destroy(struct fann *ann); /* Function: fann_run Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer. See also: This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL fann_type * FANN_API fann_run(struct fann *ann, fann_type * input); /* Function: fann_randomize_weights Give each connection a random weight between *min_weight* and *max_weight* From the beginning the weights are random between -0.1 and 0.1. See also: This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_randomize_weights(struct fann *ann, fann_type min_weight, fann_type max_weight); /* Function: fann_init_weights Initialize the weights using Widrow + Nguyen's algorithm. This function behaves similarly to fann_randomize_weights. It will use the algorithm developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up training. This technique is not always successful, and in some cases can be less efficient than a purely random initialization. The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second argument, data, which is the training data that will be used to train the network. See also: , This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL void FANN_API fann_init_weights(struct fann *ann, struct fann_train_data *train_data); /* Function: fann_print_connections Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann. The output from fann_print_connections on a small (2 2 1) network trained on the xor problem >Layer / Neuron 012345 >L 1 / N 3 BBa... >L 1 / N 4 BBA... >L 1 / N 5 ...... >L 2 / N 6 ...BBA >L 2 / N 7 ...... This network have five real neurons and two bias neurons. This gives a total of seven neurons named from 0 to 6. The connections between these neurons can be seen in the matrix. "." is a place where there is no connection, while a character tells how strong the connection is on a scale from a-z. The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) has connection from the three neurons in the previous layer as is visible in the first two lines. The output neuron (6) has connections form the three neurons in the hidden layer 3 - 5 as is visible in the fourth line. To simplify the matrix output neurons is not visible as neurons that connections can come from, and input and bias neurons are not visible as neurons that connections can go to. This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_print_connections(struct fann *ann); /* Group: Parameters */ /* Function: fann_print_parameters Prints all of the parameters and options of the ANN This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_print_parameters(struct fann *ann); /* Function: fann_get_num_input Get the number of input neurons. This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_num_input(struct fann *ann); /* Function: fann_get_num_output Get the number of output neurons. This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_num_output(struct fann *ann); /* Function: fann_get_total_neurons Get the total number of neurons in the entire network. This number does also include the bias neurons, so a 2-4-2 network has 2+4+2 +2(bias) = 10 neurons. This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_total_neurons(struct fann *ann); /* Function: fann_get_total_connections Get the total number of connections in the entire network. This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_total_connections(struct fann *ann); /* Function: fann_get_network_type Get the type of neural network it was created as. Parameters: ann - A previously created neural network structure of type pointer. Returns: The neural network type from enum See Also: This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type(struct fann *ann); /* Function: fann_get_connection_rate Get the connection rate used when the network was created Parameters: ann - A previously created neural network structure of type pointer. Returns: The connection rate This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL float FANN_API fann_get_connection_rate(struct fann *ann); /* Function: fann_get_num_layers Get the number of layers in the network Parameters: ann - A previously created neural network structure of type pointer. Returns: The number of layers in the neural network Example: > // Obtain the number of layers in a neural network > struct fann *ann = fann_create_standard(4, 2, 8, 9, 1); > unsigned int num_layers = fann_get_num_layers(ann); This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL unsigned int FANN_API fann_get_num_layers(struct fann *ann); /*Function: fann_get_layer_array Get the number of neurons in each layer in the network. Bias is not included so the layers match the fann_create functions. Parameters: ann - A previously created neural network structure of type pointer. The layers array must be preallocated to at least sizeof(unsigned int) * fann_num_layers() long. This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_get_layer_array(struct fann *ann, unsigned int *layers); /* Function: fann_get_bias_array Get the number of bias in each layer in the network. Parameters: ann - A previously created neural network structure of type pointer. The bias array must be preallocated to at least sizeof(unsigned int) * fann_num_layers() long. This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_get_bias_array(struct fann *ann, unsigned int *bias); /* Function: fann_get_connection_array Get the connections in the network. Parameters: ann - A previously created neural network structure of type pointer. The connections array must be preallocated to at least sizeof(struct fann_connection) * fann_get_total_connections() long. This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_get_connection_array(struct fann *ann, struct fann_connection *connections); /* Function: fann_set_weight_array Set connections in the network. Parameters: ann - A previously created neural network structure of type pointer. Only the weights can be changed, connections and weights are ignored if they do not already exist in the network. The array must have sizeof(struct fann_connection) * num_connections size. This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_set_weight_array(struct fann *ann, struct fann_connection *connections, unsigned int num_connections); /* Function: fann_set_weight Set a connection in the network. Parameters: ann - A previously created neural network structure of type pointer. Only the weights can be changed. The connection/weight is ignored if it does not already exist in the network. This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_set_weight(struct fann *ann, unsigned int from_neuron, unsigned int to_neuron, fann_type weight); /* Function: fann_set_user_data Store a pointer to user defined data. The pointer can be retrieved with for example in a callback. It is the user's responsibility to allocate and deallocate any data that the pointer might point to. Parameters: ann - A previously created neural network structure of type pointer. user_data - A void pointer to user defined data. This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_set_user_data(struct fann *ann, void *user_data); /* Function: fann_get_user_data Get a pointer to user defined data that was previously set with . It is the user's responsibility to allocate and deallocate any data that the pointer might point to. Parameters: ann - A previously created neural network structure of type pointer. Returns: A void pointer to user defined data. This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void * FANN_API fann_get_user_data(struct fann *ann); #ifdef FIXEDFANN /* Function: fann_get_decimal_point Returns the position of the decimal point in the ann. This function is only available when the ANN is in fixed point mode. The decimal point is described in greater detail in the tutorial . See also: , , , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_decimal_point(struct fann *ann); /* Function: fann_get_multiplier returns the multiplier that fix point data is multiplied with. This function is only available when the ANN is in fixed point mode. The multiplier is the used to convert between floating point and fixed point notation. A floating point number is multiplied with the multiplier in order to get the fixed point number and visa versa. The multiplier is described in greater detail in the tutorial . See also: , , , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_multiplier(struct fann *ann); #endif /* FIXEDFANN */ #ifdef __cplusplus #ifndef __cplusplus /* to fool automatic indention engines */ { #endif } #endif /* __cplusplus */ #endif /* __fann_h__ */ #endif /* NOT FANN_INCLUDE */ libfann-2.1.0~beta~dfsg/src/include/fann_activation.h000066400000000000000000000150131140315177100227450ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __fann_activation_h__ #define __fann_activation_h__ /* internal include file, not to be included directly */ /* Implementation of the activation functions */ /* stepwise linear functions used for some of the activation functions */ /* defines used for the stepwise linear functions */ #define fann_linear_func(v1, r1, v2, r2, sum) (((((r2)-(r1)) * ((sum)-(v1)))/((v2)-(v1))) + (r1)) #define fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6, min, max, sum) (sum < v5 ? (sum < v3 ? (sum < v2 ? (sum < v1 ? min : fann_linear_func(v1, r1, v2, r2, sum)) : fann_linear_func(v2, r2, v3, r3, sum)) : (sum < v4 ? fann_linear_func(v3, r3, v4, r4, sum) : fann_linear_func(v4, r4, v5, r5, sum))) : (sum < v6 ? fann_linear_func(v5, r5, v6, r6, sum) : max)) /* FANN_LINEAR */ /* #define fann_linear(steepness, sum) fann_mult(steepness, sum) */ #define fann_linear_derive(steepness, value) (steepness) /* FANN_SIGMOID */ /* #define fann_sigmoid(steepness, sum) (1.0f/(1.0f + exp(-2.0f * steepness * sum))) */ #define fann_sigmoid_real(sum) (1.0f/(1.0f + exp(-2.0f * sum))) #define fann_sigmoid_derive(steepness, value) (2.0f * steepness * value * (1.0f - value)) /* FANN_SIGMOID_SYMMETRIC */ /* #define fann_sigmoid_symmetric(steepness, sum) (2.0f/(1.0f + exp(-2.0f * steepness * sum)) - 1.0f) */ #define fann_sigmoid_symmetric_real(sum) (2.0f/(1.0f + exp(-2.0f * sum)) - 1.0f) #define fann_sigmoid_symmetric_derive(steepness, value) steepness * (1.0f - (value*value)) /* FANN_GAUSSIAN */ /* #define fann_gaussian(steepness, sum) (exp(-sum * steepness * sum * steepness)) */ #define fann_gaussian_real(sum) (exp(-sum * sum)) #define fann_gaussian_derive(steepness, value, sum) (-2.0f * sum * value * steepness * steepness) /* FANN_GAUSSIAN_SYMMETRIC */ /* #define fann_gaussian_symmetric(steepness, sum) ((exp(-sum * steepness * sum * steepness)*2.0)-1.0) */ #define fann_gaussian_symmetric_real(sum) ((exp(-sum * sum)*2.0f)-1.0f) #define fann_gaussian_symmetric_derive(steepness, value, sum) (-2.0f * sum * (value+1.0f) * steepness * steepness) /* FANN_ELLIOT */ /* #define fann_elliot(steepness, sum) (((sum * steepness) / 2.0f) / (1.0f + fann_abs(sum * steepness)) + 0.5f) */ #define fann_elliot_real(sum) (((sum) / 2.0f) / (1.0f + fann_abs(sum)) + 0.5f) #define fann_elliot_derive(steepness, value, sum) (steepness * 1.0f / (2.0f * (1.0f + fann_abs(sum)) * (1.0f + fann_abs(sum)))) /* FANN_ELLIOT_SYMMETRIC */ /* #define fann_elliot_symmetric(steepness, sum) ((sum * steepness) / (1.0f + fann_abs(sum * steepness)))*/ #define fann_elliot_symmetric_real(sum) ((sum) / (1.0f + fann_abs(sum))) #define fann_elliot_symmetric_derive(steepness, value, sum) (steepness * 1.0f / ((1.0f + fann_abs(sum)) * (1.0f + fann_abs(sum)))) /* FANN_SIN_SYMMETRIC */ #define fann_sin_symmetric_real(sum) (sin(sum)) #define fann_sin_symmetric_derive(steepness, sum) (steepness*cos(steepness*sum)) /* FANN_COS_SYMMETRIC */ #define fann_cos_symmetric_real(sum) (cos(sum)) #define fann_cos_symmetric_derive(steepness, sum) (steepness*-sin(steepness*sum)) /* FANN_SIN */ #define fann_sin_real(sum) (sin(sum)/2.0f+0.5f) #define fann_sin_derive(steepness, sum) (steepness*cos(steepness*sum)/2.0f) /* FANN_COS */ #define fann_cos_real(sum) (cos(sum)/2.0f+0.5f) #define fann_cos_derive(steepness, sum) (steepness*-sin(steepness*sum)/2.0f) #define fann_activation_switch(activation_function, value, result) \ switch(activation_function) \ { \ case FANN_LINEAR: \ result = (fann_type)value; \ break; \ case FANN_LINEAR_PIECE: \ result = (fann_type)((value < 0) ? 0 : (value > 1) ? 1 : value); \ break; \ case FANN_LINEAR_PIECE_SYMMETRIC: \ result = (fann_type)((value < -1) ? -1 : (value > 1) ? 1 : value); \ break; \ case FANN_SIGMOID: \ result = (fann_type)fann_sigmoid_real(value); \ break; \ case FANN_SIGMOID_SYMMETRIC: \ result = (fann_type)fann_sigmoid_symmetric_real(value); \ break; \ case FANN_SIGMOID_SYMMETRIC_STEPWISE: \ result = (fann_type)fann_stepwise(-2.64665293693542480469e+00, -1.47221934795379638672e+00, -5.49306154251098632812e-01, 5.49306154251098632812e-01, 1.47221934795379638672e+00, 2.64665293693542480469e+00, -9.90000009536743164062e-01, -8.99999976158142089844e-01, -5.00000000000000000000e-01, 5.00000000000000000000e-01, 8.99999976158142089844e-01, 9.90000009536743164062e-01, -1, 1, value); \ break; \ case FANN_SIGMOID_STEPWISE: \ result = (fann_type)fann_stepwise(-2.64665246009826660156e+00, -1.47221946716308593750e+00, -5.49306154251098632812e-01, 5.49306154251098632812e-01, 1.47221934795379638672e+00, 2.64665293693542480469e+00, 4.99999988824129104614e-03, 5.00000007450580596924e-02, 2.50000000000000000000e-01, 7.50000000000000000000e-01, 9.49999988079071044922e-01, 9.95000004768371582031e-01, 0, 1, value); \ break; \ case FANN_THRESHOLD: \ result = (fann_type)((value < 0) ? 0 : 1); \ break; \ case FANN_THRESHOLD_SYMMETRIC: \ result = (fann_type)((value < 0) ? -1 : 1); \ break; \ case FANN_GAUSSIAN: \ result = (fann_type)fann_gaussian_real(value); \ break; \ case FANN_GAUSSIAN_SYMMETRIC: \ result = (fann_type)fann_gaussian_symmetric_real(value); \ break; \ case FANN_ELLIOT: \ result = (fann_type)fann_elliot_real(value); \ break; \ case FANN_ELLIOT_SYMMETRIC: \ result = (fann_type)fann_elliot_symmetric_real(value); \ break; \ case FANN_SIN_SYMMETRIC: \ result = (fann_type)fann_sin_symmetric_real(value); \ break; \ case FANN_COS_SYMMETRIC: \ result = (fann_type)fann_cos_symmetric_real(value); \ break; \ case FANN_SIN: \ result = (fann_type)fann_sin_real(value); \ break; \ case FANN_COS: \ result = (fann_type)fann_cos_real(value); \ break; \ case FANN_GAUSSIAN_STEPWISE: \ result = 0; \ break; \ } #endif libfann-2.1.0~beta~dfsg/src/include/fann_cascade.h000066400000000000000000000451341140315177100221760ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __fann_cascade_h__ #define __fann_cascade_h__ /* Section: FANN Cascade Training Cascade training differs from ordinary training in the sense that it starts with an empty neural network and then adds neurons one by one, while it trains the neural network. The main benefit of this approach, is that you do not have to guess the number of hidden layers and neurons prior to training, but cascade training have also proved better at solving some problems. The basic idea of cascade training is that a number of candidate neurons are trained separate from the real network, then the most promissing of these candidate neurons is inserted into the neural network. Then the output connections are trained and new candidate neurons is prepared. The candidate neurons are created as shorcut connected neurons in a new hidden layer, which means that the final neural network will consist of a number of hidden layers with one shorcut connected neuron in each. */ /* Group: Cascade Training */ /* Function: fann_cascadetrain_on_data Trains on an entire dataset, for a period of time using the Cascade2 training algorithm. This algorithm adds neurons to the neural network while training, which means that it needs to start with an ANN without any hidden layers. The neural network should also use shortcut connections, so should be used to create the ANN like this: >struct fann *ann = fann_create_shortcut(2, fann_num_input_train_data(train_data), fann_num_output_train_data(train_data)); This training uses the parameters set using the fann_set_cascade_..., but it also uses another training algorithm as it's internal training algorithm. This algorithm can be set to either FANN_TRAIN_RPROP or FANN_TRAIN_QUICKPROP by , and the parameters set for these training algorithms will also affect the cascade training. Parameters: ann - The neural network data - The data, which should be used during training max_neuron - The maximum number of neurons to be added to neural network neurons_between_reports - The number of neurons between printing a status report to stdout. A value of zero means no reports should be printed. desired_error - The desired or , depending on which stop function is chosen by . Instead of printing out reports every neurons_between_reports, a callback function can be called (see ). See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data(struct fann *ann, struct fann_train_data *data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error); /* Function: fann_cascadetrain_on_file Does the same as , but reads the training data directly from a file. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file(struct fann *ann, const char *filename, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error); /* Group: Parameters */ /* Function: fann_get_cascade_output_change_fraction The cascade output change fraction is a number between 0 and 1 determining how large a fraction the value should change within during training of the output connections, in order for the training not to stagnate. If the training stagnates, the training of the output connections will be ended and new candidates will be prepared. This means: If the MSE does not change by a fraction of during a period of , the training of the output connections is stopped because the training has stagnated. If the cascade output change fraction is low, the output connections will be trained more and if the fraction is high they will be trained less. The default cascade output change fraction is 0.01, which is equalent to a 1% change in MSE. See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL float FANN_API fann_get_cascade_output_change_fraction(struct fann *ann); /* Function: fann_set_cascade_output_change_fraction Sets the cascade output change fraction. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_output_change_fraction(struct fann *ann, float cascade_output_change_fraction); /* Function: fann_get_cascade_output_stagnation_epochs The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of . See more info about this parameter in . The default number of cascade output stagnation epochs is 12. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_output_stagnation_epochs(struct fann *ann); /* Function: fann_set_cascade_output_stagnation_epochs Sets the number of cascade output stagnation epochs. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_output_stagnation_epochs(struct fann *ann, unsigned int cascade_output_stagnation_epochs); /* Function: fann_get_cascade_candidate_change_fraction The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the value should change within during training of the candidate neurons, in order for the training not to stagnate. If the training stagnates, the training of the candidate neurons will be ended and the best candidate will be selected. This means: If the MSE does not change by a fraction of during a period of , the training of the candidate neurons is stopped because the training has stagnated. If the cascade candidate change fraction is low, the candidate neurons will be trained more and if the fraction is high they will be trained less. The default cascade candidate change fraction is 0.01, which is equalent to a 1% change in MSE. See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL float FANN_API fann_get_cascade_candidate_change_fraction(struct fann *ann); /* Function: fann_set_cascade_candidate_change_fraction Sets the cascade candidate change fraction. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_change_fraction(struct fann *ann, float cascade_candidate_change_fraction); /* Function: fann_get_cascade_candidate_stagnation_epochs The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of . See more info about this parameter in . The default number of cascade candidate stagnation epochs is 12. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_candidate_stagnation_epochs(struct fann *ann); /* Function: fann_set_cascade_candidate_stagnation_epochs Sets the number of cascade candidate stagnation epochs. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_stagnation_epochs(struct fann *ann, unsigned int cascade_candidate_stagnation_epochs); /* Function: fann_get_cascade_weight_multiplier The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network. This parameter is usually between 0 and 1, and is used to make the training a bit less aggressive. The default weight multiplier is 0.4 See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL fann_type FANN_API fann_get_cascade_weight_multiplier(struct fann *ann); /* Function: fann_set_cascade_weight_multiplier Sets the weight multiplier. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_weight_multiplier(struct fann *ann, fann_type cascade_weight_multiplier); /* Function: fann_get_cascade_candidate_limit The candidate limit is a limit for how much the candidate neuron may be trained. The limit is a limit on the proportion between the MSE and candidate score. Set this to a lower value to avoid overfitting and to a higher if overfitting is not a problem. The default candidate limit is 1000.0 See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL fann_type FANN_API fann_get_cascade_candidate_limit(struct fann *ann); /* Function: fann_set_cascade_candidate_limit Sets the candidate limit. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_limit(struct fann *ann, fann_type cascade_candidate_limit); /* Function: fann_get_cascade_max_out_epochs The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron. The default max out epochs is 150 See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_out_epochs(struct fann *ann); /* Function: fann_set_cascade_max_out_epochs Sets the maximum out epochs. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_max_out_epochs(struct fann *ann, unsigned int cascade_max_out_epochs); /* Function: fann_get_cascade_max_cand_epochs The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron. The default max candidate epochs is 150 See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_cand_epochs(struct fann *ann); /* Function: fann_set_cascade_max_cand_epochs Sets the max candidate epochs. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_max_cand_epochs(struct fann *ann, unsigned int cascade_max_cand_epochs); /* Function: fann_get_cascade_num_candidates The number of candidates used during training (calculated by multiplying , and ). The actual candidates is defined by the and arrays. These arrays define the activation functions and activation steepnesses used for the candidate neurons. If there are 2 activation functions in the activation function array and 3 steepnesses in the steepness array, then there will be 2x3=6 different candidates which will be trained. These 6 different candidates can be copied into several candidate groups, where the only difference between these groups is the initial weights. If the number of groups is set to 2, then the number of candidate neurons will be 2x3x2=12. The number of candidate groups is defined by . The default number of candidates is 6x4x2 = 48 See also: , , , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidates(struct fann *ann); /* Function: fann_get_cascade_activation_functions_count The number of activation functions in the array. The default number of activation functions is 6. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_functions_count(struct fann *ann); /* Function: fann_get_cascade_activation_functions The cascade activation functions array is an array of the different activation functions used by the candidates. See for a description of which candidate neurons will be generated by this array. The default activation functions is {FANN_SIGMOID, FANN_SIGMOID_SYMMETRIC, FANN_GAUSSIAN, FANN_GAUSSIAN_SYMMETRIC, FANN_ELLIOT, FANN_ELLIOT_SYMMETRIC} See also: , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL enum fann_activationfunc_enum * FANN_API fann_get_cascade_activation_functions( struct fann *ann); /* Function: fann_set_cascade_activation_functions Sets the array of cascade candidate activation functions. The array must be just as long as defined by the count. See for a description of which candidate neurons will be generated by this array. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_activation_functions(struct fann *ann, enum fann_activationfunc_enum * cascade_activation_functions, unsigned int cascade_activation_functions_count); /* Function: fann_get_cascade_activation_steepnesses_count The number of activation steepnesses in the array. The default number of activation steepnesses is 4. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_steepnesses_count(struct fann *ann); /* Function: fann_get_cascade_activation_steepnesses The cascade activation steepnesses array is an array of the different activation functions used by the candidates. See for a description of which candidate neurons will be generated by this array. The default activation steepnesses is {0.25, 0.50, 0.75, 1.00} See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL fann_type * FANN_API fann_get_cascade_activation_steepnesses(struct fann *ann); /* Function: fann_set_cascade_activation_steepnesses Sets the array of cascade candidate activation steepnesses. The array must be just as long as defined by the count. See for a description of which candidate neurons will be generated by this array. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_activation_steepnesses(struct fann *ann, fann_type * cascade_activation_steepnesses, unsigned int cascade_activation_steepnesses_count); /* Function: fann_get_cascade_num_candidate_groups The number of candidate groups is the number of groups of identical candidates which will be used during training. This number can be used to have more candidates without having to define new parameters for the candidates. See for a description of which candidate neurons will be generated by this parameter. The default number of candidate groups is 2 See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidate_groups(struct fann *ann); /* Function: fann_set_cascade_num_candidate_groups Sets the number of candidate groups. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_cascade_num_candidate_groups(struct fann *ann, unsigned int cascade_num_candidate_groups); #endif libfann-2.1.0~beta~dfsg/src/include/fann_cpp.h000066400000000000000000003653271140315177100214060ustar00rootroot00000000000000#ifndef FANN_CPP_H_INCLUDED #define FANN_CPP_H_INCLUDED /* * * Fast Artificial Neural Network (fann) C++ Wrapper * Copyright (C) 2004-2006 created by freegoldbar (at) yahoo dot com * * This wrapper is free software; you can redistribute it and/or * modify it under the terms of the GNU Lesser General Public * License as published by the Free Software Foundation; either * version 2.1 of the License, or (at your option) any later version. * * This wrapper is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU * Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public * License along with this library; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA * */ /* * Title: FANN Wrapper for C++ * * Overview: * * The Fann Wrapper for C++ provides two classes: * and . To use the wrapper include * doublefann.h, floatfann.h or fixedfann.h before the * fann_cpp.h header file. To get started see xor_sample.cpp * in the examples directory. The license is LGPL. Copyright (C) * 2004-2006 created by . * * Note: Notes and differences from C API * * - The Fann Wrapper for C++ is a minimal wrapper without use of * templates or exception handling for efficient use in any environment. * Benefits include stricter type checking, simpler memory * management and possibly code completion in program editor. * - Method names are the same as the function names in the C * API except the fann_ prefix has been removed. Enums in the * namespace are similarly defined without the FANN_ prefix. * - The arguments to the methods are the same as the C API * except that the struct fann *ann/struct fann_train_data *data * arguments are encapsulated so they are not present in the * method signatures or are translated into class references. * - The various create methods return a boolean set to true to * indicate that the neural network was created, false otherwise. * The same goes for the read_train_from_file method. * - The neural network and training data is automatically cleaned * up in the destructors and create/read methods. * - To make the destructors virtual define USE_VIRTUAL_DESTRUCTOR * before including the header file. * - Additional methods are available on the training_data class to * give access to the underlying training data. They are get_input, * get_output and set_train_data. Finally fann_duplicate_train_data * has been replaced by a copy constructor. * * Note: Changes * * Version 2.1.0: * - General update to fann C library 2.1.0 with support for new functionality * - Due to changes in the C API the C++ API is not fully backward compatible: * The create methods have changed names and parameters. * The training callback function has different parameters and a set_callback. * Some methods have updated names. * Get activation function and steepness is available for neurons, not layers. * - Extensions are now part of fann so there is no fann_extensions.h * * Version 1.2.0: * - Changed char pointers to const std::string references * - Added const_casts where the C API required it * - Initialized enums from the C enums instead of numeric constants * - Added a method set_train_data that copies and allocates training * - data in a way that is compatible with the way the C API deallocates * - the data thus making it possible to change training data. * - The get_rprop_increase_factor method did not return its value * * Version 1.0.0: * - Initial version * */ #include #include /* Namespace: FANN The FANN namespace groups the C++ wrapper definitions */ namespace FANN { /* Enum: error_function_enum Error function used during training. ERRORFUNC_LINEAR - Standard linear error function. ERRORFUNC_TANH - Tanh error function, usually better but can require a lower learning rate. This error function agressively targets outputs that differ much from the desired, while not targetting outputs that only differ a little that much. This activation function is not recommended for cascade training and incremental training. See also: , */ enum error_function_enum { ERRORFUNC_LINEAR = FANN_ERRORFUNC_LINEAR, ERRORFUNC_TANH }; /* Enum: stop_function_enum Stop criteria used during training. STOPFUNC_MSE - Stop criteria is Mean Square Error (MSE) value. STOPFUNC_BIT - Stop criteria is number of bits that fail. The number of bits; means the number of output neurons which differ more than the bit fail limit (see , ). The bits are counted in all of the training data, so this number can be higher than the number of training data. See also: , */ enum stop_function_enum { STOPFUNC_MSE = FANN_STOPFUNC_MSE, STOPFUNC_BIT }; /* Enum: training_algorithm_enum The Training algorithms used when training on with functions like or . The incremental training looks alters the weights after each time it is presented an input pattern, while batch only alters the weights once after it has been presented to all the patterns. TRAIN_INCREMENTAL - Standard backpropagation algorithm, where the weights are updated after each training pattern. This means that the weights are updated many times during a single epoch. For this reason some problems, will train very fast with this algorithm, while other more advanced problems will not train very well. TRAIN_BATCH - Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set. This means that the weights are only updated once during a epoch. For this reason some problems, will train slower with this algorithm. But since the mean square error is calculated more correctly than in incremental training, some problems will reach a better solutions with this algorithm. TRAIN_RPROP - A more advanced batch training algorithm which achieves good results for many problems. The RPROP training algorithm is adaptive, and does therefore not use the learning_rate. Some other parameters can however be set to change the way the RPROP algorithm works, but it is only recommended for users with insight in how the RPROP training algorithm works. The RPROP training algorithm is described by [Riedmiller and Braun, 1993], but the actual learning algorithm used here is the iRPROP- training algorithm which is described by [Igel and Husken, 2000] which is an variety of the standard RPROP training algorithm. TRAIN_QUICKPROP - A more advanced batch training algorithm which achieves good results for many problems. The quickprop training algorithm uses the learning_rate parameter along with other more advanced parameters, but it is only recommended to change these advanced parameters, for users with insight in how the quickprop training algorithm works. The quickprop training algorithm is described by [Fahlman, 1988]. See also: , */ enum training_algorithm_enum { TRAIN_INCREMENTAL = FANN_TRAIN_INCREMENTAL, TRAIN_BATCH, TRAIN_RPROP, TRAIN_QUICKPROP }; /* Enum: activation_function_enum The activation functions used for the neurons during training. The activation functions can either be defined for a group of neurons by and or it can be defined for a single neuron by . The steepness of an activation function is defined in the same way by , and . The functions are described with functions where: * x is the input to the activation function, * y is the output, * s is the steepness and * d is the derivation. FANN_LINEAR - Linear activation function. * span: -inf < y < inf * y = x*s, d = 1*s * Can NOT be used in fixed point. FANN_THRESHOLD - Threshold activation function. * x < 0 -> y = 0, x >= 0 -> y = 1 * Can NOT be used during training. FANN_THRESHOLD_SYMMETRIC - Threshold activation function. * x < 0 -> y = 0, x >= 0 -> y = 1 * Can NOT be used during training. FANN_SIGMOID - Sigmoid activation function. * One of the most used activation functions. * span: 0 < y < 1 * y = 1/(1 + exp(-2*s*x)) * d = 2*s*y*(1 - y) FANN_SIGMOID_STEPWISE - Stepwise linear approximation to sigmoid. * Faster than sigmoid but a bit less precise. FANN_SIGMOID_SYMMETRIC - Symmetric sigmoid activation function, aka. tanh. * One of the most used activation functions. * span: -1 < y < 1 * y = tanh(s*x) = 2/(1 + exp(-2*s*x)) - 1 * d = s*(1-(y*y)) FANN_SIGMOID_SYMMETRIC - Stepwise linear approximation to symmetric sigmoid. * Faster than symmetric sigmoid but a bit less precise. FANN_GAUSSIAN - Gaussian activation function. * 0 when x = -inf, 1 when x = 0 and 0 when x = inf * span: 0 < y < 1 * y = exp(-x*s*x*s) * d = -2*x*s*y*s FANN_GAUSSIAN_SYMMETRIC - Symmetric gaussian activation function. * -1 when x = -inf, 1 when x = 0 and 0 when x = inf * span: -1 < y < 1 * y = exp(-x*s*x*s)*2-1 * d = -2*x*s*(y+1)*s FANN_ELLIOT - Fast (sigmoid like) activation function defined by David Elliott * span: 0 < y < 1 * y = ((x*s) / 2) / (1 + |x*s|) + 0.5 * d = s*1/(2*(1+|x*s|)*(1+|x*s|)) FANN_ELLIOT_SYMMETRIC - Fast (symmetric sigmoid like) activation function defined by David Elliott * span: -1 < y < 1 * y = (x*s) / (1 + |x*s|) * d = s*1/((1+|x*s|)*(1+|x*s|)) FANN_LINEAR_PIECE - Bounded linear activation function. * span: 0 < y < 1 * y = x*s, d = 1*s FANN_LINEAR_PIECE_SYMMETRIC - Bounded Linear activation function. * span: -1 < y < 1 * y = x*s, d = 1*s FANN_SIN_SYMMETRIC - Periodical sinus activation function. * span: -1 <= y <= 1 * y = sin(x*s) * d = s*cos(x*s) FANN_COS_SYMMETRIC - Periodical cosinus activation function. * span: -1 <= y <= 1 * y = cos(x*s) * d = s*-sin(x*s) See also: , */ enum activation_function_enum { LINEAR = FANN_LINEAR, THRESHOLD, THRESHOLD_SYMMETRIC, SIGMOID, SIGMOID_STEPWISE, SIGMOID_SYMMETRIC, SIGMOID_SYMMETRIC_STEPWISE, GAUSSIAN, GAUSSIAN_SYMMETRIC, GAUSSIAN_STEPWISE, ELLIOT, ELLIOT_SYMMETRIC, LINEAR_PIECE, LINEAR_PIECE_SYMMETRIC, SIN_SYMMETRIC, COS_SYMMETRIC }; /* Enum: network_type_enum Definition of network types used by LAYER - Each layer only has connections to the next layer SHORTCUT - Each layer has connections to all following layers See Also: , This enumeration appears in FANN >= 2.1.0 */ enum network_type_enum { LAYER = FANN_NETTYPE_LAYER, SHORTCUT }; /* Type: connection Describes a connection between two neurons and its weight from_neuron - Unique number used to identify source neuron to_neuron - Unique number used to identify destination neuron weight - The numerical value of the weight See Also: , This structure appears in FANN >= 2.1.0 */ typedef struct fann_connection connection; /* Forward declaration of class neural_net and training_data */ class neural_net; class training_data; /* Type: callback_type This callback function can be called during training when using , or . >typedef int (*callback_type) (neural_net &net, training_data &train, > unsigned int max_epochs, unsigned int epochs_between_reports, > float desired_error, unsigned int epochs, void *user_data); The callback can be set by using and is very usefull for doing custom things during training. It is recommended to use this function when implementing custom training procedures, or when visualizing the training in a GUI etc. The parameters which the callback function takes is the parameters given to the , plus an epochs parameter which tells how many epochs the training have taken so far. The callback function should return an integer, if the callback function returns -1, the training will terminate. Example of a callback function that prints information to cout: >int print_callback(FANN::neural_net &net, FANN::training_data &train, > unsigned int max_epochs, unsigned int epochs_between_reports, > float desired_error, unsigned int epochs, void *user_data) >{ > cout << "Epochs " << setw(8) << epochs << ". " > << "Current Error: " << left << net.get_MSE() << right << endl; > return 0; >} See also: , */ typedef int (*callback_type) (neural_net &net, training_data &train, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error, unsigned int epochs, void *user_data); /*************************************************************************/ /* Class: training_data Encapsulation of a training data set and associated C API functions. */ class training_data { public: /* Constructor: training_data Default constructor creates an empty neural net. Use , or to initialize. */ training_data() : train_data(NULL) { } /* Constructor: training_data Copy constructor constructs a copy of the training data. Corresponds to the C API function. */ training_data(const training_data &data) { destroy_train(); if (data.train_data != NULL) { train_data = fann_duplicate_train_data(data.train_data); } } /* Destructor: ~training_data Provides automatic cleanup of data. Define USE_VIRTUAL_DESTRUCTOR if you need the destructor to be virtual. See also: */ #ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~training_data() { destroy_train(); } /* Method: destroy Destructs the training data. Called automatically by the destructor. See also: <~training_data> */ void destroy_train() { if (train_data != NULL) { fann_destroy_train(train_data); train_data = NULL; } } /* Method: read_train_from_file Reads a file that stores training data. The file must be formatted like: >num_train_data num_input num_output >inputdata seperated by space >outputdata seperated by space > >. >. >. > >inputdata seperated by space >outputdata seperated by space See also: , , This function appears in FANN >= 1.0.0 */ bool read_train_from_file(const std::string &filename) { destroy_train(); train_data = fann_read_train_from_file(filename.c_str()); return (train_data != NULL); } /* Method: save_train Save the training structure to a file, with the format as specified in Return: The function returns true on success and false on failure. See also: , , This function appears in FANN >= 1.0.0. */ bool save_train(const std::string &filename) { if (train_data == NULL) { return false; } if (fann_save_train(train_data, filename.c_str()) == -1) { return false; } return true; } /* Method: save_train_to_fixed Saves the training structure to a fixed point data file. This function is very usefull for testing the quality of a fixed point network. Return: The function returns true on success and false on failure. See also: , This function appears in FANN >= 1.0.0. */ bool save_train_to_fixed(const std::string &filename, unsigned int decimal_point) { if (train_data == NULL) { return false; } if (fann_save_train_to_fixed(train_data, filename.c_str(), decimal_point) == -1) { return false; } return true; } /* Method: shuffle_train_data Shuffles training data, randomizing the order. This is recommended for incremental training, while it have no influence during batch training. This function appears in FANN >= 1.1.0. */ void shuffle_train_data() { if (train_data != NULL) { fann_shuffle_train_data(train_data); } } /* Method: merge_train_data Merges the data into the data contained in the . This function appears in FANN >= 1.1.0. */ void merge_train_data(const training_data &data) { fann_train_data *new_data = fann_merge_train_data(train_data, data.train_data); if (new_data != NULL) { destroy_train(); train_data = new_data; } } /* Method: length_train_data Returns the number of training patterns in the . See also: , , This function appears in FANN >= 2.0.0. */ unsigned int length_train_data() { if (train_data == NULL) { return 0; } else { return fann_length_train_data(train_data); } } /* Method: num_input_train_data Returns the number of inputs in each of the training patterns in the . See also: , , This function appears in FANN >= 2.0.0. */ unsigned int num_input_train_data() { if (train_data == NULL) { return 0; } else { return fann_num_input_train_data(train_data); } } /* Method: num_output_train_data Returns the number of outputs in each of the training patterns in the . See also: , , This function appears in FANN >= 2.0.0. */ unsigned int num_output_train_data() { if (train_data == NULL) { return 0; } else { return fann_num_output_train_data(train_data); } } /* Grant access to the encapsulated data since many situations and applications creates the data from sources other than files or uses the training data for testing and related functions */ /* Method: get_input Returns: A pointer to the array of input training data See also: , */ fann_type **get_input() { if (train_data == NULL) { return NULL; } else { return train_data->input; } } /* Method: get_output Returns: A pointer to the array of output training data See also: , */ fann_type **get_output() { if (train_data == NULL) { return NULL; } else { return train_data->output; } } /* Method: set_train_data Set the training data to the input and output data provided. A copy of the data is made so there are no restrictions on the allocation of the input/output data and the caller is responsible for the deallocation of the data pointed to by input and output. Parameters: num_data - The number of training data num_input - The number of inputs per training data num_output - The number of ouputs per training data input - The set of inputs (a pointer to an array of pointers to arrays of floating point data) output - The set of desired outputs (a pointer to an array of pointers to arrays of floating point data) See also: , */ void set_train_data(unsigned int num_data, unsigned int num_input, fann_type **input, unsigned int num_output, fann_type **output) { // Uses the allocation method used in fann struct fann_train_data *data = (struct fann_train_data *)malloc(sizeof(struct fann_train_data)); data->input = (fann_type **)calloc(num_data, sizeof(fann_type *)); data->output = (fann_type **)calloc(num_data, sizeof(fann_type *)); data->num_data = num_data; data->num_input = num_input; data->num_output = num_output; fann_type *data_input = (fann_type *)calloc(num_input*num_data, sizeof(fann_type)); fann_type *data_output = (fann_type *)calloc(num_output*num_data, sizeof(fann_type)); for (unsigned int i = 0; i < num_data; ++i) { data->input[i] = data_input; data_input += num_input; for (unsigned int j = 0; j < num_input; ++j) { data->input[i][j] = input[i][j]; } data->output[i] = data_output; data_output += num_output; for (unsigned int j = 0; j < num_output; ++j) { data->output[i][j] = output[i][j]; } } set_train_data(data); } private: /* Set the training data to the struct fann_training_data pointer. The struct has to be allocated with malloc to be compatible with fann_destroy. */ void set_train_data(struct fann_train_data *data) { destroy_train(); train_data = data; } public: /*********************************************************************/ /* Method: create_train_from_callback Creates the training data struct from a user supplied function. As the training data are numerable (data 1, data 2...), the user must write a function that receives the number of the training data set (input,output) and returns the set. Parameters: num_data - The number of training data num_input - The number of inputs per training data num_output - The number of ouputs per training data user_function - The user suplied function Parameters for the user function: num - The number of the training data set num_input - The number of inputs per training data num_output - The number of ouputs per training data input - The set of inputs output - The set of desired outputs See also: , , This function appears in FANN >= 2.1.0 */ void create_train_from_callback(unsigned int num_data, unsigned int num_input, unsigned int num_output, void (FANN_API *user_function)( unsigned int, unsigned int, unsigned int, fann_type * , fann_type * )) { destroy_train(); train_data = fann_create_train_from_callback(num_data, num_input, num_output, user_function); } /* Method: scale_input_train_data Scales the inputs in the training data to the specified range. See also: , , This function appears in FANN >= 2.0.0. */ void scale_input_train_data(fann_type new_min, fann_type new_max) { if (train_data != NULL) { fann_scale_input_train_data(train_data, new_min, new_max); } } /* Method: scale_output_train_data Scales the outputs in the training data to the specified range. See also: , , This function appears in FANN >= 2.0.0. */ void scale_output_train_data(fann_type new_min, fann_type new_max) { if (train_data != NULL) { fann_scale_output_train_data(train_data, new_min, new_max); } } /* Method: scale_train_data Scales the inputs and outputs in the training data to the specified range. See also: , , This function appears in FANN >= 2.0.0. */ void scale_train_data(fann_type new_min, fann_type new_max) { if (train_data != NULL) { fann_scale_train_data(train_data, new_min, new_max); } } /* Method: subset_train_data Changes the training data to a subset, starting at position *pos* and *length* elements forward. Use the copy constructor to work on a new copy of the training data. >FANN::training_data full_data_set; >full_data_set.read_train_from_file("somefile.train"); >FANN::training_data *small_data_set = new FANN::training_data(full_data_set); >small_data_set->subset_train_data(0, 2); // Only use first two >// Use small_data_set ... >delete small_data_set; See also: This function appears in FANN >= 2.0.0. */ void subset_train_data(unsigned int pos, unsigned int length) { if (train_data != NULL) { struct fann_train_data *temp = fann_subset_train_data(train_data, pos, length); destroy_train(); train_data = temp; } } /*********************************************************************/ protected: /* The neural_net class has direct access to the training data */ friend class neural_net; /* Pointer to the encapsulated training data */ struct fann_train_data* train_data; }; /*************************************************************************/ /* Class: neural_net Encapsulation of a neural network and associated C API functions. */ class neural_net { public: /* Constructor: neural_net Default constructor creates an empty neural net. Use one of the create functions to create the neural network. See also: , , , , , */ neural_net() : ann(NULL) { } /* Destructor: ~neural_net Provides automatic cleanup of data. Define USE_VIRTUAL_DESTRUCTOR if you need the destructor to be virtual. See also: */ #ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~neural_net() { destroy(); } /* Method: destroy Destructs the entire network. Called automatically by the destructor. See also: <~neural_net> */ void destroy() { if (ann != NULL) { user_context *user_data = static_cast(fann_get_user_data(ann)); if (user_data != NULL) delete user_data; fann_destroy(ann); ann = NULL; } } /* Method: create_standard Creates a standard fully connected backpropagation neural network. There will be a bias neuron in each layer (except the output layer), and this bias neuron will be connected to all neurons in the next layer. When running the network, the bias nodes always emits 1. Parameters: num_layers - The total number of layers including the input and the output layer. ... - Integer values determining the number of neurons in each layer starting with the input layer and ending with the output layer. Returns: Boolean true if the network was created, false otherwise. Example: >const unsigned int num_layers = 3; >const unsigned int num_input = 2; >const unsigned int num_hidden = 3; >const unsigned int num_output = 1; > >FANN::neural_net net; >net.create_standard(num_layers, num_input, num_hidden, num_output); See also: , , , This function appears in FANN >= 2.0.0. */ bool create_standard(unsigned int num_layers, ...) { va_list layers; va_start(layers, num_layers); bool status = create_standard_array(num_layers, reinterpret_cast(layers)); va_end(layers); return status; } /* Method: create_standard_array Just like , but with an array of layer sizes instead of individual parameters. See also: , , , This function appears in FANN >= 2.0.0. */ bool create_standard_array(unsigned int num_layers, const unsigned int * layers) { destroy(); ann = fann_create_standard_array(num_layers, layers); return (ann != NULL); } /* Method: create_sparse Creates a standard backpropagation neural network, which is not fully connected. Parameters: connection_rate - The connection rate controls how many connections there will be in the network. If the connection rate is set to 1, the network will be fully connected, but if it is set to 0.5 only half of the connections will be set. A connection rate of 1 will yield the same result as num_layers - The total number of layers including the input and the output layer. ... - Integer values determining the number of neurons in each layer starting with the input layer and ending with the output layer. Returns: Boolean true if the network was created, false otherwise. See also: , , , This function appears in FANN >= 2.0.0. */ bool create_sparse(float connection_rate, unsigned int num_layers, ...) { va_list layers; va_start(layers, num_layers); bool status = create_sparse_array(connection_rate, num_layers, reinterpret_cast(layers)); va_end(layers); return status; } /* Method: create_sparse_array Just like , but with an array of layer sizes instead of individual parameters. See for a description of the parameters. See also: , , , This function appears in FANN >= 2.0.0. */ bool create_sparse_array(float connection_rate, unsigned int num_layers, const unsigned int * layers) { destroy(); ann = fann_create_sparse_array(connection_rate, num_layers, layers); return (ann != NULL); } /* Method: create_shortcut Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections. Shortcut connections are connections that skip layers. A fully connected network with shortcut connections, is a network where all neurons are connected to all neurons in later layers. Including direct connections from the input layer to the output layer. See for a description of the parameters. See also: , , , This function appears in FANN >= 2.0.0. */ bool create_shortcut(unsigned int num_layers, ...) { va_list layers; va_start(layers, num_layers); bool status = create_shortcut_array(num_layers, reinterpret_cast(layers)); va_end(layers); return status; } /* Method: create_shortcut_array Just like , but with an array of layer sizes instead of individual parameters. See for a description of the parameters. See also: , , , This function appears in FANN >= 2.0.0. */ bool create_shortcut_array(unsigned int num_layers, const unsigned int * layers) { destroy(); ann = fann_create_shortcut_array(num_layers, layers); return (ann != NULL); } /* Method: run Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer. See also: , This function appears in FANN >= 1.0.0. */ fann_type* run(fann_type *input) { if (ann == NULL) { return NULL; } return fann_run(ann, input); } /* Method: randomize_weights Give each connection a random weight between *min_weight* and *max_weight* From the beginning the weights are random between -0.1 and 0.1. See also: , This function appears in FANN >= 1.0.0. */ void randomize_weights(fann_type min_weight, fann_type max_weight) { if (ann != NULL) { fann_randomize_weights(ann, min_weight, max_weight); } } /* Method: init_weights Initialize the weights using Widrow + Nguyen's algorithm. This function behaves similarly to fann_randomize_weights. It will use the algorithm developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up training. This technique is not always successful, and in some cases can be less efficient than a purely random initialization. The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second argument, data, which is the training data that will be used to train the network. See also: , , This function appears in FANN >= 1.1.0. */ void init_weights(const training_data &data) { if ((ann != NULL) && (data.train_data != NULL)) { fann_init_weights(ann, data.train_data); } } /* Method: print_connections Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann. The output from fann_print_connections on a small (2 2 1) network trained on the xor problem >Layer / Neuron 012345 >L 1 / N 3 BBa... >L 1 / N 4 BBA... >L 1 / N 5 ...... >L 2 / N 6 ...BBA >L 2 / N 7 ...... This network have five real neurons and two bias neurons. This gives a total of seven neurons named from 0 to 6. The connections between these neurons can be seen in the matrix. "." is a place where there is no connection, while a character tells how strong the connection is on a scale from a-z. The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) has connection from the three neurons in the previous layer as is visible in the first two lines. The output neuron (6) has connections form the three neurons in the hidden layer 3 - 5 as is visible in the fourth line. To simplify the matrix output neurons is not visible as neurons that connections can come from, and input and bias neurons are not visible as neurons that connections can go to. This function appears in FANN >= 1.2.0. */ void print_connections() { if (ann != NULL) { fann_print_connections(ann); } } /* Method: create_from_file Constructs a backpropagation neural network from a configuration file, which have been saved by . See also: , , This function appears in FANN >= 1.0.0. */ bool create_from_file(const std::string &configuration_file) { destroy(); ann = fann_create_from_file(configuration_file.c_str()); return (ann != NULL); } /* Method: save Save the entire network to a configuration file. The configuration file contains all information about the neural network and enables to create an exact copy of the neural network and all of the parameters associated with the neural network. These two parameters (, ) are *NOT* saved to the file because they cannot safely be ported to a different location. Also temporary parameters generated during training like is not saved. Return: The function returns 0 on success and -1 on failure. See also: , , This function appears in FANN >= 1.0.0. */ bool save(const std::string &configuration_file) { if (ann == NULL) { return false; } if (fann_save(ann, configuration_file.c_str()) == -1) { return false; } return true; } /* Method: save_to_fixed Saves the entire network to a configuration file. But it is saved in fixed point format no matter which format it is currently in. This is usefull for training a network in floating points, and then later executing it in fixed point. The function returns the bit position of the fix point, which can be used to find out how accurate the fixed point network will be. A high value indicates high precision, and a low value indicates low precision. A negative value indicates very low precision, and a very strong possibility for overflow. (the actual fix point will be set to 0, since a negative fix point does not make sence). Generally, a fix point lower than 6 is bad, and should be avoided. The best way to avoid this, is to have less connections to each neuron, or just less neurons in each layer. The fixed point use of this network is only intended for use on machines that have no floating point processor, like an iPAQ. On normal computers the floating point version is actually faster. See also: , , This function appears in FANN >= 1.0.0. */ int save_to_fixed(const std::string &configuration_file) { int fixpoint = 0; if (ann != NULL) { fixpoint = fann_save_to_fixed(ann, configuration_file.c_str()); } return fixpoint; } #ifndef FIXEDFANN /* Method: train Train one iteration with a set of inputs, and a set of desired outputs. This training is always incremental training (see ), since only one pattern is presented. Parameters: ann - The neural network structure input - an array of inputs. This array must be exactly long. desired_output - an array of desired outputs. This array must be exactly long. See also: , , This function appears in FANN >= 1.0.0. */ void train(fann_type *input, fann_type *desired_output) { if (ann != NULL) { fann_train(ann, input, desired_output); } } /* Method: train_epoch Train one epoch with a set of training data. Train one epoch with the training data stored in data. One epoch is where all of the training data is considered exactly once. This function returns the MSE error as it is calculated either before or during the actual training. This is not the actual MSE after the training epoch, but since calculating this will require to go through the entire training set once more, it is more than adequate to use this value during training. The training algorithm used by this function is chosen by the function. See also: , , This function appears in FANN >= 1.2.0. */ float train_epoch(const training_data &data) { float mse = 0.0f; if ((ann != NULL) && (data.train_data != NULL)) { mse = fann_train_epoch(ann, data.train_data); } return mse; } /* Method: train_on_data Trains on an entire dataset, for a period of time. This training uses the training algorithm chosen by , and the parameters set for these training algorithms. Parameters: ann - The neural network data - The data, which should be used during training max_epochs - The maximum number of epochs the training should continue epochs_between_reports - The number of epochs between printing a status report to stdout. A value of zero means no reports should be printed. desired_error - The desired or , depending on which stop function is chosen by . Instead of printing out reports every epochs_between_reports, a callback function can be called (see ). See also: , , This function appears in FANN >= 1.0.0. */ void train_on_data(const training_data &data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error) { if ((ann != NULL) && (data.train_data != NULL)) { fann_train_on_data(ann, data.train_data, max_epochs, epochs_between_reports, desired_error); } } /* Method: train_on_file Does the same as , but reads the training data directly from a file. See also: , This function appears in FANN >= 1.0.0. */ void train_on_file(const std::string &filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error) { if (ann != NULL) { fann_train_on_file(ann, filename.c_str(), max_epochs, epochs_between_reports, desired_error); } } #endif /* NOT FIXEDFANN */ /* Method: test Test with a set of inputs, and a set of desired outputs. This operation updates the mean square error, but does not change the network in any way. See also: , , This function appears in FANN >= 1.0.0. */ fann_type * test(fann_type *input, fann_type *desired_output) { fann_type * output = NULL; if (ann != NULL) { output = fann_test(ann, input, desired_output); } return output; } /* Method: test_data Test a set of training data and calculates the MSE for the training data. This function updates the MSE and the bit fail values. See also: , , , This function appears in FANN >= 1.2.0. */ float test_data(const training_data &data) { float mse = 0.0f; if ((ann != NULL) && (data.train_data != NULL)) { mse = fann_test_data(ann, data.train_data); } return mse; } /* Method: get_MSE Reads the mean square error from the network. Reads the mean square error from the network. This value is calculated during training or testing, and can therefore sometimes be a bit off if the weights have been changed since the last calculation of the value. See also: , This function appears in FANN >= 1.1.0. */ float get_MSE() { float mse = 0.0f; if (ann != NULL) { mse = fann_get_MSE(ann); } return mse; } /* Method: reset_MSE Resets the mean square error from the network. This function also resets the number of bits that fail. See also: , , This function appears in FANN >= 1.1.0 */ void reset_MSE() { if (ann != NULL) { fann_reset_MSE(ann); } } /* Method: set_callback Sets the callback function for use during training. The user_data is passed to the callback. It can point to arbitrary data that the callback might require and can be NULL if it is not used. See for more information about the callback function. The default callback function simply prints out some status information. This function appears in FANN >= 2.0.0. */ void set_callback(callback_type callback, void *user_data) { if (ann != NULL) { // Allocated data is also deleted in the destroy method called by the destructor user_context *user_instance = static_cast(fann_get_user_data(ann)); if (user_instance != NULL) delete user_instance; user_instance = new user_context(); user_instance->user_callback = callback; user_instance->user_data = user_data; user_instance->net = this; fann_set_user_data(ann, user_instance); if (callback != NULL) fann_set_callback(ann, &FANN::neural_net::internal_callback); else fann_set_callback(ann, NULL); } } /* Method: print_parameters Prints all of the parameters and options of the neural network See also: This function appears in FANN >= 1.2.0. */ void print_parameters() { if (ann != NULL) { fann_print_parameters(ann); } } /* Method: get_training_algorithm Return the training algorithm as described by . This training algorithm is used by and associated functions. Note that this algorithm is also used during , although only FANN::TRAIN_RPROP and FANN::TRAIN_QUICKPROP is allowed during cascade training. The default training algorithm is FANN::TRAIN_RPROP. See also: , , This function appears in FANN >= 1.0.0. */ training_algorithm_enum get_training_algorithm() { fann_train_enum training_algorithm = FANN_TRAIN_INCREMENTAL; if (ann != NULL) { training_algorithm = fann_get_training_algorithm(ann); } return static_cast(training_algorithm); } /* Method: set_training_algorithm Set the training algorithm. More info available in This function appears in FANN >= 1.0.0. */ void set_training_algorithm(training_algorithm_enum training_algorithm) { if (ann != NULL) { fann_set_training_algorithm(ann, static_cast(training_algorithm)); } } /* Method: get_learning_rate Return the learning rate. The learning rate is used to determine how aggressive training should be for some of the training algorithms (FANN::TRAIN_INCREMENTAL, FANN::TRAIN_BATCH, FANN::TRAIN_QUICKPROP). Do however note that it is not used in FANN::TRAIN_RPROP. The default learning rate is 0.7. See also: , , This function appears in FANN >= 1.0.0. */ float get_learning_rate() { float learning_rate = 0.0f; if (ann != NULL) { learning_rate = fann_get_learning_rate(ann); } return learning_rate; } /* Method: set_learning_rate Set the learning rate. More info available in This function appears in FANN >= 1.0.0. */ void set_learning_rate(float learning_rate) { if (ann != NULL) { fann_set_learning_rate(ann, learning_rate); } } /*************************************************************************************************************/ /* Method: get_activation_function Get the activation function for neuron number *neuron* in layer number *layer*, counting the input layer as layer 0. It is not possible to get activation functions for the neurons in the input layer. Information about the individual activation functions is available at . Returns: The activation function for the neuron or -1 if the neuron is not defined in the neural network. See also: , , , , , This function appears in FANN >= 2.1.0 */ activation_function_enum get_activation_function(int layer, int neuron) { unsigned int activation_function = 0; if (ann != NULL) { activation_function = fann_get_activation_function(ann, layer, neuron); } return static_cast(activation_function); } /* Method: set_activation_function Set the activation function for neuron number *neuron* in layer number *layer*, counting the input layer as layer 0. It is not possible to set activation functions for the neurons in the input layer. When choosing an activation function it is important to note that the activation functions have different range. FANN::SIGMOID is e.g. in the 0 - 1 range while FANN::SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN::LINEAR is unbound. Information about the individual activation functions is available at . The default activation function is FANN::SIGMOID_STEPWISE. See also: , , , , , This function appears in FANN >= 2.0.0. */ void set_activation_function(activation_function_enum activation_function, int layer, int neuron) { if (ann != NULL) { fann_set_activation_function(ann, static_cast(activation_function), layer, neuron); } } /* Method: set_activation_function_layer Set the activation function for all the neurons in the layer number *layer*, counting the input layer as layer 0. It is not possible to set activation functions for the neurons in the input layer. See also: , , , , This function appears in FANN >= 2.0.0. */ void set_activation_function_layer(activation_function_enum activation_function, int layer) { if (ann != NULL) { fann_set_activation_function_layer(ann, static_cast(activation_function), layer); } } /* Method: set_activation_function_hidden Set the activation function for all of the hidden layers. See also: , , , , This function appears in FANN >= 1.0.0. */ void set_activation_function_hidden(activation_function_enum activation_function) { if (ann != NULL) { fann_set_activation_function_hidden(ann, static_cast(activation_function)); } } /* Method: set_activation_function_output Set the activation function for the output layer. See also: , , , , This function appears in FANN >= 1.0.0. */ void set_activation_function_output(activation_function_enum activation_function) { if (ann != NULL) { fann_set_activation_function_output(ann, static_cast(activation_function)); } } /* Method: get_activation_steepness Get the activation steepness for neuron number *neuron* in layer number *layer*, counting the input layer as layer 0. It is not possible to get activation steepness for the neurons in the input layer. The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training. When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0). The default activation steepness is 0.5. Returns: The activation steepness for the neuron or -1 if the neuron is not defined in the neural network. See also: , , , , , This function appears in FANN >= 2.1.0 */ fann_type get_activation_steepness(int layer, int neuron) { fann_type activation_steepness = 0; if (ann != NULL) { activation_steepness = fann_get_activation_steepness(ann, layer, neuron); } return activation_steepness; } /* Method: set_activation_steepness Set the activation steepness for neuron number *neuron* in layer number *layer*, counting the input layer as layer 0. It is not possible to set activation steepness for the neurons in the input layer. The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training. When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0). The default activation steepness is 0.5. See also: , , , , , This function appears in FANN >= 2.0.0. */ void set_activation_steepness(fann_type steepness, int layer, int neuron) { if (ann != NULL) { fann_set_activation_steepness(ann, steepness, layer, neuron); } } /* Method: set_activation_steepness_layer Set the activation steepness all of the neurons in layer number *layer*, counting the input layer as layer 0. It is not possible to set activation steepness for the neurons in the input layer. See also: , , , , This function appears in FANN >= 2.0.0. */ void set_activation_steepness_layer(fann_type steepness, int layer) { if (ann != NULL) { fann_set_activation_steepness_layer(ann, steepness, layer); } } /* Method: set_activation_steepness_hidden Set the steepness of the activation steepness in all of the hidden layers. See also: , , , , This function appears in FANN >= 1.2.0. */ void set_activation_steepness_hidden(fann_type steepness) { if (ann != NULL) { fann_set_activation_steepness_hidden(ann, steepness); } } /* Method: set_activation_steepness_output Set the steepness of the activation steepness in the output layer. See also: , , , , This function appears in FANN >= 1.2.0. */ void set_activation_steepness_output(fann_type steepness) { if (ann != NULL) { fann_set_activation_steepness_output(ann, steepness); } } /*************************************************************************************************************/ /* Method: get_train_error_function Returns the error function used during training. The error functions is described further in The default error function is FANN::ERRORFUNC_TANH See also: , This function appears in FANN >= 1.2.0. */ error_function_enum get_train_error_function() { fann_errorfunc_enum train_error_function = FANN_ERRORFUNC_LINEAR; if (ann != NULL) { train_error_function = fann_get_train_error_function(ann); } return static_cast(train_error_function); } /* Method: set_train_error_function Set the error function used during training. The error functions is described further in See also: , This function appears in FANN >= 1.2.0. */ void set_train_error_function(error_function_enum train_error_function) { if (ann != NULL) { fann_set_train_error_function(ann, static_cast(train_error_function)); } } /* Method: get_quickprop_decay The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training. This is used to make sure that the weights do not become too high during training. The default decay is -0.0001. See also: , This function appears in FANN >= 1.2.0. */ float get_quickprop_decay() { float quickprop_decay = 0.0f; if (ann != NULL) { quickprop_decay = fann_get_quickprop_decay(ann); } return quickprop_decay; } /* Method: set_quickprop_decay Sets the quickprop decay factor. See also: , This function appears in FANN >= 1.2.0. */ void set_quickprop_decay(float quickprop_decay) { if (ann != NULL) { fann_set_quickprop_decay(ann, quickprop_decay); } } /* Method: get_quickprop_mu The mu factor is used to increase and decrease the step-size during quickprop training. The mu factor should always be above 1, since it would otherwise decrease the step-size when it was suppose to increase it. The default mu factor is 1.75. See also: , This function appears in FANN >= 1.2.0. */ float get_quickprop_mu() { float quickprop_mu = 0.0f; if (ann != NULL) { quickprop_mu = fann_get_quickprop_mu(ann); } return quickprop_mu; } /* Method: set_quickprop_mu Sets the quickprop mu factor. See also: , This function appears in FANN >= 1.2.0. */ void set_quickprop_mu(float quickprop_mu) { if (ann != NULL) { fann_set_quickprop_mu(ann, quickprop_mu); } } /* Method: get_rprop_increase_factor The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training. The default increase factor is 1.2. See also: , This function appears in FANN >= 1.2.0. */ float get_rprop_increase_factor() { float factor = 0.0f; if (ann != NULL) { factor = fann_get_rprop_increase_factor(ann); } return factor; } /* Method: set_rprop_increase_factor The increase factor used during RPROP training. See also: , This function appears in FANN >= 1.2.0. */ void set_rprop_increase_factor(float rprop_increase_factor) { if (ann != NULL) { fann_set_rprop_increase_factor(ann, rprop_increase_factor); } } /* Method: get_rprop_decrease_factor The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training. The default decrease factor is 0.5. See also: , This function appears in FANN >= 1.2.0. */ float get_rprop_decrease_factor() { float factor = 0.0f; if (ann != NULL) { factor = fann_get_rprop_decrease_factor(ann); } return factor; } /* Method: set_rprop_decrease_factor The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training. See also: , This function appears in FANN >= 1.2.0. */ void set_rprop_decrease_factor(float rprop_decrease_factor) { if (ann != NULL) { fann_set_rprop_decrease_factor(ann, rprop_decrease_factor); } } /* Method: get_rprop_delta_min The minimum step-size is a small positive number determining how small the minimum step-size may be. The default value delta min is 0.0. See also: , This function appears in FANN >= 1.2.0. */ float get_rprop_delta_min() { float delta = 0.0f; if (ann != NULL) { delta = fann_get_rprop_delta_min(ann); } return delta; } /* Method: set_rprop_delta_min The minimum step-size is a small positive number determining how small the minimum step-size may be. See also: , This function appears in FANN >= 1.2.0. */ void set_rprop_delta_min(float rprop_delta_min) { if (ann != NULL) { fann_set_rprop_delta_min(ann, rprop_delta_min); } } /* Method: get_rprop_delta_max The maximum step-size is a positive number determining how large the maximum step-size may be. The default delta max is 50.0. See also: , , This function appears in FANN >= 1.2.0. */ float get_rprop_delta_max() { float delta = 0.0f; if (ann != NULL) { delta = fann_get_rprop_delta_max(ann); } return delta; } /* Method: set_rprop_delta_max The maximum step-size is a positive number determining how large the maximum step-size may be. See also: , , This function appears in FANN >= 1.2.0. */ void set_rprop_delta_max(float rprop_delta_max) { if (ann != NULL) { fann_set_rprop_delta_max(ann, rprop_delta_max); } } /* Method: get_num_input Get the number of input neurons. This function appears in FANN >= 1.0.0. */ unsigned int get_num_input() { unsigned int num_input = 0; if (ann != NULL) { num_input = fann_get_num_input(ann); } return num_input; } /* Method: get_num_output Get the number of output neurons. This function appears in FANN >= 1.0.0. */ unsigned int get_num_output() { unsigned int num_output = 0; if (ann != NULL) { num_output = fann_get_num_output(ann); } return num_output; } /* Method: get_total_neurons Get the total number of neurons in the entire network. This number does also include the bias neurons, so a 2-4-2 network has 2+4+2 +2(bias) = 10 neurons. This function appears in FANN >= 1.0.0. */ unsigned int get_total_neurons() { if (ann == NULL) { return 0; } return fann_get_total_neurons(ann); } /* Method: get_total_connections Get the total number of connections in the entire network. This function appears in FANN >= 1.0.0. */ unsigned int get_total_connections() { if (ann == NULL) { return 0; } return fann_get_total_connections(ann); } #ifdef FIXEDFANN /* Method: get_decimal_point Returns the position of the decimal point in the ann. This function is only available when the ANN is in fixed point mode. The decimal point is described in greater detail in the tutorial . See also: , , , , This function appears in FANN >= 1.0.0. */ unsigned int get_decimal_point() { if (ann == NULL) { return 0; } return fann_get_decimal_point(ann); } /* Method: get_multiplier Returns the multiplier that fix point data is multiplied with. This function is only available when the ANN is in fixed point mode. The multiplier is the used to convert between floating point and fixed point notation. A floating point number is multiplied with the multiplier in order to get the fixed point number and visa versa. The multiplier is described in greater detail in the tutorial . See also: , , , , This function appears in FANN >= 1.0.0. */ unsigned int get_multiplier() { if (ann == NULL) { return 0; } return fann_get_multiplier(ann); } #endif /* FIXEDFANN */ /*********************************************************************/ /* Method: get_network_type Get the type of neural network it was created as. Returns: The neural network type from enum See Also: This function appears in FANN >= 2.1.0 */ network_type_enum get_network_type() { fann_nettype_enum network_type = FANN_NETTYPE_LAYER; if (ann != NULL) { network_type = fann_get_network_type(ann); } return static_cast(network_type); } /* Method: get_connection_rate Get the connection rate used when the network was created Returns: The connection rate See also: This function appears in FANN >= 2.1.0 */ float get_connection_rate() { if (ann == NULL) { return 0; } return fann_get_connection_rate(ann); } /* Method: get_num_layers Get the number of layers in the network Returns: The number of layers in the neural network See also: This function appears in FANN >= 2.1.0 */ unsigned int get_num_layers() { if (ann == NULL) { return 0; } return fann_get_num_layers(ann); } /* Method: get_layer_array Get the number of neurons in each layer in the network. Bias is not included so the layers match the create methods. The layers array must be preallocated to at least sizeof(unsigned int) * get_num_layers() long. See also: This function appears in FANN >= 2.1.0 */ void get_layer_array(unsigned int *layers) { if (ann != NULL) { fann_get_layer_array(ann, layers); } } /* Method: get_bias_array Get the number of bias in each layer in the network. The bias array must be preallocated to at least sizeof(unsigned int) * get_num_layers() long. See also: This function appears in FANN >= 2.1.0 */ void get_bias_array(unsigned int *bias) { if (ann != NULL) { fann_get_bias_array(ann, bias); } } /* Method: get_connection_array Get the connections in the network. The connections array must be preallocated to at least sizeof(struct fann_connection) * get_total_connections() long. See also: This function appears in FANN >= 2.1.0 */ void get_connection_array(connection *connections) { if (ann != NULL) { fann_get_connection_array(ann, connections); } } /* Method: set_weight_array Set connections in the network. Only the weights can be changed, connections and weights are ignored if they do not already exist in the network. The array must have sizeof(struct fann_connection) * num_connections size. See also: This function appears in FANN >= 2.1.0 */ void set_weight_array(connection *connections, unsigned int num_connections) { if (ann != NULL) { fann_set_weight_array(ann, connections, num_connections); } } /* Method: set_weight Set a connection in the network. Only the weights can be changed. The connection/weight is ignored if it does not already exist in the network. See also: This function appears in FANN >= 2.1.0 */ void set_weight(unsigned int from_neuron, unsigned int to_neuron, fann_type weight) { if (ann != NULL) { fann_set_weight(ann, from_neuron, to_neuron, weight); } } /*********************************************************************/ /* Method: get_learning_momentum Get the learning momentum. The learning momentum can be used to speed up FANN::TRAIN_INCREMENTAL training. A too high momentum will however not benefit training. Setting momentum to 0 will be the same as not using the momentum parameter. The recommended value of this parameter is between 0.0 and 1.0. The default momentum is 0. See also: , This function appears in FANN >= 2.0.0. */ float get_learning_momentum() { float learning_momentum = 0.0f; if (ann != NULL) { learning_momentum = fann_get_learning_momentum(ann); } return learning_momentum; } /* Method: set_learning_momentum Set the learning momentum. More info available in This function appears in FANN >= 2.0.0. */ void set_learning_momentum(float learning_momentum) { if (ann != NULL) { fann_set_learning_momentum(ann, learning_momentum); } } /* Method: get_train_stop_function Returns the the stop function used during training. The stop function is described further in The default stop function is FANN::STOPFUNC_MSE See also: , This function appears in FANN >= 2.0.0. */ stop_function_enum get_train_stop_function() { enum fann_stopfunc_enum stopfunc = FANN_STOPFUNC_MSE; if (ann != NULL) { stopfunc = fann_get_train_stop_function(ann); } return static_cast(stopfunc); } /* Method: set_train_stop_function Set the stop function used during training. The stop function is described further in See also: This function appears in FANN >= 2.0.0. */ void set_train_stop_function(stop_function_enum train_stop_function) { if (ann != NULL) { fann_set_train_stop_function(ann, static_cast(train_stop_function)); } } /* Method: get_bit_fail_limit Returns the bit fail limit used during training. The bit fail limit is used during training when the is set to FANN_STOPFUNC_BIT. The limit is the maximum accepted difference between the desired output and the actual output during training. Each output that diverges more than this limit is counted as an error bit. This difference is divided by two when dealing with symmetric activation functions, so that symmetric and not symmetric activation functions can use the same limit. The default bit fail limit is 0.35. See also: This function appears in FANN >= 2.0.0. */ fann_type get_bit_fail_limit() { fann_type bit_fail_limit = 0.0f; if (ann != NULL) { bit_fail_limit = fann_get_bit_fail_limit(ann); } return bit_fail_limit; } /* Method: set_bit_fail_limit Set the bit fail limit used during training. See also: This function appears in FANN >= 2.0.0. */ void set_bit_fail_limit(fann_type bit_fail_limit) { if (ann != NULL) { fann_set_bit_fail_limit(ann, bit_fail_limit); } } /* Method: get_bit_fail The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see , ). The bits are counted in all of the training data, so this number can be higher than the number of training data. This value is reset by and updated by all the same functions which also updates the MSE value (e.g. , ) See also: , This function appears in FANN >= 2.0.0 */ unsigned int get_bit_fail() { unsigned int bit_fail = 0; if (ann != NULL) { bit_fail = fann_get_bit_fail(ann); } return bit_fail; } /*********************************************************************/ /* Method: cascadetrain_on_data Trains on an entire dataset, for a period of time using the Cascade2 training algorithm. This algorithm adds neurons to the neural network while training, which means that it needs to start with an ANN without any hidden layers. The neural network should also use shortcut connections, so should be used to create the ANN like this: >net.create_shortcut(2, train_data.num_input_train_data(), train_data.num_output_train_data()); This training uses the parameters set using the set_cascade_..., but it also uses another training algorithm as it's internal training algorithm. This algorithm can be set to either FANN::TRAIN_RPROP or FANN::TRAIN_QUICKPROP by , and the parameters set for these training algorithms will also affect the cascade training. Parameters: data - The data, which should be used during training max_neuron - The maximum number of neurons to be added to neural network neurons_between_reports - The number of neurons between printing a status report to stdout. A value of zero means no reports should be printed. desired_error - The desired or , depending on which stop function is chosen by . Instead of printing out reports every neurons_between_reports, a callback function can be called (see ). See also: , , This function appears in FANN >= 2.0.0. */ void cascadetrain_on_data(const training_data &data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error) { if ((ann != NULL) && (data.train_data != NULL)) { fann_cascadetrain_on_data(ann, data.train_data, max_neurons, neurons_between_reports, desired_error); } } /* Method: cascadetrain_on_file Does the same as , but reads the training data directly from a file. See also: , This function appears in FANN >= 2.0.0. */ void cascadetrain_on_file(const std::string &filename, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error) { if (ann != NULL) { fann_cascadetrain_on_file(ann, filename.c_str(), max_neurons, neurons_between_reports, desired_error); } } /* Method: get_cascade_output_change_fraction The cascade output change fraction is a number between 0 and 1 determining how large a fraction the value should change within during training of the output connections, in order for the training not to stagnate. If the training stagnates, the training of the output connections will be ended and new candidates will be prepared. This means: If the MSE does not change by a fraction of during a period of , the training of the output connections is stopped because the training has stagnated. If the cascade output change fraction is low, the output connections will be trained more and if the fraction is high they will be trained less. The default cascade output change fraction is 0.01, which is equalent to a 1% change in MSE. See also: , , , This function appears in FANN >= 2.0.0. */ float get_cascade_output_change_fraction() { float change_fraction = 0.0f; if (ann != NULL) { change_fraction = fann_get_cascade_output_change_fraction(ann); } return change_fraction; } /* Method: set_cascade_output_change_fraction Sets the cascade output change fraction. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_output_change_fraction(float cascade_output_change_fraction) { if (ann != NULL) { fann_set_cascade_output_change_fraction(ann, cascade_output_change_fraction); } } /* Method: get_cascade_output_stagnation_epochs The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of . See more info about this parameter in . The default number of cascade output stagnation epochs is 12. See also: , , This function appears in FANN >= 2.0.0. */ unsigned int get_cascade_output_stagnation_epochs() { unsigned int stagnation_epochs = 0; if (ann != NULL) { stagnation_epochs = fann_get_cascade_output_stagnation_epochs(ann); } return stagnation_epochs; } /* Method: set_cascade_output_stagnation_epochs Sets the number of cascade output stagnation epochs. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_output_stagnation_epochs(unsigned int cascade_output_stagnation_epochs) { if (ann != NULL) { fann_set_cascade_output_stagnation_epochs(ann, cascade_output_stagnation_epochs); } } /* Method: get_cascade_candidate_change_fraction The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the value should change within during training of the candidate neurons, in order for the training not to stagnate. If the training stagnates, the training of the candidate neurons will be ended and the best candidate will be selected. This means: If the MSE does not change by a fraction of during a period of , the training of the candidate neurons is stopped because the training has stagnated. If the cascade candidate change fraction is low, the candidate neurons will be trained more and if the fraction is high they will be trained less. The default cascade candidate change fraction is 0.01, which is equalent to a 1% change in MSE. See also: , , , This function appears in FANN >= 2.0.0. */ float get_cascade_candidate_change_fraction() { float change_fraction = 0.0f; if (ann != NULL) { change_fraction = fann_get_cascade_candidate_change_fraction(ann); } return change_fraction; } /* Method: set_cascade_candidate_change_fraction Sets the cascade candidate change fraction. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_candidate_change_fraction(float cascade_candidate_change_fraction) { if (ann != NULL) { fann_set_cascade_candidate_change_fraction(ann, cascade_candidate_change_fraction); } } /* Method: get_cascade_candidate_stagnation_epochs The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of . See more info about this parameter in . The default number of cascade candidate stagnation epochs is 12. See also: , , This function appears in FANN >= 2.0.0. */ unsigned int get_cascade_candidate_stagnation_epochs() { unsigned int stagnation_epochs = 0; if (ann != NULL) { stagnation_epochs = fann_get_cascade_candidate_stagnation_epochs(ann); } return stagnation_epochs; } /* Method: set_cascade_candidate_stagnation_epochs Sets the number of cascade candidate stagnation epochs. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_candidate_stagnation_epochs(unsigned int cascade_candidate_stagnation_epochs) { if (ann != NULL) { fann_set_cascade_candidate_stagnation_epochs(ann, cascade_candidate_stagnation_epochs); } } /* Method: get_cascade_weight_multiplier The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network. This parameter is usually between 0 and 1, and is used to make the training a bit less aggressive. The default weight multiplier is 0.4 See also: , This function appears in FANN >= 2.0.0. */ fann_type get_cascade_weight_multiplier() { fann_type weight_multiplier = 0; if (ann != NULL) { weight_multiplier = fann_get_cascade_weight_multiplier(ann); } return weight_multiplier; } /* Method: set_cascade_weight_multiplier Sets the weight multiplier. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_weight_multiplier(fann_type cascade_weight_multiplier) { if (ann != NULL) { fann_set_cascade_weight_multiplier(ann, cascade_weight_multiplier); } } /* Method: get_cascade_candidate_limit The candidate limit is a limit for how much the candidate neuron may be trained. The limit is a limit on the proportion between the MSE and candidate score. Set this to a lower value to avoid overfitting and to a higher if overfitting is not a problem. The default candidate limit is 1000.0 See also: , This function appears in FANN >= 2.0.0. */ fann_type get_cascade_candidate_limit() { fann_type candidate_limit = 0; if (ann != NULL) { candidate_limit = fann_get_cascade_candidate_limit(ann); } return candidate_limit; } /* Method: set_cascade_candidate_limit Sets the candidate limit. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_candidate_limit(fann_type cascade_candidate_limit) { if (ann != NULL) { fann_set_cascade_candidate_limit(ann, cascade_candidate_limit); } } /* Method: get_cascade_max_out_epochs The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron. The default max out epochs is 150 See also: , This function appears in FANN >= 2.0.0. */ unsigned int get_cascade_max_out_epochs() { unsigned int max_out_epochs = 0; if (ann != NULL) { max_out_epochs = fann_get_cascade_max_out_epochs(ann); } return max_out_epochs; } /* Method: set_cascade_max_out_epochs Sets the maximum out epochs. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_max_out_epochs(unsigned int cascade_max_out_epochs) { if (ann != NULL) { fann_set_cascade_max_out_epochs(ann, cascade_max_out_epochs); } } /* Method: get_cascade_max_cand_epochs The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron. The default max candidate epochs is 150 See also: , This function appears in FANN >= 2.0.0. */ unsigned int get_cascade_max_cand_epochs() { unsigned int max_cand_epochs = 0; if (ann != NULL) { max_cand_epochs = fann_get_cascade_max_cand_epochs(ann); } return max_cand_epochs; } /* Method: set_cascade_max_cand_epochs Sets the max candidate epochs. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_max_cand_epochs(unsigned int cascade_max_cand_epochs) { if (ann != NULL) { fann_set_cascade_max_cand_epochs(ann, cascade_max_cand_epochs); } } /* Method: get_cascade_num_candidates The number of candidates used during training (calculated by multiplying , and ). The actual candidates is defined by the and arrays. These arrays define the activation functions and activation steepnesses used for the candidate neurons. If there are 2 activation functions in the activation function array and 3 steepnesses in the steepness array, then there will be 2x3=6 different candidates which will be trained. These 6 different candidates can be copied into several candidate groups, where the only difference between these groups is the initial weights. If the number of groups is set to 2, then the number of candidate neurons will be 2x3x2=12. The number of candidate groups is defined by . The default number of candidates is 6x4x2 = 48 See also: , , , , , This function appears in FANN >= 2.0.0. */ unsigned int get_cascade_num_candidates() { unsigned int num_candidates = 0; if (ann != NULL) { num_candidates = fann_get_cascade_num_candidates(ann); } return num_candidates; } /* Method: get_cascade_activation_functions_count The number of activation functions in the array. The default number of activation functions is 6. See also: , , This function appears in FANN >= 2.0.0. */ unsigned int get_cascade_activation_functions_count() { unsigned int activation_functions_count = 0; if (ann != NULL) { activation_functions_count = fann_get_cascade_activation_functions_count(ann); } return activation_functions_count; } /* Method: get_cascade_activation_functions The cascade activation functions array is an array of the different activation functions used by the candidates. See for a description of which candidate neurons will be generated by this array. See also: , , This function appears in FANN >= 2.0.0. */ activation_function_enum * get_cascade_activation_functions() { enum fann_activationfunc_enum *activation_functions = NULL; if (ann != NULL) { activation_functions = fann_get_cascade_activation_functions(ann); } return reinterpret_cast(activation_functions); } /* Method: set_cascade_activation_functions Sets the array of cascade candidate activation functions. The array must be just as long as defined by the count. See for a description of which candidate neurons will be generated by this array. See also: , , This function appears in FANN >= 2.0.0. */ void set_cascade_activation_functions(activation_function_enum *cascade_activation_functions, unsigned int cascade_activation_functions_count) { if (ann != NULL) { fann_set_cascade_activation_functions(ann, reinterpret_cast(cascade_activation_functions), cascade_activation_functions_count); } } /* Method: get_cascade_activation_steepnesses_count The number of activation steepnesses in the array. The default number of activation steepnesses is 4. See also: , , This function appears in FANN >= 2.0.0. */ unsigned int get_cascade_activation_steepnesses_count() { unsigned int activation_steepness_count = 0; if (ann != NULL) { activation_steepness_count = fann_get_cascade_activation_steepnesses_count(ann); } return activation_steepness_count; } /* Method: get_cascade_activation_steepnesses The cascade activation steepnesses array is an array of the different activation functions used by the candidates. See for a description of which candidate neurons will be generated by this array. The default activation steepnesses is {0.25, 0.50, 0.75, 1.00} See also: , , This function appears in FANN >= 2.0.0. */ fann_type *get_cascade_activation_steepnesses() { fann_type *activation_steepnesses = NULL; if (ann != NULL) { activation_steepnesses = fann_get_cascade_activation_steepnesses(ann); } return activation_steepnesses; } /* Method: set_cascade_activation_steepnesses Sets the array of cascade candidate activation steepnesses. The array must be just as long as defined by the count. See for a description of which candidate neurons will be generated by this array. See also: , , This function appears in FANN >= 2.0.0. */ void set_cascade_activation_steepnesses(fann_type *cascade_activation_steepnesses, unsigned int cascade_activation_steepnesses_count) { if (ann != NULL) { fann_set_cascade_activation_steepnesses(ann, cascade_activation_steepnesses, cascade_activation_steepnesses_count); } } /* Method: get_cascade_num_candidate_groups The number of candidate groups is the number of groups of identical candidates which will be used during training. This number can be used to have more candidates without having to define new parameters for the candidates. See for a description of which candidate neurons will be generated by this parameter. The default number of candidate groups is 2 See also: , This function appears in FANN >= 2.0.0. */ unsigned int get_cascade_num_candidate_groups() { unsigned int num_candidate_groups = 0; if (ann != NULL) { num_candidate_groups = fann_get_cascade_num_candidate_groups(ann); } return num_candidate_groups; } /* Method: set_cascade_num_candidate_groups Sets the number of candidate groups. See also: , This function appears in FANN >= 2.0.0. */ void set_cascade_num_candidate_groups(unsigned int cascade_num_candidate_groups) { if (ann != NULL) { fann_set_cascade_num_candidate_groups(ann, cascade_num_candidate_groups); } } /*********************************************************************/ #ifndef FIXEDFANN /* Method: scale_train Scale input and output data based on previously calculated parameters. See also: , This function appears in FANN >= 2.1.0. */ void scale_train(training_data &data) { if (ann != NULL) { fann_scale_train(ann, data.train_data); } } /* Method: descale_train Descale input and output data based on previously calculated parameters. See also: , This function appears in FANN >= 2.1.0. */ void descale_train(training_data &data) { if (ann != NULL) { fann_descale_train(ann, data.train_data); } } /* Method: set_input_scaling_params Calculate scaling parameters for future use based on training data. See also: , This function appears in FANN >= 2.1.0. */ bool set_input_scaling_params(const training_data &data, float new_input_min, float new_input_max) { bool status = false; if (ann != NULL) { status = (fann_set_input_scaling_params(ann, data.train_data, new_input_min, new_input_max) != -1); } return status; } /* Method: set_output_scaling_params Calculate scaling parameters for future use based on training data. See also: , This function appears in FANN >= 2.1.0. */ bool set_output_scaling_params(const training_data &data, float new_output_min, float new_output_max) { bool status = false; if (ann != NULL) { status = (fann_set_output_scaling_params(ann, data.train_data, new_output_min, new_output_max) != -1); } return status; } /* Method: set_scaling_params Calculate scaling parameters for future use based on training data. See also: , This function appears in FANN >= 2.1.0. */ bool set_scaling_params(const training_data &data, float new_input_min, float new_input_max, float new_output_min, float new_output_max) { bool status = false; if (ann != NULL) { status = (fann_set_scaling_params(ann, data.train_data, new_input_min, new_input_max, new_output_min, new_output_max) != -1); } return status; } /* Method: clear_scaling_params Clears scaling parameters. See also: , This function appears in FANN >= 2.1.0. */ bool clear_scaling_params() { bool status = false; if (ann != NULL) { status = (fann_clear_scaling_params(ann) != -1); } return status; } /* Method: scale_input Scale data in input vector before feed it to ann based on previously calculated parameters. See also: , , This function appears in FANN >= 2.1.0. */ void scale_input(fann_type *input_vector) { if (ann != NULL) { fann_scale_input(ann, input_vector ); } } /* Method: scale_output Scale data in output vector before feed it to ann based on previously calculated parameters. See also: , , This function appears in FANN >= 2.1.0. */ void scale_output(fann_type *output_vector) { if (ann != NULL) { fann_scale_output(ann, output_vector ); } } /* Method: descale_input Scale data in input vector after get it from ann based on previously calculated parameters. See also: , , This function appears in FANN >= 2.1.0. */ void descale_input(fann_type *input_vector) { if (ann != NULL) { fann_descale_input(ann, input_vector ); } } /* Method: descale_output Scale data in output vector after get it from ann based on previously calculated parameters. See also: , , This function appears in FANN >= 2.1.0. */ void descale_output(fann_type *output_vector) { if (ann != NULL) { fann_descale_output(ann, output_vector ); } } #endif /* FIXEDFANN */ /*********************************************************************/ /* Method: set_error_log Change where errors are logged to. If log_file is NULL, no errors will be printed. If neural_net is empty i.e. ann is NULL, the default log will be set. The default log is the log used when creating a neural_net. This default log will also be the default for all new structs that are created. The default behavior is to log them to stderr. See also: , This function appears in FANN >= 1.1.0. */ void set_error_log(FILE *log_file) { fann_set_error_log(reinterpret_cast(ann), log_file); } /* Method: get_errno Returns the last error number. See also: , , This function appears in FANN >= 1.1.0. */ unsigned int get_errno() { return fann_get_errno(reinterpret_cast(ann)); } /* Method: reset_errno Resets the last error number. This function appears in FANN >= 1.1.0. */ void reset_errno() { fann_reset_errno(reinterpret_cast(ann)); } /* Method: reset_errstr Resets the last error string. This function appears in FANN >= 1.1.0. */ void reset_errstr() { fann_reset_errstr(reinterpret_cast(ann)); } /* Method: get_errstr Returns the last errstr. This function calls and This function appears in FANN >= 1.1.0. */ std::string get_errstr() { return std::string(fann_get_errstr(reinterpret_cast(ann))); } /* Method: print_error Prints the last error to stderr. This function appears in FANN >= 1.1.0. */ void print_error() { fann_print_error(reinterpret_cast(ann)); } /*********************************************************************/ private: // Structure used by set_callback to hold information about a user callback typedef struct user_context_type { callback_type user_callback; // Pointer to user callback function void *user_data; // Arbitrary data pointer passed to the callback neural_net *net; // This pointer for the neural network } user_context; // Internal callback used to convert from pointers to class references static int FANN_API internal_callback(struct fann *ann, struct fann_train_data *train, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error, unsigned int epochs) { user_context *user_data = static_cast(fann_get_user_data(ann)); if (user_data != NULL) { FANN::training_data data; data.train_data = train; int result = (*user_data->user_callback)(*user_data->net, data, max_epochs, epochs_between_reports, desired_error, epochs, user_data); data.train_data = NULL; // Prevent automatic cleanup return result; } else { return -1; // This should not occur except if out of memory } } protected: // Pointer the encapsulated fann neural net structure struct fann *ann; }; /*************************************************************************/ }; #endif /* FANN_CPP_H_INCLUDED */ libfann-2.1.0~beta~dfsg/src/include/fann_data.h000066400000000000000000000613001140315177100215150ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __fann_data_h__ #define __fann_data_h__ #include /* Section: FANN Datatypes The two main datatypes used in the fann library is , which represents an artificial neural network, and , which represent training data. */ /* Type: fann_type fann_type is the type used for the weights, inputs and outputs of the neural network. fann_type is defined as a: float - if you include fann.h or floatfann.h double - if you include doublefann.h int - if you include fixedfann.h (please be aware that fixed point usage is only to be used during execution, and not during training). */ /* Enum: fann_train_enum The Training algorithms used when training on with functions like or . The incremental training looks alters the weights after each time it is presented an input pattern, while batch only alters the weights once after it has been presented to all the patterns. FANN_TRAIN_INCREMENTAL - Standard backpropagation algorithm, where the weights are updated after each training pattern. This means that the weights are updated many times during a single epoch. For this reason some problems, will train very fast with this algorithm, while other more advanced problems will not train very well. FANN_TRAIN_BATCH - Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set. This means that the weights are only updated once during a epoch. For this reason some problems, will train slower with this algorithm. But since the mean square error is calculated more correctly than in incremental training, some problems will reach a better solutions with this algorithm. FANN_TRAIN_RPROP - A more advanced batch training algorithm which achieves good results for many problems. The RPROP training algorithm is adaptive, and does therefore not use the learning_rate. Some other parameters can however be set to change the way the RPROP algorithm works, but it is only recommended for users with insight in how the RPROP training algorithm works. The RPROP training algorithm is described by [Riedmiller and Braun, 1993], but the actual learning algorithm used here is the iRPROP- training algorithm which is described by [Igel and Husken, 2000] which is an variety of the standard RPROP training algorithm. FANN_TRAIN_QUICKPROP - A more advanced batch training algorithm which achieves good results for many problems. The quickprop training algorithm uses the learning_rate parameter along with other more advanced parameters, but it is only recommended to change these advanced parameters, for users with insight in how the quickprop training algorithm works. The quickprop training algorithm is described by [Fahlman, 1988]. See also: , */ enum fann_train_enum { FANN_TRAIN_INCREMENTAL = 0, FANN_TRAIN_BATCH, FANN_TRAIN_RPROP, FANN_TRAIN_QUICKPROP }; /* Constant: FANN_TRAIN_NAMES Constant array consisting of the names for the training algorithms, so that the name of an training function can be received by: (code) char *name = FANN_TRAIN_NAMES[train_function]; (end) See Also: */ static char const *const FANN_TRAIN_NAMES[] = { "FANN_TRAIN_INCREMENTAL", "FANN_TRAIN_BATCH", "FANN_TRAIN_RPROP", "FANN_TRAIN_QUICKPROP" }; /* Enums: fann_activationfunc_enum The activation functions used for the neurons during training. The activation functions can either be defined for a group of neurons by and or it can be defined for a single neuron by . The steepness of an activation function is defined in the same way by , and . The functions are described with functions where: * x is the input to the activation function, * y is the output, * s is the steepness and * d is the derivation. FANN_LINEAR - Linear activation function. * span: -inf < y < inf * y = x*s, d = 1*s * Can NOT be used in fixed point. FANN_THRESHOLD - Threshold activation function. * x < 0 -> y = 0, x >= 0 -> y = 1 * Can NOT be used during training. FANN_THRESHOLD_SYMMETRIC - Threshold activation function. * x < 0 -> y = 0, x >= 0 -> y = 1 * Can NOT be used during training. FANN_SIGMOID - Sigmoid activation function. * One of the most used activation functions. * span: 0 < y < 1 * y = 1/(1 + exp(-2*s*x)) * d = 2*s*y*(1 - y) FANN_SIGMOID_STEPWISE - Stepwise linear approximation to sigmoid. * Faster than sigmoid but a bit less precise. FANN_SIGMOID_SYMMETRIC - Symmetric sigmoid activation function, aka. tanh. * One of the most used activation functions. * span: -1 < y < 1 * y = tanh(s*x) = 2/(1 + exp(-2*s*x)) - 1 * d = s*(1-(y*y)) FANN_SIGMOID_SYMMETRIC - Stepwise linear approximation to symmetric sigmoid. * Faster than symmetric sigmoid but a bit less precise. FANN_GAUSSIAN - Gaussian activation function. * 0 when x = -inf, 1 when x = 0 and 0 when x = inf * span: 0 < y < 1 * y = exp(-x*s*x*s) * d = -2*x*s*y*s FANN_GAUSSIAN_SYMMETRIC - Symmetric gaussian activation function. * -1 when x = -inf, 1 when x = 0 and 0 when x = inf * span: -1 < y < 1 * y = exp(-x*s*x*s)*2-1 * d = -2*x*s*(y+1)*s FANN_ELLIOT - Fast (sigmoid like) activation function defined by David Elliott * span: 0 < y < 1 * y = ((x*s) / 2) / (1 + |x*s|) + 0.5 * d = s*1/(2*(1+|x*s|)*(1+|x*s|)) FANN_ELLIOT_SYMMETRIC - Fast (symmetric sigmoid like) activation function defined by David Elliott * span: -1 < y < 1 * y = (x*s) / (1 + |x*s|) * d = s*1/((1+|x*s|)*(1+|x*s|)) FANN_LINEAR_PIECE - Bounded linear activation function. * span: 0 <= y <= 1 * y = x*s, d = 1*s FANN_LINEAR_PIECE_SYMMETRIC - Bounded linear activation function. * span: -1 <= y <= 1 * y = x*s, d = 1*s FANN_SIN_SYMMETRIC - Periodical sinus activation function. * span: -1 <= y <= 1 * y = sin(x*s) * d = s*cos(x*s) FANN_COS_SYMMETRIC - Periodical cosinus activation function. * span: -1 <= y <= 1 * y = cos(x*s) * d = s*-sin(x*s) FANN_SIN - Periodical sinus activation function. * span: 0 <= y <= 1 * y = sin(x*s)/2+0.5 * d = s*cos(x*s)/2 FANN_COS - Periodical cosinus activation function. * span: 0 <= y <= 1 * y = cos(x*s)/2+0.5 * d = s*-sin(x*s)/2 See also: , , , , */ enum fann_activationfunc_enum { FANN_LINEAR = 0, FANN_THRESHOLD, FANN_THRESHOLD_SYMMETRIC, FANN_SIGMOID, FANN_SIGMOID_STEPWISE, FANN_SIGMOID_SYMMETRIC, FANN_SIGMOID_SYMMETRIC_STEPWISE, FANN_GAUSSIAN, FANN_GAUSSIAN_SYMMETRIC, /* Stepwise linear approximation to gaussian. * Faster than gaussian but a bit less precise. * NOT implemented yet. */ FANN_GAUSSIAN_STEPWISE, FANN_ELLIOT, FANN_ELLIOT_SYMMETRIC, FANN_LINEAR_PIECE, FANN_LINEAR_PIECE_SYMMETRIC, FANN_SIN_SYMMETRIC, FANN_COS_SYMMETRIC, FANN_SIN, FANN_COS }; /* Constant: FANN_ACTIVATIONFUNC_NAMES Constant array consisting of the names for the activation function, so that the name of an activation function can be received by: (code) char *name = FANN_ACTIVATIONFUNC_NAMES[activation_function]; (end) See Also: */ static char const *const FANN_ACTIVATIONFUNC_NAMES[] = { "FANN_LINEAR", "FANN_THRESHOLD", "FANN_THRESHOLD_SYMMETRIC", "FANN_SIGMOID", "FANN_SIGMOID_STEPWISE", "FANN_SIGMOID_SYMMETRIC", "FANN_SIGMOID_SYMMETRIC_STEPWISE", "FANN_GAUSSIAN", "FANN_GAUSSIAN_SYMMETRIC", "FANN_GAUSSIAN_STEPWISE", "FANN_ELLIOT", "FANN_ELLIOT_SYMMETRIC", "FANN_LINEAR_PIECE", "FANN_LINEAR_PIECE_SYMMETRIC", "FANN_SIN_SYMMETRIC", "FANN_COS_SYMMETRIC", "FANN_SIN", "FANN_COS" }; /* Enum: fann_errorfunc_enum Error function used during training. FANN_ERRORFUNC_LINEAR - Standard linear error function. FANN_ERRORFUNC_TANH - Tanh error function, usually better but can require a lower learning rate. This error function agressively targets outputs that differ much from the desired, while not targetting outputs that only differ a little that much. This activation function is not recommended for cascade training and incremental training. See also: , */ enum fann_errorfunc_enum { FANN_ERRORFUNC_LINEAR = 0, FANN_ERRORFUNC_TANH }; /* Constant: FANN_ERRORFUNC_NAMES Constant array consisting of the names for the training error functions, so that the name of an error function can be received by: (code) char *name = FANN_ERRORFUNC_NAMES[error_function]; (end) See Also: */ static char const *const FANN_ERRORFUNC_NAMES[] = { "FANN_ERRORFUNC_LINEAR", "FANN_ERRORFUNC_TANH" }; /* Enum: fann_stopfunc_enum Stop criteria used during training. FANN_STOPFUNC_MSE - Stop criteria is Mean Square Error (MSE) value. FANN_STOPFUNC_BIT - Stop criteria is number of bits that fail. The number of bits; means the number of output neurons which differ more than the bit fail limit (see , ). The bits are counted in all of the training data, so this number can be higher than the number of training data. See also: , */ enum fann_stopfunc_enum { FANN_STOPFUNC_MSE = 0, FANN_STOPFUNC_BIT }; /* Constant: FANN_STOPFUNC_NAMES Constant array consisting of the names for the training stop functions, so that the name of a stop function can be received by: (code) char *name = FANN_STOPFUNC_NAMES[stop_function]; (end) See Also: */ static char const *const FANN_STOPFUNC_NAMES[] = { "FANN_STOPFUNC_MSE", "FANN_STOPFUNC_BIT" }; /* Enum: fann_network_type_enum Definition of network types used by FANN_NETTYPE_LAYER - Each layer only has connections to the next layer FANN_NETTYPE_SHORTCUT - Each layer has connections to all following layers See Also: This enumeration appears in FANN >= 2.1.0 */ enum fann_nettype_enum { FANN_NETTYPE_LAYER = 0, /* Each layer only has connections to the next layer */ FANN_NETTYPE_SHORTCUT /* Each layer has connections to all following layers */ }; /* Constant: FANN_NETWORK_TYPE_NAMES Constant array consisting of the names for the network types, so that the name of an network type can be received by: (code) char *network_type_name = FANN_NETWORK_TYPE_NAMES[fann_get_network_type(ann)]; (end) See Also: This constant appears in FANN >= 2.1.0 */ static char const *const FANN_NETTYPE_NAMES[] = { "FANN_NETTYPE_LAYER", "FANN_NETTYPE_SHORTCUT" }; /* forward declarations for use with the callback */ struct fann; struct fann_train_data; /* Type: fann_callback_type This callback function can be called during training when using , or . >typedef int (FANN_API * fann_callback_type) (struct fann *ann, struct fann_train_data *train, > unsigned int max_epochs, > unsigned int epochs_between_reports, > float desired_error, unsigned int epochs); The callback can be set by using and is very usefull for doing custom things during training. It is recommended to use this function when implementing custom training procedures, or when visualizing the training in a GUI etc. The parameters which the callback function takes is the parameters given to the , plus an epochs parameter which tells how many epochs the training have taken so far. The callback function should return an integer, if the callback function returns -1, the training will terminate. Example of a callback function: >int FANN_API test_callback(struct fann *ann, struct fann_train_data *train, > unsigned int max_epochs, unsigned int epochs_between_reports, > float desired_error, unsigned int epochs) >{ > printf("Epochs %8d. MSE: %.5f. Desired-MSE: %.5f\n", epochs, fann_get_MSE(ann), desired_error); > return 0; >} See also: , */ FANN_EXTERNAL typedef int (FANN_API * fann_callback_type) (struct fann *ann, struct fann_train_data *train, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error, unsigned int epochs); /* ----- Data structures ----- * No data within these structures should be altered directly by the user. */ struct fann_neuron { /* Index to the first and last connection * (actually the last is a past end index) */ unsigned int first_con; unsigned int last_con; /* The sum of the inputs multiplied with the weights */ fann_type sum; /* The value of the activation function applied to the sum */ fann_type value; /* The steepness of the activation function */ fann_type activation_steepness; /* Used to choose which activation function to use */ enum fann_activationfunc_enum activation_function; #ifdef __GNUC__ } __attribute__ ((packed)); #else }; #endif /* A single layer in the neural network. */ struct fann_layer { /* A pointer to the first neuron in the layer * When allocated, all the neurons in all the layers are actually * in one long array, this is because we wan't to easily clear all * the neurons at once. */ struct fann_neuron *first_neuron; /* A pointer to the neuron past the last neuron in the layer */ /* the number of neurons is last_neuron - first_neuron */ struct fann_neuron *last_neuron; }; /* Struct: struct fann_error Structure used to store error-related information, both and can be casted to this type. See also: , */ struct fann_error { enum fann_errno_enum errno_f; FILE *error_log; char *errstr; }; /* Struct: struct fann The fast artificial neural network(fann) structure. Data within this structure should never be accessed directly, but only by using the *fann_get_...* and *fann_set_...* functions. The fann structure is created using one of the *fann_create_...* functions and each of the functions which operates on the structure takes *struct fann * ann* as the first parameter. See also: , */ struct fann { /* The type of error that last occured. */ enum fann_errno_enum errno_f; /* Where to log error messages. */ FILE *error_log; /* A string representation of the last error. */ char *errstr; /* the learning rate of the network */ float learning_rate; /* The learning momentum used for backpropagation algorithm. */ float learning_momentum; /* the connection rate of the network * between 0 and 1, 1 meaning fully connected */ float connection_rate; /* is 1 if shortcut connections are used in the ann otherwise 0 * Shortcut connections are connections that skip layers. * A fully connected ann with shortcut connections are a ann where * neurons have connections to all neurons in all later layers. */ enum fann_nettype_enum network_type; /* pointer to the first layer (input layer) in an array af all the layers, * including the input and outputlayers */ struct fann_layer *first_layer; /* pointer to the layer past the last layer in an array af all the layers, * including the input and outputlayers */ struct fann_layer *last_layer; /* Total number of neurons. * very usefull, because the actual neurons are allocated in one long array */ unsigned int total_neurons; /* Number of input neurons (not calculating bias) */ unsigned int num_input; /* Number of output neurons (not calculating bias) */ unsigned int num_output; /* The weight array */ fann_type *weights; /* The connection array */ struct fann_neuron **connections; /* Used to contain the errors used during training * Is allocated during first training session, * which means that if we do not train, it is never allocated. */ fann_type *train_errors; /* Training algorithm used when calling fann_train_on_.. */ enum fann_train_enum training_algorithm; #ifdef FIXEDFANN /* the decimal_point, used for shifting the fix point * in fixed point integer operatons. */ unsigned int decimal_point; /* the multiplier, used for multiplying the fix point * in fixed point integer operatons. * Only used in special cases, since the decimal_point is much faster. */ unsigned int multiplier; /* When in choosen (or in fixed point), the sigmoid function is * calculated as a stepwise linear function. In the * activation_results array, the result is saved, and in the * two values arrays, the values that gives the results are saved. */ fann_type sigmoid_results[6]; fann_type sigmoid_values[6]; fann_type sigmoid_symmetric_results[6]; fann_type sigmoid_symmetric_values[6]; #endif /* Total number of connections. * very usefull, because the actual connections * are allocated in one long array */ unsigned int total_connections; /* used to store outputs in */ fann_type *output; /* the number of data used to calculate the mean square error. */ unsigned int num_MSE; /* the total error value. * the real mean square error is MSE_value/num_MSE */ float MSE_value; /* The number of outputs which would fail (only valid for classification problems) */ unsigned int num_bit_fail; /* The maximum difference between the actual output and the expected output * which is accepted when counting the bit fails. * This difference is multiplied by two when dealing with symmetric activation functions, * so that symmetric and not symmetric activation functions can use the same limit. */ fann_type bit_fail_limit; /* The error function used during training. (default FANN_ERRORFUNC_TANH) */ enum fann_errorfunc_enum train_error_function; /* The stop function used during training. (default FANN_STOPFUNC_MSE) */ enum fann_stopfunc_enum train_stop_function; /* The callback function used during training. (default NULL) */ fann_callback_type callback; /* A pointer to user defined data. (default NULL) */ void *user_data; /* Variables for use with Cascade Correlation */ /* The error must change by at least this * fraction of its old value to count as a * significant change. */ float cascade_output_change_fraction; /* No change in this number of epochs will cause * stagnation. */ unsigned int cascade_output_stagnation_epochs; /* The error must change by at least this * fraction of its old value to count as a * significant change. */ float cascade_candidate_change_fraction; /* No change in this number of epochs will cause * stagnation. */ unsigned int cascade_candidate_stagnation_epochs; /* The current best candidate, which will be installed. */ unsigned int cascade_best_candidate; /* The upper limit for a candidate score */ fann_type cascade_candidate_limit; /* Scale of copied candidate output weights */ fann_type cascade_weight_multiplier; /* Maximum epochs to train the output neurons during cascade training */ unsigned int cascade_max_out_epochs; /* Maximum epochs to train the candidate neurons during cascade training */ unsigned int cascade_max_cand_epochs; /* An array consisting of the activation functions used when doing * cascade training. */ enum fann_activationfunc_enum *cascade_activation_functions; /* The number of elements in the cascade_activation_functions array. */ unsigned int cascade_activation_functions_count; /* An array consisting of the steepnesses used during cascade training. */ fann_type *cascade_activation_steepnesses; /* The number of elements in the cascade_activation_steepnesses array. */ unsigned int cascade_activation_steepnesses_count; /* The number of candidates of each type that will be present. * The actual number of candidates is then * cascade_activation_functions_count * * cascade_activation_steepnesses_count * * cascade_num_candidate_groups */ unsigned int cascade_num_candidate_groups; /* An array consisting of the score of the individual candidates, * which is used to decide which candidate is the best */ fann_type *cascade_candidate_scores; /* The number of allocated neurons during cascade correlation algorithms. * This number might be higher than the actual number of neurons to avoid * allocating new space too often. */ unsigned int total_neurons_allocated; /* The number of allocated connections during cascade correlation algorithms. * This number might be higher than the actual number of neurons to avoid * allocating new space too often. */ unsigned int total_connections_allocated; /* Variables for use with Quickprop training */ /* Decay is used to make the weights not go so high */ float quickprop_decay; /* Mu is a factor used to increase and decrease the stepsize */ float quickprop_mu; /* Variables for use with with RPROP training */ /* Tells how much the stepsize should increase during learning */ float rprop_increase_factor; /* Tells how much the stepsize should decrease during learning */ float rprop_decrease_factor; /* The minimum stepsize */ float rprop_delta_min; /* The maximum stepsize */ float rprop_delta_max; /* The initial stepsize */ float rprop_delta_zero; /* Used to contain the slope errors used during batch training * Is allocated during first training session, * which means that if we do not train, it is never allocated. */ fann_type *train_slopes; /* The previous step taken by the quickprop/rprop procedures. * Not allocated if not used. */ fann_type *prev_steps; /* The slope values used by the quickprop/rprop procedures. * Not allocated if not used. */ fann_type *prev_train_slopes; /* The last delta applied to a connection weight. * This is used for the momentum term in the backpropagation algorithm. * Not allocated if not used. */ fann_type *prev_weights_deltas; #ifndef FIXEDFANN /* Arithmetic mean used to remove steady component in input data. */ float *scale_mean_in; /* Standart deviation used to normalize input data (mostly to [-1;1]). */ float *scale_deviation_in; /* User-defined new minimum for input data. * Resulting data values may be less than user-defined minimum. */ float *scale_new_min_in; /* Used to scale data to user-defined new maximum for input data. * Resulting data values may be greater than user-defined maximum. */ float *scale_factor_in; /* Arithmetic mean used to remove steady component in output data. */ float *scale_mean_out; /* Standart deviation used to normalize output data (mostly to [-1;1]). */ float *scale_deviation_out; /* User-defined new minimum for output data. * Resulting data values may be less than user-defined minimum. */ float *scale_new_min_out; /* Used to scale data to user-defined new maximum for output data. * Resulting data values may be greater than user-defined maximum. */ float *scale_factor_out; #endif }; /* Type: fann_connection Describes a connection between two neurons and its weight from_neuron - Unique number used to identify source neuron to_neuron - Unique number used to identify destination neuron weight - The numerical value of the weight See Also: , This structure appears in FANN >= 2.1.0 */ struct fann_connection { /* Unique number used to identify source neuron */ unsigned int from_neuron; /* Unique number used to identify destination neuron */ unsigned int to_neuron; /* The numerical value of the weight */ fann_type weight; }; #endif libfann-2.1.0~beta~dfsg/src/include/fann_error.h000066400000000000000000000122361140315177100217410ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __fann_error_h__ #define __fann_error_h__ #include #define FANN_ERRSTR_MAX 128 struct fann_error; /* Section: FANN Error Handling Errors from the fann library are usually reported on stderr. It is however possible to redirect these error messages to a file, or completely ignore them by the function. It is also possible to inspect the last error message by using the and functions. */ /* Enum: fann_errno_enum Used to define error events on and . See also: , , FANN_E_NO_ERROR - No error FANN_E_CANT_OPEN_CONFIG_R - Unable to open configuration file for reading FANN_E_CANT_OPEN_CONFIG_W - Unable to open configuration file for writing FANN_E_WRONG_CONFIG_VERSION - Wrong version of configuration file FANN_E_CANT_READ_CONFIG - Error reading info from configuration file FANN_E_CANT_READ_NEURON - Error reading neuron info from configuration file FANN_E_CANT_READ_CONNECTIONS - Error reading connections from configuration file FANN_E_WRONG_NUM_CONNECTIONS - Number of connections not equal to the number expected FANN_E_CANT_OPEN_TD_W - Unable to open train data file for writing FANN_E_CANT_OPEN_TD_R - Unable to open train data file for reading FANN_E_CANT_READ_TD - Error reading training data from file FANN_E_CANT_ALLOCATE_MEM - Unable to allocate memory FANN_E_CANT_TRAIN_ACTIVATION - Unable to train with the selected activation function FANN_E_CANT_USE_ACTIVATION - Unable to use the selected activation function FANN_E_TRAIN_DATA_MISMATCH - Irreconcilable differences between two structures FANN_E_CANT_USE_TRAIN_ALG - Unable to use the selected training algorithm FANN_E_TRAIN_DATA_SUBSET - Trying to take subset which is not within the training set FANN_E_INDEX_OUT_OF_BOUND - Index is out of bound FANN_E_SCALE_NOT_PRESENT - Scaling parameters not present */ enum fann_errno_enum { FANN_E_NO_ERROR = 0, FANN_E_CANT_OPEN_CONFIG_R, FANN_E_CANT_OPEN_CONFIG_W, FANN_E_WRONG_CONFIG_VERSION, FANN_E_CANT_READ_CONFIG, FANN_E_CANT_READ_NEURON, FANN_E_CANT_READ_CONNECTIONS, FANN_E_WRONG_NUM_CONNECTIONS, FANN_E_CANT_OPEN_TD_W, FANN_E_CANT_OPEN_TD_R, FANN_E_CANT_READ_TD, FANN_E_CANT_ALLOCATE_MEM, FANN_E_CANT_TRAIN_ACTIVATION, FANN_E_CANT_USE_ACTIVATION, FANN_E_TRAIN_DATA_MISMATCH, FANN_E_CANT_USE_TRAIN_ALG, FANN_E_TRAIN_DATA_SUBSET, FANN_E_INDEX_OUT_OF_BOUND, FANN_E_SCALE_NOT_PRESENT }; /* Group: Error Handling */ /* Function: fann_set_error_log Change where errors are logged to. Both and can be casted to , so this function can be used to set either of these. If log_file is NULL, no errors will be printed. If errdata is NULL, the default log will be set. The default log is the log used when creating and . This default log will also be the default for all new structs that are created. The default behavior is to log them to stderr. See also: This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL void FANN_API fann_set_error_log(struct fann_error *errdat, FILE * log_file); /* Function: fann_get_errno Returns the last error number. See also: , This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno(struct fann_error *errdat); /* Function: fann_reset_errno Resets the last error number. This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL void FANN_API fann_reset_errno(struct fann_error *errdat); /* Function: fann_reset_errstr Resets the last error string. This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL void FANN_API fann_reset_errstr(struct fann_error *errdat); /* Function: fann_get_errstr Returns the last errstr. This function calls and This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL char *FANN_API fann_get_errstr(struct fann_error *errdat); /* Function: fann_print_error Prints the last error to stderr. This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL void FANN_API fann_print_error(struct fann_error *errdat); extern FILE * fann_default_error_log; #endif libfann-2.1.0~beta~dfsg/src/include/fann_internal.h000066400000000000000000000123231140315177100224210ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __fann_internal_h__ #define __fann_internal_h__ /* internal include file, not to be included directly */ #include #include #include #include "fann_data.h" #define FANN_FIX_VERSION "FANN_FIX_2.0" #define FANN_FLO_VERSION "FANN_FLO_2.1" #ifdef FIXEDFANN #define FANN_CONF_VERSION FANN_FIX_VERSION #else #define FANN_CONF_VERSION FANN_FLO_VERSION #endif #define FANN_GET(type, name) \ FANN_EXTERNAL type FANN_API fann_get_ ## name(struct fann *ann) \ { \ return ann->name; \ } #define FANN_SET(type, name) \ FANN_EXTERNAL void FANN_API fann_set_ ## name(struct fann *ann, type value) \ { \ ann->name = value; \ } #define FANN_GET_SET(type, name) \ FANN_GET(type, name) \ FANN_SET(type, name) struct fann_train_data; struct fann *fann_allocate_structure(unsigned int num_layers); void fann_allocate_neurons(struct fann *ann); void fann_allocate_connections(struct fann *ann); int fann_save_internal(struct fann *ann, const char *configuration_file, unsigned int save_as_fixed); int fann_save_internal_fd(struct fann *ann, FILE * conf, const char *configuration_file, unsigned int save_as_fixed); int fann_save_train_internal(struct fann_train_data *data, const char *filename, unsigned int save_as_fixed, unsigned int decimal_point); int fann_save_train_internal_fd(struct fann_train_data *data, FILE * file, const char *filename, unsigned int save_as_fixed, unsigned int decimal_point); void fann_update_stepwise(struct fann *ann); void fann_seed_rand(); void fann_error(struct fann_error *errdat, const enum fann_errno_enum errno_f, ...); void fann_init_error_data(struct fann_error *errdat); struct fann *fann_create_from_fd(FILE * conf, const char *configuration_file); struct fann_train_data *fann_read_train_from_fd(FILE * file, const char *filename); void fann_compute_MSE(struct fann *ann, fann_type * desired_output); void fann_update_output_weights(struct fann *ann); void fann_backpropagate_MSE(struct fann *ann); void fann_update_weights(struct fann *ann); void fann_update_slopes_batch(struct fann *ann, struct fann_layer *layer_begin, struct fann_layer *layer_end); void fann_update_weights_quickprop(struct fann *ann, unsigned int num_data, unsigned int first_weight, unsigned int past_end); void fann_update_weights_batch(struct fann *ann, unsigned int num_data, unsigned int first_weight, unsigned int past_end); void fann_update_weights_irpropm(struct fann *ann, unsigned int first_weight, unsigned int past_end); void fann_clear_train_arrays(struct fann *ann); fann_type fann_activation(struct fann * ann, unsigned int activation_function, fann_type steepness, fann_type value); fann_type fann_activation_derived(unsigned int activation_function, fann_type steepness, fann_type value, fann_type sum); int fann_desired_error_reached(struct fann *ann, float desired_error); /* Some functions for cascade */ int fann_train_outputs(struct fann *ann, struct fann_train_data *data, float desired_error); float fann_train_outputs_epoch(struct fann *ann, struct fann_train_data *data); int fann_train_candidates(struct fann *ann, struct fann_train_data *data); fann_type fann_train_candidates_epoch(struct fann *ann, struct fann_train_data *data); void fann_install_candidate(struct fann *ann); int fann_initialize_candidates(struct fann *ann); void fann_set_shortcut_connections(struct fann *ann); int fann_allocate_scale(struct fann *ann); /* called fann_max, in order to not interferre with predefined versions of max */ #define fann_max(x, y) (((x) > (y)) ? (x) : (y)) #define fann_min(x, y) (((x) < (y)) ? (x) : (y)) #define fann_safe_free(x) {if(x) { free(x); x = NULL; }} #define fann_clip(x, lo, hi) (((x) < (lo)) ? (lo) : (((x) > (hi)) ? (hi) : (x))) /*#define fann_clip(x, lo, hi) (x)*/ #define fann_rand(min_value, max_value) (((float)(min_value))+(((float)(max_value)-((float)(min_value)))*rand()/(RAND_MAX+1.0f))) #define fann_abs(value) (((value) > 0) ? (value) : -(value)) #ifdef FIXEDFANN #define fann_mult(x,y) ((x*y) >> decimal_point) #define fann_div(x,y) (((x) << decimal_point)/y) #define fann_random_weight() (fann_type)(fann_rand(0,multiplier/10)) #define fann_random_bias_weight() (fann_type)(fann_rand((0-multiplier)/10,multiplier/10)) #else #define fann_mult(x,y) (x*y) #define fann_div(x,y) (x/y) #define fann_random_weight() (fann_rand(-0.1f,0.1f)) #define fann_random_bias_weight() (fann_rand(-0.1f,0.1f)) #endif #endif libfann-2.1.0~beta~dfsg/src/include/fann_io.h000066400000000000000000000067541140315177100212270ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __fann_io_h__ #define __fann_io_h__ /* Section: FANN File Input/Output It is possible to save an entire ann to a file with for future loading with . */ /* Group: File Input and Output */ /* Function: fann_create_from_file Constructs a backpropagation neural network from a configuration file, which have been saved by . See also: , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL struct fann *FANN_API fann_create_from_file(const char *configuration_file); /* Function: fann_save Save the entire network to a configuration file. The configuration file contains all information about the neural network and enables to create an exact copy of the neural network and all of the parameters associated with the neural network. These three parameters (, , ) are *NOT* saved to the file because they cannot safely be ported to a different location. Also temporary parameters generated during training like is not saved. Return: The function returns 0 on success and -1 on failure. See also: , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL int FANN_API fann_save(struct fann *ann, const char *configuration_file); /* Function: fann_save_to_fixed Saves the entire network to a configuration file. But it is saved in fixed point format no matter which format it is currently in. This is usefull for training a network in floating points, and then later executing it in fixed point. The function returns the bit position of the fix point, which can be used to find out how accurate the fixed point network will be. A high value indicates high precision, and a low value indicates low precision. A negative value indicates very low precision, and a very strong possibility for overflow. (the actual fix point will be set to 0, since a negative fix point does not make sence). Generally, a fix point lower than 6 is bad, and should be avoided. The best way to avoid this, is to have less connections to each neuron, or just less neurons in each layer. The fixed point use of this network is only intended for use on machines that have no floating point processor, like an iPAQ. On normal computers the floating point version is actually faster. See also: , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL int FANN_API fann_save_to_fixed(struct fann *ann, const char *configuration_file); #endif libfann-2.1.0~beta~dfsg/src/include/fann_train.h000066400000000000000000001146731140315177100217350ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __fann_train_h__ #define __fann_train_h__ /* Section: FANN Training There are many different ways of training neural networks and the FANN library supports a number of different approaches. Two fundementally different approaches are the most commonly used: Fixed topology training - The size and topology of the ANN is determined in advance and the training alters the weights in order to minimize the difference between the desired output values and the actual output values. This kind of training is supported by . Evolving topology training - The training start out with an empty ANN, only consisting of input and output neurons. Hidden neurons and connections is the added during training, in order to reach the same goal as for fixed topology training. This kind of training is supported by . */ /* Struct: struct fann_train_data Structure used to store data, for use with training. The data inside this structure should never be manipulated directly, but should use some of the supplied functions in . The training data structure is very usefull for storing data during training and testing of a neural network. See also: , , */ struct fann_train_data { enum fann_errno_enum errno_f; FILE *error_log; char *errstr; unsigned int num_data; unsigned int num_input; unsigned int num_output; fann_type **input; fann_type **output; }; /* Section: FANN Training */ /* Group: Training */ #ifndef FIXEDFANN /* Function: fann_train Train one iteration with a set of inputs, and a set of desired outputs. This training is always incremental training (see ), since only one pattern is presented. Parameters: ann - The neural network structure input - an array of inputs. This array must be exactly long. desired_output - an array of desired outputs. This array must be exactly long. See also: , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_train(struct fann *ann, fann_type * input, fann_type * desired_output); #endif /* NOT FIXEDFANN */ /* Function: fann_test Test with a set of inputs, and a set of desired outputs. This operation updates the mean square error, but does not change the network in any way. See also: , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL fann_type * FANN_API fann_test(struct fann *ann, fann_type * input, fann_type * desired_output); /* Function: fann_get_MSE Reads the mean square error from the network. Reads the mean square error from the network. This value is calculated during training or testing, and can therefore sometimes be a bit off if the weights have been changed since the last calculation of the value. See also: This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL float FANN_API fann_get_MSE(struct fann *ann); /* Function: fann_get_bit_fail The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see , ). The bits are counted in all of the training data, so this number can be higher than the number of training data. This value is reset by and updated by all the same functions which also updates the MSE value (e.g. , ) See also: , This function appears in FANN >= 2.0.0 */ FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail(struct fann *ann); /* Function: fann_reset_MSE Resets the mean square error from the network. This function also resets the number of bits that fail. See also: , This function appears in FANN >= 1.1.0 */ FANN_EXTERNAL void FANN_API fann_reset_MSE(struct fann *ann); /* Group: Training Data Training */ #ifndef FIXEDFANN /* Function: fann_train_on_data Trains on an entire dataset, for a period of time. This training uses the training algorithm chosen by , and the parameters set for these training algorithms. Parameters: ann - The neural network data - The data, which should be used during training max_epochs - The maximum number of epochs the training should continue epochs_between_reports - The number of epochs between printing a status report to stdout. A value of zero means no reports should be printed. desired_error - The desired or , depending on which stop function is chosen by . Instead of printing out reports every epochs_between_reports, a callback function can be called (see ). See also: , , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_train_on_data(struct fann *ann, struct fann_train_data *data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error); /* Function: fann_train_on_file Does the same as , but reads the training data directly from a file. See also: This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_train_on_file(struct fann *ann, const char *filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error); /* Function: fann_train_epoch Train one epoch with a set of training data. Train one epoch with the training data stored in data. One epoch is where all of the training data is considered exactly once. This function returns the MSE error as it is calculated either before or during the actual training. This is not the actual MSE after the training epoch, but since calculating this will require to go through the entire training set once more, it is more than adequate to use this value during training. The training algorithm used by this function is chosen by the function. See also: , This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL float FANN_API fann_train_epoch(struct fann *ann, struct fann_train_data *data); #endif /* NOT FIXEDFANN */ /* Function: fann_test_data Test a set of training data and calculates the MSE for the training data. This function updates the MSE and the bit fail values. See also: , , This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL float FANN_API fann_test_data(struct fann *ann, struct fann_train_data *data); /* Group: Training Data Manipulation */ /* Function: fann_read_train_from_file Reads a file that stores training data. The file must be formatted like: >num_train_data num_input num_output >inputdata seperated by space >outputdata seperated by space > >. >. >. > >inputdata seperated by space >outputdata seperated by space See also: , , This function appears in FANN >= 1.0.0 */ FANN_EXTERNAL struct fann_train_data *FANN_API fann_read_train_from_file(const char *filename); /* Function: fann_create_train_from_callback Creates the training data struct from a user supplied function. As the training data are numerable (data 1, data 2...), the user must write a function that receives the number of the training data set (input,output) and returns the set. Parameters: num_data - The number of training data num_input - The number of inputs per training data num_output - The number of ouputs per training data user_function - The user suplied function Parameters for the user function: num - The number of the training data set num_input - The number of inputs per training data num_output - The number of ouputs per training data input - The set of inputs output - The set of desired outputs See also: , , , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL struct fann_train_data * FANN_API fann_create_train_from_callback(unsigned int num_data, unsigned int num_input, unsigned int num_output, void (FANN_API *user_function)( unsigned int, unsigned int, unsigned int, fann_type * , fann_type * )); /* Function: fann_destroy_train Destructs the training data and properly deallocates all of the associated data. Be sure to call this function after finished using the training data. This function appears in FANN >= 1.0.0 */ FANN_EXTERNAL void FANN_API fann_destroy_train(struct fann_train_data *train_data); /* Function: fann_shuffle_train_data Shuffles training data, randomizing the order. This is recommended for incremental training, while it have no influence during batch training. This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL void FANN_API fann_shuffle_train_data(struct fann_train_data *train_data); #ifndef FIXEDFANN /* Function: fann_scale_train Scale input and output data based on previously calculated parameters. Parameters: ann - ann for which were calculated trained parameters before data - training data that needs to be scaled See also: , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_scale_train( struct fann *ann, struct fann_train_data *data ); /* Function: fann_descale_train Descale input and output data based on previously calculated parameters. Parameters: ann - ann for which were calculated trained parameters before data - training data that needs to be descaled See also: , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_descale_train( struct fann *ann, struct fann_train_data *data ); /* Function: fann_set_input_scaling_params Calculate input scaling parameters for future use based on training data. Parameters: ann - ann for wgich parameters needs to be calculated data - training data that will be used to calculate scaling parameters new_input_min - desired lower bound in input data after scaling (not strictly followed) new_input_max - desired upper bound in input data after scaling (not strictly followed) See also: This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL int FANN_API fann_set_input_scaling_params( struct fann *ann, const struct fann_train_data *data, float new_input_min, float new_input_max); /* Function: fann_set_output_scaling_params Calculate output scaling parameters for future use based on training data. Parameters: ann - ann for wgich parameters needs to be calculated data - training data that will be used to calculate scaling parameters new_output_min - desired lower bound in input data after scaling (not strictly followed) new_output_max - desired upper bound in input data after scaling (not strictly followed) See also: This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL int FANN_API fann_set_output_scaling_params( struct fann *ann, const struct fann_train_data *data, float new_output_min, float new_output_max); /* Function: fann_set_scaling_params Calculate input and output scaling parameters for future use based on training data. Parameters: ann - ann for wgich parameters needs to be calculated data - training data that will be used to calculate scaling parameters new_input_min - desired lower bound in input data after scaling (not strictly followed) new_input_max - desired upper bound in input data after scaling (not strictly followed) new_output_min - desired lower bound in input data after scaling (not strictly followed) new_output_max - desired upper bound in input data after scaling (not strictly followed) See also: , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL int FANN_API fann_set_scaling_params( struct fann *ann, const struct fann_train_data *data, float new_input_min, float new_input_max, float new_output_min, float new_output_max); /* Function: fann_clear_scaling_params Clears scaling parameters. Parameters: ann - ann for which to clear scaling parameters This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL int FANN_API fann_clear_scaling_params(struct fann *ann); /* Function: fann_scale_input Scale data in input vector before feed it to ann based on previously calculated parameters. Parameters: ann - for which scaling parameters were calculated input_vector - input vector that will be scaled See also: , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_scale_input( struct fann *ann, fann_type *input_vector ); /* Function: fann_scale_output Scale data in output vector before feed it to ann based on previously calculated parameters. Parameters: ann - for which scaling parameters were calculated output_vector - output vector that will be scaled See also: , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_scale_output( struct fann *ann, fann_type *output_vector ); /* Function: fann_descale_input Scale data in input vector after get it from ann based on previously calculated parameters. Parameters: ann - for which scaling parameters were calculated input_vector - input vector that will be descaled See also: , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_descale_input( struct fann *ann, fann_type *input_vector ); /* Function: fann_descale_output Scale data in output vector after get it from ann based on previously calculated parameters. Parameters: ann - for which scaling parameters were calculated output_vector - output vector that will be descaled See also: , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL void FANN_API fann_descale_output( struct fann *ann, fann_type *output_vector ); #endif /* Function: fann_scale_input_train_data Scales the inputs in the training data to the specified range. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_scale_input_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max); /* Function: fann_scale_output_train_data Scales the outputs in the training data to the specified range. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_scale_output_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max); /* Function: fann_scale_train_data Scales the inputs and outputs in the training data to the specified range. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_scale_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max); /* Function: fann_merge_train_data Merges the data from *data1* and *data2* into a new . This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL struct fann_train_data *FANN_API fann_merge_train_data(struct fann_train_data *data1, struct fann_train_data *data2); /* Function: fann_duplicate_train_data Returns an exact copy of a . This function appears in FANN >= 1.1.0. */ FANN_EXTERNAL struct fann_train_data *FANN_API fann_duplicate_train_data(struct fann_train_data *data); /* Function: fann_subset_train_data Returns an copy of a subset of the , starting at position *pos* and *length* elements forward. >fann_subset_train_data(train_data, 0, fann_length_train_data(train_data)) Will do the same as . See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL struct fann_train_data *FANN_API fann_subset_train_data(struct fann_train_data *data, unsigned int pos, unsigned int length); /* Function: fann_length_train_data Returns the number of training patterns in the . This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_length_train_data(struct fann_train_data *data); /* Function: fann_num_input_train_data Returns the number of inputs in each of the training patterns in the . See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_num_input_train_data(struct fann_train_data *data); /* Function: fann_num_output_train_data Returns the number of outputs in each of the training patterns in the . See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL unsigned int FANN_API fann_num_output_train_data(struct fann_train_data *data); /* Function: fann_save_train Save the training structure to a file, with the format as specified in Return: The function returns 0 on success and -1 on failure. See also: , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL int FANN_API fann_save_train(struct fann_train_data *data, const char *filename); /* Function: fann_save_train_to_fixed Saves the training structure to a fixed point data file. This function is very usefull for testing the quality of a fixed point network. Return: The function returns 0 on success and -1 on failure. See also: This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL int FANN_API fann_save_train_to_fixed(struct fann_train_data *data, const char *filename, unsigned int decimal_point); /* Group: Parameters */ /* Function: fann_get_training_algorithm Return the training algorithm as described by . This training algorithm is used by and associated functions. Note that this algorithm is also used during , although only FANN_TRAIN_RPROP and FANN_TRAIN_QUICKPROP is allowed during cascade training. The default training algorithm is FANN_TRAIN_RPROP. See also: , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL enum fann_train_enum FANN_API fann_get_training_algorithm(struct fann *ann); /* Function: fann_set_training_algorithm Set the training algorithm. More info available in This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_set_training_algorithm(struct fann *ann, enum fann_train_enum training_algorithm); /* Function: fann_get_learning_rate Return the learning rate. The learning rate is used to determine how aggressive training should be for some of the training algorithms (FANN_TRAIN_INCREMENTAL, FANN_TRAIN_BATCH, FANN_TRAIN_QUICKPROP). Do however note that it is not used in FANN_TRAIN_RPROP. The default learning rate is 0.7. See also: , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL float FANN_API fann_get_learning_rate(struct fann *ann); /* Function: fann_set_learning_rate Set the learning rate. More info available in This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_set_learning_rate(struct fann *ann, float learning_rate); /* Function: fann_get_learning_momentum Get the learning momentum. The learning momentum can be used to speed up FANN_TRAIN_INCREMENTAL training. A too high momentum will however not benefit training. Setting momentum to 0 will be the same as not using the momentum parameter. The recommended value of this parameter is between 0.0 and 1.0. The default momentum is 0. See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL float FANN_API fann_get_learning_momentum(struct fann *ann); /* Function: fann_set_learning_momentum Set the learning momentum. More info available in This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_learning_momentum(struct fann *ann, float learning_momentum); /* Function: fann_get_activation_function Get the activation function for neuron number *neuron* in layer number *layer*, counting the input layer as layer 0. It is not possible to get activation functions for the neurons in the input layer. Information about the individual activation functions is available at . Returns: The activation function for the neuron or -1 if the neuron is not defined in the neural network. See also: , , , , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL enum fann_activationfunc_enum FANN_API fann_get_activation_function(struct fann *ann, int layer, int neuron); /* Function: fann_set_activation_function Set the activation function for neuron number *neuron* in layer number *layer*, counting the input layer as layer 0. It is not possible to set activation functions for the neurons in the input layer. When choosing an activation function it is important to note that the activation functions have different range. FANN_SIGMOID is e.g. in the 0 - 1 range while FANN_SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN_LINEAR is unbound. Information about the individual activation functions is available at . The default activation function is FANN_SIGMOID_STEPWISE. See also: , , , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_activation_function(struct fann *ann, enum fann_activationfunc_enum activation_function, int layer, int neuron); /* Function: fann_set_activation_function_layer Set the activation function for all the neurons in the layer number *layer*, counting the input layer as layer 0. It is not possible to set activation functions for the neurons in the input layer. See also: , , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_activation_function_layer(struct fann *ann, enum fann_activationfunc_enum activation_function, int layer); /* Function: fann_set_activation_function_hidden Set the activation function for all of the hidden layers. See also: , , , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden(struct fann *ann, enum fann_activationfunc_enum activation_function); /* Function: fann_set_activation_function_output Set the activation function for the output layer. See also: , , , This function appears in FANN >= 1.0.0. */ FANN_EXTERNAL void FANN_API fann_set_activation_function_output(struct fann *ann, enum fann_activationfunc_enum activation_function); /* Function: fann_get_activation_steepness Get the activation steepness for neuron number *neuron* in layer number *layer*, counting the input layer as layer 0. It is not possible to get activation steepness for the neurons in the input layer. The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training. When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0). The default activation steepness is 0.5. Returns: The activation steepness for the neuron or -1 if the neuron is not defined in the neural network. See also: , , , , This function appears in FANN >= 2.1.0 */ FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness(struct fann *ann, int layer, int neuron); /* Function: fann_set_activation_steepness Set the activation steepness for neuron number *neuron* in layer number *layer*, counting the input layer as layer 0. It is not possible to set activation steepness for the neurons in the input layer. The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training. When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0). The default activation steepness is 0.5. See also: , , , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_activation_steepness(struct fann *ann, fann_type steepness, int layer, int neuron); /* Function: fann_set_activation_steepness_layer Set the activation steepness all of the neurons in layer number *layer*, counting the input layer as layer 0. It is not possible to set activation steepness for the neurons in the input layer. See also: , , , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer(struct fann *ann, fann_type steepness, int layer); /* Function: fann_set_activation_steepness_hidden Set the steepness of the activation steepness in all of the hidden layers. See also: , , , This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden(struct fann *ann, fann_type steepness); /* Function: fann_set_activation_steepness_output Set the steepness of the activation steepness in the output layer. See also: , , , This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output(struct fann *ann, fann_type steepness); /* Function: fann_get_train_error_function Returns the error function used during training. The error functions is described further in The default error function is FANN_ERRORFUNC_TANH See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL enum fann_errorfunc_enum FANN_API fann_get_train_error_function(struct fann *ann); /* Function: fann_set_train_error_function Set the error function used during training. The error functions is described further in See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_train_error_function(struct fann *ann, enum fann_errorfunc_enum train_error_function); /* Function: fann_get_train_stop_function Returns the the stop function used during training. The stop function is described further in The default stop function is FANN_STOPFUNC_MSE See also: , This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL enum fann_stopfunc_enum FANN_API fann_get_train_stop_function(struct fann *ann); /* Function: fann_set_train_stop_function Set the stop function used during training. Returns the the stop function used during training. The stop function is described further in See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_train_stop_function(struct fann *ann, enum fann_stopfunc_enum train_stop_function); /* Function: fann_get_bit_fail_limit Returns the bit fail limit used during training. The bit fail limit is used during training where the is set to FANN_STOPFUNC_BIT. The limit is the maximum accepted difference between the desired output and the actual output during training. Each output that diverges more than this limit is counted as an error bit. This difference is divided by two when dealing with symmetric activation functions, so that symmetric and not symmetric activation functions can use the same limit. The default bit fail limit is 0.35. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL fann_type FANN_API fann_get_bit_fail_limit(struct fann *ann); /* Function: fann_set_bit_fail_limit Set the bit fail limit used during training. See also: This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_bit_fail_limit(struct fann *ann, fann_type bit_fail_limit); /* Function: fann_set_callback Sets the callback function for use during training. See for more information about the callback function. The default callback function simply prints out some status information. This function appears in FANN >= 2.0.0. */ FANN_EXTERNAL void FANN_API fann_set_callback(struct fann *ann, fann_callback_type callback); /* Function: fann_get_quickprop_decay The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training. This is used to make sure that the weights do not become too high during training. The default decay is -0.0001. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL float FANN_API fann_get_quickprop_decay(struct fann *ann); /* Function: fann_set_quickprop_decay Sets the quickprop decay factor. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_quickprop_decay(struct fann *ann, float quickprop_decay); /* Function: fann_get_quickprop_mu The mu factor is used to increase and decrease the step-size during quickprop training. The mu factor should always be above 1, since it would otherwise decrease the step-size when it was suppose to increase it. The default mu factor is 1.75. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL float FANN_API fann_get_quickprop_mu(struct fann *ann); /* Function: fann_set_quickprop_mu Sets the quickprop mu factor. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_quickprop_mu(struct fann *ann, float quickprop_mu); /* Function: fann_get_rprop_increase_factor The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training. The default increase factor is 1.2. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL float FANN_API fann_get_rprop_increase_factor(struct fann *ann); /* Function: fann_set_rprop_increase_factor The increase factor used during RPROP training. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_rprop_increase_factor(struct fann *ann, float rprop_increase_factor); /* Function: fann_get_rprop_decrease_factor The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training. The default decrease factor is 0.5. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL float FANN_API fann_get_rprop_decrease_factor(struct fann *ann); /* Function: fann_set_rprop_decrease_factor The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_rprop_decrease_factor(struct fann *ann, float rprop_decrease_factor); /* Function: fann_get_rprop_delta_min The minimum step-size is a small positive number determining how small the minimum step-size may be. The default value delta min is 0.0. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL float FANN_API fann_get_rprop_delta_min(struct fann *ann); /* Function: fann_set_rprop_delta_min The minimum step-size is a small positive number determining how small the minimum step-size may be. See also: This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_rprop_delta_min(struct fann *ann, float rprop_delta_min); /* Function: fann_get_rprop_delta_max The maximum step-size is a positive number determining how large the maximum step-size may be. The default delta max is 50.0. See also: , This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL float FANN_API fann_get_rprop_delta_max(struct fann *ann); /* Function: fann_set_rprop_delta_max The maximum step-size is a positive number determining how large the maximum step-size may be. See also: , This function appears in FANN >= 1.2.0. */ FANN_EXTERNAL void FANN_API fann_set_rprop_delta_max(struct fann *ann, float rprop_delta_max); /* Function: fann_get_rprop_delta_zero The initial step-size is a positive number determining the initial step size. The default delta zero is 0.1. See also: , , This function appears in FANN >= 2.1.0. */ FANN_EXTERNAL float FANN_API fann_get_rprop_delta_zero(struct fann *ann); /* Function: fann_set_rprop_delta_zero The initial step-size is a positive number determining the initial step size. See also: , This function appears in FANN >= 2.1.0. */ FANN_EXTERNAL void FANN_API fann_set_rprop_delta_zero(struct fann *ann, float rprop_delta_max); #endif libfann-2.1.0~beta~dfsg/src/include/fixedfann.h000066400000000000000000000017601140315177100215500ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __fixedfann_h__ #define __fixedfann_h__ typedef int fann_type; #undef FIXEDFANN #define FIXEDFANN #define FANNPRINTF "%d" #define FANNSCANF "%d" #define FANN_INCLUDE #include "fann.h" #endif libfann-2.1.0~beta~dfsg/src/include/floatfann.h000066400000000000000000000017651140315177100215630ustar00rootroot00000000000000/* Fast Artificial Neural Network Library (fann) Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk) This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef __floatfann_h__ #define __floatfann_h__ typedef float fann_type; #undef FLOATFANN #define FLOATFANN #define FANNPRINTF "%.20e" #define FANNSCANF "%f" #define FANN_INCLUDE #include "fann.h" #endif