e1071/0000755000175100001440000000000013567267365011051 5ustar hornikuserse1071/NAMESPACE0000655000175100001440000000316313475420020012246 0ustar hornikusersuseDynLib("e1071", .registration = TRUE, .fixes = "R_") import(graphics) import(grDevices) import(stats) importFrom("methods", "as", "getClass", "new") importFrom("class", "knn", "knn1") if(getRversion() >= "2.5.0") importFrom("utils", "write.table") export(ddiscrete, pdiscrete, qdiscrete, rdiscrete, bclust, hclust.bclust, centers.bclust, clusters.bclust, bincombinations, bootstrap.lca, classAgreement, cmeans, countpattern, cshell, element, fclustIndex, hamming.distance, hamming.window, hanning.window, ica, impute, interpolate, kurtosis, lca, matchControls, matchClasses, compareMatchedClasses, moment, naiveBayes, permutations, rbridge, read.matrix.csr, write.matrix.csr, rectangle.window, rwiener, allShortestPaths, extractPath, sigmoid, dsigmoid, d2sigmoid, skewness, stft, svm, tune, tune.control, write.svm, probplot, hsv_palette) exportPattern("tune\\..+", "best\\..+") S3method(boxplot, bclust) S3method(coef, svm) S3method(lines, probplot) S3method(naiveBayes, default) S3method(naiveBayes, formula) S3method(plot, bclust) S3method(plot, ica) S3method(plot, stft) S3method(plot, svm) S3method(plot, tune) S3method(predict, lca) S3method(predict, naiveBayes) S3method(predict, svm) S3method(print, bootstrap.lca) S3method("print", "fclust") S3method(print, ica) S3method(print, lca) S3method(print, summary.lca) S3method(print, naiveBayes) S3method(print, svm) S3method(print, summary.svm) S3method(print, tune) S3method(print, summary.tune) S3method(summary, lca) S3method(summary, svm) S3method(summary, tune) S3method(svm, default) S3method(svm, formula) e1071/man/0000755000175100001440000000000013475425174011614 5ustar hornikuserse1071/man/svm.Rd0000655000175100001440000002546313475425174012724 0ustar hornikusers\name{svm} \alias{svm} \alias{svm.default} \alias{svm.formula} \alias{summary.svm} \alias{print.summary.svm} \alias{coef.svm} \alias{print.svm} \title{Support Vector Machines} \description{ \code{svm} is used to train a support vector machine. It can be used to carry out general regression and classification (of nu and epsilon-type), as well as density-estimation. A formula interface is provided. } \usage{ \method{svm}{formula}(formula, data = NULL, ..., subset, na.action = na.omit, scale = TRUE) \method{svm}{default}(x, y = NULL, scale = TRUE, type = NULL, kernel = "radial", degree = 3, gamma = if (is.vector(x)) 1 else 1 / ncol(x), coef0 = 0, cost = 1, nu = 0.5, class.weights = NULL, cachesize = 40, tolerance = 0.001, epsilon = 0.1, shrinking = TRUE, cross = 0, probability = FALSE, fitted = TRUE, ..., subset, na.action = na.omit) } \arguments{ \item{formula}{a symbolic description of the model to be fit.} \item{data}{an optional data frame containing the variables in the model. By default the variables are taken from the environment which \sQuote{svm} is called from.} \item{x}{a data matrix, a vector, or a sparse matrix (object of class \code{\link[Matrix]{Matrix}} provided by the \pkg{Matrix} package, or of class \code{\link[SparseM]{matrix.csr}} provided by the \pkg{SparseM} package, or of class \code{\link[slam]{simple_triplet_matrix}} provided by the \pkg{slam} package).} \item{y}{a response vector with one label for each row/component of \code{x}. Can be either a factor (for classification tasks) or a numeric vector (for regression).} \item{scale}{A logical vector indicating the variables to be scaled. If \code{scale} is of length 1, the value is recycled as many times as needed. Per default, data are scaled internally (both \code{x} and \code{y} variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions.} \item{type}{\code{svm} can be used as a classification machine, as a regression machine, or for novelty detection. Depending of whether \code{y} is a factor or not, the default setting for \code{type} is \code{C-classification} or \code{eps-regression}, respectively, but may be overwritten by setting an explicit value.\cr Valid options are: \itemize{ \item \code{C-classification} \item \code{nu-classification} \item \code{one-classification} (for novelty detection) \item \code{eps-regression} \item \code{nu-regression} } } \item{kernel}{the kernel used in training and predicting. You might consider changing some of the following parameters, depending on the kernel type.\cr \describe{ \item{linear:}{\eqn{u'v}{u'*v}} \item{polynomial:}{\eqn{(\gamma u'v + coef0)^{degree}}{(gamma*u'*v + coef0)^degree}} \item{radial basis:}{\eqn{e^(-\gamma |u-v|^2)}{exp(-gamma*|u-v|^2)}} \item{sigmoid:}{\eqn{tanh(\gamma u'v + coef0)}{tanh(gamma*u'*v + coef0)}} } } \item{degree}{parameter needed for kernel of type \code{polynomial} (default: 3)} \item{gamma}{parameter needed for all kernels except \code{linear} (default: 1/(data dimension))} \item{coef0}{parameter needed for kernels of type \code{polynomial} and \code{sigmoid} (default: 0)} \item{cost}{cost of constraints violation (default: 1)---it is the \sQuote{C}-constant of the regularization term in the Lagrange formulation.} \item{nu}{parameter needed for \code{nu-classification}, \code{nu-regression}, and \code{one-classification}} \item{class.weights}{a named vector of weights for the different classes, used for asymmetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named. Specifying \code{"inverse"} will choose the weights \emph{inversely} proportional to the class distribution.} \item{cachesize}{cache memory in MB (default 40)} \item{tolerance}{tolerance of termination criterion (default: 0.001)} \item{epsilon}{epsilon in the insensitive-loss function (default: 0.1)} \item{shrinking}{option whether to use the shrinking-heuristics (default: \code{TRUE})} \item{cross}{if a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model: the accuracy rate for classification and the Mean Squared Error for regression} \item{fitted}{logical indicating whether the fitted values should be computed and included in the model or not (default: \code{TRUE})} \item{probability}{logical indicating whether the model should allow for probability predictions.} \item{\dots}{additional parameters for the low level fitting function \code{svm.default}} \item{subset}{An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)} \item{na.action}{A function to specify the action to be taken if \code{NA}s are found. The default action is \code{na.omit}, which leads to rejection of cases with missing values on any required variable. An alternative is \code{na.fail}, which causes an error if \code{NA} cases are found. (NOTE: If given, this argument must be named.)} } \value{ An object of class \code{"svm"} containing the fitted model, including: \item{SV}{The resulting support vectors (possibly scaled).} \item{index}{The index of the resulting support vectors in the data matrix. Note that this index refers to the preprocessed data (after the possible effect of \code{na.omit} and \code{subset})} \item{coefs}{The corresponding coefficients times the training labels.} \item{rho}{The negative intercept.} \item{sigma}{In case of a probabilistic regression model, the scale parameter of the hypothesized (zero-mean) laplace distribution estimated by maximum likelihood.} \item{probA, probB}{numeric vectors of length k(k-1)/2, k number of classes, containing the parameters of the logistic distributions fitted to the decision values of the binary classifiers (1 / (1 + exp(a x + b))).} } \details{ For multiclass-classification with k levels, k>2, \code{libsvm} uses the \sQuote{one-against-one}-approach, in which k(k-1)/2 binary classifiers are trained; the appropriate class is found by a voting scheme. \code{libsvm} internally uses a sparse data representation, which is also high-level supported by the package \pkg{SparseM}. If the predictor variables include factors, the formula interface must be used to get a correct model matrix. \code{plot.svm} allows a simple graphical visualization of classification models. The probability model for classification fits a logistic distribution using maximum likelihood to the decision values of all binary classifiers, and computes the a-posteriori class probabilities for the multi-class problem using quadratic optimization. The probabilistic regression model assumes (zero-mean) laplace-distributed errors for the predictions, and estimates the scale parameter using maximum likelihood. For linear kernel, the coefficients of the regression/decision hyperplane can be extracted using the \code{coef} method (see examples). } \note{ Data are scaled internally, usually yielding better results. Parameters of SVM-models usually \emph{must} be tuned to yield sensible results! } \references{ \itemize{ \item Chang, Chih-Chung and Lin, Chih-Jen:\cr \emph{LIBSVM: a library for Support Vector Machines}\cr \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm} \item Exact formulations of models, algorithms, etc. can be found in the document:\cr Chang, Chih-Chung and Lin, Chih-Jen:\cr \emph{LIBSVM: a library for Support Vector Machines}\cr \url{http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz} \item More implementation details and speed benchmarks can be found on: Rong-En Fan and Pai-Hsune Chen and Chih-Jen Lin:\cr \emph{Working Set Selection Using the Second Order Information for Training SVM}\cr \url{http://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset.pdf} } } \author{ David Meyer (based on C/C++-code by Chih-Chung Chang and Chih-Jen Lin)\cr \email{David.Meyer@R-project.org} } \seealso{ \code{\link{predict.svm}} \code{\link{plot.svm}} \code{\link{tune.svm}} \code{\link[SparseM]{matrix.csr}} (in package \pkg{SparseM}) } \examples{ data(iris) attach(iris) ## classification mode # default with factor response: model <- svm(Species ~ ., data = iris) # alternatively the traditional interface: x <- subset(iris, select = -Species) y <- Species model <- svm(x, y) print(model) summary(model) # test with train data pred <- predict(model, x) # (same as:) pred <- fitted(model) # Check accuracy: table(pred, y) # compute decision values and probabilities: pred <- predict(model, x, decision.values = TRUE) attr(pred, "decision.values")[1:4,] # visualize (classes by color, SV by crosses): plot(cmdscale(dist(iris[,-5])), col = as.integer(iris[,5]), pch = c("o","+")[1:150 \%in\% model$index + 1]) ## try regression mode on two dimensions # create data x <- seq(0.1, 5, by = 0.05) y <- log(x) + rnorm(x, sd = 0.2) # estimate model and predict input values m <- svm(x, y) new <- predict(m, x) # visualize plot(x, y) points(x, log(x), col = 2) points(x, new, col = 4) ## density-estimation # create 2-dim. normal with rho=0: X <- data.frame(a = rnorm(1000), b = rnorm(1000)) attach(X) # traditional way: m <- svm(X, gamma = 0.1) # formula interface: m <- svm(~., data = X, gamma = 0.1) # or: m <- svm(~ a + b, gamma = 0.1) # test: newdata <- data.frame(a = c(0, 4), b = c(0, 4)) predict (m, newdata) # visualize: plot(X, col = 1:1000 \%in\% m$index + 1, xlim = c(-5,5), ylim=c(-5,5)) points(newdata, pch = "+", col = 2, cex = 5) ## weights: (example not particularly sensible) i2 <- iris levels(i2$Species)[3] <- "versicolor" summary(i2$Species) wts <- 100 / table(i2$Species) wts m <- svm(Species ~ ., data = i2, class.weights = wts) ## extract coefficients for linear kernel # a. regression x <- 1:100 y <- x + rnorm(100) m <- svm(y ~ x, scale = FALSE, kernel = "linear") coef(m) plot(y ~ x) abline(m, col = "red") # b. classification # transform iris data to binary problem, and scale data setosa <- as.factor(iris$Species == "setosa") iris2 = scale(iris[,-5]) # fit binary C-classification model m <- svm(setosa ~ Petal.Width + Petal.Length, data = iris2, kernel = "linear") # plot data and separating hyperplane plot(Petal.Length ~ Petal.Width, data = iris2, col = setosa) (cf <- coef(m)) abline(-cf[1]/cf[3], -cf[2]/cf[3], col = "red") # plot margin and mark support vectors abline(-(cf[1] + 1)/cf[3], -cf[2]/cf[3], col = "blue") abline(-(cf[1] - 1)/cf[3], -cf[2]/cf[3], col = "blue") points(m$SV, pch = 5, cex = 2) } \keyword{neural} \keyword{nonlinear} \keyword{classif} e1071/man/plot.svm.Rd0000755000175100001440000000437311633216702013664 0ustar hornikusers\name{plot.svm} \alias{plot.svm} %- Also NEED an `\alias' for EACH other topic documented here. \title{Plot SVM Objects} \description{ Generates a scatter plot of the input data of a \code{svm} fit for classification models by highlighting the classes and support vectors. Optionally, draws a filled contour plot of the class regions. } \usage{ \method{plot}{svm}(x, data, formula, fill = TRUE, grid = 50, slice = list(), symbolPalette = palette(), svSymbol = "x", dataSymbol = "o", ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{An object of class \code{svm}} \item{data}{data to visualize. Should be the same used for fitting.} \item{formula}{formula selecting the visualized two dimensions. Only needed if more than two input variables are used.} \item{fill}{switch indicating whether a contour plot for the class regions should be added.} \item{grid}{granularity for the contour plot.} \item{slice}{a list of named values for the dimensions held constant (only needed if more than two variables are used). The defaults for unspecified dimensions are 0 (for numeric variables) and the first level (for factors). Factor levels can either be specified as factors or character vectors of length 1.} \item{symbolPalette}{Color palette used for the class the data points and support vectors belong to.} \item{svSymbol}{Symbol used for support vectors.} \item{dataSymbol}{Symbol used for data points (other than support vectors).} \item{\dots}{additional graphics parameters passed to \code{filled.contour} and \code{plot}.} } \author{David Meyer\cr \email{David.Meyer@R-project.org}} \seealso{\code{\link{svm}}} \examples{ ## a simple example data(cats, package = "MASS") m <- svm(Sex~., data = cats) plot(m, cats) ## more than two variables: fix 2 dimensions data(iris) m2 <- svm(Species~., data = iris) plot(m2, iris, Petal.Width ~ Petal.Length, slice = list(Sepal.Width = 3, Sepal.Length = 4)) ## plot with custom symbols and colors plot(m, cats, svSymbol = 1, dataSymbol = 2, symbolPalette = rainbow(4), color.palette = terrain.colors) } \keyword{neural}% at least one, from doc/KEYWORDS \keyword{classif}% __ONLY ONE__ keyword per line \keyword{nonlinear}% __ONLY ONE__ keyword per line e1071/man/tune.Rd0000655000175100001440000001063312714054325013052 0ustar hornikusers\name{tune} \alias{tune} \alias{best.tune} \alias{print.tune} \alias{summary.tune} \alias{print.summary.tune} \title{Parameter Tuning of Functions Using Grid Search} \description{ This generic function tunes hyperparameters of statistical methods using a grid search over supplied parameter ranges. } \usage{ tune(method, train.x, train.y = NULL, data = list(), validation.x = NULL, validation.y = NULL, ranges = NULL, predict.func = predict, tunecontrol = tune.control(), ...) best.tune(...) } \arguments{ \item{method}{either the function to be tuned, or a character string naming such a function.} \item{train.x}{either a formula or a matrix of predictors.} \item{train.y}{the response variable if \code{train.x} is a predictor matrix. Ignored if \code{train.x} is a formula.} \item{data}{data, if a formula interface is used. Ignored, if predictor matrix and response are supplied directly.} \item{validation.x}{an optional validation set. Depending on whether a formula interface is used or not, the response can be included in \code{validation.x} or separately specified using \code{validation.y}. Only used for bootstrap and fixed validation set (see \code{\link{tune.control}})} \item{validation.y}{if no formula interface is used, the response of the (optional) validation set. Only used for bootstrap and fixed validation set (see \code{\link{tune.control}})} \item{ranges}{a named list of parameter vectors spanning the sampling space. The vectors will usually be created by \code{seq}.} \item{predict.func}{optional predict function, if the standard \code{predict} behavior is inadequate.} \item{tunecontrol}{object of class \code{"tune.control"}, as created by the function \code{tune.control()}. If omitted, \code{tune.control()} gives the defaults.} \item{\dots}{Further parameters passed to the training functions.} } \value{ For \code{tune}, an object of class \code{tune}, including the components: \item{best.parameters}{a 1 x k data frame, k number of parameters.} \item{best.performance}{best achieved performance.} \item{performances}{if requested, a data frame of all parameter combinations along with the corresponding performance results.} \item{train.ind}{list of index vectors used for splits into training and validation sets.} \item{best.model}{if requested, the model trained on the complete training data using the best parameter combination.} \code{best.tune()} returns the best model detected by \code{tune}. } \details{ As performance measure, the classification error is used for classification, and the mean squared error for regression. It is possible to specify only one parameter combination (i.e., vectors of length 1) to obtain an error estimation of the specified type (bootstrap, cross-classification, etc.) on the given data set. For convenience, there are several \code{tune.foo()} wrappers defined, e.g., for \code{nnet()}, \code{randomForest()}, \code{rpart()}, \code{svm()}, and \code{knn()}. Cross-validation randomizes the data set before building the splits which---once created---remain constant during the training process. The splits can be recovered through the \code{train.ind} component of the returned object. } \author{ David Meyer\cr \email{David.Meyer@R-project.org} } \seealso{\code{\link{tune.control}}, \code{\link{plot.tune}}, \code{\link{tune.svm}}, \link{tune.wrapper}} \examples{ data(iris) ## tune `svm' for classification with RBF-kernel (default in svm), ## using one split for training/validation set obj <- tune(svm, Species~., data = iris, ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)), tunecontrol = tune.control(sampling = "fix") ) ## alternatively: ## obj <- tune.svm(Species~., data = iris, gamma = 2^(-1:1), cost = 2^(2:4)) summary(obj) plot(obj) ## tune `knn' using a convenience function; this time with the ## conventional interface and bootstrap sampling: x <- iris[,-5] y <- iris[,5] obj2 <- tune.knn(x, y, k = 1:5, tunecontrol = tune.control(sampling = "boot")) summary(obj2) plot(obj2) ## tune `rpart' for regression, using 10-fold cross validation (default) data(mtcars) obj3 <- tune.rpart(mpg~., data = mtcars, minsplit = c(5,10,15)) summary(obj3) plot(obj3) ## simple error estimation for lm using 10-fold cross validation tune(lm, mpg~., data = mtcars) } \keyword{models} e1071/man/hanning.window.Rd0000755000175100001440000000142311400421345015014 0ustar hornikusers\name{hanning.window} \title{Computes the Coefficients of a Hanning Window.} \usage{hanning.window(n)} \alias{hanning.window} \arguments{ \item{n}{The length of the window.} } \description{The filter coefficients \eqn{w_i}{w(i)} of a Hanning window of length \code{n} are computed according to the formula \deqn{w_i = 0.5 - 0.5 \cos\frac{2\pi i}{n-1}}{ w(i) = 0.5 - 0.5*cos(2*pi*i/(n-1))} } \value{A vector containing the filter coefficients.} \references{For a definition of the Hanning window, see for example\cr Alan V. Oppenheim and Roland W. Schafer: "Discrete-Time Signal Processing", Prentice-Hall, 1989.} \author{Andreas Weingessel} \seealso{stft, hamming.window} \examples{hanning.window(10) x<-rnorm(500) y<-stft(x, wtype="hanning.window") plot(y) } \keyword{ts} e1071/man/kurtosis.Rd0000755000175100001440000000317011400421345013750 0ustar hornikusers\name{kurtosis} \alias{kurtosis} \title{Kurtosis} \description{ Computes the kurtosis. } \usage{ kurtosis(x, na.rm = FALSE, type = 3) } \arguments{ \item{x}{a numeric vector containing the values whose kurtosis is to be computed.} \item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.} \item{type}{an integer between 1 and 3 selecting one of the algorithms for computing skewness detailed below.} } \details{ If \code{x} contains missings and these are not removed, the skewness is \code{NA}. Otherwise, write \eqn{x_i} for the non-missing elements of \code{x}, \eqn{n} for their number, \eqn{\mu}{mu} for their mean, \eqn{s} for their standard deviation, and \eqn{m_r = \sum_i (x_i - \mu)^r / n}{m_r = \sum_i (x_i - mu)^r / n} for the sample moments of order \eqn{r}. Joanes and Gill (1998) discuss three methods for estimating kurtosis: \describe{ \item{Type 1:}{ \eqn{g_2 = m_4 / m_2^2 - 3}. This is the typical definition used in many older textbooks.} \item{Type 2:}{ \eqn{G_2 = ((n+1) g_2 + 6) * (n-1) / ((n-2)(n-3))}. Used in SAS and SPSS. } \item{Type 3:}{ \eqn{b_2 = m_4 / s^4 - 3 = (g_2 + 3) (1 - 1/n)^2 - 3}. Used in MINITAB and BMDP.} } Only \eqn{G_2} (corresponding to \code{type = 2}) is unbiased under normality. } \value{ The estimated kurtosis of \code{x}. } \references{ D. N. Joanes and C. A. Gill (1998), Comparing measures of sample skewness and kurtosis. \emph{The Statistician}, \bold{47}, 183--189. } \examples{ x <- rnorm(100) kurtosis(x) } \keyword{univar} e1071/man/hamming.distance.Rd0000755000175100001440000000122712505565430015312 0ustar hornikusers\name{hamming.distance} \alias{hamming.distance} \title{Hamming Distances of Vectors} \usage{ hamming.distance(x, y) } \arguments{ \item{x}{a vector or matrix.} \item{y}{an optional vector.} } \description{ If both \code{x} and \code{y} are vectors, \code{hamming.distance} returns the Hamming distance (number of different elements) between this two vectors. If \code{x} is a matrix, the Hamming distances between the rows of \code{x} are computed and \code{y} is ignored. } \examples{ x <- c(1, 0, 0) y <- c(1, 0, 1) hamming.distance(x, y) z <- rbind(x,y) rownames(z) <- c("Fred", "Tom") hamming.distance(z) hamming.distance(1:3, 3:1) } \keyword{multivariate} e1071/man/tune.control.Rd0000755000175100001440000000523012444323171014525 0ustar hornikusers\name{tune.control} \alias{tune.control} \title{Control Parameters for the Tune Function} \description{ Creates an object of class \code{tune.control} to be used with the \code{tune} function, containing various control parameters. } \usage{ tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = mean, sampling = c("cross", "fix", "bootstrap"), sampling.aggregate = mean, sampling.dispersion = sd, cross = 10, fix = 2/3, nboot = 10, boot.size = 9/10, best.model = TRUE, performances = TRUE, error.fun = NULL) } \arguments{ \item{random}{if an integer value is specified, \code{random} parameter vectors are drawn from the parameter space.} \item{nrepeat}{specifies how often training shall be repeated.} \item{repeat.aggregate}{function for aggregating the repeated training results.} \item{sampling}{sampling scheme. If \code{sampling = "cross"}, a \code{cross}-times cross validation is performed. If \code{sampling = "boot"}, \code{nboot} training sets of size \code{boot.size} (part) are sampled (with replacement) from the supplied data. If \code{sampling = "fix"}, a single split into training/validation set is used, the training set containing a \code{fix} part of the supplied data. Note that a separate validation set can be supplied via \code{validation.x} and \code{validation.y}. It is only used for \code{sampling = "boot"} and \code{sampling = "fix"}; in the latter case, \code{fix} is set to 1.} \item{sampling.aggregate,sampling.dispersion}{functions for aggregating the training results on the generated training samples (default: mean and standard deviation).} \item{cross}{number of partitions for cross-validation.} \item{fix}{part of the data used for training in fixed sampling.} \item{nboot}{number of bootstrap replications.} \item{boot.size}{size of the bootstrap samples.} \item{best.model}{if \code{TRUE}, the best model is trained and returned (the best parameter set is used for training on the complete training set).} \item{performances}{if \code{TRUE}, the performance results for all parameter combinations are returned.} \item{error.fun}{function returning the error measure to be minimized. It takes two arguments: a vector of true values and a vector of predicted values. If \code{NULL}, the misclassification error is used for categorical predictions and the mean squared error for numeric predictions.} } \value{ An object of class \code{"tune.control"} containing all the above parameters (either the defaults or the user specified values). } \author{ David Meyer\cr \email{David.Meyer@R-project.org} } \seealso{\code{\link{tune}}} \keyword{models} e1071/man/hamming.window.Rd0000755000175100001440000000142711400421345015016 0ustar hornikusers\name{hamming.window} \title{Computes the Coefficients of a Hamming Window.} \usage{hamming.window(n)} \alias{hamming.window} \arguments{ \item{n}{The length of the window.} } \description{The filter coefficients \eqn{w_i}{w(i)} of a Hamming window of length \code{n} are computed according to the formula \deqn{w_i = 0.54 - 0.46 \cos\frac{2\pi i}{n-1}}{ w(i) = 0.54 - 0.46*cos(2*pi*i/(n-1))} } \value{A vector containing the filter coefficients.} \references{For a definition of the Hamming window, see for example\cr Alan V. Oppenheim and Roland W. Schafer: "Discrete-Time Signal Processing", Prentice-Hall, 1989.} \author{Andreas Weingessel} \seealso{stft, hanning.window} \examples{hamming.window(10) x<-rnorm(500) y<-stft(x, wtype="hamming.window") plot(y) } \keyword{ts} e1071/man/bclust.Rd0000755000175100001440000001160012547045550013373 0ustar hornikusers\name{bclust} \alias{bclust} \alias{hclust.bclust} \alias{plot.bclust} \alias{centers.bclust} \alias{clusters.bclust} \title{Bagged Clustering} \usage{ bclust(x, centers=2, iter.base=10, minsize=0, dist.method="euclidian", hclust.method="average", base.method="kmeans", base.centers=20, verbose=TRUE, final.kmeans=FALSE, docmdscale=FALSE, resample=TRUE, weights=NULL, maxcluster=base.centers, ...) hclust.bclust(object, x, centers, dist.method=object$dist.method, hclust.method=object$hclust.method, final.kmeans=FALSE, docmdscale = FALSE, maxcluster=object$maxcluster) \method{plot}{bclust}(x, maxcluster=x$maxcluster, main, ...) centers.bclust(object, k) clusters.bclust(object, k, x=NULL) } \arguments{ \item{x}{Matrix of inputs (or object of class \code{"bclust"} for plot).} \item{centers, k}{Number of clusters.} \item{iter.base}{Number of runs of the base cluster algorithm.} \item{minsize}{Minimum number of points in a base cluster.} \item{dist.method}{Distance method used for the hierarchical clustering, see \code{\link{dist}} for available distances.} \item{hclust.method}{Linkage method used for the hierarchical clustering, see \code{\link{hclust}} for available methods.} \item{base.method}{Partitioning cluster method used as base algorithm.} \item{base.centers}{Number of centers used in each repetition of the base method.} \item{verbose}{Output status messages.} \item{final.kmeans}{If \code{TRUE}, a final kmeans step is performed using the output of the bagged clustering as initialization.} \item{docmdscale}{Logical, if \code{TRUE} a \code{\link{cmdscale}} result is included in the return value.} \item{resample}{Logical, if \code{TRUE} the base method is run on bootstrap samples of \code{x}, else directly on \code{x}.} \item{weights}{Vector of length \code{nrow(x)}, weights for the resampling. By default all observations have equal weight.} \item{maxcluster}{Maximum number of clusters memberships are to be computed for.} \item{object}{Object of class \code{"bclust"}.} \item{main}{Main title of the plot.} \item{\dots}{Optional arguments top be passed to the base method in \code{bclust}, ignored in \code{plot}.} } \description{ Cluster the data in \code{x} using the bagged clustering algorithm. A partitioning cluster algorithm such as \code{\link{kmeans}} is run repeatedly on bootstrap samples from the original data. The resulting cluster centers are then combined using the hierarchical cluster algorithm \code{\link{hclust}}. } \details{ First, \code{iter.base} bootstrap samples of the original data in \code{x} are created by drawing with replacement. The base cluster method is run on each of these samples with \code{base.centers} centers. The \code{base.method} must be the name of a partitioning cluster function returning a list with the same components as the return value of \code{\link{kmeans}}. This results in a collection of \code{iter.base * base.centers} centers, which are subsequently clustered using the hierarchical method \code{\link{hclust}}. Base centers with less than \code{minsize} points in there respective partitions are removed before the hierarchical clustering. The resulting dendrogram is then cut to produce \code{centers} clusters. Hence, the name of the argument \code{centers} is a little bit misleading as the resulting clusters need not be convex, e.g., when single linkage is used. The name was chosen for compatibility with standard partitioning cluster methods such as \code{\link{kmeans}}. A new hierarchical clustering (e.g., using another \code{hclust.method}) re-using previous base runs can be performed by running \code{hclust.bclust} on the return value of \code{bclust}. } \value{ \code{bclust} and \code{hclust.bclust} return objects of class \code{"bclust"} including the components \item{hclust}{Return value of the hierarchical clustering of the collection of base centers (Object of class \code{"hclust"}).} \item{cluster}{Vector with indices of the clusters the inputs are assigned to.} \item{centers}{Matrix of centers of the final clusters. Only useful, if the hierarchical clustering method produces convex clusters.} \item{allcenters}{Matrix of all \code{iter.base * base.centers} centers found in the base runs.} } \author{Friedrich Leisch} \references{ Friedrich Leisch. Bagged clustering. Working Paper 51, SFB ``Adaptive Information Systems and Modeling in Economics and Management Science'', August 1999. \url{http://epub.wu.ac.at/1272/1/document.pdf}} \seealso{\code{\link{hclust}}, \code{\link{kmeans}}, \code{\link{boxplot.bclust}}} \keyword{multivariate} \keyword{cluster} \examples{ data(iris) bc1 <- bclust(iris[,1:4], 3, base.centers=5) plot(bc1) table(clusters.bclust(bc1, 3)) centers.bclust(bc1, 3) } e1071/man/lca.Rd0000755000175100001440000000410211400421345012620 0ustar hornikusers\name{lca} \alias{lca} \alias{print.lca} \alias{summary.lca} \alias{print.summary.lca} \alias{predict.lca} \title{Latent Class Analysis (LCA)} \usage{ lca(x, k, niter=100, matchdata=FALSE, verbose=FALSE) } \arguments{ \item{x}{Either a data matrix of binary observations or a list of patterns as created by \code{\link{countpattern}}} \item{k}{Number of classes used for LCA} \item{niter}{Number of Iterations} \item{matchdata}{If \code{TRUE} and \code{x} is a data matrix, the class membership of every data point is returned, otherwise the class membership of every pattern is returned.} \item{verbose}{If \code{TRUE} some output is printed during the computations.} } \description{ A latent class analysis with \code{k} classes is performed on the data given by \code{x}. } \value{ An object of class \code{"lca"} is returned, containing \item{w}{Probabilities to belong to each class} \item{p}{Probabilities of a `1' for each variable in each class} \item{matching}{Depending on \code{matchdata} either the class membership of each pattern or of each data point} \item{logl, loglsat}{The LogLikelihood of the model and of the saturated model} \item{bic, bicsat}{The BIC of the model and of the saturated model} \item{chisq}{Pearson's Chisq} \item{lhquot}{Likelihood quotient of the model and the saturated model} \item{n}{Number of data points.} \item{np}{Number of free parameters.} } \references{Anton K. Formann: ``Die Latent-Class-Analysis'', Beltz Verlag 1984} \author{Andreas Weingessel} \seealso{ \code{\link{countpattern}}, \code{\link{bootstrap.lca}} } \examples{ ## Generate a 4-dim. sample with 2 latent classes of 500 data points each. ## The probabilities for the 2 classes are given by type1 and type2. type1 <- c(0.8,0.8,0.2,0.2) type2 <- c(0.2,0.2,0.8,0.8) x <- matrix(runif(4000),nr=1000) x[1:500,] <- t(t(x[1:500,])}{N}=\frac{||U||^2}{N}} \itemize{ \item \eqn{F(U;k)} shows the fuzziness or the overlap of the partition and depends on \eqn{kN} elements. \item \eqn{1/k\leq F(U;k)\leq 1}, where if \eqn{F(U;k)=1} then \eqn{U} is a hard partition and if \eqn{F(U;k)=1/k} then \eqn{U=[1/k]} is the centroid of the fuzzy partion space \eqn{P_{fk}}. The converse is also valid. } } \item{\bold{partition.entropy}:}{ It is a measure that provides information about the membership matrix without also considering the data itself. The minimum values imply a good partition in the meaning of a more crisp partition. \eqn{H(U;k)=\sum_{i=1}^{N} h(u_i)/N}, where \eqn{h(u)=-\sum_{j=1}^{k} u_j\,\log _a (u_j)} the Shannon's entropy. \itemize{ \item \eqn{H(U;k)} shows the uncertainty of a fuzzy partition and depends also on \eqn{kN} elements. Specifically, \eqn{h(u_i)} is interpreted as the amount of fuzzy information about the membership of \eqn{x_i} in \eqn{k} classes that is retained by column \eqn{u_j}. Thus, at \eqn{U=[1/k]} the most information is withheld since the membership is the fuzziest possible. \item \eqn{0\leq H(U;k)\leq \log_a(k)}, where for \eqn{H(U;k)=0} \eqn{U} is a hard partition and for \eqn{H(U;k)=\log_a(k)} \eqn{U=[1/k]}. } } \item{\bold{proportion.exponent}:}{ It is a measure \eqn{P(U;k)} of fuzziness adept to detect structural variations in the partition matrix as it becomes more fuzzier. A crisp cluster in the partition matrix can drive it to infinity when the partition coefficient and the partition entropy are more sensitive to small changes when approaching a hard partition. Its evaluation does not also involve the data or the algorithm used to partition them and its maximum implies the optimal partition but without knowing what maximum is a statistically significant maximum. \itemize{ \item \eqn{0\leq P(U;k)<\infty}, since the \eqn{[0,1]} values explode to \eqn{[0,\infty)} due to the natural logarithm. Specifically, \eqn{P=0} when and only when \eqn{U=[1/k]}, while \eqn{P\rightarrow\infty} when any column of \eqn{U} is crisp. \item \eqn{P(U;k)} can easily explode and it is good for partitions with large column maximums and at detecting structural variations. } } \item{\bold{separation.index (known as CS Index)}:}{ This index identifies unique cluster structure with well-defined properties that depend on the data and a measure of distance. It answers the question if the clusters are compact and separated, but it rather seems computationally infeasible for big data sets since a distance matrix between all the data membership values has to be calculated. It also presupposes that a hard partition is derived from the fuzzy one.\cr \eqn{D_1(U;k;X,d)=\min_{i+1\,\leq\,l\,\leq\,k-1}\left\{\min_{1\,\leq\,j\,\leq\,k}\left\{\frac{dis(u_j,u_l)}{\max_{1\leq m\leq k}\{dia(u_m)\}}\right\}\right\}}, where \eqn{dia} is the diameter of the subset, \eqn{dis} the distance of two subsets, and \eqn{d} a metric. \eqn{U} is a CS partition of \eqn{X} \eqn{\Leftrightarrow D_1>1}. When this holds then \eqn{U} is unique. } } } \value{ Returns a vector with the validity measures values. } \references{ James C. Bezdek, \emph{Pattern Recognition with Fuzzy Objective Function Algorithms}, Plenum Press, 1981, NY.\cr L. X. Xie and G. Beni, \emph{Validity measure for fuzzy clustering}, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. \bold{3}, n. 8, p. 841-847, 1991.\cr I. Gath and A. B. Geva, \emph{Unsupervised Optimal Fuzzy Clustering}, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. \bold{11}, n. 7, p. 773-781, 1989.\cr Y. Fukuyama and M. Sugeno, \emph{A new method of choosing the number of clusters for the fuzzy $c$-means method}, Proc. 5th Fuzzy Syst. Symp., p. 247-250, 1989 (in japanese).} \author{Evgenia Dimitriadou} \seealso{\code{\link{cmeans}}} \examples{ # a 2-dimensional example x<-rbind(matrix(rnorm(100,sd=0.3),ncol=2), matrix(rnorm(100,mean=1,sd=0.3),ncol=2)) cl<-cmeans(x,2,20,verbose=TRUE,method="cmeans") resultindexes <- fclustIndex(cl,x, index="all") resultindexes } \keyword{cluster} e1071/man/bincombinations.Rd0000755000175100001440000000052311400421345015242 0ustar hornikusers\name{bincombinations} \title{Binary Combinations} \usage{ bincombinations(p) } \alias{bincombinations} \arguments{ \item{p}{Length of binary vectors} } \description{ Returns a matrix containing the \eqn{2^p} vectors of length \code{p}. } \author{Friedrich Leisch} \examples{ bincombinations(2) bincombinations(3) } \keyword{utilities} e1071/man/permutations.Rd0000755000175100001440000000054411400421345014621 0ustar hornikusers\name{permutations} \alias{permutations} \title{All Permutations of Integers 1:n} \description{ Returns a matrix containing all permutations of the integers \code{1:n} (one permutation per row). } \usage{ permutations(n) } \arguments{ \item{n}{Number of element to permute.} } \author{Friedrich Leisch} \examples{ permutations(3) } \keyword{datagen} e1071/man/boxplot.bclust.Rd0000755000175100001440000000214511400421345015050 0ustar hornikusers\name{boxplot.bclust} \alias{boxplot.bclust} \title{Boxplot of Cluster Profiles} \usage{ \method{boxplot}{bclust}(x, n=nrow(x$centers), bycluster=TRUE, main=deparse(substitute(x)), oneplot=TRUE, which=1:n, ...) } \arguments{ \item{x}{Clustering result, object of class \code{"bclust"}.}% \item{n}{Number of clusters to plot, by default the number of clusters used in the call of \code{\link{bclust}}.} \item{bycluster}{If \code{TRUE} (default), a boxplot for each cluster is plotted. If \code{FALSE}, a boxplot for each variable is plotted.} \item{main}{Main title of the plot, by default the name of the cluster object.} \item{oneplot}{If \code{TRUE}, all boxplots appear on one screen (using an appropriate rectangular layout).} \item{which}{Number of clusters which should be plotted, default is all clusters.} \item{...}{Additional arguments for \code{\link{boxplot}}.} } \description{ Makes boxplots of the results of a bagged clustering run. } \author{Friedrich Leisch} \keyword{hplot} \examples{ data(iris) bc1 <- bclust(iris[,1:4], 3, base.centers=5) boxplot(bc1) } e1071/man/stft.Rd0000755000175100001440000000317211400421345013047 0ustar hornikusers\name{stft} \title{Computes the Short Time Fourier Transform of a Vector} \usage{stft(X, win=min(80,floor(length(X)/10)), inc=min(24, floor(length(X)/30)), coef=64, wtype="hanning.window")} \alias{stft} \arguments{ \item{X}{The vector from which the stft is computed.} \item{win}{Length of the window. For long vectors the default window size is 80, for short vectors the window size is chosen so that 10 windows fit in the vector.} \item{inc}{Increment by which the window is shifted. For long vectors the default increment is 24, for short vectors the increment is chosen so that 30 increments fit in the vector.} \item{coef}{Number of Fourier coefficients} \item{wtype}{Type of window used} } \description{This function computes the Short Time Fourier Transform of a given vector \code{X}. First, time-slices of length \code{win} are extracted from the vector. The shift of one time-slice to the next one is given by \code{inc}. The values of these time-slices are smoothed by mulitplying them with a window function specified in \code{wtype}. For the thus obtained windows, the Fast Fourier Transform is computed.} \value{Object of type stft. Contains the values of the stft and information about the parameters. \item{values}{A matrix containing the results of the stft. Each row of the matrix contains the \code{coef} Fourier coefficients of one window.} \item{windowsize}{The value of the parameter \code{win}} \item{increment}{The value of the parameter \code{inc}} \item{windowtype}{The value of the parameter \code{wtype}} } \author{Andreas Weingessel} \seealso{plot.stft} \examples{x<-rnorm(500) y<-stft(x) plot(y) } \keyword{ts} e1071/man/matchControls.Rd0000755000175100001440000000545011400421345014710 0ustar hornikusers\name{matchControls} \alias{matchControls} \title{Find Matched Control Group} \usage{ matchControls(formula, data = list(), subset, contlabel = "con", caselabel = NULL, dogrep = TRUE, replace = FALSE) } \arguments{ \item{formula}{A formula indicating cases, controls and the variables to be matched. Details are described below.} \item{data}{an optional data frame containing the variables in the model. By default the variables are taken from the environment which \code{matchControls} is called from.} \item{subset}{an optional vector specifying a subset of observations to be used in the matching process.} \item{contlabel}{A string giving the label of the control group.} \item{caselabel}{A string giving the labels of the cases.} \item{dogrep}{If \code{TRUE}, then \code{contlabel} and \code{contlabel} are matched using \code{\link{grep}}, else string comparison (exact equality) is used.} \item{replace}{If \code{FALSE}, then every control is used only once.} } \description{ Finds controls matching the cases as good as possible. } \details{ The left hand side of the \code{formula} must be a factor determining whether an observation belongs to the case or the control group. By default, all observations where a grep of \code{contlabel} matches, are used as possible controls, the rest is taken as cases. If \code{caselabel} is given, then only those observations are taken as cases. If \code{dogrep = TRUE}, then both \code{contlabel} and \code{caselabel} can be regular expressions. The right hand side of the \code{formula} gives the variables that should be matched. The matching is done using the \code{\link{daisy}} distance from the \code{cluster} package, i.e., a model frame is built from the formula and used as input for \code{\link{daisy}}. For each case, the nearest control is selected. If \code{replace = FALSE}, each control is used only once. } \value{ Returns a list with components \item{cases}{Row names of cases.} \item{controls}{Row names of matched controls.} \item{factor}{A factor with 2 levels indicating cases and controls (the rest is set to \code{NA}.} } \author{Friedrich Leisch} \examples{ Age.case <- 40 + 5 * rnorm(50) Age.cont <- 45 + 10 * rnorm(150) Age <- c(Age.case, Age.cont) Sex.case <- sample(c("M", "F"), 50, prob = c(.4, .6), replace = TRUE) Sex.cont <- sample(c("M", "F"), 150, prob = c(.6, .4), replace = TRUE) Sex <- as.factor(c(Sex.case, Sex.cont)) casecont <- as.factor(c(rep("case", 50), rep("cont", 150))) ## now look at the group properties: boxplot(Age ~ casecont) barplot(table(Sex, casecont), beside = TRUE) m <- matchControls(casecont ~ Sex + Age) ## properties of the new groups: boxplot(Age ~ m$factor) barplot(table(Sex, m$factor)) } \keyword{manip} e1071/man/interpolate.Rd0000755000175100001440000000214211400421345014411 0ustar hornikusers\name{interpolate} \title{Interpolate Values of Array} \usage{ interpolate(x, a, adims=lapply(dimnames(a), as.numeric), method="linear") } \alias{interpolate} \arguments{ \item{x}{Matrix of values at which interpolation shall take place.} \item{a}{Array of arbitrary dimension.} \item{adims}{List of the same structure as \code{dimnames(a)}.} \item{method}{Interpolation method, one of \code{"linear"} or \code{"constant"}.} } \description{ For each row in matrix \code{x}, the hypercube of \code{a} containing this point is searched. The corners of the hypercube are linearly interpolated. By default, \code{dimnames(a)} is taken to contain the coordinate values for each point in \code{a}. This can be overridden using \code{adims}. If \code{method=="constant"}, the value of the ``lower left'' corner of the hypercube is returned. } \author{Friedrich Leisch} \seealso{\code{\link{approx}}, \code{\link{spline}}} \examples{ x <- seq(0,3,0.2) z <- outer(x,x, function(x,y) sin(x*y)) dimnames(z) <- list(x,x) sin(1.1*2.1) interpolate(c(1.1, 2.1),z) } \keyword{arith} \keyword{multivariate} e1071/man/rwiener.Rd0000755000175100001440000000075711400421345013550 0ustar hornikusers\name{rwiener} \alias{rwiener} \title{Simulation of Wiener Process} \usage{ rwiener(end = 1, frequency = 1000) } \arguments{ \item{end}{the time of the last observation.} \item{frequency}{the number of observations per unit of time.} } \description{ \code{rwiener} returns a time series containing a simulated realization of the Wiener process on the interval [0,\code{end}] } \examples{ # simulate a Wiener process on [0,1] and plot it x <- rwiener() plot(x,type="l") } \keyword{distribution} e1071/man/shortestPaths.Rd0000755000175100001440000000422011400421345014735 0ustar hornikusers\name{allShortestPaths} \alias{allShortestPaths} \alias{extractPath} \title{Find Shortest Paths Between All Nodes in a Directed Graph} \description{ \code{allShortestPaths} finds all shortest paths in a directed (or undirected) graph using Floyd's algorithm. \code{extractPath} can be used to actually extract the path between a given pair of nodes. } \usage{ allShortestPaths(x) extractPath(obj, start, end) } \arguments{ \item{x}{matrix or distance object} \item{obj}{return value of \code{allShortestPaths}} \item{start}{integer, starting point of path} \item{end}{integer, end point of path} } \details{ If \code{x} is a matrix, then \code{x[i,j]} has to be the length of the direct path from point \code{i} to point \code{j}. If no direct connection from point \code{i} to point \code{j} exist, then \code{x[i,j]} should be either \code{NA} or \code{Inf}. Note that the graph can be directed, hence \code{x[i,j]} need not be the same as \code{x[j,i]}. The main diagonal of \code{x} is ignored. Alternatively, \code{x} can be a distance object as returned by \code{\link{dist}} (corresponding to an undirected graph). } \value{ \code{allShortestPaths} returns a list with components \item{length}{A matrix with the total lengths of the shortest path between each pair of points.} \item{middlePoints}{A matrix giving a point in the middle of each shortest path (or 0 if the direct connection is the shortest path), this is mainly used as input for \code{extractPath}.} \code{extractPath} returns a vector of node numbers giving with the shortest path between two points. } \references{Kumar, V., Grama, A., Gupta, A. and Karypis, G. Introduction to Parallel Programming - Design and Analysis of Algorithms, Benjamin Cummings Publishing, 1994, ISBN 0-8053-3170-0} \author{Friedrich Leisch} \examples{ ## build a graph with 5 nodes x <- matrix(NA, 5, 5) diag(x) <- 0 x[1,2] <- 30; x[1,3] <- 10 x[2,4] <- 70; x[2,5] <- 40 x[3,4] <- 50; x[3,5] <- 20 x[4,5] <- 60 x[5,4] <- 10 print(x) ## compute all path lengths z <- allShortestPaths(x) print(z) ## the following should give 1 -> 3 -> 5 -> 4 extractPath(z, 1, 4) } \keyword{optimize} e1071/man/tune.wrapper.Rd0000755000175100001440000000470512263003667014537 0ustar hornikusers\name{tune.wrapper} \alias{tune.wrapper} \alias{tune.rpart} \alias{best.rpart} \alias{tune.svm} \alias{best.svm} \alias{tune.nnet} \alias{best.nnet} \alias{tune.randomForest} \alias{best.randomForest} \alias{tune.knn} \title{Convenience Tuning Wrapper Functions} \description{ Convenience tuning wrapper functions, using \code{tune}. } \usage{ tune.svm(x, y = NULL, data = NULL, degree = NULL, gamma = NULL, coef0 = NULL, cost = NULL, nu = NULL, class.weights = NULL, epsilon = NULL, ...) best.svm(x, tunecontrol = tune.control(), ...) tune.nnet(x, y = NULL, data = NULL, size = NULL, decay = NULL, trace = FALSE, tunecontrol = tune.control(nrepeat = 5), ...) best.nnet(x, tunecontrol = tune.control(nrepeat = 5), ...) tune.rpart(formula, data, na.action = na.omit, minsplit = NULL, minbucket = NULL, cp = NULL, maxcompete = NULL, maxsurrogate = NULL, usesurrogate = NULL, xval = NULL, surrogatestyle = NULL, maxdepth = NULL, predict.func = NULL, ...) best.rpart(formula, tunecontrol = tune.control(), ...) tune.randomForest(x, y = NULL, data = NULL, nodesize = NULL, mtry = NULL, ntree = NULL, ...) best.randomForest(x, tunecontrol = tune.control(), ...) tune.knn(x, y, k = NULL, l = NULL, ...) } \arguments{ \item{formula, x, y, data}{formula and data arguments of function to be tuned.} \item{predict.func}{predicting function.} \item{na.action}{function handling missingness.} \item{minsplit, minbucket, cp, maxcompete, maxsurrogate, usesurrogate, xval, surrogatestyle, maxdepth}{\code{rpart} parameters.} \item{degree, gamma, coef0, cost, nu, class.weights, epsilon}{\code{svm} parameters.} \item{k, l}{\code{knn} parameters.} \item{mtry, nodesize, ntree}{\code{randomForest} parameters.} \item{size, decay, trace}{parameters passed to \code{nnet}.} \item{tunecontrol}{object of class \code{"tune.control"} containing tuning parameters.} \item{\dots}{Further parameters passed to \code{tune}.} } \value{ \code{tune.foo()} returns a tuning object including the best parameter set obtained by optimizing over the specified parameter vectors. \code{best.foo()} directly returns the best model, i.e. the fit of a new model using the optimal parameters found by \code{tune.foo}. } \details{For examples, see the help page of \code{tune()}.} \author{ David Meyer\cr \email{David.Meyer@R-project.org} } \seealso{\code{\link{tune}}} \keyword{models} e1071/man/e1071-deprecated.Rd0000755000175100001440000000051211400421345014715 0ustar hornikusers\name{e1071-deprecated} \alias{e1071-deprecated} \title{Deprecated Functions in Package e1071} \description{ These functions are provided for compatibility with older versions of package \pkg{e1071} only, and may be defunct as soon as of the next release. } %\usage{ %} \seealso{ \code{\link{Deprecated}} } \keyword{misc} e1071/man/plot.stft.Rd0000755000175100001440000000160411400421345014022 0ustar hornikusers\name{plot.stft} \alias{plot.stft} \title{Plot Short Time Fourier Transforms} \description{ An object of class \code{"stft"} is plotted as a gray scale image. The x-axis corresponds to time, the y-axis to frequency. If the default colormap is used, dark regions in the plot correspond to high values at the particular time/frequency location. } \usage{ \method{plot}{stft}(x, col = gray(63:0/63), \dots) } \arguments{ \item{x}{An object of class \code{"stft"} as obtained by the function \code{stft}.} \item{col}{An optional colormap. By default 64 gray values are used, where white corresponds to the minimum value and black to the maximum.} \item{\dots}{further arguments to be passed to or from methods.} } \value{No return value. This function is only for plotting.} \author{Andreas Weingessel} \seealso{stft} \examples{x<-rnorm(500) y<-stft(x) plot(y) } \keyword{ts} e1071/man/Discrete.Rd0000755000175100001440000000302511400421345013626 0ustar hornikusers\name{Discrete} \alias{ddiscrete} \alias{pdiscrete} \alias{qdiscrete} \alias{rdiscrete} \title{Discrete Distribution} \description{ These functions provide information about the discrete distribution where the probability of the elements of \code{values} is proportional to the values given in \code{probs}, which are normalized to sum up to 1. \code{ddiscrete} gives the density, \code{pdiscrete} gives the distribution function, \code{qdiscrete} gives the quantile function and \code{rdiscrete} generates random deviates. } \usage{ ddiscrete(x, probs, values = 1:length(probs)) pdiscrete(q, probs, values = 1:length(probs)) qdiscrete(p, probs, values = 1:length(probs)) rdiscrete(n, probs, values = 1:length(probs), ...) } \arguments{ \item{x,q}{vector or array of quantiles.} \item{p}{vector or array of probabilities.} \item{n}{number of observations.} \item{probs}{probabilities of the distribution.} \item{values}{values of the distribution.} \item{...}{ignored (only there for backwards compatibility)} } \details{ The random number generator is simply a wrapper for \code{\link{sample}} and provided for backwards compatibility only. } \author{Andreas Weingessel and Friedrich Leisch} \examples{ ## a vector of length 30 whose elements are 1 with probability 0.2 ## and 2 with probability 0.8. rdiscrete (30, c(0.2, 0.8)) ## a vector of length 100 whose elements are A, B, C, D. ## The probabilities of the four values have the relation 1:2:3:3 rdiscrete (100, c(1,2,3,3), c("A","B","C","D")) } \keyword{distribution} e1071/DESCRIPTION0000655000175100001440000000311513567267365012561 0ustar hornikusersPackage: e1071 Version: 1.7-3 Title: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien Imports: graphics, grDevices, class, stats, methods, utils Suggests: cluster, mlbench, nnet, randomForest, rpart, SparseM, xtable, Matrix, MASS, slam Authors@R: c(person(given = "David", family = "Meyer", role = c("aut", "cre"), email = "David.Meyer@R-project.org"), person(given = "Evgenia", family = "Dimitriadou", role = c("aut","cph")), person(given = "Kurt", family = "Hornik", role = "aut"), person(given = "Andreas", family = "Weingessel", role = "aut"), person(given = "Friedrich", family = "Leisch", role = "aut"), person(given = "Chih-Chung", family = "Chang", role = c("ctb","cph"), comment = "libsvm C++-code"), person(given = "Chih-Chen", family = "Lin", role = c("ctb","cph"), comment = "libsvm C++-code")) Description: Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ... License: GPL-2 | GPL-3 LazyLoad: yes NeedsCompilation: yes Packaged: 2019-11-25 17:01:14 UTC; meyer Author: David Meyer [aut, cre], Evgenia Dimitriadou [aut, cph], Kurt Hornik [aut], Andreas Weingessel [aut], Friedrich Leisch [aut], Chih-Chung Chang [ctb, cph] (libsvm C++-code), Chih-Chen Lin [ctb, cph] (libsvm C++-code) Maintainer: David Meyer Repository: CRAN Date/Publication: 2019-11-26 18:29:09 UTC e1071/build/0000755000175100001440000000000013567004332012127 5ustar hornikuserse1071/build/vignette.rds0000644000175100001440000000052413567004332014467 0ustar hornikusers‹Å‘MOÂ@†[J«?H8xÝ£ ròn‰1&Fñ:l§eC»Ûì® 79°-Ó ¨g›t»óÌgßyëzž×ò‚È÷Z»}wDîíU¼íuÜ·ky¬øðI.‰ôÒ¢–™ŠïgÜ>¿…Ò–½"·J³à3!Ñ ;CvW¦&À‘YÅ21uÕ˜¬>‡Ž®oFTéÜù.¯XÓìÑŠ8ùu´’ïg×?Cvùô+ÿ6. îNê´%#ú°0Íâýt·ZHÞ3ž1"¬P²ž^cªÑñ†ôò­R,CÐRÈ”øÉ%Ÿå çßìÂÈ‹­È”jþµu»µ£I(!o扶'ÂÉD/Â6Fð8žÐÕ¯·s4ÆelêåÝãçRéø°QG«å°nvZêÿåŽõz½:œ¨ã`uÝ, íòµÚkŽie1071/tests/0000755000175100001440000000000012212345174012170 5ustar hornikuserse1071/tests/clustering.R0000755000175100001440000000033311400421345014465 0ustar hornikusers## cmeans clustering should also work on data frames library(e1071) data(iris) set.seed(123) cm1 <- cmeans(iris[,1:4], 10) bc1 <- bclust(iris[,1:4], 3, base.centers=20,iter.base=50, base.method="cmeans") e1071/configure.ac0000755000175100001440000000072111400421345013310 0ustar hornikusersAC_PREREQ(2.50) AC_INIT([DESCRIPTION]) : ${R_HOME=`R RHOME`} if test -z "${R_HOME}"; then echo "could not determine R_HOME" exit 1 fi CXX=`${R_HOME}/bin/R CMD config CXX` AC_PROG_CXX if test "${GXX}" = yes; then gxx_version=`${CXX} --version` case ${gxx_version} in 2.96*) AC_MSG_WARN([g++ 2.96 cannot reliably be used with this package.]) AC_MSG_ERROR([Please use a newer version of g++ or a different C++ compiler.]) ;; esac fi e1071/src/0000755000175100001440000000000013567004332011617 5ustar hornikuserse1071/src/floyd.c0000755000175100001440000000140311400421345013070 0ustar hornikusersint e1071_floyd(int *n, double *A, double *C, int *P) /* this function takes an nxn matrix C of edge costs and produces */ /* an nxn matrix A of lengths of shortest paths, and an nxn */ /* matrix P giving a point in the middle of each shortest path */ { int i,j,k; for (i=0; i<*n; i++) for (j=0; j<*n; j++) { A[i + *n * j] = C[i + *n * j]; P[i + *n * j] = -1; } for (i=0; i<*n; i++) A[i + *n * i] = 0; /* no self cycle */ for (k=0; k<*n; k++) for (i=0; i<*n; i++) for (j=0; j<*n; j++) if (A[i + *n * k]+A[k + *n * j] < A[i + *n * j]) { A[i + *n * j] = A[i + *n * k] + A[k + *n * j]; P[i + *n * j] = k; /* k is included in shortest path */ } return 0; } e1071/src/init.c0000644000175100001440000000627513044152377012743 0ustar hornikusers #include #include #include void cmeans(double *x, int *nr_x, int *nc, double *p, int *nr_p, double *w, double *f, int *dist, int *itermax, double *reltol, int *verbose, double *u, double *ermin, int *iter); int cshell(int *xrows, int *xcols, double *x, int *ncenters, double *centers, int *itermax, int *iter, int *verbose, int *dist, double *U, double *UANT, double *f, double *ermin, double *radius, int *flag); int e1071_floyd(int *n, double *A, double *C, int *P); void ufcl(double *x, int *nr_x, int *nc, double *p, int *nr_p, double *w, double *f, int *dist, int *itermax, double *reltol, int *verbose, double *rate_par, double *u, double *ermin, int *iter); void svmtrain (double *x, int *r, int *c, double *y, int *rowindex, int *colindex, int *svm_type, int *kernel_type, int *degree, double *gamma, double *coef0, double *cost, double *nu, int *weightlabels, double *weights, int *nweights, double *cache, double *tolerance, double *epsilon, int *shrinking, int *cross, int *sparse, int *probability, int *nclasses, int *nr, int *index, int *labels, int *nSV, double *rho, double *coefs, double *sigma, double *probA, double *probB, double *cresults, double *ctotal1, double *ctotal2, char **error); void svmpredict (int *decisionvalues, int *probability, double *v, int *r, int *c, int *rowindex, int *colindex, double *coefs, double *rho, int *compprob, double *probA, double *probB, int *nclasses, int *totnSV, int *labels, int *nSV, int *sparsemodel, int *svm_type, int *kernel_type, int *degree, double *gamma, double *coef0, double *x, int *xr, int *xrowindex, int *xcolindex, int *sparsex, double *ret, double *dec, double *prob); void svmwrite (double *v, int *r, int *c, int *rowindex, int *colindex, double *coefs, double *rho, int *compprob, double *probA, double *probB, int *nclasses, int *totnSV, int *labels, int *nSV, int *sparsemodel, int *svm_type, int *kernel_type, int *degree, double *gamma, double *coef0, char **filename); static const R_CMethodDef CEntries[] = { {"cmeans", (DL_FUNC) &cmeans, 14}, {"cshell", (DL_FUNC) &cshell, 15}, {"e1071_floyd", (DL_FUNC) &e1071_floyd, 4}, {"svmpredict", (DL_FUNC) &svmpredict, 30}, {"svmtrain", (DL_FUNC) &svmtrain, 37}, {"svmwrite", (DL_FUNC) &svmwrite, 21}, {"ufcl", (DL_FUNC) &ufcl, 15}, {NULL, NULL, 0} }; void R_init_e1071(DllInfo *dll) { R_registerRoutines(dll, CEntries, NULL, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } e1071/src/svm.cpp0000655000175100001440000020001013325342027013121 0ustar hornikusers#include #include #include #include #include #include #include #include #include #include #include #include "svm.h" int libsvm_version = LIBSVM_VERSION; typedef float Qfloat; typedef signed char schar; #ifndef min template static inline T min(T x,T y) { return (x static inline T max(T x,T y) { return (x>y)?x:y; } #endif template static inline void swap(T& x, T& y) { T t=x; x=y; y=t; } template static inline void clone(T*& dst, S* src, int n) { dst = new T[n]; memcpy((void *)dst,(void *)src,sizeof(T)*n); } static inline double powi(double base, int times) { double tmp = base, ret = 1.0; for(int t=times; t>0; t/=2) { if(t%2==1) ret*=tmp; tmp = tmp * tmp; } return ret; } #define INF HUGE_VAL #define TAU 1e-12 #define Malloc(type,n) (type *)malloc((n)*sizeof(type)) static void print_string_stdout(const char *s) { /* fputs(s,stdout); fflush(stdout); */ Rprintf(s); } static void (*svm_print_string) (const char *) = &print_string_stdout; #if 0 static void info(const char *fmt,...) { char buf[BUFSIZ]; va_list ap; va_start(ap,fmt); vsprintf(buf,fmt,ap); va_end(ap); (*svm_print_string)(buf); } #else static void info(const char *fmt,...) {} #endif // // Kernel Cache // // l is the number of total data items // size is the cache size limit in bytes // class Cache { public: Cache(int l,long int size); ~Cache(); // request data [0,len) // return some position p where [p,len) need to be filled // (p >= len if nothing needs to be filled) int get_data(const int index, Qfloat **data, int len); void swap_index(int i, int j); private: int l; long int size; struct head_t { head_t *prev, *next; // a circular list Qfloat *data; int len; // data[0,len) is cached in this entry }; head_t *head; head_t lru_head; void lru_delete(head_t *h); void lru_insert(head_t *h); }; Cache::Cache(int l_,long int size_):l(l_),size(size_) { head = (head_t *)calloc(l,sizeof(head_t)); // initialized to 0 size /= sizeof(Qfloat); size -= l * sizeof(head_t) / sizeof(Qfloat); size = max(size, 2 * (long int) l); // cache must be large enough for two columns lru_head.next = lru_head.prev = &lru_head; } Cache::~Cache() { for(head_t *h = lru_head.next; h != &lru_head; h=h->next) free(h->data); free(head); } void Cache::lru_delete(head_t *h) { // delete from current location h->prev->next = h->next; h->next->prev = h->prev; } void Cache::lru_insert(head_t *h) { // insert to last position h->next = &lru_head; h->prev = lru_head.prev; h->prev->next = h; h->next->prev = h; } int Cache::get_data(const int index, Qfloat **data, int len) { head_t *h = &head[index]; if(h->len) lru_delete(h); int more = len - h->len; if(more > 0) { // free old space while(size < more) { head_t *old = lru_head.next; lru_delete(old); free(old->data); size += old->len; old->data = 0; old->len = 0; } // allocate new space h->data = (Qfloat *)realloc(h->data,sizeof(Qfloat)*len); size -= more; swap(h->len,len); } lru_insert(h); *data = h->data; return len; } void Cache::swap_index(int i, int j) { if(i==j) return; if(head[i].len) lru_delete(&head[i]); if(head[j].len) lru_delete(&head[j]); swap(head[i].data,head[j].data); swap(head[i].len,head[j].len); if(head[i].len) lru_insert(&head[i]); if(head[j].len) lru_insert(&head[j]); if(i>j) swap(i,j); for(head_t *h = lru_head.next; h!=&lru_head; h=h->next) { if(h->len > i) { if(h->len > j) swap(h->data[i],h->data[j]); else { // give up lru_delete(h); free(h->data); size += h->len; h->data = 0; h->len = 0; } } } } // // Kernel evaluation // // the static method k_function is for doing single kernel evaluation // the constructor of Kernel prepares to calculate the l*l kernel matrix // the member function get_Q is for getting one column from the Q Matrix // class QMatrix { public: virtual Qfloat *get_Q(int column, int len) const = 0; virtual double *get_QD() const = 0; virtual void swap_index(int i, int j) const = 0; virtual ~QMatrix() {} }; class Kernel: public QMatrix { public: Kernel(int l, svm_node * const * x, const svm_parameter& param); virtual ~Kernel(); static double k_function(const svm_node *x, const svm_node *y, const svm_parameter& param); virtual Qfloat *get_Q(int column, int len) const = 0; virtual double *get_QD() const = 0; virtual void swap_index(int i, int j) const // no so const... { swap(x[i],x[j]); if(x_square) swap(x_square[i],x_square[j]); } protected: double (Kernel::*kernel_function)(int i, int j) const; private: const svm_node **x; double *x_square; // svm_parameter const int kernel_type; const int degree; const double gamma; const double coef0; static double dot(const svm_node *px, const svm_node *py); double kernel_linear(int i, int j) const { return dot(x[i],x[j]); } double kernel_poly(int i, int j) const { return powi(gamma*dot(x[i],x[j])+coef0,degree); } double kernel_rbf(int i, int j) const { return exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j]))); } double kernel_sigmoid(int i, int j) const { return tanh(gamma*dot(x[i],x[j])+coef0); } double kernel_precomputed(int i, int j) const { return x[i][(int)(x[j][0].value)].value; } }; Kernel::Kernel(int l, svm_node * const * x_, const svm_parameter& param) :kernel_type(param.kernel_type), degree(param.degree), gamma(param.gamma), coef0(param.coef0) { switch(kernel_type) { case LINEAR: kernel_function = &Kernel::kernel_linear; break; case POLY: kernel_function = &Kernel::kernel_poly; break; case RBF: kernel_function = &Kernel::kernel_rbf; break; case SIGMOID: kernel_function = &Kernel::kernel_sigmoid; break; case PRECOMPUTED: kernel_function = &Kernel::kernel_precomputed; break; } clone(x,x_,l); if(kernel_type == RBF) { x_square = new double[l]; for(int i=0;iindex != -1 && py->index != -1) { if(px->index == py->index) { sum += px->value * py->value; ++px; ++py; } else { if(px->index > py->index) ++py; else ++px; } } return sum; } double Kernel::k_function(const svm_node *x, const svm_node *y, const svm_parameter& param) { switch(param.kernel_type) { case LINEAR: return dot(x,y); case POLY: return powi(param.gamma*dot(x,y)+param.coef0,param.degree); case RBF: { double sum = 0; while(x->index != -1 && y->index !=-1) { if(x->index == y->index) { double d = x->value - y->value; sum += d*d; ++x; ++y; } else { if(x->index > y->index) { sum += y->value * y->value; ++y; } else { sum += x->value * x->value; ++x; } } } while(x->index != -1) { sum += x->value * x->value; ++x; } while(y->index != -1) { sum += y->value * y->value; ++y; } return exp(-param.gamma*sum); } case SIGMOID: return tanh(param.gamma*dot(x,y)+param.coef0); case PRECOMPUTED: //x: test (validation), y: SV return x[(int)(y->value)].value; default: return 0; // Unreachable } } // An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918 // Solves: // // min 0.5(\alpha^T Q \alpha) + p^T \alpha // // y^T \alpha = \delta // y_i = +1 or -1 // 0 <= alpha_i <= Cp for y_i = 1 // 0 <= alpha_i <= Cn for y_i = -1 // // Given: // // Q, p, y, Cp, Cn, and an initial feasible point \alpha // l is the size of vectors and matrices // eps is the stopping tolerance // // solution will be put in \alpha, objective value will be put in obj // class Solver { public: Solver() {}; virtual ~Solver() {}; struct SolutionInfo { double obj; double rho; double upper_bound_p; double upper_bound_n; double r; // for Solver_NU }; void Solve(int l, const QMatrix& Q, const double *p_, const schar *y_, double *alpha_, double Cp, double Cn, double eps, SolutionInfo* si, int shrinking); protected: int active_size; schar *y; double *G; // gradient of objective function enum { LOWER_BOUND, UPPER_BOUND, FREE }; char *alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE double *alpha; const QMatrix *Q; const double *QD; double eps; double Cp,Cn; double *p; int *active_set; double *G_bar; // gradient, if we treat free variables as 0 int l; bool unshrink; // XXX double get_C(int i) { return (y[i] > 0)? Cp : Cn; } void update_alpha_status(int i) { if(alpha[i] >= get_C(i)) alpha_status[i] = UPPER_BOUND; else if(alpha[i] <= 0) alpha_status[i] = LOWER_BOUND; else alpha_status[i] = FREE; } bool is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; } bool is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; } bool is_free(int i) { return alpha_status[i] == FREE; } void swap_index(int i, int j); void reconstruct_gradient(); virtual int select_working_set(int &i, int &j); virtual double calculate_rho(); virtual void do_shrinking(); private: bool be_shrunk(int i, double Gmax1, double Gmax2); }; void Solver::swap_index(int i, int j) { Q->swap_index(i,j); swap(y[i],y[j]); swap(G[i],G[j]); swap(alpha_status[i],alpha_status[j]); swap(alpha[i],alpha[j]); swap(p[i],p[j]); swap(active_set[i],active_set[j]); swap(G_bar[i],G_bar[j]); } void Solver::reconstruct_gradient() { // reconstruct inactive elements of G from G_bar and free variables if(active_size == l) return; int i,j; int nr_free = 0; for(j=active_size;j 2*active_size*(l-active_size)) { for(i=active_size;iget_Q(i,active_size); for(j=0;jget_Q(i,l); double alpha_i = alpha[i]; for(j=active_size;jl = l; this->Q = &Q; QD=Q.get_QD(); clone(p, p_,l); clone(y, y_,l); clone(alpha,alpha_,l); this->Cp = Cp; this->Cn = Cn; this->eps = eps; unshrink = false; // initialize alpha_status { alpha_status = new char[l]; for(int i=0;iINT_MAX/100 ? INT_MAX : 100*l); int counter = min(l,1000)+1; while(iter < max_iter) { // show progress and do shrinking if(--counter == 0) { counter = min(l,1000); if(shrinking) do_shrinking(); info("."); } int i,j; if(select_working_set(i,j)!=0) { // reconstruct the whole gradient reconstruct_gradient(); // reset active set size and check active_size = l; info("*"); if(select_working_set(i,j)!=0) break; else counter = 1; // do shrinking next iteration } ++iter; // update alpha[i] and alpha[j], handle bounds carefully const Qfloat *Q_i = Q.get_Q(i,active_size); const Qfloat *Q_j = Q.get_Q(j,active_size); double C_i = get_C(i); double C_j = get_C(j); double old_alpha_i = alpha[i]; double old_alpha_j = alpha[j]; if(y[i]!=y[j]) { double quad_coef = QD[i]+QD[j]+2*Q_i[j]; if (quad_coef <= 0) quad_coef = TAU; double delta = (-G[i]-G[j])/quad_coef; double diff = alpha[i] - alpha[j]; alpha[i] += delta; alpha[j] += delta; if(diff > 0) { if(alpha[j] < 0) { alpha[j] = 0; alpha[i] = diff; } } else { if(alpha[i] < 0) { alpha[i] = 0; alpha[j] = -diff; } } if(diff > C_i - C_j) { if(alpha[i] > C_i) { alpha[i] = C_i; alpha[j] = C_i - diff; } } else { if(alpha[j] > C_j) { alpha[j] = C_j; alpha[i] = C_j + diff; } } } else { double quad_coef = QD[i]+QD[j]-2*Q_i[j]; if (quad_coef <= 0) quad_coef = TAU; double delta = (G[i]-G[j])/quad_coef; double sum = alpha[i] + alpha[j]; alpha[i] -= delta; alpha[j] += delta; if(sum > C_i) { if(alpha[i] > C_i) { alpha[i] = C_i; alpha[j] = sum - C_i; } } else { if(alpha[j] < 0) { alpha[j] = 0; alpha[i] = sum; } } if(sum > C_j) { if(alpha[j] > C_j) { alpha[j] = C_j; alpha[i] = sum - C_j; } } else { if(alpha[i] < 0) { alpha[i] = 0; alpha[j] = sum; } } } // update G double delta_alpha_i = alpha[i] - old_alpha_i; double delta_alpha_j = alpha[j] - old_alpha_j; for(int k=0;k= max_iter) { if(active_size < l) { // reconstruct the whole gradient to calculate objective value reconstruct_gradient(); active_size = l; info("*"); } REprintf("\nWARNING: reaching max number of iterations\n"); } // calculate rho si->rho = calculate_rho(); // calculate objective value { double v = 0; int i; for(i=0;iobj = v/2; } // put back the solution { for(int i=0;iupper_bound_p = Cp; si->upper_bound_n = Cn; info("\noptimization finished, #iter = %d\n",iter); delete[] p; delete[] y; delete[] alpha; delete[] alpha_status; delete[] active_set; delete[] G; delete[] G_bar; } // return 1 if already optimal, return 0 otherwise int Solver::select_working_set(int &out_i, int &out_j) { // return i,j such that // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha) // j: minimizes the decrease of obj value // (if quadratic coefficeint <= 0, replace it with tau) // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha) double Gmax = -INF; double Gmax2 = -INF; int Gmax_idx = -1; int Gmin_idx = -1; double obj_diff_min = INF; for(int t=0;t= Gmax) { Gmax = -G[t]; Gmax_idx = t; } } else { if(!is_lower_bound(t)) if(G[t] >= Gmax) { Gmax = G[t]; Gmax_idx = t; } } int i = Gmax_idx; const Qfloat *Q_i = NULL; if(i != -1) // NULL Q_i not accessed: Gmax=-INF if i=-1 Q_i = Q->get_Q(i,active_size); for(int j=0;j= Gmax2) Gmax2 = G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } else { if (!is_upper_bound(j)) { double grad_diff= Gmax-G[j]; if (-G[j] >= Gmax2) Gmax2 = -G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } } if(Gmax+Gmax2 < eps || Gmin_idx == -1) return 1; out_i = Gmax_idx; out_j = Gmin_idx; return 0; } bool Solver::be_shrunk(int i, double Gmax1, double Gmax2) { if(is_upper_bound(i)) { if(y[i]==+1) return(-G[i] > Gmax1); else return(-G[i] > Gmax2); } else if(is_lower_bound(i)) { if(y[i]==+1) return(G[i] > Gmax2); else return(G[i] > Gmax1); } else return(false); } void Solver::do_shrinking() { int i; double Gmax1 = -INF; // max { -y_i * grad(f)_i | i in I_up(\alpha) } double Gmax2 = -INF; // max { y_i * grad(f)_i | i in I_low(\alpha) } // find maximal violating pair first for(i=0;i= Gmax1) Gmax1 = -G[i]; } if(!is_lower_bound(i)) { if(G[i] >= Gmax2) Gmax2 = G[i]; } } else { if(!is_upper_bound(i)) { if(-G[i] >= Gmax2) Gmax2 = -G[i]; } if(!is_lower_bound(i)) { if(G[i] >= Gmax1) Gmax1 = G[i]; } } } if(unshrink == false && Gmax1 + Gmax2 <= eps*10) { unshrink = true; reconstruct_gradient(); active_size = l; info("*"); } for(i=0;i i) { if (!be_shrunk(active_size, Gmax1, Gmax2)) { swap_index(i,active_size); break; } active_size--; } } } double Solver::calculate_rho() { double r; int nr_free = 0; double ub = INF, lb = -INF, sum_free = 0; for(int i=0;i0) r = sum_free/nr_free; else r = (ub+lb)/2; return r; } // // Solver for nu-svm classification and regression // // additional constraint: e^T \alpha = constant // class Solver_NU: public Solver { public: Solver_NU() {} void Solve(int l, const QMatrix& Q, const double *p, const schar *y, double *alpha, double Cp, double Cn, double eps, SolutionInfo* si, int shrinking) { this->si = si; Solver::Solve(l,Q,p,y,alpha,Cp,Cn,eps,si,shrinking); } private: SolutionInfo *si; int select_working_set(int &i, int &j); double calculate_rho(); bool be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4); void do_shrinking(); }; // return 1 if already optimal, return 0 otherwise int Solver_NU::select_working_set(int &out_i, int &out_j) { // return i,j such that y_i = y_j and // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha) // j: minimizes the decrease of obj value // (if quadratic coefficeint <= 0, replace it with tau) // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha) double Gmaxp = -INF; double Gmaxp2 = -INF; int Gmaxp_idx = -1; double Gmaxn = -INF; double Gmaxn2 = -INF; int Gmaxn_idx = -1; int Gmin_idx = -1; double obj_diff_min = INF; for(int t=0;t= Gmaxp) { Gmaxp = -G[t]; Gmaxp_idx = t; } } else { if(!is_lower_bound(t)) if(G[t] >= Gmaxn) { Gmaxn = G[t]; Gmaxn_idx = t; } } int ip = Gmaxp_idx; int in = Gmaxn_idx; const Qfloat *Q_ip = NULL; const Qfloat *Q_in = NULL; if(ip != -1) // NULL Q_ip not accessed: Gmaxp=-INF if ip=-1 Q_ip = Q->get_Q(ip,active_size); if(in != -1) Q_in = Q->get_Q(in,active_size); for(int j=0;j= Gmaxp2) Gmaxp2 = G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef = QD[ip]+QD[j]-2*Q_ip[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } else { if (!is_upper_bound(j)) { double grad_diff=Gmaxn-G[j]; if (-G[j] >= Gmaxn2) Gmaxn2 = -G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef = QD[in]+QD[j]-2*Q_in[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } } if(max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps || Gmin_idx == -1) return 1; if (y[Gmin_idx] == +1) out_i = Gmaxp_idx; else out_i = Gmaxn_idx; out_j = Gmin_idx; return 0; } bool Solver_NU::be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4) { if(is_upper_bound(i)) { if(y[i]==+1) return(-G[i] > Gmax1); else return(-G[i] > Gmax4); } else if(is_lower_bound(i)) { if(y[i]==+1) return(G[i] > Gmax2); else return(G[i] > Gmax3); } else return(false); } void Solver_NU::do_shrinking() { double Gmax1 = -INF; // max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) } double Gmax2 = -INF; // max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) } double Gmax3 = -INF; // max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) } double Gmax4 = -INF; // max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) } // find maximal violating pair first int i; for(i=0;i Gmax1) Gmax1 = -G[i]; } else if(-G[i] > Gmax4) Gmax4 = -G[i]; } if(!is_lower_bound(i)) { if(y[i]==+1) { if(G[i] > Gmax2) Gmax2 = G[i]; } else if(G[i] > Gmax3) Gmax3 = G[i]; } } if(unshrink == false && max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10) { unshrink = true; reconstruct_gradient(); active_size = l; } for(i=0;i i) { if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4)) { swap_index(i,active_size); break; } active_size--; } } } double Solver_NU::calculate_rho() { int nr_free1 = 0,nr_free2 = 0; double ub1 = INF, ub2 = INF; double lb1 = -INF, lb2 = -INF; double sum_free1 = 0, sum_free2 = 0; for(int i=0;i 0) r1 = sum_free1/nr_free1; else r1 = (ub1+lb1)/2; if(nr_free2 > 0) r2 = sum_free2/nr_free2; else r2 = (ub2+lb2)/2; si->r = (r1+r2)/2; return (r1-r2)/2; } // // Q matrices for various formulations // class SVC_Q: public Kernel { public: SVC_Q(const svm_problem& prob, const svm_parameter& param, const schar *y_) :Kernel(prob.l, prob.x, param) { clone(y,y_,prob.l); cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20))); QD = new double[prob.l]; for(int i=0;i*kernel_function)(i,i); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int start, j; if((start = cache->get_data(i,&data,len)) < len) { for(j=start;j*kernel_function)(i,j)); } return data; } double *get_QD() const { return QD; } void swap_index(int i, int j) const { cache->swap_index(i,j); Kernel::swap_index(i,j); swap(y[i],y[j]); swap(QD[i],QD[j]); } ~SVC_Q() { delete[] y; delete cache; delete[] QD; } private: schar *y; Cache *cache; double *QD; }; class ONE_CLASS_Q: public Kernel { public: ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param) :Kernel(prob.l, prob.x, param) { cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20))); QD = new double[prob.l]; for(int i=0;i*kernel_function)(i,i); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int start, j; if((start = cache->get_data(i,&data,len)) < len) { for(j=start;j*kernel_function)(i,j); } return data; } double *get_QD() const { return QD; } void swap_index(int i, int j) const { cache->swap_index(i,j); Kernel::swap_index(i,j); swap(QD[i],QD[j]); } ~ONE_CLASS_Q() { delete cache; delete[] QD; } private: Cache *cache; double *QD; }; class SVR_Q: public Kernel { public: SVR_Q(const svm_problem& prob, const svm_parameter& param) :Kernel(prob.l, prob.x, param) { l = prob.l; cache = new Cache(l,(long int)(param.cache_size*(1<<20))); QD = new double[2*l]; sign = new schar[2*l]; index = new int[2*l]; for(int k=0;k*kernel_function)(k,k); QD[k+l] = QD[k]; } buffer[0] = new Qfloat[2*l]; buffer[1] = new Qfloat[2*l]; next_buffer = 0; } void swap_index(int i, int j) const { swap(sign[i],sign[j]); swap(index[i],index[j]); swap(QD[i],QD[j]); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int j, real_i = index[i]; if(cache->get_data(real_i,&data,l) < l) { for(j=0;j*kernel_function)(real_i,j); } // reorder and copy Qfloat *buf = buffer[next_buffer]; next_buffer = 1 - next_buffer; schar si = sign[i]; for(j=0;jl; double *minus_ones = new double[l]; schar *y = new schar[l]; int i; for(i=0;iy[i] > 0) y[i] = +1; else y[i] = -1; } Solver s; s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y, alpha, Cp, Cn, param->eps, si, param->shrinking); double sum_alpha=0; for(i=0;il)); for(i=0;il; double nu = param->nu; schar *y = new schar[l]; for(i=0;iy[i]>0) y[i] = +1; else y[i] = -1; double sum_pos = nu*l/2; double sum_neg = nu*l/2; for(i=0;ieps, si, param->shrinking); double r = si->r; info("C = %f\n",1/r); for(i=0;irho /= r; si->obj /= (r*r); si->upper_bound_p = 1/r; si->upper_bound_n = 1/r; delete[] y; delete[] zeros; } static void solve_one_class( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double *zeros = new double[l]; schar *ones = new schar[l]; int i; int n = (int)(param->nu*prob->l); // # of alpha's at upper bound for(i=0;il) alpha[n] = param->nu * prob->l - n; for(i=n+1;ieps, si, param->shrinking); delete[] zeros; delete[] ones; } static void solve_epsilon_svr( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double *alpha2 = new double[2*l]; double *linear_term = new double[2*l]; schar *y = new schar[2*l]; int i; for(i=0;ip - prob->y[i]; y[i] = 1; alpha2[i+l] = 0; linear_term[i+l] = param->p + prob->y[i]; y[i+l] = -1; } Solver s; s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y, alpha2, param->C, param->C, param->eps, si, param->shrinking); double sum_alpha = 0; for(i=0;iC*l)); delete[] alpha2; delete[] linear_term; delete[] y; } static void solve_nu_svr( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double C = param->C; double *alpha2 = new double[2*l]; double *linear_term = new double[2*l]; schar *y = new schar[2*l]; int i; double sum = C * param->nu * l / 2; for(i=0;iy[i]; y[i] = 1; linear_term[i+l] = prob->y[i]; y[i+l] = -1; } Solver_NU s; s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y, alpha2, C, C, param->eps, si, param->shrinking); info("epsilon = %f\n",-si->r); for(i=0;il); Solver::SolutionInfo si; switch(param->svm_type) { case C_SVC: solve_c_svc(prob,param,alpha,&si,Cp,Cn); break; case NU_SVC: solve_nu_svc(prob,param,alpha,&si); break; case ONE_CLASS: solve_one_class(prob,param,alpha,&si); break; case EPSILON_SVR: solve_epsilon_svr(prob,param,alpha,&si); break; case NU_SVR: solve_nu_svr(prob,param,alpha,&si); break; } info("obj = %f, rho = %f\n",si.obj,si.rho); // output SVs int nSV = 0; int nBSV = 0; for(int i=0;il;i++) { if(fabs(alpha[i]) > 0) { ++nSV; if(prob->y[i] > 0) { if(fabs(alpha[i]) >= si.upper_bound_p) ++nBSV; } else { if(fabs(alpha[i]) >= si.upper_bound_n) ++nBSV; } } } info("nSV = %d, nBSV = %d\n",nSV,nBSV); decision_function f; f.alpha = alpha; f.rho = si.rho; return f; } // Platt's binary SVM Probablistic Output: an improvement from Lin et al. static void sigmoid_train( int l, const double *dec_values, const double *labels, double& A, double& B) { double prior1=0, prior0 = 0; int i; for (i=0;i 0) prior1+=1; else prior0+=1; int max_iter=100; // Maximal number of iterations double min_step=1e-10; // Minimal step taken in line search double sigma=1e-12; // For numerically strict PD of Hessian double eps=1e-5; double hiTarget=(prior1+1.0)/(prior1+2.0); double loTarget=1/(prior0+2.0); double *t=Malloc(double,l); double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize; double newA,newB,newf,d1,d2; int iter; // Initial Point and Initial Fun Value A=0.0; B=log((prior0+1.0)/(prior1+1.0)); double fval = 0.0; for (i=0;i0) t[i]=hiTarget; else t[i]=loTarget; fApB = dec_values[i]*A+B; if (fApB>=0) fval += t[i]*fApB + log(1+exp(-fApB)); else fval += (t[i] - 1)*fApB +log(1+exp(fApB)); } for (iter=0;iter= 0) { p=exp(-fApB)/(1.0+exp(-fApB)); q=1.0/(1.0+exp(-fApB)); } else { p=1.0/(1.0+exp(fApB)); q=exp(fApB)/(1.0+exp(fApB)); } d2=p*q; h11+=dec_values[i]*dec_values[i]*d2; h22+=d2; h21+=dec_values[i]*d2; d1=t[i]-p; g1+=dec_values[i]*d1; g2+=d1; } // Stopping Criteria if (fabs(g1)= min_step) { newA = A + stepsize * dA; newB = B + stepsize * dB; // New function value newf = 0.0; for (i=0;i= 0) newf += t[i]*fApB + log(1+exp(-fApB)); else newf += (t[i] - 1)*fApB +log(1+exp(fApB)); } // Check sufficient decrease if (newf=max_iter) info("Reaching maximal iterations in two-class probability estimates\n"); free(t); } static double sigmoid_predict(double decision_value, double A, double B) { double fApB = decision_value*A+B; // 1-p used later; avoid catastrophic cancellation if (fApB >= 0) return exp(-fApB)/(1.0+exp(-fApB)); else return 1.0/(1+exp(fApB)) ; } // Method 2 from the multiclass_prob paper by Wu, Lin, and Weng static void multiclass_probability(int k, double **r, double *p) { int t,j; int iter = 0, max_iter=max(100,k); double **Q=Malloc(double *,k); double *Qp=Malloc(double,k); double pQp, eps=0.005/k; for (t=0;tmax_error) max_error=error; } if (max_error=max_iter) info("Exceeds max_iter in multiclass_prob\n"); for(t=0;tl); double *dec_values = Malloc(double,prob->l); // random shuffle GetRNGstate(); for(i=0;il;i++) perm[i]=i; for(i=0;il;i++) { int j = i+((int) (unif_rand() * (prob->l-i))) % (prob->l-i); swap(perm[i],perm[j]); } PutRNGstate(); for(i=0;il/nr_fold; int end = (i+1)*prob->l/nr_fold; int j,k; struct svm_problem subprob; subprob.l = prob->l-(end-begin); subprob.x = Malloc(struct svm_node*,subprob.l); subprob.y = Malloc(double,subprob.l); k=0; for(j=0;jx[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } for(j=end;jl;j++) { subprob.x[k] = prob->x[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } int p_count=0,n_count=0; for(j=0;j0) p_count++; else n_count++; if(p_count==0 && n_count==0) for(j=begin;j 0 && n_count == 0) for(j=begin;j 0) for(j=begin;jx[perm[j]],&(dec_values[perm[j]])); // ensure +1 -1 order; reason not using CV subroutine dec_values[perm[j]] *= submodel->label[0]; } svm_free_and_destroy_model(&submodel); svm_destroy_param(&subparam); } free(subprob.x); free(subprob.y); } sigmoid_train(prob->l,dec_values,prob->y,probA,probB); free(dec_values); free(perm); } // Return parameter of a Laplace distribution static double svm_svr_probability( const svm_problem *prob, const svm_parameter *param) { int i; int nr_fold = 5; double *ymv = Malloc(double,prob->l); double mae = 0; svm_parameter newparam = *param; newparam.probability = 0; svm_cross_validation(prob,&newparam,nr_fold,ymv); for(i=0;il;i++) { ymv[i]=prob->y[i]-ymv[i]; mae += fabs(ymv[i]); } mae /= prob->l; double std=sqrt(2*mae*mae); int count=0; mae=0; for(i=0;il;i++) if (fabs(ymv[i]) > 5*std) count=count+1; else mae+=fabs(ymv[i]); mae /= (prob->l-count); info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g\n",mae); free(ymv); return mae; } // label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data // perm, length l, must be allocated before calling this subroutine static void svm_group_classes(const svm_problem *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm) { int l = prob->l; int max_nr_class = 16; int nr_class = 0; int *label = Malloc(int,max_nr_class); int *count = Malloc(int,max_nr_class); int *data_label = Malloc(int,l); int i; for(i=0;iy[i]; int j; for(j=0;jparam = *param; model->free_sv = 0; // XXX if(param->svm_type == ONE_CLASS || param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR) { // regression or one-class-svm model->nr_class = 2; model->label = NULL; model->nSV = NULL; model->probA = NULL; model->probB = NULL; model->sv_coef = Malloc(double *,1); if(param->probability && (param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR)) { model->probA = Malloc(double,1); model->probA[0] = svm_svr_probability(prob,param); } decision_function f = svm_train_one(prob,param,0,0); model->rho = Malloc(double,1); model->rho[0] = f.rho; int nSV = 0; int i; for(i=0;il;i++) if(fabs(f.alpha[i]) > 0) ++nSV; model->l = nSV; model->SV = Malloc(svm_node *,nSV); model->sv_coef[0] = Malloc(double,nSV); model->sv_indices = Malloc(int,nSV); int j = 0; for(i=0;il;i++) if(fabs(f.alpha[i]) > 0) { model->SV[j] = prob->x[i]; model->sv_coef[0][j] = f.alpha[i]; model->sv_indices[j] = i+1; ++j; } free(f.alpha); } else { // classification int l = prob->l; int nr_class; int *label = NULL; int *start = NULL; int *count = NULL; int *perm = Malloc(int,l); // group training data of the same class svm_group_classes(prob,&nr_class,&label,&start,&count,perm); if(nr_class == 1) info("WARNING: training data in only one class. See README for details.\n"); svm_node **x = Malloc(svm_node *,l); int i; for(i=0;ix[perm[i]]; // calculate weighted C double *weighted_C = Malloc(double, nr_class); for(i=0;iC; for(i=0;inr_weight;i++) { int j; for(j=0;jweight_label[i] == label[j]) break; if(j == nr_class) REprintf("WARNING: class label %d specified in weight is not found\n", param->weight_label[i]); else weighted_C[j] *= param->weight[i]; } // train k*(k-1)/2 models bool *nonzero = Malloc(bool,l); for(i=0;iprobability) { probA=Malloc(double,nr_class*(nr_class-1)/2); probB=Malloc(double,nr_class*(nr_class-1)/2); } int p = 0; for(i=0;iprobability) svm_binary_svc_probability(&sub_prob,param,weighted_C[i],weighted_C[j],probA[p],probB[p]); f[p] = svm_train_one(&sub_prob,param,weighted_C[i],weighted_C[j]); for(k=0;k 0) nonzero[si+k] = true; for(k=0;k 0) nonzero[sj+k] = true; free(sub_prob.x); free(sub_prob.y); ++p; } // build output model->nr_class = nr_class; model->label = Malloc(int,nr_class); for(i=0;ilabel[i] = label[i]; model->rho = Malloc(double,nr_class*(nr_class-1)/2); for(i=0;irho[i] = f[i].rho; if(param->probability) { model->probA = Malloc(double,nr_class*(nr_class-1)/2); model->probB = Malloc(double,nr_class*(nr_class-1)/2); for(i=0;iprobA[i] = probA[i]; model->probB[i] = probB[i]; } } else { model->probA=NULL; model->probB=NULL; } int total_sv = 0; int *nz_count = Malloc(int,nr_class); model->nSV = Malloc(int,nr_class); for(i=0;inSV[i] = nSV; nz_count[i] = nSV; } info("Total nSV = %d\n",total_sv); model->l = total_sv; model->SV = Malloc(svm_node *,total_sv); model->sv_indices = Malloc(int,total_sv); p = 0; for(i=0;iSV[p] = x[i]; model->sv_indices[p++] = perm[i] + 1; } int *nz_start = Malloc(int,nr_class); nz_start[0] = 0; for(i=1;isv_coef = Malloc(double *,nr_class-1); for(i=0;isv_coef[i] = Malloc(double,total_sv); p = 0; for(i=0;isv_coef[j-1][q++] = f[p].alpha[k]; q = nz_start[j]; for(k=0;ksv_coef[i][q++] = f[p].alpha[ci+k]; ++p; } free(label); free(probA); free(probB); free(count); free(perm); free(start); free(x); free(weighted_C); free(nonzero); for(i=0;il; int *perm = Malloc(int,l); int nr_class; GetRNGstate(); if (nr_fold > l) { nr_fold = l; REprintf("WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)\n"); } fold_start = Malloc(int,nr_fold+1); // stratified cv may not give leave-one-out rate // Each class to l folds -> some folds may have zero elements if((param->svm_type == C_SVC || param->svm_type == NU_SVC) && nr_fold < l) { int *start = NULL; int *label = NULL; int *count = NULL; svm_group_classes(prob,&nr_class,&label,&start,&count,perm); // random shuffle and then data grouped by fold using the array perm int *fold_count = Malloc(int,nr_fold); int c; int *index = Malloc(int,l); for(i=0;ix[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } for(j=end;jx[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } struct svm_model *submodel = svm_train(&subprob,param); if(param->probability && (param->svm_type == C_SVC || param->svm_type == NU_SVC)) { double *prob_estimates=Malloc(double,svm_get_nr_class(submodel)); for(j=begin;jx[perm[j]],prob_estimates); free(prob_estimates); } else for(j=begin;jx[perm[j]]); svm_free_and_destroy_model(&submodel); free(subprob.x); free(subprob.y); } free(fold_start); free(perm); PutRNGstate(); } int svm_get_svm_type(const svm_model *model) { return model->param.svm_type; } int svm_get_nr_class(const svm_model *model) { return model->nr_class; } void svm_get_labels(const svm_model *model, int* label) { if (model->label != NULL) for(int i=0;inr_class;i++) label[i] = model->label[i]; } void svm_get_sv_indices(const svm_model *model, int* indices) { if (model->sv_indices != NULL) for(int i=0;il;i++) indices[i] = model->sv_indices[i]; } int svm_get_nr_sv(const svm_model *model) { return model->l; } double svm_get_svr_probability(const svm_model *model) { if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) && model->probA!=NULL) return model->probA[0]; else { REprintf("Model doesn't contain information for SVR probability inference\n"); return 0; } } double svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values) { int i; if(model->param.svm_type == ONE_CLASS || model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) { double *sv_coef = model->sv_coef[0]; double sum = 0; for(i=0;il;i++) sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param); sum -= model->rho[0]; *dec_values = sum; if(model->param.svm_type == ONE_CLASS) return (sum>0)?1:-1; else return sum; } else { int nr_class = model->nr_class; int l = model->l; double *kvalue = Malloc(double,l); for(i=0;iSV[i],model->param); int *start = Malloc(int,nr_class); start[0] = 0; for(i=1;inSV[i-1]; int *vote = Malloc(int,nr_class); for(i=0;inSV[i]; int cj = model->nSV[j]; int k; double *coef1 = model->sv_coef[j-1]; double *coef2 = model->sv_coef[i]; for(k=0;krho[p]; dec_values[p] = sum; if(dec_values[p] > 0) ++vote[i]; else ++vote[j]; p++; } int vote_max_idx = 0; for(i=1;i vote[vote_max_idx]) vote_max_idx = i; free(kvalue); free(start); free(vote); return model->label[vote_max_idx]; } } double svm_predict(const svm_model *model, const svm_node *x) { int nr_class = model->nr_class; double *dec_values; if(model->param.svm_type == ONE_CLASS || model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) dec_values = Malloc(double, 1); else dec_values = Malloc(double, nr_class*(nr_class-1)/2); double pred_result = svm_predict_values(model, x, dec_values); free(dec_values); return pred_result; } double svm_predict_probability( const svm_model *model, const svm_node *x, double *prob_estimates) { if ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) && model->probA!=NULL && model->probB!=NULL) { int i; int nr_class = model->nr_class; double *dec_values = Malloc(double, nr_class*(nr_class-1)/2); svm_predict_values(model, x, dec_values); double min_prob=1e-7; double **pairwise_prob=Malloc(double *,nr_class); for(i=0;iprobA[k],model->probB[k]),min_prob),1-min_prob); pairwise_prob[j][i]=1-pairwise_prob[i][j]; k++; } if (nr_class == 2) { prob_estimates[0] = pairwise_prob[0][1]; prob_estimates[1] = pairwise_prob[1][0]; } else multiclass_probability(nr_class,pairwise_prob,prob_estimates); int prob_max_idx = 0; for(i=1;i prob_estimates[prob_max_idx]) prob_max_idx = i; for(i=0;ilabel[prob_max_idx]; } else return svm_predict(model, x); } static const char *svm_type_table[] = { "c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL }; static const char *kernel_type_table[]= { "linear","polynomial","rbf","sigmoid","precomputed",NULL }; int svm_save_model(const char *model_file_name, const svm_model *model) { FILE *fp = fopen(model_file_name,"w"); if(fp==NULL) return -1; char *old_locale = setlocale(LC_ALL, NULL); if (old_locale) { old_locale = strdup(old_locale); } setlocale(LC_ALL, "C"); const svm_parameter& param = model->param; (void) fprintf(fp,"svm_type %s\n", svm_type_table[param.svm_type]); (void) fprintf(fp,"kernel_type %s\n", kernel_type_table[param.kernel_type]); if(param.kernel_type == POLY) (void) fprintf(fp,"degree %d\n", param.degree); if(param.kernel_type == POLY || param.kernel_type == RBF || param.kernel_type == SIGMOID) (void) fprintf(fp,"gamma %.17g\n", param.gamma); if(param.kernel_type == POLY || param.kernel_type == SIGMOID) (void) fprintf(fp,"coef0 %.17g\n", param.coef0); int nr_class = model->nr_class; int l = model->l; (void) fprintf(fp, "nr_class %d\n", nr_class); (void) fprintf(fp, "total_sv %d\n",l); { (void) fprintf(fp, "rho"); for(int i=0;irho[i]); (void) fprintf(fp, "\n"); } if(model->label) { (void) fprintf(fp, "label"); for(int i=0;ilabel[i]); (void) fprintf(fp, "\n"); } if(model->probA) // regression has probA only { (void) fprintf(fp, "probA"); for(int i=0;iprobA[i]); (void) fprintf(fp, "\n"); } if(model->probB) { (void) fprintf(fp, "probB"); for(int i=0;iprobB[i]); (void) fprintf(fp, "\n"); } if(model->nSV) { (void) fprintf(fp, "nr_sv"); for(int i=0;inSV[i]); (void) fprintf(fp, "\n"); } (void) fprintf(fp, "SV\n"); const double * const *sv_coef = model->sv_coef; const svm_node * const *SV = model->SV; for(int i=0;ivalue)); else while(p->index != -1) { (void) fprintf(fp,"%d:%.8g ",p->index,p->value); p++; } (void) fprintf(fp, "\n"); } setlocale(LC_ALL, old_locale); free(old_locale); if (ferror(fp) != 0 || fclose(fp) != 0) return -1; else return 0; } static char *line = NULL; static int max_line_len; static char* readline(FILE *input) { int len; if(fgets(line,max_line_len,input) == NULL) return NULL; while(strrchr(line,'\n') == NULL) { max_line_len *= 2; line = (char *) realloc(line,max_line_len); len = (int) strlen(line); if(fgets(line+len,max_line_len-len,input) == NULL) break; } return line; } // // FSCANF helps to handle fscanf failures. // Its do-while block avoids the ambiguity when // if (...) // FSCANF(); // is used // #define FSCANF(_stream, _format, _var) do{ if (fscanf(_stream, _format, _var) != 1) return false; }while(0) bool read_model_header(FILE *fp, svm_model* model) { svm_parameter& param = model->param; // parameters for training only won't be assigned, but arrays are assigned as NULL for safety param.nr_weight = 0; param.weight_label = NULL; param.weight = NULL; char cmd[81]; while(1) { FSCANF(fp,"%80s",cmd); if(strcmp(cmd,"svm_type")==0) { FSCANF(fp,"%80s",cmd); int i; for(i=0;svm_type_table[i];i++) { if(strcmp(svm_type_table[i],cmd)==0) { param.svm_type=i; break; } } if(svm_type_table[i] == NULL) { REprintf("unknown svm type.\n"); return false; } } else if(strcmp(cmd,"kernel_type")==0) { FSCANF(fp,"%80s",cmd); int i; for(i=0;kernel_type_table[i];i++) { if(strcmp(kernel_type_table[i],cmd)==0) { param.kernel_type=i; break; } } if(kernel_type_table[i] == NULL) { REprintf("unknown kernel function.\n"); return false; } } else if(strcmp(cmd,"degree")==0) FSCANF(fp,"%d",¶m.degree); else if(strcmp(cmd,"gamma")==0) FSCANF(fp,"%lf",¶m.gamma); else if(strcmp(cmd,"coef0")==0) FSCANF(fp,"%lf",¶m.coef0); else if(strcmp(cmd,"nr_class")==0) FSCANF(fp,"%d",&model->nr_class); else if(strcmp(cmd,"total_sv")==0) FSCANF(fp,"%d",&model->l); else if(strcmp(cmd,"rho")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->rho = Malloc(double,n); for(int i=0;irho[i]); } else if(strcmp(cmd,"label")==0) { int n = model->nr_class; model->label = Malloc(int,n); for(int i=0;ilabel[i]); } else if(strcmp(cmd,"probA")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->probA = Malloc(double,n); for(int i=0;iprobA[i]); } else if(strcmp(cmd,"probB")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->probB = Malloc(double,n); for(int i=0;iprobB[i]); } else if(strcmp(cmd,"nr_sv")==0) { int n = model->nr_class; model->nSV = Malloc(int,n); for(int i=0;inSV[i]); } else if(strcmp(cmd,"SV")==0) { while(1) { int c = getc(fp); if(c==EOF || c=='\n') break; } break; } else { REprintf("unknown text in model file: [%s]\n",cmd); return false; } } return true; } svm_model *svm_load_model(const char *model_file_name) { FILE *fp = fopen(model_file_name,"rb"); if(fp==NULL) return NULL; char *old_locale = setlocale(LC_ALL, NULL); if (old_locale) { old_locale = strdup(old_locale); } setlocale(LC_ALL, "C"); // read parameters svm_model *model = Malloc(svm_model,1); model->rho = NULL; model->probA = NULL; model->probB = NULL; model->sv_indices = NULL; model->label = NULL; model->nSV = NULL; // read header if (!read_model_header(fp, model)) { REprintf("ERROR: fscanf failed to read model\n"); setlocale(LC_ALL, old_locale); free(old_locale); free(model->rho); free(model->label); free(model->nSV); free(model); return NULL; } // read sv_coef and SV int elements = 0; long pos = ftell(fp); max_line_len = 1024; line = Malloc(char,max_line_len); char *p,*endptr,*idx,*val; while(readline(fp)!=NULL) { p = strtok(line,":"); while(1) { p = strtok(NULL,":"); if(p == NULL) break; ++elements; } } elements += model->l; fseek(fp,pos,SEEK_SET); int m = model->nr_class - 1; int l = model->l; model->sv_coef = Malloc(double *,m); int i; for(i=0;isv_coef[i] = Malloc(double,l); model->SV = Malloc(svm_node*,l); svm_node *x_space = NULL; if(l>0) x_space = Malloc(svm_node,elements); int j=0; for(i=0;iSV[i] = &x_space[j]; p = strtok(line, " \t"); model->sv_coef[0][i] = strtod(p,&endptr); for(int k=1;ksv_coef[k][i] = strtod(p,&endptr); } while(1) { idx = strtok(NULL, ":"); val = strtok(NULL, " \t"); if(val == NULL) break; x_space[j].index = (int) strtol(idx,&endptr,10); x_space[j].value = strtod(val,&endptr); ++j; } x_space[j++].index = -1; } free(line); setlocale(LC_ALL, old_locale); free(old_locale); if (ferror(fp) != 0 || fclose(fp) != 0) return NULL; model->free_sv = 1; // XXX return model; } void svm_free_model_content(svm_model* model_ptr) { if(model_ptr->free_sv && model_ptr->l > 0 && model_ptr->SV != NULL) free((void *)(model_ptr->SV[0])); if(model_ptr->sv_coef) { for(int i=0;inr_class-1;i++) free(model_ptr->sv_coef[i]); } free(model_ptr->SV); model_ptr->SV = NULL; free(model_ptr->sv_coef); model_ptr->sv_coef = NULL; free(model_ptr->rho); model_ptr->rho = NULL; free(model_ptr->label); model_ptr->label= NULL; free(model_ptr->probA); model_ptr->probA = NULL; free(model_ptr->probB); model_ptr->probB= NULL; free(model_ptr->sv_indices); model_ptr->sv_indices = NULL; free(model_ptr->nSV); model_ptr->nSV = NULL; } void svm_free_and_destroy_model(svm_model** model_ptr_ptr) { if(model_ptr_ptr != NULL && *model_ptr_ptr != NULL) { svm_free_model_content(*model_ptr_ptr); free(*model_ptr_ptr); *model_ptr_ptr = NULL; } } void svm_destroy_param(svm_parameter* param) { free(param->weight_label); free(param->weight); } const char *svm_check_parameter(const svm_problem *prob, const svm_parameter *param) { // svm_type int svm_type = param->svm_type; if(svm_type != C_SVC && svm_type != NU_SVC && svm_type != ONE_CLASS && svm_type != EPSILON_SVR && svm_type != NU_SVR) return "unknown svm type"; // kernel_type, degree int kernel_type = param->kernel_type; if(kernel_type != LINEAR && kernel_type != POLY && kernel_type != RBF && kernel_type != SIGMOID && kernel_type != PRECOMPUTED) return "unknown kernel type"; if(param->gamma < 0) return "gamma < 0"; if(param->degree < 0) return "degree of polynomial kernel < 0"; // cache_size,eps,C,nu,p,shrinking if(param->cache_size <= 0) return "cache_size <= 0"; if(param->eps <= 0) return "eps <= 0"; if(svm_type == C_SVC || svm_type == EPSILON_SVR || svm_type == NU_SVR) if(param->C <= 0) return "C <= 0"; if(svm_type == NU_SVC || svm_type == ONE_CLASS || svm_type == NU_SVR) if(param->nu <= 0 || param->nu > 1) return "nu <= 0 or nu > 1"; if(svm_type == EPSILON_SVR) if(param->p < 0) return "p < 0"; if(param->shrinking != 0 && param->shrinking != 1) return "shrinking != 0 and shrinking != 1"; if(param->probability != 0 && param->probability != 1) return "probability != 0 and probability != 1"; if(param->probability == 1 && svm_type == ONE_CLASS) return "one-class SVM probability output not supported yet"; // check whether nu-svc is feasible if(svm_type == NU_SVC) { int l = prob->l; int max_nr_class = 16; int nr_class = 0; int *label = Malloc(int,max_nr_class); int *count = Malloc(int,max_nr_class); int i; for(i=0;iy[i]; int j; for(j=0;jnu*(n1+n2)/2 > min(n1,n2)) { free(label); free(count); return "specified nu is infeasible"; } } } free(label); free(count); } return NULL; } int svm_check_probability_model(const svm_model *model) { return ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) && model->probA!=NULL && model->probB!=NULL) || ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) && model->probA!=NULL); } void svm_set_print_string_function(void (*print_func)(const char *)) { if(print_func == NULL) svm_print_string = &print_string_stdout; else svm_print_string = print_func; } e1071/src/cmeans.c0000755000175100001440000001774011400421345013234 0ustar hornikusers/* C code for (weighted) fuzzy c-means, rewritten from scratch by KH. */ #include #include #include /* Enhance readability of matrix-subscripting for matrices stored in row-major order. */ #define MSUB(x, i, j, n) x[(i) + (n) * (j)] static double *d; static double *dwrk, *dwrk_x, *dwrk_w; static int *iwrk; static void cmeans_setup(int nr_x, int nr_p, int dist) { int len_u_d = nr_x * nr_p; d = (double *) R_alloc(len_u_d, sizeof(double)); if(dist == 1) { /* Needed for weighted medians. */ dwrk_x = (double *) R_alloc(nr_x, sizeof(double)); dwrk_w = (double *) R_alloc(nr_x, sizeof(double)); dwrk = (double *) R_alloc(nr_x, sizeof(double)); iwrk = (int *) R_alloc(nr_x, sizeof(int)); } } /* static void cmeans_copy_vector(double *from, double *to, int len) { int i; for(i = 0; i < len; i++) to[i] = from[i]; } static double cmeans_delta_old_new(double *old, double *new, int len) { int i; double sum = 0; for(i = 0; i < len; i++) sum += fabs(new[i] - old[i]); return(sum / len); } */ static int cmeans_sign(double x) { if(x == 0) return(0); return((x > 0) ? 1 : -1); } static double cmeans_weighted_median(double *x, double *w, int len) { int i; double sum, val, marg, mval, cumsum_w, cumsum_w_x; /* Sort x. */ for(i = 0; i < len; i++) iwrk[i] = i; rsort_with_index(x, iwrk, len); /* Permute w using iwrk, and normalize. */ sum = 0; for(i = 0; i < len; i++) { dwrk[i] = w[iwrk[i]]; sum += dwrk[i]; } for(i = 0; i < len; i++) { w[i] = dwrk[i] / sum; } cumsum_w = cumsum_w_x = 0; mval = R_PosInf; marg = *x; /* -Wall */ for(i = 0; i < len; i++) { cumsum_w += w[i]; cumsum_w_x += w[i] * x[i]; val = x[i] * (cumsum_w - .5) - cumsum_w_x; if(val < mval) { marg = x[i]; mval = val; } } return(marg); } /* Update the dissimilarities (between objects and prototypes) for a * single object (i.e., a single row of the dissimilarity matrix. */ static void ufcl_dissimilarities(double *x, double *p, int nr_x, int nc, int nr_p, int dist, int ix, double *d) { int ip, j; double sum, v; for(ip = 0; ip < nr_p; ip++) { sum = 0; for(j = 0; j < nc; j++) { v = MSUB(x, ix, j, nr_x) - MSUB(p, ip, j, nr_p); if(dist == 0) sum += v * v; else if(dist == 1) sum += fabs(v); } MSUB(d, ix, ip, nr_x) = sum; } } static void cmeans_dissimilarities(double *x, double *p, int nr_x, int nc, int nr_p, int dist, double *d) { int ix; for(ix = 0; ix < nr_x; ix++) { /* Loop over all objects ... */ ufcl_dissimilarities(x, p, nr_x, nc, nr_p, dist, ix, d); } } /* Update the memberships for a single object (i.e., a single row of the * membership matrix.) */ static void ufcl_memberships(double *d, int nr_x, int nr_p, double exponent, int ix, double *u) { int ip, n_of_zeroes; double sum, v; n_of_zeroes = 0; for(ip = 0; ip < nr_p; ip++) { if(MSUB(d, ix, ip, nr_x) == 0) n_of_zeroes++; } if(n_of_zeroes > 0) { v = 1 / n_of_zeroes; for(ip = 0; ip < nr_p; ip++) MSUB(u, ix, ip, nr_x) = ((MSUB(d, ix, ip, nr_x) == 0) ? v : 0); } else { /* Use the assumption that in general, pow() is more * expensive than subscripting. */ sum = 0; for(ip = 0; ip < nr_p; ip++) { v = 1 / pow(MSUB(d, ix, ip, nr_x), exponent); sum += v; MSUB(u, ix, ip, nr_x) = v; } for(ip = 0; ip < nr_p; ip++) MSUB(u, ix, ip, nr_x) /= sum; } } static void cmeans_memberships(double *d, int nr_x, int nr_p, double exponent, double *u) { int ix; for(ix = 0; ix < nr_x; ix++) { /* Loop over all objects ... */ ufcl_memberships(d, nr_x, nr_p, exponent, ix, u); } } static void cmeans_prototypes(double *x, double *u, double *w, int nr_x, int nc, int nr_p, double f, int dist, double *p) { int ix, ip, j; double sum, v; if(dist == 0) { /* Euclidean: weighted means. */ for(ip = 0; ip < nr_p; ip++) { for(j = 0; j < nc; j++) MSUB(p, ip, j, nr_p) = 0; sum = 0; for(ix = 0; ix < nr_x; ix++) { v = w[ix] * pow(MSUB(u, ix, ip, nr_x), f); sum += v; for(j = 0; j < nc; j++) MSUB(p, ip, j, nr_p) += v * MSUB(x, ix, j, nr_x); } for(j = 0; j < nc; j++) MSUB(p, ip, j, nr_p) /= sum; } } else { /* Manhattan: weighted medians. */ for(ip = 0; ip < nr_p; ip++) for(j = 0; j < nc; j++) { for(ix = 0; ix < nr_x; ix++) { dwrk_x[ix] = MSUB(x, ix, j, nr_x); dwrk_w[ix] = w[ix] * pow(MSUB(u, ix, ip, nr_x), f); } MSUB(p, ip, j, nr_p) = cmeans_weighted_median(dwrk_x, dwrk_w, nr_x); } } } static double cmeans_error_fn(double *u, double *d, double *w, int nr_x, int nr_p, double f) { int ix, ip; double sum; sum = 0; for(ix = 0; ix < nr_x; ix++) for(ip = 0; ip < nr_p; ip++) sum += w[ix] * pow(MSUB(u, ix, ip, nr_x), f) * MSUB(d, ix, ip, nr_x); return(sum); } void cmeans(double *x, int *nr_x, int *nc, double *p, int *nr_p, double *w, double *f, int *dist, int *itermax, double *reltol, int *verbose, double *u, double *ermin, int *iter) { double exponent = 1 / (*f - 1); double old_value, new_value; cmeans_setup(*nr_x, *nr_p, *dist); cmeans_dissimilarities(x, p, *nr_x, *nc, *nr_p, *dist, d); cmeans_memberships(d, *nr_x, *nr_p, exponent, u); old_value = new_value = cmeans_error_fn(u, d, w, *nr_x, *nr_p, *f); *iter = 0; while((*iter)++ < *itermax) { cmeans_prototypes(x, u, w, *nr_x, *nc, *nr_p, *f, *dist, p); cmeans_dissimilarities(x, p, *nr_x, *nc, *nr_p, *dist, d); cmeans_memberships(d, *nr_x, *nr_p, exponent, u); new_value = cmeans_error_fn(u, d, w, *nr_x, *nr_p, *f); if(fabs(old_value - new_value) < *reltol * (old_value + *reltol)) { if(*verbose) Rprintf("Iteration: %3d converged, Error: %13.10f\n", *iter, new_value); break; } else { if(*verbose) { *ermin = cmeans_error_fn(u, d, w, *nr_x, *nr_p, *f); Rprintf("Iteration: %3d, Error: %13.10f\n", *iter, new_value); } old_value = new_value; } } *ermin = new_value; } /* Update prototypes based on a single object. */ static void ufcl_prototypes(double *x, double *u, double *w, int nr_x, int nc, int nr_p, double f, int dist, double lrate, int ix, double *p) { int ip, j; double grad; for(ip = 0; ip < nr_p; ip++) { for(j = 0; j < nc; j++) { grad = MSUB(x, ix, j, nr_x) - MSUB(p, ip, j, nr_p); if(dist == 1) grad = cmeans_sign(grad); MSUB(p, ip, j, nr_p) += lrate * w[ix] * pow(MSUB(u, ix, ip, nr_x), f) * grad; } } } void ufcl(double *x, int *nr_x, int *nc, double *p, int *nr_p, double *w, double *f, int *dist, int *itermax, double *reltol, int *verbose, double *rate_par, double *u, double *ermin, int *iter) { double exponent = 1 / (*f - 1); double old_value, new_value; int ix; double lrate; cmeans_setup(*nr_x, *nr_p, 0); /* Need some starting values ... */ cmeans_dissimilarities(x, p, *nr_x, *nc, *nr_p, *dist, d); cmeans_memberships(d, *nr_x, *nr_p, exponent, u); old_value = new_value = cmeans_error_fn(u, d, w, *nr_x, *nr_p, *f); *iter = 0; while((*iter)++ < *itermax) { /* Turns out that sampling the objects is a bad idea ... */ lrate = *rate_par * (1 - (double) *iter / *itermax); for(ix = 0; ix < *nr_x; ix++) { ufcl_dissimilarities(x, p, *nr_x, *nc, *nr_p, *dist, ix, d); ufcl_memberships(d, *nr_x, *nr_p, exponent, ix, u); ufcl_prototypes(x, u, w, *nr_x, *nc, *nr_p, *f, *dist, lrate, ix, p); } new_value = cmeans_error_fn(u, d, w, *nr_x, *nr_p, *f); if(fabs(old_value - new_value) < *reltol * (old_value + *reltol)) { if(*verbose) Rprintf("Iteration: %3d converged, Error: %13.10f\n", *iter, new_value); break; } else { if(*verbose) { *ermin = cmeans_error_fn(u, d, w, *nr_x, *nr_p, *f); Rprintf("Iteration: %3d, Error: %13.10f\n", *iter, new_value); } old_value = new_value; } } *ermin = new_value; } e1071/src/svm.h0000655000175100001440000000660513421370756012613 0ustar hornikusers#ifndef _LIBSVM_H #define _LIBSVM_H #define LIBSVM_VERSION 323 #ifdef __cplusplus extern "C" { #endif extern int libsvm_version; struct svm_node { int index; double value; }; struct svm_problem { int l; double *y; struct svm_node **x; }; enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR }; /* svm_type */ enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */ struct svm_parameter { int svm_type; int kernel_type; int degree; /* for poly */ double gamma; /* for poly/rbf/sigmoid */ double coef0; /* for poly/sigmoid */ /* these are for training only */ double cache_size; /* in MB */ double eps; /* stopping criteria */ double C; /* for C_SVC, EPSILON_SVR and NU_SVR */ int nr_weight; /* for C_SVC */ int *weight_label; /* for C_SVC */ double* weight; /* for C_SVC */ double nu; /* for NU_SVC, ONE_CLASS, and NU_SVR */ double p; /* for EPSILON_SVR */ int shrinking; /* use the shrinking heuristics */ int probability; /* do probability estimates */ }; /* // // svm_model // */ struct svm_model { struct svm_parameter param; /* parameter */ int nr_class; /* number of classes, = 2 in regression/one class svm */ int l; /* total #SV */ struct svm_node **SV; /* SVs (SV[l]) */ double **sv_coef; /* coefficients for SVs in decision functions (sv_coef[k-1][l]) */ double *rho; /* constants in decision functions (rho[k*(k-1)/2]) */ double *probA; /* pariwise probability information */ double *probB; int *sv_indices; /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */ /* for classification only */ int *label; /* label of each class (label[k]) */ int *nSV; /* number of SVs for each class (nSV[k]) */ /* nSV[0] + nSV[1] + ... + nSV[k-1] = l */ /* XXX */ int free_sv; /* 1 if svm_model is created by svm_load_model*/ /* 0 if svm_model is created by svm_train */ }; struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param); void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target); int svm_save_model(const char *model_file_name, const struct svm_model *model); struct svm_model *svm_load_model(const char *model_file_name); int svm_get_svm_type(const struct svm_model *model); int svm_get_nr_class(const struct svm_model *model); void svm_get_labels(const struct svm_model *model, int *label); void svm_get_sv_indices(const struct svm_model *model, int *sv_indices); int svm_get_nr_sv(const struct svm_model *model); double svm_get_svr_probability(const struct svm_model *model); double svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values); double svm_predict(const struct svm_model *model, const struct svm_node *x); double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates); void svm_free_model_content(struct svm_model *model_ptr); void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr); void svm_destroy_param(struct svm_parameter *param); const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param); int svm_check_probability_model(const struct svm_model *model); //void svm_set_print_string_function(void (*print_func)(const char *)); #ifdef __cplusplus } #endif void svm_set_print_string_function(void (*print_func)(const char *)); #endif /* _LIBSVM_H */ e1071/src/cshell.c0000755000175100001440000001766711400421345013250 0ustar hornikusers/*****************************************************************/ /* * Copyright (C)2000 Evgenia Dimitriadou * * * 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., 675 Mass Ave, Cambridge, MA 02139, USA. */ #include #include #include "R.h" int subcshell(int *xrows, int *xcols, double *x, int *ncenters, double *centers, int *itermax, int *iter, int *verbose, int *dist, double *U, double *UANT, double *f, double *ermin, double *radius, int *flag) { int k, col, i, m, n ; double serror; /*convergence parameters*/ double epsi1, epsi2, conv; double sum2; double temp,tempu, tempu1, tempu2, distance; int j; double suma; double exponente; /* *ermin=0.0;*/ serror=0.0; sum2=0; if ((*flag==0) || (*flag==5)){ /* UPDATE CENTERS*/ for(i=0;i<*ncenters;i++) { sum2=0; for(col=0;col<*xcols;col++) centers[i+(*ncenters)*col]=0.0; for(k=0;k<*xrows;k++) { temp=pow(U[k+(*xrows)*i],*f); sum2=sum2+temp; for(col=0;col<*xcols;col++) { centers[i+(*ncenters)*col]+= temp*x[k+(*xrows)*col]; } } for(col=0;col<*xcols;col++) centers[i+(*ncenters)*col]/=sum2; } /*UPDATE radius*/ for(i=0;i<*ncenters;i++) { sum2=0; radius[i]=0.0; for(k=0;k<*xrows;k++) { distance=0.0; temp=pow(U[k+(*xrows)*i],*f); sum2=sum2+temp; for(col=0;col<*xcols;col++) { if (*dist==0){ distance+= (x[k+(*xrows)*col]-centers[i+(*ncenters)*col])*(x[k+(*xrows)*col]-centers[i+(*ncenters)*col]); } else if(*dist ==1){ distance+=fabs(x[k+(*xrows)*col]-centers[i+(*ncenters)*col]); } } if (*dist==0){ radius[i]+= temp*sqrt(distance);} else if(*dist ==1){ radius[i]+= temp*distance;} } radius[i]/=sum2; } }/*flag=0*/ /*update UANT*/ for(i=0;i<*ncenters;i++){ for(k=0;k<*xrows;k++){ UANT[k+(*xrows)*i]=U[k+(*xrows)*i];}} /* UPDATE Membership Matrix */ exponente=2.0/(*f-1.0); for(i=0;i<*ncenters;i++) { for(k=0;k<*xrows;k++) { suma=0; for(j=0;j<*ncenters;j++) { tempu=0; tempu1=0; tempu2=0; for (col=0;col<*xcols;col++) { if (*dist==0){ tempu1+=(x[k+(*xrows)*col]-centers[i+(*ncenters)*col])*(x[k+(*xrows)*col]-centers[i+(*ncenters)*col]); tempu2+=(x[k+(*xrows)*col]-centers[j+(*ncenters)*col])*(x[k+(*xrows)*col]-centers[j+(*ncenters)*col]); } else if(*dist ==1){ tempu1+=fabs(x[k+(*xrows)*col]-centers[i+(*ncenters)*col]); tempu2+=fabs(x[k+(*xrows)*col]-centers[j+(*ncenters)*col]); } } if (*dist==0){ tempu=fabs(sqrt(tempu1)-radius[i])/fabs(sqrt(tempu2)-radius[j]); } else if(*dist ==1){ tempu=fabs((tempu1-radius[i])/(tempu2-radius[j])); } suma=suma+pow(tempu,exponente); } U[k+(*xrows)*i]=1.0/suma; } } /*ERROR MINIMIZATION*/ epsi1=0.002; epsi2=0.2; conv=0.0; for (m=0;m<*ncenters;m++){ for (k=0;k<*xrows;k++){ serror = 0.0; for(n=0;n<*xcols;n++){ if(*dist == 0){ serror += (x[k+(*xrows)*n] - centers[m+(*ncenters)*n])*(x[k+(*xrows)*n] - centers[m +(*ncenters)*n]); } else if(*dist ==1){ serror += fabs(x[k+(*xrows)*n] - centers[m + (*ncenters)*n]); } } if (*dist == 0){ serror=fabs(sqrt(serror)-radius[m]);} else if(*dist ==1){ serror=fabs(serror-radius[m]);} *ermin+=pow(U[k+(*xrows)*m],*f)*pow(serror,2); /* *ermin=*ermin/(*xrows));*/ /*Convergence check*/ conv += fabs(U[k+(*xrows)*m]-UANT[k+(*xrows)*m]); } } if (conv<= ((*xrows)*(*xcols)*epsi1)){ *flag=2; if (*verbose){ Rprintf("Iteration: %3d converged, Error: %13.10f\n",*iter,conv); }} else if (conv<= ((*xrows)*(*xcols)*epsi2)){ if (*verbose){ Rprintf("Iteration: %3d Epsi2: %13.10f\n",*iter,conv);} if (*flag==3) *flag=4; else *flag=1; } else if(*flag==3) *flag=5; if (*verbose){ Rprintf("Iteration: %3d Error: %13.10f\n",*iter,*ermin/(*xrows)); } return 0; } int cshell(int *xrows, int *xcols, double *x, int *ncenters, double *centers, int *itermax, int *iter, int *verbose, int *dist, double *U, double *UANT, double *f, double *ermin, double *radius, int *flag) { int k; int i,j,col; double suma,tempu,exponente,tempu1,tempu2; exponente=2.0/(*f-1.0); /* *flag=0;*/ if (*flag==0){ *iter=0; /*Initialize Membership Matrix */ for(i=0;i<*ncenters;i++) { for(k=0;k<*xrows;k++) { suma=0; for(j=0;j<*ncenters;j++) { tempu=0; tempu1=0; tempu2=0; for (col=0;col<*xcols;col++) { if (*dist==0){ tempu1+=(x[k+(*xrows)*col]-centers[i+(*ncenters)*col])*(x[k+(*xrows)*col]-centers[i+(*ncenters)*col]); tempu2+=(x[k+(*xrows)*col]-centers[j+(*ncenters)*col])*(x[k+(*xrows)*col]-centers[j+(*ncenters)*col]); } else if(*dist ==1){ tempu1+=fabs(x[k+(*xrows)*col]-centers[i+(*ncenters)*col]); tempu2+=fabs(x[k+(*xrows)*col]-centers[j+(*ncenters)*col]); } } if (*dist==0){ tempu=fabs(sqrt(tempu1)-radius[i])/fabs(sqrt(tempu2)-radius[j]); } else if(*dist ==1){ tempu=fabs((tempu1-radius[i])/(tempu2-radius[j])); } suma=suma+pow(tempu,exponente); } UANT[k+(*xrows)*i]=1.0/suma; } } for(i=0;i<*ncenters;i++) { for(j=0;j<*xrows;j++) U[j+(*xrows)*i]=UANT[j+(*xrows)*i]; } } while(((*iter)++ < *itermax) && ((*flag)!=1 && (*flag)!=2) && (*flag)!=4) { *ermin=0.0; subcshell(xrows, xcols, x, ncenters, centers, itermax, iter, verbose, dist, U, UANT, f, ermin, radius, flag); } return 0; } /*****************************************************************/ /*******only for prediction***************************************/ /*****************************************************************/ int cshell_assign(int *xrows, int *xcols, double *x, int *ncenters, double *centers, int *dist, double *U, double *f, double *radius) { int k, col, i; double tempu, tempu1, tempu2; int j; double suma; double exponente; exponente=2.0/(*f-1.0); for(i=0;i<*ncenters;i++) { for(k=0;k<*xrows;k++) { suma=0; for(j=0;j<*ncenters;j++) { tempu=0; tempu1=0; tempu2=0; for (col=0;col<*xcols;col++) { if (*dist==0){ tempu1+=(x[k+(*xrows)*col]-centers[i+(*ncenters)*col])*(x[k+(*xrows)*col]-centers[i+(*ncenters)*col]); tempu2+=(x[k+(*xrows)*col]-centers[j+(*ncenters)*col])*(x[k+(*xrows)*col]-centers[j+(*ncenters)*col]); } else if(*dist ==1){ tempu1+=fabs(x[k+(*xrows)*col]-centers[i+(*ncenters)*col]); tempu2+=fabs(x[k+(*xrows)*col]-centers[j+(*ncenters)*col]); } } if (*dist==0){ tempu=fabs(sqrt(tempu1)-radius[i])/fabs(sqrt(tempu2)-radius[j]); } else if(*dist ==1){ tempu=fabs((tempu1-radius[i])/(tempu2-radius[j])); } suma=suma+pow(tempu,exponente); } U[k+(*xrows)*i]=1.0/suma; } } return 0; } e1071/src/Rsvm.c0000755000175100001440000002754612562114411012725 0ustar hornikusers #include #include #include #include #include #include "svm.h" #define Malloc(type,n) (type *)malloc((n)*sizeof(type)) /* * results from cross-validation */ struct crossresults { double* results; double total1; double total2; }; struct svm_node ** sparsify (double *x, int r, int c) { struct svm_node** sparse; int i, ii, count; sparse = (struct svm_node **) malloc (r * sizeof(struct svm_node *)); for (i = 0; i < r; i++) { /* determine nr. of non-zero elements */ for (count = ii = 0; ii < c; ii++) if (x[i * c + ii] != 0) count++; /* allocate memory for column elements */ sparse[i] = (struct svm_node *) malloc ((count + 1) * sizeof(struct svm_node)); /* set column elements */ for (count = ii = 0; ii < c; ii++) if (x[i * c + ii] != 0) { sparse[i][count].index = ii + 1; sparse[i][count].value = x[i * c + ii]; count++; } /* set termination element */ sparse[i][count].index = -1; } return sparse; } struct svm_node ** transsparse (double *x, int r, int *rowindex, int *colindex) { struct svm_node** sparse; int i, ii, count = 0, nnz = 0; sparse = (struct svm_node **) malloc (r * sizeof(struct svm_node*)); for (i = 0; i < r; i++) { /* allocate memory for column elements */ nnz = rowindex[i+1] - rowindex[i]; sparse[i] = (struct svm_node *) malloc ((nnz + 1) * sizeof(struct svm_node)); /* set column elements */ for (ii = 0; ii < nnz; ii++) { sparse[i][ii].index = colindex[count]; sparse[i][ii].value = x[count]; count++; } /* set termination element */ sparse[i][ii].index = -1; } return sparse; } /* Cross-Validation-routine from svm-train */ void do_cross_validation(struct svm_problem *prob, struct svm_parameter *param, int nr_fold, double* cresults, double* ctotal1, double* ctotal2) { int i; int total_correct = 0; double total_error = 0; double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0; /* random shuffle */ GetRNGstate(); for(i=0; il; i++) { int j = i+((int) (unif_rand() * (prob->l-i)))%(prob->l-i); struct svm_node *tx; double ty; tx = prob->x[i]; prob->x[i] = prob->x[j]; prob->x[j] = tx; ty = prob->y[i]; prob->y[i] = prob->y[j]; prob->y[j] = ty; } PutRNGstate(); for(i=0; il/nr_fold; int end = (i+1)*prob->l/nr_fold; int j,k; struct svm_problem subprob; subprob.l = prob->l-(end-begin); subprob.x = Malloc(struct svm_node*,subprob.l); subprob.y = Malloc(double,subprob.l); k=0; for(j = 0; j < begin; j++) { subprob.x[k] = prob->x[j]; subprob.y[k] = prob->y[j]; ++k; } for(j = end; jl; j++) { subprob.x[k] = prob->x[j]; subprob.y[k] = prob->y[j]; ++k; } if(param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR) { struct svm_model *submodel = svm_train(&subprob,param); double error = 0; for(j=begin;jx[j]); double y = prob->y[j]; error += (v-y)*(v-y); sumv += v; sumy += y; sumvv += v*v; sumyy += y*y; sumvy += v*y; } svm_free_and_destroy_model(&submodel); /* printf("Mean squared error = %g\n", error/(end-begin)); */ cresults[i] = error/(end-begin); total_error += error; } else { struct svm_model *submodel = svm_train(&subprob,param); int correct = 0; for(j=begin;jx[j]); if(v == prob->y[j]) ++correct; } svm_free_and_destroy_model(&submodel); /* printf("Accuracy = %g%% (%d/%d)\n", */ /* 100.0*correct/(end-begin),correct,(end-begin)); */ cresults[i] = 100.0*correct/(end-begin); total_correct += correct; } free(subprob.x); free(subprob.y); } if(param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR) { /* printf("Cross Validation Mean squared error = %g\n",total_error/prob.l); printf("Cross Validation Squared correlation coefficient = %g\n", ((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/ ((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy)) ); */ *ctotal1 = total_error/prob->l; *ctotal2 = ((prob->l * sumvy - sumv * sumy) * (prob->l * sumvy - sumv*sumy)) / ((prob->l * sumvv - sumv * sumv) * (prob->l * sumyy - sumy * sumy)); } else /* printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l); */ *ctotal1 = 100.0 * total_correct / prob->l; } void svmtrain (double *x, int *r, int *c, double *y, int *rowindex, int *colindex, int *svm_type, int *kernel_type, int *degree, double *gamma, double *coef0, double *cost, double *nu, int *weightlabels, double *weights, int *nweights, double *cache, double *tolerance, double *epsilon, int *shrinking, int *cross, int *sparse, int *probability, int *nclasses, int *nr, int *index, int *labels, int *nSV, double *rho, double *coefs, double *sigma, double *probA, double *probB, double *cresults, double *ctotal1, double *ctotal2, char **error) { struct svm_parameter par; struct svm_problem prob; struct svm_model *model = NULL; int i; const char* s; /* set parameters */ par.svm_type = *svm_type; par.kernel_type = *kernel_type; par.degree = *degree; par.gamma = *gamma; par.coef0 = *coef0; par.cache_size = *cache; par.eps = *tolerance; par.C = *cost; par.nu = *nu; par.nr_weight = *nweights; if (par.nr_weight > 0) { par.weight = (double *) malloc (sizeof(double) * par.nr_weight); memcpy(par.weight, weights, par.nr_weight * sizeof(double)); par.weight_label = (int *) malloc (sizeof(int) * par.nr_weight); memcpy(par.weight_label, weightlabels, par.nr_weight * sizeof(int)); } par.p = *epsilon; par.shrinking = *shrinking; par.probability = *probability; /* set problem */ prob.l = *r; prob.y = y; if (*sparse > 0) prob.x = transsparse(x, *r, rowindex, colindex); else prob.x = sparsify(x, *r, *c); /* check parameters & copy error message */ s = svm_check_parameter(&prob, &par); if (s) { strcpy(*error, s); } else { /* call svm_train */ model = svm_train(&prob, &par); /* set up return values */ /* for (ii = 0; ii < model->l; ii++) for (i = 0; i < *r; i++) if (prob.x[i] == model->SV[ii]) index[ii] = i+1; */ svm_get_sv_indices(model, index); *nr = model->l; *nclasses = model->nr_class; memcpy (rho, model->rho, *nclasses * (*nclasses - 1)/2 * sizeof(double)); if (*probability && par.svm_type != ONE_CLASS) { if (par.svm_type == EPSILON_SVR || par.svm_type == NU_SVR) *sigma = svm_get_svr_probability(model); else { memcpy(probA, model->probA, *nclasses * (*nclasses - 1)/2 * sizeof(double)); memcpy(probB, model->probB, *nclasses * (*nclasses - 1)/2 * sizeof(double)); } } for (i = 0; i < *nclasses-1; i++) memcpy (coefs + i * *nr, model->sv_coef[i], *nr * sizeof (double)); if (*svm_type < 2) { memcpy (labels, model->label, *nclasses * sizeof(int)); memcpy (nSV, model->nSV, *nclasses * sizeof(int)); } /* Perform cross-validation, if requested */ if (*cross > 0) do_cross_validation (&prob, &par, *cross, cresults, ctotal1, ctotal2); /* clean up memory */ svm_free_and_destroy_model(&model); } /* clean up memory */ if (par.nr_weight > 0) { free(par.weight); free(par.weight_label); } for (i = 0; i < *r; i++) free (prob.x[i]); free (prob.x); } void svmpredict (int *decisionvalues, int *probability, double *v, int *r, int *c, int *rowindex, int *colindex, double *coefs, double *rho, int *compprob, double *probA, double *probB, int *nclasses, int *totnSV, int *labels, int *nSV, int *sparsemodel, int *svm_type, int *kernel_type, int *degree, double *gamma, double *coef0, double *x, int *xr, int *xrowindex, int *xcolindex, int *sparsex, double *ret, double *dec, double *prob) { struct svm_model m; struct svm_node ** train; int i; /* set up model */ m.l = *totnSV; m.nr_class = *nclasses; m.sv_coef = (double **) malloc (m.nr_class * sizeof(double*)); for (i = 0; i < m.nr_class - 1; i++) { m.sv_coef[i] = (double *) malloc (m.l * sizeof (double)); memcpy (m.sv_coef[i], coefs + i*m.l, m.l * sizeof (double)); } if (*sparsemodel > 0) m.SV = transsparse(v, *r, rowindex, colindex); else m.SV = sparsify(v, *r, *c); m.rho = rho; m.probA = probA; m.probB = probB; m.label = labels; m.nSV = nSV; /* set up parameter */ m.param.svm_type = *svm_type; m.param.kernel_type = *kernel_type; m.param.degree = *degree; m.param.gamma = *gamma; m.param.coef0 = *coef0; m.param.probability = *compprob; m.free_sv = 1; /* create sparse training matrix */ if (*sparsex > 0) train = transsparse(x, *xr, xrowindex, xcolindex); else train = sparsify(x, *xr, *c); /* call svm-predict-function for each x-row, possibly using probability estimator, if requested */ if (*probability && svm_check_probability_model(&m)) { for (i = 0; i < *xr; i++) ret[i] = svm_predict_probability(&m, train[i], prob + i * *nclasses); } else { for (i = 0; i < *xr; i++) ret[i] = svm_predict(&m, train[i]); } /* optionally, compute decision values */ if (*decisionvalues) for (i = 0; i < *xr; i++) svm_predict_values(&m, train[i], dec + i * *nclasses * (*nclasses - 1) / 2); /* clean up memory */ for (i = 0; i < *xr; i++) free (train[i]); free (train); for (i = 0; i < *r; i++) free (m.SV[i]); free (m.SV); for (i = 0; i < m.nr_class - 1; i++) free(m.sv_coef[i]); free(m.sv_coef); } void svmwrite (double *v, int *r, int *c, int *rowindex, int *colindex, double *coefs, double *rho, int *compprob, double *probA, double *probB, int *nclasses, int *totnSV, int *labels, int *nSV, int *sparsemodel, int *svm_type, int *kernel_type, int *degree, double *gamma, double *coef0, char **filename) { struct svm_model m; int i; char *fname = *filename; /* set up model */ m.l = *totnSV; m.nr_class = *nclasses; m.sv_coef = (double **) malloc (m.nr_class * sizeof(double*)); for (i = 0; i < m.nr_class - 1; i++) { m.sv_coef[i] = (double *) malloc (m.l * sizeof (double)); memcpy (m.sv_coef[i], coefs + i*m.l, m.l * sizeof (double)); } if (*sparsemodel > 0) m.SV = transsparse(v, *r, rowindex, colindex); else m.SV = sparsify(v, *r, *c); m.rho = rho; m.label = labels; m.nSV = nSV; if (*compprob) { m.probA = probA; m.probB = probB; } else { m.probA = NULL; m.probB = NULL; } /* set up parameter */ m.param.svm_type = *svm_type; 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e1071/vignettes/svminternals.Rnw0000755000175100001440000001611312547044621016264 0ustar hornikusers\documentclass[a4paper]{article} \usepackage{hyperref, graphicx, color, alltt,a4wide} \usepackage{Sweave} \newcommand{\pkg}[1]{\texttt{#1}} \definecolor{Red}{rgb}{0.7,0,0} \definecolor{Blue}{rgb}{0,0,0.8} \definecolor{hellgrau}{rgb}{0.55,0.55,0.55} \newenvironment{smallexample}{\begin{alltt}\small}{\end{alltt}} \begin{document} %\VignetteIndexEntry{svm() internals} %\VignetteDepends{xtable} %\VignetteKeywords{classification, regression, machine learning, benchmarking, support vector machines} %\VignettePackage{e1071} \SweaveOpts{engine=R,eps=FALSE} \setkeys{Gin}{width=0.8\textwidth} \title{\texttt{svm()} internals\\ \large Some technical notes about the \texttt{svm()} in package \pkg{e1071}} \author{by David Meyer\\ FH Technikum Wien, Austria\\ \url{David.Meyer@R-Project.org} } \maketitle \sloppy This document explains how to use the parameters in an object returned by \texttt{svm()} for own prediction functions. \section{Binary Classifier} For class prediction in the binary case, the class of a new data vector $n$ is usually given by \emph{the sign} of \begin{equation} \sum_i{a_i y_i K(x_i, n)} + \rho \end{equation} \noindent where $x_i$ is the $i$-th support vector, $y_i$ the corresponding label, $a_i$ the corresponding coefficiant, and $K$ is the kernel (for example the linear one, i.e. $K(u,v) = u ^{\top} v$). Now, the \texttt{libsvm} library interfaced by the \texttt{svm()} function actually returns $a_i y_i$ as $i$-th coefficiant and the \emph{negative} $\rho$, so in fact uses the formula: \[ \sum_i{\mathrm{coef}_i K(x_i, n)} - \rho \] \noindent where the training examples (=training data) are labeled \{1,-1\} (!). A simplified \textsf{R} function for prediction with linear kernel would be: \begin{smallexample} svmpred <- function (m, newdata, K=crossprod) \{ ## this guy does the computation: pred.one <- function (x) sign(sum(sapply(1:m$tot.nSV, function (j) K(m$SV[j,], x) * m$coefs[j] ) ) - m$rho ) ## this is just for convenience: if (is.vector(newdata)) newdata <- t(as.matrix(x)) sapply (1:nrow(newdata), function (i) pred.one(newdata[i,])) \} \end{smallexample} \noindent where \texttt{pred.one()} does the actual prediction for one new data vector, the remainder is just a convenience for prediction of multiple new examples. It is easy to extend this to other kernels, just replace \texttt{K()} with the appropriate function (see the help page for the formulas used) and supply the additional constants. As we will see in the next section, the multi-class prediction is more complicated, because the coefficiants of the diverse binary SVMs are stored in a compressed format. \section{Multiclass-classifier} To handle $k$ classes, $k>2$, \texttt{svm()} trains all binary subclassifiers (one-against-one-method) and then uses a voting mechanism to determine the actual class. Now, this means $k(k-1)/2$ classifiers, hence in principle $k(k-1)/2$ sets of SVs, coefficiants and rhos. These are stored in a compressed format: \begin{enumerate} \item Only one SV is stored in case it were used by several classifiers. The \texttt{model\$SV-matrix} is ordered by classes, and you find the starting indices by using \texttt{nSV} (number of SVs): \begin{smallexample} start <- c(1, cumsum(model$nSV)) start <- start[-length(start)] \end{smallexample} \texttt{sum(nSV)} equals the total number of (distinct) SVs. \item The coefficients of the SVs are stored in the \texttt{model\$coefs}-matrix, grouped by classes. Because the separating hyperplanes found by the SVM algorithm has SVs on both sides, you will have two sets of coefficients per binary classifier, and e.g., for 3 classes, you could build a \emph{block}-matrix like this for the classifiers $(i, j)$ ($i$,$j$=class numbers): \begin{table}[h] \center \begin{tabular}{|c|c|c|c|} \hline i $\backslash$ j & 0 & 1 & 2 \\\hline 0 & X & set (0, 1)& set (0, 2)\\\hline 1 & set (1, 0) & X & set (1, 2)\\\hline 2 & set (2, 0) & set (2, 1) & X\\\hline \end{tabular} \end{table} \noindent where set(i, j) are the coefficients for the classifier (i,j), lying on the side of class j. Because there are no entries for (i, i), we can save the diagonal and shift up the lower triangular matrix to get \begin{table}[h] \center \begin{tabular}{|c|c|c|c|} \hline i $\backslash$ j & 0 & 1 & 2 \\\hline 0 & set (1,0) & set (0,1) & set (0,2) \\\hline 1 & set (2,0) & set (2,1) & set (1,2) \\\hline \end{tabular} \end{table} \noindent Each set (., j) has length \texttt{nSV[j]}, so of course, there will be some filling 0s in some sets. \texttt{model\$coefs} is the \emph{transposed} of such a matrix, therefore for a data set with, say, 6 classes, you get 6-1=5 columns. The coefficients of (i, j) start at \texttt{model\$coefs[start[i],j]} and those of (j, i) at \texttt{model\$coefs[start[j],i-1]}. \item The $k(k-1)/2$ rhos are just linearly stored in the vector \texttt{model\$rho}. \end{enumerate} \newpage \noindent The following code shows how to use this for prediction: \begin{smallexample} ## Linear Kernel function K <- function(i,j) crossprod(i,j) predsvm <- function(object, newdata) \{ ## compute start-index start <- c(1, cumsum(object$nSV)+1) start <- start[-length(start)] ## compute kernel values kernel <- sapply (1:object$tot.nSV, function (x) K(object$SV[x,], newdata)) ## compute raw prediction for classifier (i,j) predone <- function (i,j) \{ ## ranges for class i and j: ri <- start[i] : (start[i] + object$nSV[i] - 1) rj <- start[j] : (start[j] + object$nSV[j] - 1) ## coefs for (i,j): coef1 <- object$coefs[ri, j-1] coef2 <- object$coefs[rj, i] ## return raw values: crossprod(coef1, kernel[ri]) + crossprod(coef2, kernel[rj]) \} ## compute votes for all classifiers votes <- rep(0,object$nclasses) c <- 0 # rho counter for (i in 1 : (object$nclasses - 1)) for (j in (i + 1) : object$nclasses) if (predone(i,j) > object$rho[c <- c + 1]) votes[i] <- votes[i] + 1 else votes[j] <- votes[j] + 1 ## return winner (index with max. votes) object$levels[which(votes %in% max(votes))[1]] \} \end{smallexample} In case data were scaled prior fitting the model (note that this is the default for \texttt{svm()}, the new data needs to be scaled as well before applying the predition functions, for example using the following code snipped (object is an object returned by \texttt{svm()}, \texttt{newdata} a data frame): \begin{smallexample} if (any(object$scaled)) newdata[,object$scaled] <- scale(newdata[,object$scaled, drop = FALSE], center = object$x.scale$"scaled:center", scale = object$x.scale$"scaled:scale" ) \end{smallexample} \noindent For regression, the response needs to be scaled as well before training, and the predictions need to be scaled back accordingly. \end{document} e1071/vignettes/svmdoc.Rnw0000655000175100001440000004340313475431256015040 0ustar hornikusers\documentclass[a4paper]{article} \usepackage{hyperref, graphicx, color, alltt} \usepackage{Sweave} \usepackage[round]{natbib} \definecolor{Red}{rgb}{0.7,0,0} \definecolor{Blue}{rgb}{0,0,0.8} \definecolor{hellgrau}{rgb}{0.55,0.55,0.55} \newcommand{\pkg}[1]{\texttt{#1}} \newenvironment{smallexample}{\begin{alltt}\small}{\end{alltt}} \begin{document} %\VignetteIndexEntry{Support Vector Machines---the Interface to libsvm in package e1071} %\VignetteDepends{e1071,rpart,xtable} %\VignetteKeywords{classification, regression, machine learning, benchmarking, support vector machines} %\VignettePackage{e1071} \SweaveOpts{engine=R,eps=FALSE} \setkeys{Gin}{width=0.8\textwidth} \title{Support Vector Machines \footnote{A smaller version of this article appeared in R-News, Vol.1/3, 9.2001}\\ \large The Interface to \texttt{libsvm} in package \pkg{e1071}} \author{by David Meyer\\ FH Technikum Wien, Austria\\ \url{David.Meyer@R-Project.org} } \maketitle \sloppy ``Hype or Hallelujah?'' is the provocative title used by \cite{svm:bennett+campbell:2000} in an overview of Support Vector Machines (SVM). SVMs are currently a hot topic in the machine learning community, creating a similar enthusiasm at the moment as Artificial Neural Networks used to do before. Far from being a panacea, SVMs yet represent a powerful technique for general (nonlinear) classification, regression and outlier detection with an intuitive model representation. The package \pkg{e1071} offers an interface to the award-winning\footnote{The library won the IJCNN 2001 Challenge by solving two of three problems: the Generalization Ability Challenge (GAC) and the Text Decoding Challenge (TDC). For more information, see: \url{http://www.csie.ntu.edu.tw/~cjlin/papers/ijcnn.ps.gz}.} C++-implementation by Chih-Chung Chang and Chih-Jen Lin, \texttt{libsvm} (current version: 2.6), featuring: \begin{itemize} \item $C$- and $\nu$-classification \item one-class-classification (novelty detection) \item $\epsilon$- and $\nu$-regression \end{itemize} and includes: \begin{itemize} \item linear, polynomial, radial basis function, and sigmoidal kernels \item formula interface \item $k$-fold cross validation \end{itemize} For further implementation details on \texttt{libsvm}, see \cite{svm:chang+lin:2001}. \section*{Basic concept} SVMs were developed by \cite{svm:cortes+vapnik:1995} for binary classification. Their approach may be roughly sketched as follows: \begin{description} \item[Class separation:] basically, we are looking for the optimal separating hyperplane between the two classes by maximizing the \textit{margin} between the classes' closest points (see Figure \ref{fig:svm1})---the points lying on the boundaries are called \textit{support vectors}, and the middle of the margin is our optimal separating hyperplane; \item[Overlapping classes:] data points on the ``wrong'' side of the discriminant margin are weighted down to reduce their influence (\textit{``soft margin''}); \item[Nonlinearity:] when we cannot find a \textit{linear} separator, data points are projected into an (usually) higher-dimensional space where the data points effectively become linearly separable (this projection is realised via \textit{kernel techniques}); \item[Problem solution:] the whole task can be formulated as a quadratic optimization problem which can be solved by known techniques. \end{description} \noindent A program able to perform all these tasks is called a \textit{Support Vector Machine}. \begin{figure}[htbp] \begin{center} \includegraphics[width=8cm]{svm} \caption{Classification (linear separable case)} \label{fig:svm1} \end{center} \end{figure} Several extensions have been developed; the ones currently included in \texttt{libsvm} are: \begin{description} \item[$\nu$-classification:] this model allows for more control over the number of support vectors \cite[see][]{svm:scholkopf+smola+williamson:2000} by specifying an additional parameter $\nu$ which approximates the fraction of support vectors; \item[One-class-classification:] this model tries to find the support of a distribution and thus allows for outlier/novelty detection; \item[Multi-class classification:] basically, SVMs can only solve binary classification problems. To allow for multi-class classification, \texttt{libsvm} uses the \textit{one-against-one} technique by fitting all binary subclassifiers and finding the correct class by a voting mechanism; \item[$\epsilon$-regression:] here, the data points lie \textit{in between} the two borders of the margin which is maximized under suitable conditions to avoid outlier inclusion; \item[$\nu$-regression:] with analogue modifications of the regression model as in the classification case. \end{description} \section*{Usage in R} The R interface to \texttt{libsvm} in package \pkg{e1071}, \texttt{svm()}, was designed to be as intuitive as possible. Models are fitted and new data are predicted as usual, and both the vector/matrix and the formula interface are implemented. As expected for R's statistical functions, the engine tries to be smart about the mode to be chosen, using the dependent variable's type ($y$): if $y$ is a factor, the engine switches to classification mode, otherwise, it behaves as a regression machine; if $y$ is omitted, the engine assumes a novelty detection task. \section*{Examples} In the following two examples, we demonstrate the practical use of \texttt{svm()} along with a comparison to classification and regression trees as implemented in \texttt{rpart()}. \subsection*{Classification} In this example, we use the glass data from the \href{http://www.ics.uci.edu/mlearn/MLRepository.html}{UCI Repository of Machine Learning Databases} for classification. The task is to predict the type of a glass on basis of its chemical analysis. We start by splitting the data into a train and test set: <<>>= library(e1071) library(rpart) data(Glass, package="mlbench") ## split data into a train and test set index <- 1:nrow(Glass) testindex <- sample(index, trunc(length(index)/3)) testset <- Glass[testindex,] trainset <- Glass[-testindex,] @ Both for the SVM and the partitioning tree (via \texttt{rpart()}), we fit the model and try to predict the test set values: <<>>= ## svm svm.model <- svm(Type ~ ., data = trainset, cost = 100, gamma = 1) svm.pred <- predict(svm.model, testset[,-10]) @ (The dependent variable, \texttt{Type}, has column number 10. \texttt{cost} is a general penalizing parameter for $C$-classification and \texttt{gamma} is the radial basis function-specific kernel parameter.) <<>>= ## rpart rpart.model <- rpart(Type ~ ., data = trainset) rpart.pred <- predict(rpart.model, testset[,-10], type = "class") @ A cross-tabulation of the true versus the predicted values yields: <<>>= ## compute svm confusion matrix table(pred = svm.pred, true = testset[,10]) ## compute rpart confusion matrix table(pred = rpart.pred, true = testset[,10]) @ %% results table <>= library(xtable) rp.acc <- c() sv.acc <- c() rp.kap <- c() sv.kap <- c() reps <- 10 for (i in 1:reps) { ## split data into a train and test set index <- 1:nrow(Glass) testindex <- sample(index, trunc(length(index)/3)) testset <- na.omit(Glass[testindex,]) trainset <- na.omit(Glass[-testindex,]) ## svm svm.model <- svm(Type ~ ., data = trainset, cost = 100, gamma = 1) svm.pred <- predict(svm.model, testset[,-10]) tab <- classAgreement(table(svm.pred, testset[,10])) sv.acc[i] <- tab$diag sv.kap[i] <- tab$kappa ## rpart rpart.model <- rpart(Type ~ ., data = trainset) rpart.pred <- predict(rpart.model, testset[,-10], type = "class") tab <- classAgreement(table(rpart.pred, testset[,10])) rp.acc[i] <- tab$diag rp.kap[i] <- tab$kappa } x <- rbind(summary(sv.acc), summary(sv.kap), summary(rp.acc), summary(rp.kap)) rownames <- c() tab <- cbind(rep(c("svm","rpart"),2), round(x,2)) colnames(tab)[1] <- "method" rownames(tab) <- c("Accuracy","","Kappa"," ") xtable(tab, label = "tab:class", caption = "Performance of \\texttt{svm()} and\ \\texttt{rpart()} for classification (10 replications)") @ \noindent Finally, we compare the performance of the two methods by computing the respective accuracy rates and the kappa indices (as computed by \texttt{classAgreement()} also contained in package \pkg{e1071}). In Table \ref{tab:class}, we summarize the results of \Sexpr{reps} replications---Support Vector Machines show better results. \subsection*{Non-linear $\epsilon$-Regression} The regression capabilities of SVMs are demonstrated on the ozone data. Again, we split the data into a train and test set. <<>>= library(e1071) library(rpart) data(Ozone, package="mlbench") ## split data into a train and test set index <- 1:nrow(Ozone) testindex <- sample(index, trunc(length(index)/3)) testset <- na.omit(Ozone[testindex,-3]) trainset <- na.omit(Ozone[-testindex,-3]) ## svm svm.model <- svm(V4 ~ ., data = trainset, cost = 1000, gamma = 0.0001) svm.pred <- predict(svm.model, testset[,-3]) crossprod(svm.pred - testset[,3]) / length(testindex) ## rpart rpart.model <- rpart(V4 ~ ., data = trainset) rpart.pred <- predict(rpart.model, testset[,-3]) crossprod(rpart.pred - testset[,3]) / length(testindex) @ <>= rp.res <- c() sv.res <- c() reps <- 10 for (i in 1:reps) { ## split data into a train and test set index <- 1:nrow(Ozone) testindex <- sample(index, trunc(length(index)/3)) testset <- na.omit(Ozone[testindex,-3]) trainset <- na.omit(Ozone[-testindex,-3]) ## svm svm.model <- svm(V4 ~ ., data = trainset, cost = 1000, gamma = 0.0001) svm.pred <- predict(svm.model, testset[,-3]) sv.res[i] <- crossprod(svm.pred - testset[,3]) / length(testindex) ## rpart rpart.model <- rpart(V4 ~ ., data = trainset) rpart.pred <- predict(rpart.model, testset[,-3]) rp.res[i] <- crossprod(rpart.pred - testset[,3]) / length(testindex) } xtable(rbind(svm = summary(sv.res), rpart = summary(rp.res)), label = "tab:reg", caption = "Performance of \\texttt{svm()} and\ \\texttt{rpart()} for regression (Mean Squared Error, 10 replications)") @ \noindent We compare the two methods by the mean squared error (MSE)---see Table \ref{tab:reg} for a summary of \Sexpr{reps} replications. Again, as for classification, \texttt{svm()} does a better job than \texttt{rpart()}---in fact, much better. \section*{Elements of the \texttt{svm} object} The function \texttt{svm()} returns an object of class ``\texttt{svm}'', which partly includes the following components: \begin{description} \item[\textbf{\texttt{SV}:}] matrix of support vectors found; \item[\textbf{\texttt{labels}:}] their labels in classification mode; \item[\textbf{\texttt{index}:}] index of the support vectors in the input data (could be used e.g., for their visualization as part of the data set). \end{description} If the cross-classification feature is enabled, the \texttt{svm} object will contain some additional information described below. \section*{Other main features} \begin{description} \item[Class Weighting:] if one wishes to weight the classes differently (e.g., in case of asymmetric class sizes to avoid possibly overproportional influence of bigger classes on the margin), weights may be specified in a vector with named components. In case of two classes A and B, we could use something like: \texttt{m <- svm(x, y, class.weights = c(A = 0.3, B = 0.7))} \item[Cross-classification:] to assess the quality of the training result, we can perform a $k$-fold cross-classification on the training data by setting the parameter \texttt{cross} to $k$ (default: 0). The \texttt{svm} object will then contain some additional values, depending on whether classification or regression is performed. Values for classification: \begin{description} \item[\texttt{accuracies}:] vector of accuracy values for each of the $k$ predictions \item[\texttt{tot.accuracy}:] total accuracy \end{description} Values for regression: \begin{description} \item[\texttt{MSE}:] vector of mean squared errors for each of the $k$ predictions \item[\texttt{tot.MSE}:] total mean squared error \item[\texttt{scorrcoef}:] Squared correlation coefficient (of the predicted and the true values of the dependent variable) \end{description} \end{description} \section*{Tips on practical use} \begin{itemize} \item Note that SVMs may be very sensitive to the proper choice of parameters, so allways check a range of parameter combinations, at least on a reasonable subset of your data. \item For classification tasks, you will most likely use $C$-classification with the RBF kernel (default), because of its good general performance and the few number of parameters (only two: $C$ and $\gamma$). The authors of \pkg{libsvm} suggest to try small and large values for $C$---like 1 to 1000---first, then to decide which are better for the data by cross validation, and finally to try several $\gamma$'s for the better $C$'s. \item However, better results are obtained by using a grid search over all parameters. For this, we recommend to use the \texttt{tune.svm()} function in \pkg{e1071}. \item Be careful with large datasets as training times may increase rather fast. \item Scaling of the data usually drastically improves the results. Therefore, \texttt{svm()} scales the data by default. \end{itemize} \section*{Model Formulations and Kernels} Dual representation of models implemented: \begin{itemize} \item $C$-classification:\\ \begin{eqnarray} \min_\alpha&&\frac{1}{2}\alpha^\top \mathbf{Q} \alpha-\mathbf{e}^\top\alpha \nonumber\\ \mbox{s.t.} &&0\le\alpha_i\le C,~i=1,\ldots,l,\\ &&\mathbf{y}^\top\alpha=0~, \nonumber \end{eqnarray} where $\mathbf{e}$ is the unity vector, $C$ is the upper bound, $\mathbf{Q}$ is an $l$ by $l$ positive semidefinite matrix, $Q_{ij} \equiv y_i y_j K(x_i, x_j)$, and $K(x_i, x_j) \equiv \phi(x_i)^\top\phi(x_j)$ is the kernel. \item $\nu$-classification:\\ \begin{eqnarray} \min_\alpha&&\frac{1}{2}\alpha^\top \mathbf{Q} \alpha \nonumber\\ \mbox{s.t.}&&0\le\alpha_i\le 1/l,~i=1,\ldots,l,\\ &&\mathbf{e}^\top \alpha \ge \nu, \nonumber\\ &&\mathbf{y}^\top\alpha=0~. \nonumber \end{eqnarray} where $\nu \in (0,1]$. \item one-class classification:\\ \begin{eqnarray} \min_\alpha&&\frac{1}{2}\alpha^\top \mathbf{Q} \alpha \nonumber\\ \mbox{s.t.} &&0\le\alpha_i\le 1/(\nu l),~i=1,\ldots,l,\\ &&\mathbf{e}^\top\alpha=1~,\nonumber \end{eqnarray} \item $\epsilon$-regression:\\ \begin{eqnarray} \min_{\alpha, \alpha^*}&&\frac{1}{2}(\alpha-\alpha^*)^\top \mathbf{Q} (\alpha-\alpha^*) + \nonumber\\ &&\epsilon\sum_{i=1}^{l}(\alpha_i+\alpha_i^*) + \sum_{i=1}^{l}y_i(\alpha_i-\alpha_i^*) \nonumber\\ \mbox{s.t.} &&0\le\alpha_i, \alpha_i^*\le C,~i=1,\ldots,l,\\ &&\sum_{i=1}^{l}(\alpha_i-\alpha_i^*)=0~.\nonumber \end{eqnarray} \item $\nu$-regression:\\ \begin{eqnarray} \min_{\alpha, \alpha^*}&&\frac{1}{2}(\alpha-\alpha^*)^\top \mathbf{Q} (\alpha-\alpha^*) + \mathbf{z}^\top(\alpha_i-\alpha_i^*) \nonumber\\ \mbox{s.t.} &&0\le\alpha_i, \alpha_i^*\le C,~i=1,\ldots,l,\\ &&\mathbf{e}^\top(\alpha-\alpha^*)=0\nonumber\\ &&\mathbf{e}^\top(\alpha+\alpha^*)=C\nu~.\nonumber \end{eqnarray} \end{itemize} \noindent Available kernels:\\ \\ \noindent \begin{table}[h] \centering \begin{tabular}{|l|l|l|} \hline kernel & formula & parameters \\ \hline \hline linear & $\bf u^\top v$& (none) \\ polynomial & $(\gamma \mathbf{u^\top v}+c_0)^d$ & $\gamma, d, c_0$\\ radial basis fct. & $\exp\{-\gamma|\mathbf{u-v}|^2\}$&$\gamma$\\ sigmoid & $\tanh\{\gamma \mathbf{u^\top v}+c_0\}$ &$\gamma, c_0$\\ \hline \end{tabular} \end{table} \section*{Conclusion} We hope that \texttt{svm} provides an easy-to-use interface to the world of SVMs, which nowadays have become a popular technique in flexible modelling. There are some drawbacks, though: SVMs scale rather badly with the data size due to the quadratic optimization algorithm and the kernel transformation. Furthermore, the correct choice of kernel parameters is crucial for obtaining good results, which practically means that an extensive search must be conducted on the parameter space before results can be trusted, and this often complicates the task (the authors of \texttt{libsvm} currently conduct some work on methods of efficient automatic parameter selection). Finally, the current implementation is optimized for the radial basis function kernel only, which clearly might be suboptimal for your data. \begin{thebibliography}{5} \bibitem[Bennett \& Campbell(2000)]{svm:bennett+campbell:2000} Bennett, K.~P. \& Campbell, C. (2000). \newblock Support vector machines: Hype or hallelujah? \newblock \emph{SIGKDD Explorations}, \textbf{2}(2). \newblock \url{http://www.acm.org/sigs/sigkdd/explorations/issue2-2/bennett.pdf}. \bibitem[Chang \& Lin(2001)]{svm:chang+lin:2001} Chang, C.-C. \& Lin, C.-J. (2001). \newblock {LIBSVM}: a library for support vector machines. \newblock Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}, detailed documentation (algorithms, formulae, \dots) can be found in \url{http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz} \bibitem[Cortes \& Vapnik(1995)]{svm:cortes+vapnik:1995} Cortes, C. \& Vapnik, V. (1995). \newblock Support-vector network. \newblock \emph{Machine Learning}, \textbf{20}, 1--25. \bibitem[Sch\"olkopf et~al.(2000)Sch\"olkopf, Smola, Williamson, \& Bartlett]{svm:scholkopf+smola+williamson:2000} Sch\"olkopf, B., Smola, A., Williamson, R.~C., \& Bartlett, P. (2000). \newblock New support vector algorithms. \newblock \emph{Neural Computation}, \textbf{12}, 1207--1245. \bibitem[Vapnik(1998)]{svm:vapnik:1998} Vapnik, V. (1998). \newblock \emph{Statistical learning theory}. \newblock New York: Wiley. \end{thebibliography} \end{document} e1071/R/0000755000175100001440000000000013567004332011231 5ustar hornikuserse1071/R/moment.R0000755000175100001440000000033711400421345012650 0ustar hornikusersmoment <- function(x, order = 1, center = FALSE, absolute = FALSE, na.rm = FALSE) { if (na.rm) x <- x[!is.na(x)] if (center) x <- x - mean(x) if (absolute) x <- abs(x) sum(x ^ order) / length(x) } e1071/R/naiveBayes.R0000655000175100001440000001271313464750021013447 0ustar hornikusersnaiveBayes <- function(x, ...) UseMethod("naiveBayes") naiveBayes.default <- function(x, y, laplace = 0, ...) { call <- match.call() Yname <- deparse(substitute(y)) x <- as.data.frame(x) ## estimation-function est <- function(var) if (is.numeric(var)) { cbind(tapply(var, y, mean, na.rm = TRUE), tapply(var, y, sd, na.rm = TRUE)) } else { tab <- table(y, var) (tab + laplace) / (rowSums(tab) + laplace * nlevels(var)) } ## create tables apriori <- table(y) tables <- lapply(x, est) isnumeric <- sapply(x, is.numeric) ## fix dimname names for (i in 1:length(tables)) names(dimnames(tables[[i]])) <- c(Yname, colnames(x)[i]) names(dimnames(apriori)) <- Yname structure(list(apriori = apriori, tables = tables, levels = if (is.logical(y)) c(FALSE, TRUE) else levels(y), isnumeric = isnumeric, call = call ), class = "naiveBayes" ) } naiveBayes.formula <- function(formula, data, laplace = 0, ..., subset, na.action = na.pass) { call <- match.call() Yname <- as.character(formula[[2]]) if (is.data.frame(data)) { ## handle formula m <- match.call(expand.dots = FALSE) m$... <- NULL m$laplace = NULL m$na.action <- na.action m[[1L]] <- quote(stats::model.frame) m <- eval(m, parent.frame()) Terms <- attr(m, "terms") if (any(attr(Terms, "order") > 1)) stop("naiveBayes cannot handle interaction terms") Y <- model.extract(m, "response") X <- m[,gsub("`", "", labels(Terms)), drop = FALSE] return(naiveBayes(X, Y, laplace = laplace, ...)) } else if (is.array(data)) { nam <- names(dimnames(data)) ## Find Class dimension Yind <- which(nam == Yname) ## Create Variable index # deps <- strsplit(as.character(formula)[3], ".[+].")[[1]] deps <- labels(terms(formula, data = data)) if (length(deps) == 1 && deps == ".") deps <- nam[-Yind] Vind <- which(nam %in% deps) ## create tables apriori <- margin.table(data, Yind) tables <- lapply(Vind, function(i) (margin.table(data, c(Yind, i)) + laplace) / (as.numeric(apriori) + laplace * dim(data)[i])) names(tables) <- nam[Vind] isnumeric = rep(FALSE, length(Vind)) names(isnumeric) <- nam[Vind] structure(list(apriori = apriori, tables = tables, levels = names(apriori), isnumeric = isnumeric, call = call ), class = "naiveBayes" ) } else stop("naiveBayes formula interface handles data frames or arrays only") } print.naiveBayes <- function(x, ...) { cat("\nNaive Bayes Classifier for Discrete Predictors\n\n") cat("Call:\n") print(x$call) cat("\nA-priori probabilities:\n") print(x$apriori / sum(x$apriori)) cat("\nConditional probabilities:\n") for (i in x$tables) {print(i); cat("\n")} } predict.naiveBayes <- function(object, newdata, type = c("class", "raw"), threshold = 0.001, eps = 0, ...) { type <- match.arg(type) newdata <- as.data.frame(newdata) ## fix factor levels to be identical with training data for (i in names(object$tables)) { if (!is.null(newdata[[i]]) && !is.numeric(newdata[[i]])) newdata[[i]] <- factor(newdata[[i]], levels = colnames(object$tables[[i]])) if (object$isnumeric[i] != is.numeric(newdata[[i]])) warning(paste0("Type mismatch between training and new data for variable '", i, "'. Did you use factors with numeric labels for training, and numeric values for new data?")) } attribs <- match(names(object$tables), names(newdata)) isnumeric <- sapply(newdata, is.numeric) islogical <- sapply(newdata, is.logical) newdata <- data.matrix(newdata) L <- sapply(1:nrow(newdata), function(i) { ndata <- newdata[i, ] L <- log(object$apriori) + apply(log(sapply(seq_along(attribs), function(v) { nd <- ndata[attribs[v]] if (is.na(nd)) rep(1, length(object$apriori)) else { prob <- if (isnumeric[attribs[v]]) { msd <- object$tables[[v]] msd[, 2][msd[, 2] <= eps] <- threshold dnorm(nd, msd[, 1], msd[, 2]) } else object$tables[[v]][, nd + islogical[attribs[v]]] prob[prob <= eps] <- threshold prob } })), 1, sum) if (type == "class") L else { ## Numerically unstable: ## L <- exp(L) ## L / sum(L) ## instead, we use: sapply(L, function(lp) { 1/sum(exp(L - lp)) }) } }) if (type == "class") { if (is.logical(object$levels)) L[2,] > L[1,] else factor(object$levels[apply(L, 2, which.max)], levels = object$levels) } else t(L) } e1071/R/tune.R0000655000175100001440000004307613443760517012352 0ustar hornikuserstune.control <- function(random = FALSE, nrepeat = 1, repeat.aggregate = mean, sampling = c("cross", "fix", "bootstrap"), sampling.aggregate = mean, sampling.dispersion = sd, cross = 10, fix = 2 / 3, nboot = 10, boot.size = 9 / 10, best.model = TRUE, performances = TRUE, error.fun = NULL) { structure(list(random = random, nrepeat = nrepeat, repeat.aggregate = repeat.aggregate, sampling = match.arg(sampling), sampling.aggregate = sampling.aggregate, sampling.dispersion = sampling.dispersion, cross = cross, fix = fix, nboot = nboot, boot.size = boot.size, best.model = best.model, performances = performances, error.fun = error.fun ), class = "tune.control" ) } tune <- function(method, train.x, train.y = NULL, data = list(), validation.x = NULL, validation.y = NULL, ranges = NULL, predict.func = predict, tunecontrol = tune.control(), ... ) { call <- match.call() ## internal helper functions resp <- function(formula, data) { model.response(model.frame(formula, data)) } classAgreement <- function (tab) { n <- sum(tab) if (!is.null(dimnames(tab))) { lev <- intersect(colnames(tab), rownames(tab)) p0 <- sum(diag(tab[lev, lev])) / n } else { m <- min(dim(tab)) p0 <- sum(diag(tab[1:m, 1:m])) / n } p0 } ## parameter handling if (tunecontrol$sampling == "cross") validation.x <- validation.y <- NULL useFormula <- is.null(train.y) if (useFormula && (is.null(data) || length(data) == 0)) data <- model.frame(train.x) if (is.vector(train.x)) train.x <- t(t(train.x)) if (is.data.frame(train.y)) train.y <- as.matrix(train.y) ## prepare training indices if (!is.null(validation.x)) tunecontrol$fix <- 1 n <- nrow(if (useFormula) data else train.x) perm.ind <- sample(n) if (tunecontrol$sampling == "cross") { if (tunecontrol$cross > n) stop(sQuote("cross"), " must not exceed sampling size!") if (tunecontrol$cross == 1) stop(sQuote("cross"), " must be greater than 1!") } train.ind <- if (tunecontrol$sampling == "cross") tapply(1:n, cut(1:n, breaks = tunecontrol$cross), function(x) perm.ind[-x]) else if (tunecontrol$sampling == "fix") list(perm.ind[1:trunc(n * tunecontrol$fix)]) else ## bootstrap lapply(1:tunecontrol$nboot, function(x) sample(n, n * tunecontrol$boot.size, replace = TRUE)) ## find best model parameters <- if (is.null(ranges)) data.frame(dummyparameter = 0) else expand.grid(ranges) p <- nrow(parameters) if (!is.logical(tunecontrol$random)) { if (tunecontrol$random < 1) stop("random must be a strictly positive integer") if (tunecontrol$random > p) tunecontrol$random <- p parameters <- parameters[sample(1:p, tunecontrol$random),] p <- nrow(parameters) } model.variances <- model.errors <- c() ## - loop over all models for (para.set in 1:p) { sampling.errors <- c() ## - loop over all training samples for (sample in 1:length(train.ind)) { repeat.errors <- c() ## - repeat training `nrepeat' times for (reps in 1:tunecontrol$nrepeat) { ## train one model pars <- if (is.null(ranges)) NULL else lapply(parameters[para.set,,drop = FALSE], unlist) model <- if (useFormula) do.call(method, c(list(train.x, data = data, subset = train.ind[[sample]]), pars, list(...) ) ) else do.call(method, c(list(train.x[train.ind[[sample]],], y = train.y[train.ind[[sample]]]), pars, list(...) ) ) ## predict validation set pred <- predict.func(model, if (!is.null(validation.x)) validation.x else if (useFormula) data[-train.ind[[sample]],,drop = FALSE] else if (inherits(train.x, "matrix.csr")) train.x[-train.ind[[sample]],] else train.x[-train.ind[[sample]],,drop = FALSE] ) ## compute performance measure true.y <- if (!is.null(validation.y)) validation.y else if (useFormula) { if (!is.null(validation.x)) resp(train.x, validation.x) else resp(train.x, data[-train.ind[[sample]],]) } else train.y[-train.ind[[sample]]] if (is.null(true.y)) true.y <- rep(TRUE, length(pred)) repeat.errors[reps] <- if (!is.null(tunecontrol$error.fun)) tunecontrol$error.fun(true.y, pred) else if ((is.logical(true.y) || is.factor(true.y)) && (is.logical(pred) || is.factor(pred) || is.character(pred))) ## classification error 1 - classAgreement(table(pred, true.y)) else if (is.numeric(true.y) && is.numeric(pred)) ## mean squared error crossprod(pred - true.y) / length(pred) else stop("Dependent variable has wrong type!") } sampling.errors[sample] <- tunecontrol$repeat.aggregate(repeat.errors) } model.errors[para.set] <- tunecontrol$sampling.aggregate(sampling.errors) model.variances[para.set] <- tunecontrol$sampling.dispersion(sampling.errors) } ## return results best <- which.min(model.errors) pars <- if (is.null(ranges)) NULL else lapply(parameters[best,,drop = FALSE], unlist) structure(list(best.parameters = parameters[best,,drop = FALSE], best.performance = model.errors[best], method = if (!is.character(method)) deparse(substitute(method)) else method, nparcomb = nrow(parameters), train.ind = train.ind, sampling = switch(tunecontrol$sampling, fix = "fixed training/validation set", bootstrap = "bootstrapping", cross = if (tunecontrol$cross == n) "leave-one-out" else paste(tunecontrol$cross,"-fold cross validation", sep="") ), performances = if (tunecontrol$performances) cbind(parameters, error = model.errors, dispersion = model.variances), best.model = if (tunecontrol$best.model) { modeltmp <- if (useFormula) do.call(method, c(list(train.x, data = data), pars, list(...))) else do.call(method, c(list(x = train.x, y = train.y), pars, list(...))) call[[1]] <- as.symbol("best.tune") modeltmp$call <- call modeltmp } ), class = "tune" ) } best.tune <- function(...) { call <- match.call() modeltmp <- tune(...)$best.model modeltmp$call <- call modeltmp } print.tune <- function(x, ...) { if (x$nparcomb > 1) { cat("\nParameter tuning of ", sQuote(x$method), ":\n\n", sep="") cat("- sampling method:", x$sampling,"\n\n") cat("- best parameters:\n") tmp <- x$best.parameters rownames(tmp) <- "" print(tmp) cat("\n- best performance:", x$best.performance, "\n") cat("\n") } else { cat("\nError estimation of ", sQuote(x$method), " using ", x$sampling, ": ", x$best.performance, "\n\n", sep="") } } summary.tune <- function(object, ...) structure(object, class = "summary.tune") print.summary.tune <- function(x, ...) { print.tune(x) if (!is.null(x$performances) && (x$nparcomb > 1)) { cat("- Detailed performance results:\n") print(x$performances) cat("\n") } } hsv_palette <- function(h = 2/3, from = 0.7, to = 0.2, v = 1) function(n) hsv(h = h, s = seq(from, to, length.out = n), v = v) plot.tune <- function(x, type=c("contour","perspective"), theta=60, col="lightblue", main = NULL, xlab = NULL, ylab = NULL, swapxy = FALSE, transform.x = NULL, transform.y = NULL, transform.z = NULL, color.palette = hsv_palette(), nlevels = 20, ...) { if (is.null(x$performances)) stop("Object does not contain detailed performance measures!") k <- ncol(x$performances) if (k > 4) stop("Cannot visualize more than 2 parameters") type = match.arg(type) if (is.null(main)) main <- paste("Performance of `", x$method, "'", sep="") if (k == 3) plot(x$performances[,1:2], type = "b", main = main) else { if (!is.null(transform.x)) x$performances[,1] <- transform.x(x$performances[,1]) if (!is.null(transform.y)) x$performances[,2] <- transform.y(x$performances[,2]) if (!is.null(transform.z)) x$performances[,3] <- transform.z(x$performances[,3]) if (swapxy) x$performances[,1:2] <- x$performances[,2:1] x <- xtabs(error~., data = x$performances[,-k]) if (is.null(xlab)) xlab <- names(dimnames(x))[1 + swapxy] if (is.null(ylab)) ylab <- names(dimnames(x))[2 - swapxy] if (type == "perspective") persp(x=as.double(rownames(x)), y=as.double(colnames(x)), z=x, xlab=xlab, ylab=ylab, zlab="accuracy", theta=theta, col=col, ticktype="detailed", main = main, ... ) else filled.contour(x=as.double(rownames(x)), y=as.double(colnames(x)), xlab=xlab, ylab=ylab, nlevels=nlevels, color.palette = color.palette, main = main, x, ...) } } ############################################# ## convenience functions for some methods ############################################# tune.svm <- function(x, y = NULL, data = NULL, degree = NULL, gamma = NULL, coef0 = NULL, cost = NULL, nu = NULL, class.weights = NULL, epsilon = NULL, ...) { call <- match.call() call[[1]] <- as.symbol("best.svm") ranges <- list(degree = degree, gamma = gamma, coef0 = coef0, cost = cost, nu = nu, class.weights = class.weights, epsilon = epsilon) ranges[sapply(ranges, is.null)] <- NULL if (length(ranges) < 1) ranges = NULL modeltmp <- if (inherits(x, "formula")) tune("svm", train.x = x, data = data, ranges = ranges, ...) else tune("svm", train.x = x, train.y = y, ranges = ranges, ...) if (!is.null(modeltmp$best.model)) modeltmp$best.model$call <- call modeltmp } best.svm <- function(x, tunecontrol = tune.control(), ...) { call <- match.call() tunecontrol$best.model = TRUE modeltmp <- tune.svm(x, ..., tunecontrol = tunecontrol)$best.model modeltmp$call <- call modeltmp } tune.nnet <- function(x, y = NULL, data = NULL, size = NULL, decay = NULL, trace = FALSE, tunecontrol = tune.control(nrepeat = 5), ...) { call <- match.call() call[[1]] <- as.symbol("best.nnet") loadNamespace("nnet") predict.func <- predict useFormula <- inherits(x, "formula") if (is.factor(y) || (useFormula && is.factor(model.response(model.frame(formula = x, data = data)))) ) predict.func = function(...) predict(..., type = "class") ranges <- list(size = size, decay = decay) ranges[sapply(ranges, is.null)] <- NULL if (length(ranges) < 1) ranges = NULL modeltmp <- if (useFormula) tune("nnet", train.x = x, data = data, ranges = ranges, predict.func = predict.func, tunecontrol = tunecontrol, trace = trace, ...) else tune("nnet", train.x = x, train.y = y, ranges = ranges, predict.func = predict.func, tunecontrol = tunecontrol, trace = trace, ...) if (!is.null(modeltmp$best.model)) modeltmp$best.model$call <- call modeltmp } best.nnet <- function(x, tunecontrol = tune.control(nrepeat = 5), ...) { call <- match.call() tunecontrol$best.model = TRUE modeltmp <- tune.nnet(x, ..., tunecontrol = tunecontrol)$best.model modeltmp$call <- call modeltmp } tune.randomForest <- function(x, y = NULL, data = NULL, nodesize = NULL, mtry = NULL, ntree = NULL, ...) { call <- match.call() call[[1]] <- as.symbol("best.randomForest") loadNamespace("randomForest") ranges <- list(nodesize = nodesize, mtry = mtry, ntree = ntree) ranges[sapply(ranges, is.null)] <- NULL if (length(ranges) < 1) ranges = NULL modeltmp <- if (inherits(x, "formula")) tune("randomForest", train.x = x, data = data, ranges = ranges, ...) else tune("randomForest", train.x = x, train.y = y, ranges = ranges, ...) if (!is.null(modeltmp$best.model)) modeltmp$best.model$call <- call modeltmp } best.randomForest <- function(x, tunecontrol = tune.control(), ...) { call <- match.call() tunecontrol$best.model = TRUE modeltmp <- tune.randomForest(x, ..., tunecontrol = tunecontrol)$best.model modeltmp$call <- call modeltmp } knn.wrapper <- function(x, y, k = 1, l = 0, ...) list(train = x, cl = y, k = k, l = l, ...) tune.knn <- function(x, y, k = NULL, l = NULL, ...) { loadNamespace("class") ranges <- list(k = k, l = l) ranges[sapply(ranges, is.null)] <- NULL if (length(ranges) < 1) ranges = NULL tune("knn.wrapper", train.x = x, train.y = y, ranges = ranges, predict.func = function(x, ...) knn(train = x$train, cl = x$cl, k = x$k, l = x$l, ...), ...) } rpart.wrapper <- function(formula, minsplit=20, minbucket=round(minsplit/3), cp=0.01, maxcompete=4, maxsurrogate=5, usesurrogate=2, xval=10, surrogatestyle=0, maxdepth=30, ...) rpart::rpart(formula, control = rpart::rpart.control(minsplit=minsplit, minbucket=minbucket, cp=cp, maxcompete=maxcompete, maxsurrogate=maxsurrogate, usesurrogate=usesurrogate, xval=xval, surrogatestyle=surrogatestyle, maxdepth=maxdepth), ... ) tune.rpart <- function(formula, data, na.action = na.omit, minsplit=NULL, minbucket=NULL, cp=NULL, maxcompete=NULL, maxsurrogate=NULL, usesurrogate=NULL, xval=NULL, surrogatestyle=NULL, maxdepth=NULL, predict.func = NULL, ...) { call <- match.call() call[[1]] <- as.symbol("best.rpart") loadNamespace("rpart") ranges <- list(minsplit=minsplit, minbucket=minbucket, cp=cp, maxcompete=maxcompete, maxsurrogate=maxsurrogate, usesurrogate=usesurrogate, xval=xval, surrogatestyle=surrogatestyle, maxdepth=maxdepth) ranges[sapply(ranges, is.null)] <- NULL if (length(ranges) < 1) ranges <- NULL predict.func <- if (is.factor(model.response(model.frame(formula, data)))) function(...) predict(..., type = "class") else predict modeltmp <- tune("rpart.wrapper", train.x = formula, data = data, ranges = ranges, predict.func = predict.func, na.action = na.action, ...) if (!is.null(modeltmp$best.model)) modeltmp$best.model$call <- call modeltmp } best.rpart <- function(formula, tunecontrol = tune.control(), ...) { call <- match.call() tunecontrol$best.model = TRUE modeltmp <- tune.rpart(formula, ..., tunecontrol = tunecontrol)$best.model modeltmp$call <- call modeltmp } e1071/R/sigmoid.R0000755000175100001440000000023311400421345012777 0ustar hornikuserssigmoid <- function(x) 1/(1 + exp(-x)) dsigmoid <- function(x) sigmoid(x) * (1 - sigmoid(x)) d2sigmoid <- function(x) dsigmoid(x) * (1 - 2 * sigmoid(x)) e1071/R/interpolate.R0000755000175100001440000000364211400421345013701 0ustar hornikusersinterpolate <- function(x, a, adims=lapply(dimnames(a), as.numeric), method="linear"){ if(is.vector(x)) x<- matrix(x, ncol=length(x)) if(!is.array(a)) stop("a is not an array") ad <- length(dim(a)) method <- pmatch(method, c("linear", "constant")) if (is.na(method)) stop("invalid interpolation method") if(any(unlist(lapply(adims, diff))<0)) stop("dimensions of a not ordered") retval <- rep(0, nrow(x)) bincombi <- bincombinations(ad) convexcoeff <- function(x, y) { ok <- y>0 x[ok] <- y[ok]-x[ok] x } for(n in 1:nrow(x)){ ## the "leftmost" corner of the enclosing hypercube leftidx <- rep(0, ad) xabstand <- rep(0, ad) aabstand <- rep(0, ad) for(k in 1:ad){ if(x[n,k] < min(adims[[k]]) || x[n,k] > max(adims[[k]])) stop("No extrapolation allowed") else{ leftidx[k] <- max(seq(adims[[k]])[adims[[k]] <= x[n,k]]) ## if at the right border, go one step to the left if(leftidx[k] == length(adims[[k]])) leftidx[k] <- leftidx[k] - 1 xabstand[k] <- x[n,k] - adims[[k]][leftidx[k]] aabstand[k] <- adims[[k]][leftidx[k]+1] - adims[[k]][leftidx[k]] } } coefs <- list() if(method==1){ for(k in 1:(2^ad)){ retval[n] <- retval[n] + element(a, leftidx+bincombi[k,]) * prod((aabstand- convexcoeff(xabstand, aabstand*bincombi[k,]))/aabstand) } } else if(method==2){ retval[n] <- element(a, leftidx) } } names(retval) <- rownames(x) retval } e1071/R/impute.R0000755000175100001440000000060711400421345012654 0ustar hornikusersimpute <- function(x, what=c("median", "mean")){ what <- match.arg(what) if(what == "median"){ retval <- apply(x, 2, function(z) {z[is.na(z)] <- median(z, na.rm=TRUE); z}) } else if(what == "mean"){ retval <- apply(x, 2, function(z) {z[is.na(z)] <- mean(z, na.rm=TRUE); z}) } retval } e1071/R/discrete.R0000755000175100001440000000407511400421345013156 0ustar hornikusersrdiscrete <- function (n, probs, values = 1:length(probs), ...) { sample(values, size=n, replace=TRUE, prob=probs) } ddiscrete <- function (x, probs, values = 1:length(probs)) { if (length(probs) != length(values)) stop("ddiscrete: probs and values must have the same length.") if (sum(probs < 0) > 0) stop("ddiscrete: probs must not contain negative values.") if (!is.array(x) && !is.vector(x) && !is.factor(x)) stop("ddiscrete: x must be an array or a vector or a factor.") p <- probs/sum(probs) y <- as.vector(x) l <- length(y) z <- rep(0,l) for (i in 1:l) if (any(values == y[i])) z[i] <- p[values == y[i]] z <- as.numeric(z) if (is.array(x)) dim(z) <- dim(x) return(z) } pdiscrete <- function (q, probs, values = 1:length(probs)) { if (length(probs) != length(values)) stop("pdiscrete: probs and values must have the same length.") if (sum(probs < 0) > 0) stop("pdiscrete: probs must not contain negative values.") if (!is.array(q) & !is.vector(q)) stop("pdiscrete: q must be an array or a vector") p <- probs/sum(probs) y <- as.vector(q) l <- length(y) z <- rep(0,l) for (i in 1:l) z[i] <- sum(p[values <= y[i]]) z <- as.numeric(z) if (is.array(q)) dim(z) <- dim(q) return(z) } qdiscrete <- function (p, probs, values = 1:length(probs)) { if (length(probs) != length(values)) stop("qdiscrete: probs and values must have the same length.") if (sum(probs < 0) > 0) stop("qdiscrete: probs must not contain negative values.") if (!is.array(p) & !is.vector(p)) stop("qdiscrete: p must be an array or a vector") probs <- cumsum(probs)/sum(probs) y <- as.vector(p) l <- length(y) z <- rep(0,l) for (i in 1:l) z[i] <- length(values) - sum(y[i] <= probs) + 1 z <- as.numeric(z) z <- values[z] if (is.array(p)) dim(z) <- dim(p) return(z) } e1071/R/countpattern.R0000755000175100001440000000117211633216055014105 0ustar hornikuserscountpattern <- function(x, matching=FALSE) { nvar <- dim(x)[2] n <- dim(x)[1] ## build matrix of all possible binary vectors b <- matrix(0, 2^nvar, nvar) for (i in 1:nvar) b[, nvar+1-i] <- rep(rep(c(0,1),c(2^(i-1),2^(i-1))),2^(nvar-i)) namespat <- b[,1] for (i in 2:nvar) namespat <- paste(namespat, b[,i], sep="") xpat <- x[,1] for (i in 2:nvar) xpat <- 2*xpat+x[,i] xpat <- xpat+1 pat <- tabulate(xpat, nbins=2^nvar) names(pat) <- namespat if (matching) return(list(pat=pat, matching=xpat)) else return(pat) } e1071/R/rbridge.R0000755000175100001440000000026511400421345012767 0ustar hornikusersrbridge <- function(end=1, frequency=1000) { z <- rwiener(end=end, frequency=frequency) ts(z - time(z)*as.vector(z)[frequency], start=1/frequency, frequency=frequency) } e1071/R/element.R0000755000175100001440000000050611400421345013000 0ustar hornikuserselement <- function(x, i) { if(!is.array(x)) stop("x is not an array") ni <- length(i) dx <- dim(x) if(length(i)!=length(dx)) stop("Wrong number of subscripts") if(ni==1){ return(x[i]) } else{ m1 <- c(i[1], i[2:ni]-1) m2 <- c(1,cumprod(dx)[1:(ni-1)]) return(x[sum(m1*m2)]) } } e1071/R/ica.R0000755000175100001440000000306611400421345012107 0ustar hornikusersica <- function(X, lrate, epochs=100, ncomp=dim(X)[2], fun="negative") { if (!is.matrix(X)) { if (is.data.frame(X)) X <- as.matrix(X) else stop("ica: X must be a matrix or a data frame") } if (!is.numeric(X)) stop("ica: X contains non numeric elements") m <- dim(X)[1] n <- dim(X)[2] Winit <- matrix(rnorm(n*ncomp), ncomp, n) W <- Winit if (!is.function(fun)) { funlist <- c("negative kurtosis", "positive kurtosis", "4th moment") p <- pmatch(fun, funlist) if (is.na(p)) stop("ica: invalid fun") funname <- funlist[p] if (p == 1) fun <- tanh else if (p == 2) fun <- function(x) {x - tanh(x)} else if (p == 3) fun <- function(x) {sign(x)*x^2} } else funname <- as.character(substitute(fun)) for (i in 1:epochs) for (j in 1:m) { x <- X[j,, drop=FALSE] y <- W%*%t(x) gy <- fun(y) W <- W + lrate*gy%*%(x-t(gy)%*%W) } colnames(W) <- NULL pr <- X%*%t(W) retval <- list(weights = W, projection = pr, epochs = epochs, fun = funname, lrate = lrate, initweights = Winit) class(retval) <- "ica" return(retval) } print.ica <- function(x, ...) { cat(x$epochs, "Trainingssteps with a learning rate of", x$lrate, "\n") cat("Function used:", x$fun,"\n\n") cat("Weightmatrix\n") print(x$weights, ...) } plot.ica <- function(x, ...) pairs(x$pr, ...) e1071/R/matchClasses.R0000755000175100001440000001504012520201245013757 0ustar hornikusersclassAgreement <- function (tab, match.names=FALSE) { n <- sum(tab) ni <- apply(tab, 1, sum) nj <- apply(tab, 2, sum) ## patch for matching factors if (match.names && !is.null(dimnames(tab))) { lev <- intersect (colnames (tab), rownames(tab)) p0 <- sum(diag(tab[lev,lev]))/n pc <- sum(ni[lev] * nj[lev])/n^2 } else { # cutoff larger dimension m <- min(length(ni), length(nj)) p0 <- sum(diag(tab[1:m, 1:m]))/n pc <- sum((ni[1:m] / n) * (nj[1:m] / n)) } n2 <- choose(n, 2) rand <- 1 + (sum(tab^2) - (sum(ni^2) + sum(nj^2))/2)/n2 nis2 <- sum(choose(ni[ni > 1], 2)) njs2 <- sum(choose(nj[nj > 1], 2)) crand <- (sum(choose(tab[tab > 1], 2)) - (nis2 * njs2)/n2)/((nis2 + njs2)/2 - (nis2 * njs2)/n2) list(diag = p0, kappa = (p0 - pc)/(1 - pc), rand = rand, crand = crand) } matchClasses <- function(tab, method = "rowmax", iter=1, maxexact=9, verbose=TRUE){ methods <- c("rowmax", "greedy", "exact") method <- pmatch(method, methods) rmax <- apply(tab,1,which.max) myseq <- 1:ncol(tab) cn <- colnames(tab) rn <- rownames(tab) if(is.null(cn)){ cn <- myseq } if(is.null(rn)){ rn <- myseq } if(method==1){ retval <- rmax } if(method==2 | method==3){ if(ncol(tab)!=nrow(tab)){ stop("Unique matching only for square tables.") } dimnames(tab) <- list(myseq, myseq) cmax <- apply(tab,2,which.max) retval <- rep(NA, ncol(tab)) names(retval) <- colnames(tab) baseok <- cmax[rmax]==myseq for(k in myseq[baseok]){ therow <- (tab[k,])[-rmax[k]] thecol <- (tab[, rmax[k]])[-k] if(max(outer(therow, thecol, "+")) < tab[k, rmax[k]]){ retval[k] <- rmax[k] } else{ baseok[k] <- FALSE } } if(verbose){ cat("Direct agreement:", sum(baseok), "of", ncol(tab), "pairs\n") } if(!all(baseok)){ if(method==3){ if(sum(!baseok)>maxexact){ method <- 2 warning(paste("Would need permutation of", sum(!baseok), "numbers, resetting to greedy search\n")) } else{ iter <- gamma(ncol(tab)-sum(baseok)+1) if(verbose){ cat("Iterations for permutation matching:", iter, "\n") } perm <- permutations(ncol(tab)-sum(baseok)) } } ## rest for permute matching if(any(baseok)){ rest <- myseq[-retval[baseok]] } else{ rest <- myseq } for(l in 1:iter){ newretval <- retval if(method == 2){ ok <- baseok while(sum(!ok)>1){ rest <- myseq[!ok] k <- sample(rest, 1) if(any(ok)){ rmax <- tab[k, -newretval[ok]] } else{ rmax <- tab[k,] } newretval[k] <- as.numeric(names(rmax)[which.max(rmax)]) ok[k] <- TRUE } newretval[!ok] <- myseq[-newretval[ok]] } else{ newretval[!baseok] <- rest[perm[l,]] } if(l>1){ agree <- sum(diag(tab[,newretval]))/sum(tab) if(agree>oldagree){ retval <- newretval oldagree <- agree } } else{ retval <- newretval agree <- oldagree <- sum(diag(tab[,newretval]))/sum(tab) } } } } if(verbose){ cat("Cases in matched pairs:", round(100*sum(diag(tab[,retval]))/sum(tab), 2), "%\n") } if(any(as.character(myseq)!=cn)){ retval <- cn[retval] } names(retval) <- rn retval } compareMatchedClasses <- function(x, y, method="rowmax", iter=1, maxexact=9, verbose=FALSE) { if(missing(y)){ retval <- list(diag=matrix(NA, nrow=ncol(x), ncol=ncol(x)), kappa=matrix(NA, nrow=ncol(x), ncol=ncol(x)), rand=matrix(NA, nrow=ncol(x), ncol=ncol(x)), crand=matrix(NA, nrow=ncol(x), ncol=ncol(x))) for(k in 1:(ncol(x)-1)){ for(l in (k+1):ncol(x)){ tab <- table(x[,k], x[,l]) m <- matchClasses(tab, method=method, iter=iter, verbose=verbose, maxexact=maxexact) a <- classAgreement(tab[,m]) retval$diag[k,l] <- a$diag retval$kappa[k,l] <- a$kappa retval$rand[k,l] <- a$rand retval$crand[k,l] <- a$crand } } } else{ x <- as.matrix(x) y <- as.matrix(y) retval <- list(diag=matrix(NA, nrow=ncol(x), ncol=ncol(y)), kappa=matrix(NA, nrow=ncol(x), ncol=ncol(y)), rand=matrix(NA, nrow=ncol(x), ncol=ncol(y)), crand=matrix(NA, nrow=ncol(x), ncol=ncol(y))) for(k in 1:ncol(x)){ for(l in 1:ncol(y)){ tab <- table(x[,k], y[,l]) m <- matchClasses(tab, method=method, iter=iter, verbose=verbose, maxexact=maxexact) a <- classAgreement(tab[,m]) retval$diag[k,l] <- a$diag retval$kappa[k,l] <- a$kappa retval$rand[k,l] <- a$rand retval$crand[k,l] <- a$crand } } } retval } permutations <- function(n) { if(n ==1) return(matrix(1)) else if(n<2) stop("n must be a positive integer") z <- matrix(1) for (i in 2:n) { x <- cbind(z, i) a <- c(1:i, 1:(i - 1)) z <- matrix(0, ncol=ncol(x), nrow=i*nrow(x)) z[1:nrow(x),] <- x for (j in 2:i-1) { z[j*nrow(x)+1:nrow(x),] <- x[, a[1:i+j]] } } dimnames(z) <- NULL z } e1071/R/stft.R0000755000175100001440000000144611400421345012333 0ustar hornikusersstft <- function(X, win=min(80,floor(length(X)/10)), inc=min(24, floor(length(X)/30)), coef=64, wtype="hanning.window") { numcoef <- 2*coef if (win > numcoef) { win <- numcoef cat ("stft: window size adjusted to", win, ".\n") } numwin <- trunc ((length(X) - win) / inc) ## compute the windows coefficients wincoef <- eval(parse(text=wtype))(win) ## create a matrix Z whose columns contain the windowed time-slices z <- matrix (0, numwin + 1, numcoef) y <- z st <- 1 for (i in 0:numwin) { z[i+1, 1:win] <- X[st:(st+win-1)] * wincoef y[i+1,] <- fft(z[i+1,]) st <- st + inc } Y<- list (values = Mod(y[,1:coef]), windowsize=win, increment=inc, windowtype=wtype) class(Y) <- "stft" return(Y) } e1071/R/cmeans.R0000655000175100001440000001131513044147326012626 0ustar hornikuserscmeans <- function(x, centers, iter.max = 100, verbose = FALSE, dist = "euclidean", method = "cmeans", m = 2, rate.par = NULL, weights = 1, control = list()) { x <- as.matrix(x) xrows <- nrow(x) xcols <- ncol(x) if(missing(centers)) stop("Argument 'centers' must be a number or a matrix.") dist <- pmatch(dist, c("euclidean", "manhattan")) if(is.na(dist)) stop("invalid distance") if(dist == -1) stop("ambiguous distance") method <- pmatch(method, c("cmeans", "ufcl")) if(is.na(method)) stop("invalid clustering method") if(method == -1) stop("ambiguous clustering method") if(length(centers) == 1) { ncenters <- centers centers <- x[sample(1 : xrows, ncenters), , drop = FALSE] if(any(duplicated(centers))) { cn <- unique(x) mm <- nrow(cn) if(mm < ncenters) stop("More cluster centers than distinct data points.") centers <- cn[sample(1 : mm, ncenters), , drop = FALSE] } } else { centers <- as.matrix(centers) if(any(duplicated(centers))) stop("Initial centers are not distinct.") cn <- NULL ncenters <- nrow(centers) if (xrows < ncenters) stop("More cluster centers than data points.") } if(xcols != ncol(centers)) stop("Must have same number of columns in 'x' and 'centers'.") if(iter.max < 1) stop("Argument 'iter.max' must be positive.") if(method == 2) { if(missing(rate.par)) { rate.par <- 0.3 } } reltol <- control$reltol if(is.null(reltol)) reltol <- sqrt(.Machine$double.eps) if(reltol <= 0) stop("Control parameter 'reltol' must be positive.") if(any(weights < 0)) stop("Argument 'weights' has negative elements.") if(!any(weights > 0)) stop("Argument 'weights' has no positive elements.") weights <- rep(weights, length = xrows) weights <- weights / sum(weights) ## ## Do we really want to do this? perm <- sample(xrows) x <- x[perm, ] weights <- weights[perm] ## initcenters <- centers pos <- as.factor(1 : ncenters) rownames(centers) <- pos if(method == 1) { retval <- .C(R_cmeans, as.double(x), as.integer(xrows), as.integer(xcols), centers = as.double(centers), as.integer(ncenters), as.double(weights), as.double(m), as.integer(dist - 1), as.integer(iter.max), as.double(reltol), as.integer(verbose), u = double(xrows * ncenters), ermin = double(1), iter = integer(1)) } else if(method == 2) { retval <- .C(R_ufcl, x = as.double(x), as.integer(xrows), as.integer(xcols), centers = as.double(centers), as.integer(ncenters), as.double(weights), as.double(m), as.integer(dist - 1), as.integer(iter.max), as.double(reltol), as.integer(verbose), as.double(rate.par), u = double(xrows * ncenters), ermin = double(1), iter = integer(1) ) } centers <- matrix(retval$centers, ncol = xcols, dimnames = list(1 : ncenters, colnames(initcenters))) u <- matrix(retval$u, ncol = ncenters, dimnames = list(rownames(x), 1 : ncenters)) u <- u[order(perm), ] iter <- retval$iter - 1 withinerror <- retval$ermin cluster <- apply(u, 1, which.max) clustersize <- as.integer(table(cluster)) retval <- list(centers = centers, size = clustersize, cluster = cluster, membership = u, iter = iter, withinerror = withinerror, call = match.call()) class(retval) <- c("fclust") return(retval) } print.fclust <- function(x, ...) { cat("Fuzzy c-means clustering with", length(x$size), "clusters\n") cat("\nCluster centers:\n") print(x$centers, ...) cat("\nMemberships:\n") print(x$membership, ...) cat("\nClosest hard clustering:\n") print(x$cluster, ...) cat("\nAvailable components:\n") print(names(x), ...) invisible(x) } e1071/R/probplot.R0000755000175100001440000000367111400421345013216 0ustar hornikusersprobplot <- function(x, qdist=qnorm, probs=NULL, line=TRUE, xlab=NULL, ylab="Probability in %", ...) { DOTARGS <- as.list(substitute(list(...)))[-1] DOTARGS <- paste(names(DOTARGS), DOTARGS, sep="=", collapse=", ") xlab=deparse(substitute(x)) x <- sort(x) QNAME <- deparse(substitute(qdist)) DOTS <- list(...) qdist <- match.fun(qdist) QFUN <- function(p){ args=DOTS args$p=p do.call("qdist", args) } y <- QFUN(ppoints(length(x))) if(is.null(probs)){ probs <- c(.01, .05, seq(.1,.9, by=.1), .95, .99) if(length(x)>=1000) probs <- c(0.001, probs, .999) } qprobs <- QFUN(probs) plot(x, y, axes=FALSE, type="n", ylim=range(c(y,qprobs)), xlab=xlab, ylab=ylab) box() abline(h=qprobs, col="grey") axis(1) axis(2, at=qprobs, labels=100*probs) points(x, y) QTEXT <- paste("Quantile: ", QNAME, sep="") if(nchar(DOTARGS)) QTEXT <- paste(QTEXT, DOTARGS, sep=", ") mtext(QTEXT, side=1, line=3, adj=1) xl <- quantile(x, c(0.25, 0.75)) yl <- qdist(c(0.25, 0.75), ...) slope <- diff(yl)/diff(xl) int <- yl[1] - slope * xl[1] if(line){ abline(int, slope, col="red") } z <- list(qdist=QFUN, int=int, slope=slope) class(z) <- "probplot" invisible(z) } lines.probplot <- function(x, h=NULL, v=NULL, bend=FALSE, ...) { if(is.null(h) & is.null(v)){ abline(x$int, x$slope, ...) } pu <- par("usr") if(!is.null(h)){ h <- x$qdist(h) if(!bend){ abline(h=h, ...) } else{ v <- c(v, (h-x$int)/x$slope) } } if(!is.null(v)){ if(!bend){ abline(v=v, ...) } else{ h <- v*x$slope+x$int segments(v, pu[3], v, h, ...) segments(pu[1], h, v, h, ...) } } } e1071/R/e1071-deprecated.R0000755000175100001440000000000011400421345014167 0ustar hornikuserse1071/R/svm.R0000655000175100001440000006527513567003614012204 0ustar hornikuserssvm <- function (x, ...) UseMethod ("svm") svm.formula <- function (formula, data = NULL, ..., subset, na.action = na.omit, scale = TRUE) { call <- match.call() if (!inherits(formula, "formula")) stop("method is only for formula objects") m <- match.call(expand.dots = FALSE) if (inherits(eval.parent(m$data), "matrix")) m$data <- as.data.frame(eval.parent(m$data)) m$... <- NULL m$scale <- NULL m[[1L]] <- quote(stats::model.frame) m$na.action <- na.action m <- eval(m, parent.frame()) Terms <- attr(m, "terms") attr(Terms, "intercept") <- 0 x <- model.matrix(Terms, m) y <- model.extract(m, "response") attr(x, "na.action") <- attr(y, "na.action") <- attr(m, "na.action") if (length(scale) == 1) scale <- rep(scale, ncol(x)) if (any(scale)) { remove <- unique(c(which(labels(Terms) %in% names(attr(x, "contrasts"))), which(!scale) ) ) scale <- !attr(x, "assign") %in% remove } ret <- svm.default (x, y, scale = scale, ..., na.action = na.action) ret$call <- call ret$call[[1]] <- as.name("svm") ret$terms <- Terms if (!is.null(attr(m, "na.action"))) ret$na.action <- attr(m, "na.action") class(ret) <- c("svm.formula", class(ret)) return (ret) } svm.default <- function (x, y = NULL, scale = TRUE, type = NULL, kernel = "radial", degree = 3, gamma = if (is.vector(x)) 1 else 1 / ncol(x), coef0 = 0, cost = 1, nu = 0.5, class.weights = NULL, cachesize = 40, tolerance = 0.001, epsilon = 0.1, shrinking = TRUE, cross = 0, probability = FALSE, fitted = TRUE, ..., subset, na.action = na.omit) { yorig <- y if(inherits(x, "Matrix")) { loadNamespace("SparseM") loadNamespace("Matrix") x <- as(x, "matrix.csr") } if(inherits(x, "simple_triplet_matrix")) { loadNamespace("SparseM") ind <- order(x$i, x$j) x <- new("matrix.csr", ra = x$v[ind], ja = x$j[ind], ia = as.integer(cumsum(c(1, tabulate(x$i[ind])))), dimension = c(x$nrow, x$ncol)) } if (sparse <- inherits(x, "matrix.csr")) loadNamespace("SparseM") ## NULL parameters? if(is.null(degree)) stop(sQuote("degree"), " must not be NULL!") if(is.null(gamma)) stop(sQuote("gamma"), " must not be NULL!") if(is.null(coef0)) stop(sQuote("coef0"), " must not be NULL!") if(is.null(cost)) stop(sQuote("cost"), " must not be NULL!") if(is.null(nu)) stop(sQuote("nu"), " must not be NULL!") if(is.null(epsilon)) stop(sQuote("epsilon"), " must not be NULL!") if(is.null(tolerance)) stop(sQuote("tolerance"), " must not be NULL!") xhold <- if (fitted) x else NULL x.scale <- y.scale <- NULL formula <- inherits(x, "svm.formula") ## determine model type if (is.null(type)) type <- if (is.null(y)) "one-classification" else if (is.factor(y)) "C-classification" else "eps-regression" type <- pmatch(type, c("C-classification", "nu-classification", "one-classification", "eps-regression", "nu-regression"), 99) - 1 if (type > 10) stop("wrong type specification!") kernel <- pmatch(kernel, c("linear", "polynomial", "radial", "sigmoid"), 99) - 1 if (kernel > 10) stop("wrong kernel specification!") nac <- attr(x, "na.action") ## scaling, subsetting, and NA handling if (sparse) { scale <- rep(FALSE, ncol(x)) if(!is.null(y)) na.fail(y) x <- SparseM::t(SparseM::t(x)) ## make shure that col-indices are sorted } else { x <- as.matrix(x) ## subsetting and na-handling for matrices if (!formula) { if (!missing(subset)) { x <- x[subset,] y <- y[subset] if (!is.null(xhold)) xhold <- as.matrix(xhold)[subset,] } if (is.null(y)) x <- na.action(x) else { df <- na.action(data.frame(y, x)) y <- df[,1] x <- as.matrix(df[,-1], rownames.force = TRUE) nac <- attr(x, "na.action") <- attr(y, "na.action") <- attr(df, "na.action") } } ## scaling if (length(scale) == 1) scale <- rep(scale, ncol(x)) if (any(scale)) { co <- !apply(x[,scale, drop = FALSE], 2, var) if (any(co)) { warning(paste("Variable(s)", paste(sQuote(colnames(x[,scale, drop = FALSE])[co]), sep="", collapse=" and "), "constant. Cannot scale data.") ) scale <- rep(FALSE, ncol(x)) } else { xtmp <- scale(x[,scale]) x[,scale] <- xtmp x.scale <- attributes(xtmp)[c("scaled:center","scaled:scale")] if (is.numeric(y) && (type > 2)) { yorig <- y y <- scale(y) y.scale <- attributes(y)[c("scaled:center","scaled:scale")] y <- as.vector(y) } } } } ## further parameter checks nr <- nrow(x) if (cross > nr) stop(sQuote("cross"), " cannot exceed the number of observations!") ytmp <- y attributes(ytmp) <- NULL if (!is.vector(ytmp) && !is.factor(y) && type != 2) stop("y must be a vector or a factor.") if (type != 2 && length(y) != nr) stop("x and y don't match.") if (cachesize < 0.1) cachesize <- 0.1 if (type > 2 && !is.numeric(y)) stop("Need numeric dependent variable for regression.") lev <- NULL weightlabels <- NULL ## in case of classification: transform factors into integers if (type == 2) # one class classification --> set dummy y <- rep(1, nr) else if (is.factor(y)) { lev <- levels(y) y <- as.integer(y) } else { if (type < 3) { if(any(as.integer(y) != y)) stop("dependent variable has to be of factor or integer type for classification mode.") y <- as.factor(y) lev <- levels(y) y <- as.integer(y) } else lev <- unique(y) } if (type < 3 && !is.null(class.weights)) { if (is.character(class.weights) && class.weights == "inverse") class.weights <- 1 / table(y) if (is.null(names(class.weights))) stop("Weights have to be specified along with their according level names !") weightlabels <- match (names(class.weights), lev) if (any(is.na(weightlabels))) stop("At least one level name is missing or misspelled.") } nclass <- 2 if (type < 2) nclass <- length(lev) if (type > 1 && length(class.weights) > 0) { class.weights <- NULL warning(sQuote("class.weights"), " are set to NULL for regression mode. For classification, use a _factor_ for ", sQuote("y"), ", or specify the correct ", sQuote("type"), " argument.") } err <- empty_string <- paste(rep(" ", 255), collapse = "") if (is.null(type)) stop("type argument must not be NULL!") if (is.null(kernel)) stop("kernel argument must not be NULL!") if (is.null(degree)) stop("degree argument must not be NULL!") if (is.null(gamma)) stop("gamma argument must not be NULL!") if (is.null(coef0)) stop("coef0 seed argument must not be NULL!") if (is.null(cost)) stop("cost argument must not be NULL!") if (is.null(nu)) stop("nu argument must not be NULL!") if (is.null(cachesize)) stop("cachesize argument must not be NULL!") if (is.null(tolerance)) stop("tolerance argument must not be NULL!") if (is.null(epsilon)) stop("epsilon argument must not be NULL!") if (is.null(shrinking)) stop("shrinking argument must not be NULL!") if (is.null(cross)) stop("cross argument must not be NULL!") if (is.null(sparse)) stop("sparse argument must not be NULL!") if (is.null(probability)) stop("probability argument must not be NULL!") cret <- .C (R_svmtrain, ## data as.double (if (sparse) x@ra else t(x)), as.integer (nr), as.integer(ncol(x)), as.double (y), ## sparse index info as.integer (if (sparse) x@ia else 0), as.integer (if (sparse) x@ja else 0), ## parameters as.integer (type), as.integer (kernel), as.integer (degree), as.double (gamma), as.double (coef0), as.double (cost), as.double (nu), as.integer (weightlabels), as.double (class.weights), as.integer (length (class.weights)), as.double (cachesize), as.double (tolerance), as.double (epsilon), as.integer (shrinking), as.integer (cross), as.integer (sparse), as.integer (probability), ## results nclasses = integer (1), nr = integer (1), # nr of support vectors index = integer (nr), labels = integer (nclass), nSV = integer (nclass), rho = double (nclass * (nclass - 1) / 2), coefs = double (nr * (nclass - 1)), sigma = double (1), probA = double (nclass * (nclass - 1) / 2), probB = double (nclass * (nclass - 1) / 2), cresults = double (cross), ctotal1 = double (1), ctotal2 = double (1), error = err ) if (cret$error != empty_string) stop(paste(cret$error, "!", sep="")) cret$index <- cret$index[1:cret$nr] ret <- list ( call = match.call(), type = type, kernel = kernel, cost = cost, degree = degree, gamma = gamma, coef0 = coef0, nu = nu, epsilon = epsilon, sparse = sparse, scaled = scale, x.scale = x.scale, y.scale = y.scale, nclasses = cret$nclasses, #number of classes levels = lev, tot.nSV = cret$nr, #total number of sv nSV = cret$nSV[1:cret$nclasses], #number of SV in diff. classes labels = cret$labels[1:cret$nclasses], #labels of the SVs. SV = if (sparse) SparseM::t(SparseM::t(x[cret$index])) else t(t(x[cret$index,,drop = FALSE])), #copy of SV index = cret$index, #indexes of sv in x ##constants in decision functions rho = cret$rho[1:(cret$nclasses * (cret$nclasses - 1) / 2)], ##probabilites compprob = probability, probA = if (!probability) NULL else cret$probA[1:(cret$nclasses * (cret$nclasses - 1) / 2)], probB = if (!probability) NULL else cret$probB[1:(cret$nclasses * (cret$nclasses - 1) / 2)], sigma = if (probability) cret$sigma else NULL, ##coefficiants of sv coefs = if (cret$nr == 0) NULL else t(matrix(cret$coefs[1:((cret$nclasses - 1) * cret$nr)], nrow = cret$nclasses - 1, byrow = TRUE)), na.action = nac ) ## cross-validation-results if (cross > 0) if (type > 2) { scale.factor <- if (any(scale)) crossprod(y.scale$"scaled:scale") else 1; ret$MSE <- cret$cresults * scale.factor; ret$tot.MSE <- cret$ctotal1 * scale.factor; ret$scorrcoeff <- cret$ctotal2; } else { ret$accuracies <- cret$cresults; ret$tot.accuracy <- cret$ctotal1; } class (ret) <- "svm" if (fitted) { ret$fitted <- na.action(predict(ret, xhold, decision.values = TRUE)) ret$decision.values <- attr(ret$fitted, "decision.values") attr(ret$fitted, "decision.values") <- NULL if (type > 1) ret$residuals <- yorig - ret$fitted } ret } predict.svm <- function (object, newdata, decision.values = FALSE, probability = FALSE, ..., na.action = na.omit) { if (missing(newdata)) return(fitted(object)) if (object$tot.nSV < 1) stop("Model is empty!") if(inherits(newdata, "Matrix")) { loadNamespace("SparseM") loadNamespace("Matrix") newdata <- as(newdata, "matrix.csr") } if(inherits(newdata, "simple_triplet_matrix")) { loadNamespace("SparseM") ind <- order(newdata$i, newdata$j) newdata <- new("matrix.csr", ra = newdata$v[ind], ja = newdata$j[ind], ia = as.integer(cumsum(c(1, tabulate(newdata$i[ind])))), dimension = c(newdata$nrow, newdata$ncol)) } sparse <- inherits(newdata, "matrix.csr") if (object$sparse || sparse) loadNamespace("SparseM") act <- NULL if ((is.vector(newdata) && is.atomic(newdata))) newdata <- t(t(newdata)) if (sparse) newdata <- SparseM::t(SparseM::t(newdata)) preprocessed <- !is.null(attr(newdata, "na.action")) rowns <- if (!is.null(rownames(newdata))) rownames(newdata) else 1:nrow(newdata) if (!object$sparse) { if (inherits(object, "svm.formula")) { if(is.null(colnames(newdata))) colnames(newdata) <- colnames(object$SV) newdata <- na.action(newdata) act <- attr(newdata, "na.action") newdata <- model.matrix(delete.response(terms(object)), as.data.frame(newdata)) } else { newdata <- na.action(as.matrix(newdata)) act <- attr(newdata, "na.action") } } if (!is.null(act) && !preprocessed) rowns <- rowns[-act] if (any(object$scaled)) newdata[,object$scaled] <- scale(newdata[,object$scaled, drop = FALSE], center = object$x.scale$"scaled:center", scale = object$x.scale$"scaled:scale" ) if (ncol(object$SV) != ncol(newdata)) stop ("test data does not match model !") ret <- .C (R_svmpredict, as.integer (decision.values), as.integer (probability), ## model as.double (if (object$sparse) object$SV@ra else t(object$SV)), as.integer (nrow(object$SV)), as.integer(ncol(object$SV)), as.integer (if (object$sparse) object$SV@ia else 0), as.integer (if (object$sparse) object$SV@ja else 0), as.double (as.vector(object$coefs)), as.double (object$rho), as.integer (object$compprob), as.double (if (object$compprob) object$probA else 0), as.double (if (object$compprob) object$probB else 0), as.integer (object$nclasses), as.integer (object$tot.nSV), as.integer (object$labels), as.integer (object$nSV), as.integer (object$sparse), ## parameter as.integer (object$type), as.integer (object$kernel), as.integer (object$degree), as.double (object$gamma), as.double (object$coef0), ## test matrix as.double (if (sparse) newdata@ra else t(newdata)), as.integer (nrow(newdata)), as.integer (if (sparse) newdata@ia else 0), as.integer (if (sparse) newdata@ja else 0), as.integer (sparse), ## decision-values ret = double(nrow(newdata)), dec = double(nrow(newdata) * object$nclasses * (object$nclasses - 1) / 2), prob = double(nrow(newdata) * object$nclasses) ) ret2 <- if (is.character(object$levels)) # classification: return factors factor (object$levels[ret$ret], levels = object$levels) else if (object$type == 2) # one-class-classification: return TRUE/FALSE ret$ret == 1 else if (any(object$scaled) && !is.null(object$y.scale)) # return raw values, possibly scaled back ret$ret * object$y.scale$"scaled:scale" + object$y.scale$"scaled:center" else ret$ret names(ret2) <- rowns ret2 <- napredict(act, ret2) if (decision.values) { colns = c() for (i in 1:(object$nclasses - 1)) for (j in (i + 1):object$nclasses) colns <- c(colns, paste(object$levels[object$labels[i]], "/", object$levels[object$labels[j]], sep = "")) attr(ret2, "decision.values") <- napredict(act, matrix(ret$dec, nrow = nrow(newdata), byrow = TRUE, dimnames = list(rowns, colns) ) ) } if (probability && object$type < 2) { if (!object$compprob) warning("SVM has not been trained using `probability = TRUE`, probabilities not available for predictions.") else attr(ret2, "probabilities") <- napredict(act, matrix(ret$prob, nrow = nrow(newdata), byrow = TRUE, dimnames = list(rowns, object$levels[object$labels]) ) ) } ret2 } print.svm <- function (x, ...) { cat("\nCall:", deparse(x$call, 0.8 * getOption("width")), "\n", sep="\n") cat("Parameters:\n") cat(" SVM-Type: ", c("C-classification", "nu-classification", "one-classification", "eps-regression", "nu-regression")[x$type+1], "\n") cat(" SVM-Kernel: ", c("linear", "polynomial", "radial", "sigmoid")[x$kernel+1], "\n") if (x$type==0 || x$type==3 || x$type==4) cat(" cost: ", x$cost, "\n") if (x$kernel==1) cat(" degree: ", x$degree, "\n") if (x$type==1 || x$type==2 || x$type==3) cat(" gamma: ", x$gamma, "\n") if (x$kernel==1 || x$kernel==3) cat(" coef.0: ", x$coef0, "\n") if (x$type==1 || x$type==2 || x$type==4) cat(" nu: ", x$nu, "\n") if (x$type==3) { cat(" epsilon: ", x$epsilon, "\n\n") if (x$compprob) cat("Sigma: ", x$sigma, "\n\n") } cat("\nNumber of Support Vectors: ", x$tot.nSV) cat("\n\n") } summary.svm <- function(object, ...) structure(object, class="summary.svm") print.summary.svm <- function (x, ...) { print.svm(x) if (x$type<2) { cat(" (", x$nSV, ")\n\n") cat("\nNumber of Classes: ", x$nclasses, "\n\n") cat("Levels:", if(is.numeric(x$levels)) "(as integer)", "\n", x$levels) } cat("\n\n") if (x$type==2) cat("\nNumber of Classes: 1\n\n\n") if ("MSE" %in% names(x)) { cat(length (x$MSE), "-fold cross-validation on training data:\n\n", sep="") cat("Total Mean Squared Error:", x$tot.MSE, "\n") cat("Squared Correlation Coefficient:", x$scorrcoef, "\n") cat("Mean Squared Errors:\n", x$MSE, "\n\n") } if ("accuracies" %in% names(x)) { cat(length (x$accuracies), "-fold cross-validation on training data:\n\n", sep="") cat("Total Accuracy:", x$tot.accuracy, "\n") cat("Single Accuracies:\n", x$accuracies, "\n\n") } cat("\n\n") } scale.data.frame <- function(x, center = TRUE, scale = TRUE) { i <- sapply(x, is.numeric) if (ncol(x[, i, drop = FALSE])) { x[, i] <- tmp <- scale.default(x[, i, drop = FALSE], na.omit(center), na.omit(scale)) if(center || !is.logical(center)) attr(x, "scaled:center")[i] <- attr(tmp, "scaled:center") if(scale || !is.logical(scale)) attr(x, "scaled:scale")[i] <- attr(tmp, "scaled:scale") } x } plot.svm <- function(x, data, formula = NULL, fill = TRUE, grid = 50, slice = list(), symbolPalette = palette(), svSymbol = "x", dataSymbol = "o", ...) { if (x$type < 3) { if (is.null(formula) && ncol(data) == 3) { formula <- formula(delete.response(terms(x))) formula[2:3] <- formula[[2]][2:3] } if (is.null(formula)) stop("missing formula.") if (fill) { sub <- model.frame(formula, data) xr <- seq(min(sub[, 2]), max(sub[, 2]), length = grid) yr <- seq(min(sub[, 1]), max(sub[, 1]), length = grid) l <- length(slice) if (l < ncol(data) - 3) { slnames <- names(slice) slice <- c(slice, rep(list(0), ncol(data) - 3 - l)) names <- labels(delete.response(terms(x))) names(slice) <- c(slnames, names[!names %in% c(colnames(sub), slnames)]) } for (i in names(which(sapply(data, is.factor)))) if (!is.factor(slice[[i]])) { levs <- levels(data[[i]]) lev <- if (is.character(slice[[i]])) slice[[i]] else levs[1] fac <- factor(lev, levels = levs) if (is.na(fac)) stop(paste("Level", dQuote(lev), "could not be found in factor", sQuote(i))) slice[[i]] <- fac } lis <- c(list(yr), list(xr), slice) names(lis)[1:2] <- colnames(sub) new <- expand.grid(lis)[, labels(terms(x))] preds <- predict(x, new) filled.contour(xr, yr, matrix(as.numeric(preds), nrow = length(xr), byrow = TRUE), plot.axes = { axis(1) axis(2) colind <- as.numeric(model.response(model.frame(x, data))) dat1 <- data[-x$index,] dat2 <- data[x$index,] coltmp1 <- symbolPalette[colind[-x$index]] coltmp2 <- symbolPalette[colind[x$index]] points(formula, data = dat1, pch = dataSymbol, col = coltmp1) points(formula, data = dat2, pch = svSymbol, col = coltmp2) }, levels = 1:(length(levels(preds)) + 1), key.axes = axis(4, 1:(length(levels(preds))) + 0.5, labels = levels(preds), las = 3), plot.title = title(main = "SVM classification plot", xlab = names(lis)[2], ylab = names(lis)[1]), ...) } else { plot(formula, data = data, type = "n", ...) colind <- as.numeric(model.response(model.frame(x, data))) dat1 <- data[-x$index,] dat2 <- data[x$index,] coltmp1 <- symbolPalette[colind[-x$index]] coltmp2 <- symbolPalette[colind[x$index]] points(formula, data = dat1, pch = dataSymbol, col = coltmp1) points(formula, data = dat2, pch = svSymbol, col = coltmp2) invisible() } } } write.svm <- function (object, svm.file = "Rdata.svm", scale.file = "Rdata.scale", yscale.file = "Rdata.yscale") { ret <- .C (R_svmwrite, ## model as.double (if (object$sparse) object$SV@ra else t(object$SV)), as.integer (nrow(object$SV)), as.integer(ncol(object$SV)), as.integer (if (object$sparse) object$SV@ia else 0), as.integer (if (object$sparse) object$SV@ja else 0), as.double (as.vector(object$coefs)), as.double (object$rho), as.integer (object$compprob), as.double (if (object$compprob) object$probA else 0), as.double (if (object$compprob) object$probB else 0), as.integer (object$nclasses), as.integer (object$tot.nSV), as.integer (object$labels), as.integer (object$nSV), as.integer (object$sparse), ## parameter as.integer (object$type), as.integer (object$kernel), as.integer (object$degree), as.double (object$gamma), as.double (object$coef0), ## filename as.character(svm.file) )$ret write.table(data.frame(center = object$x.scale$"scaled:center", scale = object$x.scale$"scaled:scale"), file=scale.file, col.names=FALSE, row.names=FALSE) if (!is.null(object$y.scale)) write.table(data.frame(center = object$y.scale$"scaled:center", scale = object$y.scale$"scaled:scale"), file=yscale.file, col.names=FALSE, row.names=FALSE) } coef.svm <- function(object, ...) { if (object$kernel != 0 || object$nclasses > 2) stop("Only implemented for regression or binary classification with linear kernel.") ret <- drop(crossprod(object$coefs, object$SV)) trm <- object$terms if(!is.null(trm)) names(ret) <- labels(trm) c(`(Intercept)` = -object$rho, ret) } e1071/R/hanning.window.R0000755000175100001440000000022711400421345014277 0ustar hornikusershanning.window <- function (n) { if (n == 1) c <- 1 else { n <- n-1 c <- 0.5 - 0.5*cos(2*pi*(0:n)/n) } return(c) } e1071/R/sparse.R0000755000175100001440000000374312374611431012662 0ustar hornikusersread.matrix.csr <- function(file, fac = TRUE, ncol = NULL) { l <- strsplit(readLines(file), "[ ]+") ## extract y-values, if any y <- if (is.na(l[[1]][1]) || length(grep(":",l[[1]][1]))) NULL else sapply(l, function(x) x[1]) ## x-values rja <- do.call("rbind", lapply(l, function(x) do.call("rbind", strsplit(if (is.null(y)) x else x[-1], ":") ) ) ) ja <- as.integer(rja[,1]) ia <- cumsum(c(1, sapply(l, length) - !is.null(y))) max.ja <- max(ja) dimension <- c(length(l), if (is.null(ncol)) max.ja else max(ncol, max.ja)) x = new(getClass("matrix.csr", where = asNamespace("SparseM")), ra = as.numeric(rja[,2]), ja = ja, ia = as.integer(ia), dimension = as.integer(dimension)) if (length(y)) list(x = x, y = if (fac) as.factor(y) else as.numeric(y)) else x } write.matrix.csr <- function (x, file = "out.dat", y = NULL, fac = TRUE) { on.exit(sink()) x <- SparseM::as.matrix.csr(x) if (!is.null(y) & (length(y) != nrow(x))) stop(paste("Length of y (=", length(y), ") does not match number of rows of x (=", nrow(x), ")!", sep="")) sink(file) l <- length(x@ra) zerocols <- all(x@ja < ncol(x)) if (!is.null(y) && is.factor(y) && fac) y <- as.character(y) for (i in 1:nrow(x)) { if (!is.null(y)) cat (y[i],"") if ((x@ia[i] <= l) && (x@ia[i] < x@ia[i + 1])) { for (j in x@ia[i] : (x@ia[i + 1] - 1)) cat(x@ja[j], ":", x@ra[j], " ", sep="") if (zerocols) { cat(ncol(x), ":", 0, " ", sep="") zerocols <- FALSE } } cat("\n") } } na.fail.matrix.csr <- function(object, ...) { if (any(is.na(object@ra))) stop("missing values in object") else return(object) } e1071/R/hamming.window.R0000755000175100001440000000023111400421345014270 0ustar hornikusershamming.window <- function (n) { if (n == 1) c <- 1 else { n <- n-1 c <- 0.54 - 0.46*cos(2*pi*(0:n)/n) } return(c) } e1071/R/lca.R0000655000175100001440000001571613063003326012120 0ustar hornikuserslca <- function(x, k, niter=100, matchdata=FALSE, verbose=FALSE) { ## if x is a data matrix -> create patterns if (is.matrix(x)) { if (matchdata) { x <- countpattern(x, matching=TRUE) xmat <- x$matching x <- x$pat } else x <- countpattern(x, matching=FALSE) } else ## if no data ist given, matchdata must be FALSE matchdata <- FALSE n <- sum(x) npat <- length(x) nvar <- round(log(npat)/log(2)) ## build matrix of all possible binary vectors b <- matrix(0, 2^nvar, nvar) for (i in 1:nvar) b[, nvar+1-i] <- rep(rep(c(0,1),c(2^(i-1),2^(i-1))),2^(nvar-i)) ## initialize probabilities classprob <- runif(k) classprob <- classprob/sum(classprob) names(classprob) <- 1:k p <- matrix(runif(nvar*k), k) pas <- matrix(0, k, npat) classsize <- numeric(k) for (i in 1:niter) { for (j in 1:k) { ## P(pattern|class) mp <- t(b)*p[j,]+(1-t(b))*(1-p[j,]) pas[j,] <- drop(exp(rep(1,nvar)%*%log(mp))) # column product } ## P(pattern|class)*P(class) pas <- pas * classprob ## P(class|pattern) sump <- drop(rep(1,k)%*%pas) # column sums pas <- t(t(pas)/sump) spas <- t(t(pas)*x) classsize <- drop(spas%*%rep(1,npat)) # row sums classprob <- classsize/n p <- pas%*%(x*b)/classsize if (verbose) cat("Iteration:", i, "\n") } for (j in 1:k) { mp <- t(b)*p[j,]+(1-t(b))*(1-p[j,]) pas[j,] <- drop(exp(rep(1,nvar)%*%log(mp)))*classprob[j] # column product } ## LogLikelihood pmust <- drop(rep(1,k)%*%pas) # column sums ll <- sum(x*log(pmust)) ## Likelihoodquotient xg0 <- x[x>0] ll0 <- sum(xg0*log(xg0/n)) lq <- 2*(ll0-ll) ## bic bic <- -2*ll+log(n)*(k*(nvar+1)-1) bicsat <- -2*ll0+log(n)*(2^nvar-1) ## chisq ch <- sum((x-n*pmust)^2/(n*pmust)) ## P(class|pattern) sump <- drop(rep(1,k)%*%pas) # column sums pas <- t(t(pas)/sump) mat <- max.col(t(pas)) if (matchdata) mat <- mat[xmat] colnames(p) <- 1:nvar rownames(p) <- 1:k lcaresult <- list(classprob=classprob, p=p, matching=mat, logl=ll, loglsat=ll0, chisq=ch, lhquot=lq, bic=bic, bicsat=bicsat, n=n, np=(k*(nvar+1)-1), matchdata=matchdata) class(lcaresult) <- "lca" return(lcaresult) } print.lca <- function(x, ...) { cat("LCA-Result\n") cat("----------\n\n") cat("Datapoints:", x$n, "\n") cat("Classes: ", length(x$classprob), "\n") cat("Probability of classes\n") print(round(x$classprob,3)) cat("Itemprobabilities\n") print(round(x$p,2)) } summary.lca <- function(object, ...) { nvar <- ncol(object$p) object$npsat <- 2^nvar-1 object$df <- 2^nvar-1-object$np object$pvallhquot <- 1-pchisq(object$lhquot,object$df) object$pvalchisq <- 1-pchisq(object$chisq,object$df) object$k <- length(object$classprob) ## remove unnecessary list elements object$classprob <- NULL object$p <- NULL object$matching <- NULL class(object) <- "summary.lca" return(object) } print.summary.lca <- function(x, ...) { cat("LCA-Result\n") cat("----------\n\n") cat("Datapoints:", x$n, "\n") cat("Classes: ", x$k, "\n") cat("\nGoodness of fit statistics:\n\n") cat("Number of parameters, estimated model:", x$np, "\n") cat("Number of parameters, saturated model:", x$npsat, "\n") cat("Log-Likelihood, estimated model: ", x$logl, "\n") cat("Log-Likelihood, saturated model: ", x$loglsat, "\n") cat("\nInformation Criteria:\n\n") cat("BIC, estimated model:", x$bic, "\n") cat("BIC, saturated model:", x$bicsat, "\n") cat("\nTestStatistics:\n\n") cat("Likelihood ratio: ", x$lhquot, " p-val:", x$pvallhquot, "\n") cat("Pearson Chi^2: ", x$chisq, " p-val:", x$pvalchisq, "\n") cat("Degress of freedom:", x$df, "\n") } bootstrap.lca <- function(l, nsamples=10, lcaiter=30, verbose=FALSE) { n <- l$n classprob <- l$classprob nclass <- length(l$classprob) prob <- l$p nvar <- ncol(l$p) npat <- 2^nvar ## build matrix of all possible binary vectors b <- matrix(0, npat, nvar) for (i in 1:nvar) b[, nvar+1-i] <- rep(rep(c(0,1),c(2^(i-1),2^(i-1))),2^(nvar-i)) ll <- lq <- ll0 <- ch <- numeric(nsamples) for (i in 1:nsamples) { ## generate data cm <- sample(1:nclass, size=n, replace=TRUE, prob=classprob) x <- matrix(runif(n*nvar), nrow=n) x <- (xX): ", x$pvalzratio, "\n") cat("P-Val: ", x$pvalratio, "\n\n") cat("Pearson's Chisquare\n\n") cat("Mean:", x$chisqmean, "\n") cat("SDev:", x$chisqsd, "\n") cat("Value in Data Set:", x$chisqorg, "\n") cat("Z-Statistics: ", x$zchisq, "\n") cat("P(Z>X): ", x$pvalzchisq, "\n") cat("P-Val: ", x$pvalchisq, "\n\n") } predict.lca <- function(object, x, ...) { if (object$matchdata) stop("predict.lca: only possible, if lca has been called with matchdata=FALSE") else { x <- countpattern(x, matching=TRUE) return(object$matching[x$matching]) } } e1071/R/bincombinations.R0000755000175100001440000000034311633216751014537 0ustar hornikusers## Kopie in mlbench bincombinations <- function(p) { retval <- matrix(0, nrow=2^p, ncol=p) for(n in 1:p){ retval[,n] <- rep(c(rep(0, (2^p/2^n)), rep(1, (2^p/2^n))), length=2^p) } retval } e1071/R/rwiener.R0000755000175100001440000000022311400421345013016 0ustar hornikusersrwiener <- function(end=1, frequency=1000) { z<-cumsum(rnorm(end*frequency)/sqrt(frequency)) ts(z, start=1/frequency, frequency=frequency) } e1071/R/cshell.R0000655000175100001440000001446513044147404012640 0ustar hornikuserscshell <- function (x, centers, iter.max = 100, verbose = FALSE, dist = "euclidean", method = "cshell", m=2, radius= NULL) { x <- as.matrix(x) xrows <- dim(x)[1] xcols <- dim(x)[2] xold <- x perm <- sample(xrows) x <- x[perm, ] ## initial values are given if (is.matrix(centers)) ncenters <- dim(centers)[1] else { ## take centers random vectors as initial values ncenters <- centers centers <- x[rank(runif(xrows))[1:ncenters], ]+0.001 } ## initialize radius if (missing(radius)) radius <- rep(0.2,ncenters) else radius <- as.double(radius) dist <- pmatch(dist, c("euclidean", "manhattan")) if (is.na(dist)) stop("invalid distance") if (dist == -1) stop("ambiguous distance") method <- pmatch(method, c("cshell")) if (is.na(method)) stop("invalid clustering method") if (method == -1) stop("ambiguous clustering method") initcenters <- centers ## dist <- matrix(0, xrows, ncenters) ## necessary for empty clusters pos <- as.factor(1 : ncenters) rownames(centers) <- pos iter <- integer(1) flag <- integer(1) retval <- .C(R_cshell, xrows = as.integer(xrows), xcols = as.integer(xcols), x = as.double(x), ncenters = as.integer(ncenters), centers = as.double(centers), iter.max = as.integer(iter.max), iter = as.integer(iter), verbose = as.integer(verbose), dist = as.integer(dist-1), U = double(xrows*ncenters), UANT = double(xrows*ncenters), m = as.double(m), ermin = double(1), radius = as.double(radius), flag = as.integer(flag) ) centers <- matrix(retval$centers, ncol = xcols, dimnames = dimnames(initcenters)) radius <- as.double(retval$radius) U <- retval$U U <- matrix(U, ncol=ncenters) UANT <- retval$UANT UANT <- matrix(UANT, ncol=ncenters) iter <- retval$iter flag <- as.integer(retval$flag) ## Optimization part while (((flag == 1) || (flag==4)) && (iter<=iter.max)) { flag <- 3 system <- function (spar=c(centers,radius), x, U, m, i) { k <- dim(x)[1] d <- dim(x)[2] nparam<-length(spar) v<-spar[1:(nparam-1)] r<-spar[nparam] ##distance matrix x_k - v_i distmat <- t(t(x)-v) ##norm from x_k - v_i normdist <- distmat[,1]^2 for (j in 2:d) normdist<-normdist+distmat[,j]^2 normdist <- sqrt(normdist) ##equation 5 op <- sum( (U[,i]^m) * (normdist-r) )^2 ##equation 4 equationmatrix <- ((U[,i]^m) * (1-r/normdist))*distmat ## ## This had just apply(), but optim() really needs a scalar ## fn. ## What do we really want here? op<- op+sum(apply(equationmatrix, 2, sum)^2) ## } for (i in 1:ncenters) { spar <- c(centers[i,],radius[i]) npar <- length(spar) optimres <- optim(spar, system, method="CG", x=x, U=U, m=m, i=i) centers[i,] <- optimres$par[1:(npar-1)] radius[i] <- optimres$par[npar] } retval <- .C(R_cshell, xrows = as.integer(xrows), xcols = as.integer(xcols), x = as.double(x), ncenters = as.integer(ncenters), centers = as.double(centers), iter.max = as.integer(iter.max), iter = as.integer(iter-1), verbose = as.integer(verbose), dist = as.integer(dist-1), U = as.double(U), UANT = as.double(UANT), m = as.double(m), ermin = double(1), radius = as.double(radius), flag = as.integer(flag) ) flag<-retval$flag if (retval$flag!=2) flag<-1 centers <- matrix(retval$centers, ncol = xcols, dimnames = dimnames(initcenters)) radius <- as.double(retval$radius) U <- retval$U U <- matrix(U, ncol=ncenters) UANT <- retval$UANT UANT <- matrix(UANT, ncol=ncenters) iter <- retval$iter } centers <- matrix(retval$centers, ncol = xcols, dimnames = list(pos, colnames(initcenters))) U <- matrix(retval$U, ncol = ncenters, dimnames = list(rownames(x), 1 : ncenters)) U <- U[order(perm),] clusterU <- apply(U, 1, which.max) clustersize <- as.integer(table(clusterU)) radius <- as.double(retval$radius) retval <- list(centers = centers, radius=radius, size = clustersize, cluster = clusterU, iter = retval$iter - 1, membership=U, withinerror = retval$ermin, call = match.call()) class(retval) <- c("cshell", "fclust") return(retval) } #predict.cshell <- function( clobj, x){ # xrows<-dim(x)[1] # xcols<-dim(x)[2] # ncenters <- clobj$ncenters # cluster <- integer(xrows) # clustersize <- integer(ncenters) # f <- clobj$m # radius <- clobj$radius # if(dim(clobj$centers)[2] != xcols){ # stop("Number of variables in cluster object and x are not the same!") # } # retval <- .C("cshell_assign", # xrows = as.integer(xrows), # xcols = as.integer(xcols), # x = as.double(x), # ncenters = as.integer(ncenters), # centers = as.double(clobj$centers), # dist = as.integer(clobj$dist-1), # U = double(xrows*ncenters), # f = as.double(f), # radius = as.double(radius)) # U <- retval$U # U <- matrix(U, ncol=ncenters) # clusterU <- apply(U,1,which.max) # clustersize <- as.integer(table(clusterU)) # clobj$iter <- NULL # clobj$cluster <- clusterU # clobj$size <- retval$clustersize # clobj$membership <- U # return(clobj) #} e1071/R/kurtosis.R0000755000175100001440000000111511400421345013227 0ustar hornikuserskurtosis <- function(x, na.rm = FALSE, type = 3) { if(any(ina <- is.na(x))) { if(na.rm) x <- x[!ina] else return(NA) } if(!(type %in% (1 : 3))) stop("Invalid 'type' argument.") n <- length(x) x <- x - mean(x) r <- n * sum(x ^ 4) / (sum(x ^ 2) ^ 2) y <- if(type == 1) r - 3 else if(type == 2) { if(n < 4) stop("Need at least 4 complete observations.") ((n + 1) * (r - 3) + 6) * (n - 1) / ((n - 2) * (n - 3)) } else r * (1 - 1 / n) ^ 2 - 3 y } e1071/R/plot.stft.R0000755000175100001440000000021011400421345013274 0ustar hornikusersplot.stft <- function (x, col = gray (63:0/63), ...) { x <- x$values image(x=1:dim(x)[1], y=1:dim(x)[2], z=x, col=col, ...) } e1071/R/shortestPaths.R0000655000175100001440000000146613044147472014243 0ustar hornikusersallShortestPaths <- function(x){ x <- as.matrix(x) x[is.na(x)] <- .Machine$double.xmax x[is.infinite(x) & x>0] <- .Machine$double.xmax if(ncol(x) != nrow(x)) stop("x is not a square matrix") n <- ncol(x) z <- .C(R_e1071_floyd, as.integer(n), double(n^2), as.double(x), integer(n^2) ) z <- list(length = matrix(z[[2]], n), middlePoints = matrix(z[[4]]+1, n)) z$length[z$length == .Machine$double.xmax] <- NA z } extractPath <- function(obj, start, end){ z <- integer(0) path <- function(i, j){ k <- obj$middlePoints[i, j] if (k != 0) { path(i,k); z <<- c(z, k) path(k,j); } } path(start,end) c(start, z, end) } e1071/R/rectangle.window.R0000755000175100001440000000005611400421345014621 0ustar hornikusersrectangle.window <- function (n) rep (1, n) e1071/R/matchControls.R0000655000175100001440000000420013044210525014163 0ustar hornikusersmatchControls <- function(formula, data = list(), subset, contlabel = "con", caselabel = NULL, dogrep = TRUE, replace = FALSE) { if (system.file(package = "cluster") == "") stop("Could not load required package 'cluster'!") if (system.file(package = "stats") == "") stop("Could not load required package 'stats'!") m <- match.call() m$contlabel <- m$caselabel <- m$dogrep <- m$replace <- NULL m$na.action <- function(x) x m[[1L]] <- quote(stats::model.frame) m1 <- eval(m, sys.frame(sys.parent())) ## the full model.frame is used only to determine the number of rows ## of the complete data frame m$subset <- NULL m2 <- eval(m, sys.frame(sys.parent())) if (dogrep) { ok <- grep(contlabel, as.character(model.response(m1))) controls <- rownames(m1)[ok] if (is.null(caselabel)) { cases <- rownames(m1)[-ok] } else { ok <- grep(caselabel, as.character(model.response(m1))) cases <- rownames(m1)[ok] } } else { controls <- rownames(m1)[model.response(m1) == contlabel] if (is.null(caselabel)){ cases <- rownames(m1)[model.response(m1) != contlabel] } else { ok <- rep(FALSE, nrow(m1)) for (l in caselabel){ ok <- ok | (model.response(m1) == l) } cases <- rownames(m1)[ok] } } d <- as.matrix(stats::as.dist(cluster::daisy(m1[,-1,drop=FALSE]))) which.is.min <- function (x) { y <- seq(length(x))[(x == min(x, na.rm = TRUE)) & !is.na(x)] if (length(y) > 1) sample(y, 1) else y } retval <- rep("", length(cases)) for (k in 1 : length(cases)) { retval[k] <- controls[which.is.min(d[cases[k], controls])] if (!replace) controls <- controls[controls != retval[k]] } fac <- rep(NA, nrow(m2)) names(fac) <- rownames(m2) fac[cases] <- "case" fac[retval] <- "cont" fac <- factor(fac) list(cases = cases, controls = retval, factor = fac) } e1071/R/hamming.distance.R0000755000175100001440000000060012505565304014566 0ustar hornikusershamming.distance <- function(x,y){ z<-NULL if(is.vector(x) && is.vector(y)){ z <- sum(x != y) } else{ z <- matrix(0,nrow=nrow(x),ncol=nrow(x)) for(k in 1:(nrow(x)-1)){ for(l in (k+1):nrow(x)){ z[k,l] <- hamming.distance(x[k,], x[l,]) z[l,k] <- z[k,l] } } dimnames(z) <- list(dimnames(x)[[1]], dimnames(x)[[1]]) } z } e1071/R/skewness.R0000755000175100001440000000105011400421345013204 0ustar hornikusersskewness <- function(x, na.rm = FALSE, type = 3) { if(any(ina <- is.na(x))) { if(na.rm) x <- x[!ina] else return(NA) } if(!(type %in% (1 : 3))) stop("Invalid 'type' argument.") n <- length(x) x <- x - mean(x) y <- sqrt(n) * sum(x ^ 3) / (sum(x ^ 2) ^ (3/2)) if(type == 2) { if(n < 3) stop("Need at least 3 complete observations.") y <- y * sqrt(n * (n - 1)) / (n - 2) } else if(type == 3) y <- y * ((1 - 1 / n)) ^ (3/2) y } e1071/R/bclust.R0000755000175100001440000001604612310166154012656 0ustar hornikusers"bclust" <- function (x, centers = 2, iter.base = 10, minsize = 0, dist.method = "euclidian", hclust.method = "average", base.method = "kmeans", base.centers = 20, verbose = TRUE, final.kmeans = FALSE, docmdscale=FALSE, resample=TRUE, weights=NULL, maxcluster=base.centers, ...) { x <- as.matrix(x) xr <- nrow(x) xc <- ncol(x) CLUSFUN <- get(base.method) object <- list(allcenters = matrix(0, ncol = xc, nrow = iter.base * base.centers), allcluster = NULL, hclust = NULL, members = NULL, cluster = NULL, centers = NULL, iter.base = iter.base, base.centers = base.centers, prcomp = NULL, datamean = apply(x, 2, mean), colnames = colnames(x), dist.method = dist.method, hclust.method = hclust.method, maxcluster = maxcluster) class(object) <- "bclust" optSEM <- getOption("show.error.messages") if(is.null(optSEM)) optSEM <- TRUE on.exit(options(show.error.messages = optSEM)) if (verbose) cat("Committee Member:") for (n in 1:iter.base) { if (verbose){ cat(" ", n, sep = "") } if(resample){ x1 <- x[sample(xr, replace = TRUE, prob=weights), ] } else{ x1 <- x } for(m in 1:20){ if(verbose) cat("(",m,")",sep="") options(show.error.messages = FALSE) tryres <- try(CLUSFUN(x1, centers = base.centers, ...)) if(!inherits(tryres, "try-error")) break } options(show.error.messages = optSEM) if(m==20) stop("Could not find valid cluster solution in 20 replications\n") object$allcenters[((n - 1) * base.centers + 1):(n * base.centers),] <- tryres$centers } object$allcenters <- object$allcenters[complete.cases(object$allcenters),,drop=FALSE] object$allcluster <- knn1(object$allcenters, x, factor(1:nrow(object$allcenters))) if(minsize > 0){ object <- prune.bclust(object, x, minsize=minsize) } if (verbose) cat("\nComputing Hierarchical Clustering\n") object <- hclust.bclust(object, x = x, centers = centers, final.kmeans = final.kmeans, docmdscale=docmdscale) object } "centers.bclust" <- function (object, k) { centers <- matrix(0, nrow = k, ncol = ncol(object$allcenters)) for (m in 1:k) { centers[m, ] <- apply(object$allcenters[object$members[,k-1] == m, , drop = FALSE], 2, mean) } centers } "clusters.bclust" <- function (object, k, x=NULL) { if(missing(x)) allcluster <- object$allcluster else allcluster <- knn1(object$allcenters, x, factor(1:nrow(object$allcenters))) return(object$members[allcluster, k - 1]) } "hclust.bclust" <- function (object, x, centers, dist.method = object$dist.method, hclust.method = object$hclust.method, final.kmeans = FALSE, docmdscale = FALSE, maxcluster=object$maxcluster) { d <- dist(object$allcenters, method = dist.method) if(hclust.method=="diana"){ if (system.file(package = "cluster") == "") stop("Could not load required package 'cluster'!") object$hclust <- stats::as.hclust(cluster::diana(d, diss=TRUE)) } else object$hclust <- stats::hclust(d, method = hclust.method) if(docmdscale){ object$cmdscale <- cmdscale(d) } object$members <- cutree(object$hclust, 2:maxcluster) object$cluster <- clusters.bclust(object, centers) object$centers <- centers.bclust(object, centers) if (final.kmeans) { kmeansres <- kmeans(x, centers = object$centers) object$centers <- kmeansres$centers object$cluster <- kmeansres$cluster } object } "plot.bclust" <- function (x, maxcluster=x$maxcluster, main = deparse(substitute(x)), ...) { opar <- par(c("mar", "oma")) on.exit(par(opar)) par(oma = c(0, 0, 3, 0)) layout(matrix(c(1, 1, 2, 2), 2, 2, byrow = TRUE)) par(mar = c(0, 4, 4, 1)) plot(x$hclust, labels = FALSE, hang = -1) x1 <- 1:maxcluster x2 <- 2:maxcluster y <- rev(x$hclust$height)[x1] z <- abs(diff(y)) par(mar = c(4, 4, 1, 2)) plot(x1, ((y - min(y))/(max(y) - min(y))), ty = "l", xlab = "", ylab = "", ylim = c(0, 1)) lines(x2, z/sum(z), col = "grey") text(x2, z/sum(z), labels = as.character(x2)) # lx2 <- length(x2) # abline(h=qexp(.95, rate = length(x2)), lty=3, col="grey") # abline(h=qexp(.95^(1/lx2), rate = length(x2)), lty=3, col="grey") mtext(main, outer = TRUE, cex = 1.5) layout(1) } "boxplot.bclust" <- function (x, n = nrow(x$centers), bycluster = TRUE, main = deparse(substitute(x)), oneplot=TRUE, which=1:n, ...) { N <- length(which) opar <- par(c("mfrow", "oma", "mgp","xpd")) on.exit(par(opar)) par(xpd=NA) memb <- x$members[, (n - 1)] tmemb <- table(memb) cendf <- as.data.frame(x$allcenters) ylim <- range(x$allcenters) if (bycluster) { if(oneplot){ if (N <= 3) { par(mfrow = c(N, 1)) } else { par(mfrow = c(ceiling(N/2), 2)) } } tcluster <- table(clusters.bclust(x, n)) for (k in which) { boxplot(cendf[memb == k, ], col = "grey", names = rep("",ncol(cendf)), ylim = ylim, ...) if (!is.null(x$datamean)) { lines(x$datamean, col = "red") } if(!is.null(x$colnames)){ text(1:length(x$colnames)+0.2, par("usr")[3], adj=1,srt=35, paste(x$colnames, " ")) } title(main = paste("Cluster ", k, ": ", tmemb[k], " centers, ", tcluster[k], " data points", sep = "")) } } else { a <- ceiling(sqrt(ncol(cendf))) if(oneplot){ par(mfrow = c(a, ceiling(ncol(cendf)/a))) } memb <- as.factor(memb) for (k in 1:ncol(cendf)) { boxplot(cendf[, k] ~ memb, col = "grey", ylim = ylim, ...) title(main = x$colnames[k]) abline(h = x$datamean[k], col = "red") } } } ### prune centers that contain not at least minsize data points prune.bclust <- function(object, x, minsize=1, dohclust=FALSE, ...){ ok <- FALSE while(!all(ok)){ object$allcluster <- knn1(object$allcenters, x, factor(1:nrow(object$allcenters))) ok <- table(object$allcluster) >= minsize object$allcenters <- object$allcenters[ok, ] } if(dohclust){ object <- hclust.bclust(object, x, nrow(object$centers), ...) } object } e1071/R/fclustIndex.R0000755000175100001440000001461212052476443013657 0ustar hornikusersfclustIndex <- function ( y, x, index= "all" ) { clres <- y ########################################################################### ################SESSION 1: MEASURES######################################### ########################################################################### gath.geva <- function (clres,x)#for m=2 { xrows <- dim(clres$me)[1] xcols <- dim(clres$ce)[2] ncenters <- dim(clres$centers)[1] scatter <- array(0.0, c(xcols, xcols, ncenters)) scatternew <- array(0.0, c(xcols, xcols, ncenters)) fhv <-as.double(0) apd <-as.double(0) pd <- as.double(0) control <- as.double(0) for (i in 1:ncenters){ paronomastis <- as.double(0) paronomastis2 <- as.double(0) for (j in 1:xrows){ paronomastis <- paronomastis+clres$me[j,i] diff <- x[j,]-clres$ce[i,] scatternew[,,i] <- clres$me[j,i]*(t(t(diff))%*%t(diff)) scatter[,,i] <- scatter[,,i]+scatternew[,,i] }#xrows scatter[,,i] <- scatter[,,i]/paronomastis for (j in 1:xrows){ diff <- x[j,]-clres$ce[i,] control <- (t(diff)%*%solve(scatter[,,i]))%*%t(t(diff)) if (control<1.0) paronomastis2 <- paronomastis2+clres$me[j,i] ## else ## cat("...") }#xrows fhv <- fhv+sqrt(det(scatter[,,i])) apd <- apd+paronomastis2/sqrt(det(scatter[,,i])) pd <- pd+paronomastis2 }#ncenters pd <- pd/fhv apd <- apd/ncenters retval <- list(fuzzy.hypervolume=fhv,average.partition.density=apd, partition.density=pd) 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Now, this means $k(k-1)/2$ classifiers, hence in principle $k(k-1)/2$ sets of SVs, coefficiants and rhos. These are stored in a compressed format: \begin{enumerate} \item Only one SV is stored in case it were used by several classifiers. The \texttt{model\$SV-matrix} is ordered by classes, and you find the starting indices by using \texttt{nSV} (number of SVs): \begin{smallexample} start <- c(1, cumsum(model$nSV)) start <- start[-length(start)] \end{smallexample} \texttt{sum(nSV)} equals the total number of (distinct) SVs. \item The coefficients of the SVs are stored in the \texttt{model\$coefs}-matrix, grouped by classes. Because the separating hyperplanes found by the SVM algorithm has SVs on both sides, you will have two sets of coefficients per binary classifier, and e.g., for 3 classes, you could build a \emph{block}-matrix like this for the classifiers $(i, j)$ ($i$,$j$=class numbers): \begin{table}[h] \center \begin{tabular}{|c|c|c|c|} \hline i $\backslash$ j & 0 & 1 & 2 \\\hline 0 & X & set (0, 1)& set (0, 2)\\\hline 1 & set (1, 0) & X & set (1, 2)\\\hline 2 & set (2, 0) & set (2, 1) & X\\\hline \end{tabular} \end{table} \noindent where set(i, j) are the coefficients for the classifier (i,j), lying on the side of class j. Because there are no entries for (i, i), we can save the diagonal and shift up the lower triangular matrix to get \begin{table}[h] \center \begin{tabular}{|c|c|c|c|} \hline i $\backslash$ j & 0 & 1 & 2 \\\hline 0 & set (1,0) & set (0,1) & set (0,2) \\\hline 1 & set (2,0) & set (2,1) & set (1,2) \\\hline \end{tabular} \end{table} \noindent Each set (., j) has length \texttt{nSV[j]}, so of course, there will be some filling 0s in some sets. \texttt{model\$coefs} is the \emph{transposed} of such a matrix, therefore for a data set with, say, 6 classes, you get 6-1=5 columns. The coefficients of (i, j) start at \texttt{model\$coefs[start[i],j]} and those of (j, i) at \texttt{model\$coefs[start[j],i-1]}. \item The $k(k-1)/2$ rhos are just linearly stored in the vector \texttt{model\$rho}. \end{enumerate} \newpage \noindent The following code shows how to use this for prediction: \begin{smallexample} ## Linear Kernel function K <- function(i,j) crossprod(i,j) predsvm <- function(object, newdata) \{ ## compute start-index start <- c(1, cumsum(object$nSV)+1) start <- start[-length(start)] ## compute kernel values kernel <- sapply (1:object$tot.nSV, function (x) K(object$SV[x,], newdata)) ## compute raw prediction for classifier (i,j) predone <- function (i,j) \{ ## ranges for class i and j: ri <- start[i] : (start[i] + object$nSV[i] - 1) rj <- start[j] : (start[j] + object$nSV[j] - 1) ## coefs for (i,j): coef1 <- object$coefs[ri, j-1] coef2 <- object$coefs[rj, i] ## return raw values: crossprod(coef1, kernel[ri]) + crossprod(coef2, kernel[rj]) \} ## compute votes for all classifiers votes <- rep(0,object$nclasses) c <- 0 # rho counter for (i in 1 : (object$nclasses - 1)) for (j in (i + 1) : object$nclasses) if (predone(i,j) > object$rho[c <- c + 1]) votes[i] <- votes[i] + 1 else votes[j] <- votes[j] + 1 ## return winner (index with max. votes) object$levels[which(votes %in% max(votes))[1]] \} \end{smallexample} In case data were scaled prior fitting the model (note that this is the default for \texttt{svm()}, the new data needs to be scaled as well before applying the predition functions, for example using the following code snipped (object is an object returned by \texttt{svm()}, \texttt{newdata} a data frame): \begin{smallexample} if (any(object$scaled)) newdata[,object$scaled] <- scale(newdata[,object$scaled, drop = FALSE], center = object$x.scale$"scaled:center", scale = object$x.scale$"scaled:scale" ) \end{smallexample} \noindent For regression, the response needs to be scaled as well before training, and the predictions need to be scaled back accordingly. \end{document} e1071/inst/doc/svmdoc.Rnw0000655000175100001440000004340313475431256014552 0ustar hornikusers\documentclass[a4paper]{article} \usepackage{hyperref, graphicx, color, alltt} \usepackage{Sweave} \usepackage[round]{natbib} \definecolor{Red}{rgb}{0.7,0,0} \definecolor{Blue}{rgb}{0,0,0.8} \definecolor{hellgrau}{rgb}{0.55,0.55,0.55} \newcommand{\pkg}[1]{\texttt{#1}} \newenvironment{smallexample}{\begin{alltt}\small}{\end{alltt}} \begin{document} %\VignetteIndexEntry{Support Vector Machines---the Interface to libsvm in package e1071} %\VignetteDepends{e1071,rpart,xtable} %\VignetteKeywords{classification, regression, machine learning, benchmarking, support vector machines} %\VignettePackage{e1071} \SweaveOpts{engine=R,eps=FALSE} \setkeys{Gin}{width=0.8\textwidth} \title{Support Vector Machines \footnote{A smaller version of this article appeared in R-News, Vol.1/3, 9.2001}\\ \large The Interface to \texttt{libsvm} in package \pkg{e1071}} \author{by David Meyer\\ FH Technikum Wien, Austria\\ \url{David.Meyer@R-Project.org} } \maketitle \sloppy ``Hype or Hallelujah?'' is the provocative title used by \cite{svm:bennett+campbell:2000} in an overview of Support Vector Machines (SVM). SVMs are currently a hot topic in the machine learning community, creating a similar enthusiasm at the moment as Artificial Neural Networks used to do before. Far from being a panacea, SVMs yet represent a powerful technique for general (nonlinear) classification, regression and outlier detection with an intuitive model representation. The package \pkg{e1071} offers an interface to the award-winning\footnote{The library won the IJCNN 2001 Challenge by solving two of three problems: the Generalization Ability Challenge (GAC) and the Text Decoding Challenge (TDC). For more information, see: \url{http://www.csie.ntu.edu.tw/~cjlin/papers/ijcnn.ps.gz}.} C++-implementation by Chih-Chung Chang and Chih-Jen Lin, \texttt{libsvm} (current version: 2.6), featuring: \begin{itemize} \item $C$- and $\nu$-classification \item one-class-classification (novelty detection) \item $\epsilon$- and $\nu$-regression \end{itemize} and includes: \begin{itemize} \item linear, polynomial, radial basis function, and sigmoidal kernels \item formula interface \item $k$-fold cross validation \end{itemize} For further implementation details on \texttt{libsvm}, see \cite{svm:chang+lin:2001}. \section*{Basic concept} SVMs were developed by \cite{svm:cortes+vapnik:1995} for binary classification. Their approach may be roughly sketched as follows: \begin{description} \item[Class separation:] basically, we are looking for the optimal separating hyperplane between the two classes by maximizing the \textit{margin} between the classes' closest points (see Figure \ref{fig:svm1})---the points lying on the boundaries are called \textit{support vectors}, and the middle of the margin is our optimal separating hyperplane; \item[Overlapping classes:] data points on the ``wrong'' side of the discriminant margin are weighted down to reduce their influence (\textit{``soft margin''}); \item[Nonlinearity:] when we cannot find a \textit{linear} separator, data points are projected into an (usually) higher-dimensional space where the data points effectively become linearly separable (this projection is realised via \textit{kernel techniques}); \item[Problem solution:] the whole task can be formulated as a quadratic optimization problem which can be solved by known techniques. \end{description} \noindent A program able to perform all these tasks is called a \textit{Support Vector Machine}. \begin{figure}[htbp] \begin{center} \includegraphics[width=8cm]{svm} \caption{Classification (linear separable case)} \label{fig:svm1} \end{center} \end{figure} Several extensions have been developed; the ones currently included in \texttt{libsvm} are: \begin{description} \item[$\nu$-classification:] this model allows for more control over the number of support vectors \cite[see][]{svm:scholkopf+smola+williamson:2000} by specifying an additional parameter $\nu$ which approximates the fraction of support vectors; \item[One-class-classification:] this model tries to find the support of a distribution and thus allows for outlier/novelty detection; \item[Multi-class classification:] basically, SVMs can only solve binary classification problems. To allow for multi-class classification, \texttt{libsvm} uses the \textit{one-against-one} technique by fitting all binary subclassifiers and finding the correct class by a voting mechanism; \item[$\epsilon$-regression:] here, the data points lie \textit{in between} the two borders of the margin which is maximized under suitable conditions to avoid outlier inclusion; \item[$\nu$-regression:] with analogue modifications of the regression model as in the classification case. \end{description} \section*{Usage in R} The R interface to \texttt{libsvm} in package \pkg{e1071}, \texttt{svm()}, was designed to be as intuitive as possible. Models are fitted and new data are predicted as usual, and both the vector/matrix and the formula interface are implemented. As expected for R's statistical functions, the engine tries to be smart about the mode to be chosen, using the dependent variable's type ($y$): if $y$ is a factor, the engine switches to classification mode, otherwise, it behaves as a regression machine; if $y$ is omitted, the engine assumes a novelty detection task. \section*{Examples} In the following two examples, we demonstrate the practical use of \texttt{svm()} along with a comparison to classification and regression trees as implemented in \texttt{rpart()}. \subsection*{Classification} In this example, we use the glass data from the \href{http://www.ics.uci.edu/mlearn/MLRepository.html}{UCI Repository of Machine Learning Databases} for classification. The task is to predict the type of a glass on basis of its chemical analysis. We start by splitting the data into a train and test set: <<>>= library(e1071) library(rpart) data(Glass, package="mlbench") ## split data into a train and test set index <- 1:nrow(Glass) testindex <- sample(index, trunc(length(index)/3)) testset <- Glass[testindex,] trainset <- Glass[-testindex,] @ Both for the SVM and the partitioning tree (via \texttt{rpart()}), we fit the model and try to predict the test set values: <<>>= ## svm svm.model <- svm(Type ~ ., data = trainset, cost = 100, gamma = 1) svm.pred <- predict(svm.model, testset[,-10]) @ (The dependent variable, \texttt{Type}, has column number 10. \texttt{cost} is a general penalizing parameter for $C$-classification and \texttt{gamma} is the radial basis function-specific kernel parameter.) <<>>= ## rpart rpart.model <- rpart(Type ~ ., data = trainset) rpart.pred <- predict(rpart.model, testset[,-10], type = "class") @ A cross-tabulation of the true versus the predicted values yields: <<>>= ## compute svm confusion matrix table(pred = svm.pred, true = testset[,10]) ## compute rpart confusion matrix table(pred = rpart.pred, true = testset[,10]) @ %% results table <>= library(xtable) rp.acc <- c() sv.acc <- c() rp.kap <- c() sv.kap <- c() reps <- 10 for (i in 1:reps) { ## split data into a train and test set index <- 1:nrow(Glass) testindex <- sample(index, trunc(length(index)/3)) testset <- na.omit(Glass[testindex,]) trainset <- na.omit(Glass[-testindex,]) ## svm svm.model <- svm(Type ~ ., data = trainset, cost = 100, gamma = 1) svm.pred <- predict(svm.model, testset[,-10]) tab <- classAgreement(table(svm.pred, testset[,10])) sv.acc[i] <- tab$diag sv.kap[i] <- tab$kappa ## rpart rpart.model <- rpart(Type ~ ., data = trainset) rpart.pred <- predict(rpart.model, testset[,-10], type = "class") tab <- classAgreement(table(rpart.pred, testset[,10])) rp.acc[i] <- tab$diag rp.kap[i] <- tab$kappa } x <- rbind(summary(sv.acc), summary(sv.kap), summary(rp.acc), summary(rp.kap)) rownames <- c() tab <- cbind(rep(c("svm","rpart"),2), round(x,2)) colnames(tab)[1] <- "method" rownames(tab) <- c("Accuracy","","Kappa"," ") xtable(tab, label = "tab:class", caption = "Performance of \\texttt{svm()} and\ \\texttt{rpart()} for classification (10 replications)") @ \noindent Finally, we compare the performance of the two methods by computing the respective accuracy rates and the kappa indices (as computed by \texttt{classAgreement()} also contained in package \pkg{e1071}). In Table \ref{tab:class}, we summarize the results of \Sexpr{reps} replications---Support Vector Machines show better results. \subsection*{Non-linear $\epsilon$-Regression} The regression capabilities of SVMs are demonstrated on the ozone data. Again, we split the data into a train and test set. <<>>= library(e1071) library(rpart) data(Ozone, package="mlbench") ## split data into a train and test set index <- 1:nrow(Ozone) testindex <- sample(index, trunc(length(index)/3)) testset <- na.omit(Ozone[testindex,-3]) trainset <- na.omit(Ozone[-testindex,-3]) ## svm svm.model <- svm(V4 ~ ., data = trainset, cost = 1000, gamma = 0.0001) svm.pred <- predict(svm.model, testset[,-3]) crossprod(svm.pred - testset[,3]) / length(testindex) ## rpart rpart.model <- rpart(V4 ~ ., data = trainset) rpart.pred <- predict(rpart.model, testset[,-3]) crossprod(rpart.pred - testset[,3]) / length(testindex) @ <>= rp.res <- c() sv.res <- c() reps <- 10 for (i in 1:reps) { ## split data into a train and test set index <- 1:nrow(Ozone) testindex <- sample(index, trunc(length(index)/3)) testset <- na.omit(Ozone[testindex,-3]) trainset <- na.omit(Ozone[-testindex,-3]) ## svm svm.model <- svm(V4 ~ ., data = trainset, cost = 1000, gamma = 0.0001) svm.pred <- predict(svm.model, testset[,-3]) sv.res[i] <- crossprod(svm.pred - testset[,3]) / length(testindex) ## rpart rpart.model <- rpart(V4 ~ ., data = trainset) rpart.pred <- predict(rpart.model, testset[,-3]) rp.res[i] <- crossprod(rpart.pred - testset[,3]) / length(testindex) } xtable(rbind(svm = summary(sv.res), rpart = summary(rp.res)), label = "tab:reg", caption = "Performance of \\texttt{svm()} and\ \\texttt{rpart()} for regression (Mean Squared Error, 10 replications)") @ \noindent We compare the two methods by the mean squared error (MSE)---see Table \ref{tab:reg} for a summary of \Sexpr{reps} replications. Again, as for classification, \texttt{svm()} does a better job than \texttt{rpart()}---in fact, much better. \section*{Elements of the \texttt{svm} object} The function \texttt{svm()} returns an object of class ``\texttt{svm}'', which partly includes the following components: \begin{description} \item[\textbf{\texttt{SV}:}] matrix of support vectors found; \item[\textbf{\texttt{labels}:}] their labels in classification mode; \item[\textbf{\texttt{index}:}] index of the support vectors in the input data (could be used e.g., for their visualization as part of the data set). \end{description} If the cross-classification feature is enabled, the \texttt{svm} object will contain some additional information described below. \section*{Other main features} \begin{description} \item[Class Weighting:] if one wishes to weight the classes differently (e.g., in case of asymmetric class sizes to avoid possibly overproportional influence of bigger classes on the margin), weights may be specified in a vector with named components. In case of two classes A and B, we could use something like: \texttt{m <- svm(x, y, class.weights = c(A = 0.3, B = 0.7))} \item[Cross-classification:] to assess the quality of the training result, we can perform a $k$-fold cross-classification on the training data by setting the parameter \texttt{cross} to $k$ (default: 0). The \texttt{svm} object will then contain some additional values, depending on whether classification or regression is performed. Values for classification: \begin{description} \item[\texttt{accuracies}:] vector of accuracy values for each of the $k$ predictions \item[\texttt{tot.accuracy}:] total accuracy \end{description} Values for regression: \begin{description} \item[\texttt{MSE}:] vector of mean squared errors for each of the $k$ predictions \item[\texttt{tot.MSE}:] total mean squared error \item[\texttt{scorrcoef}:] Squared correlation coefficient (of the predicted and the true values of the dependent variable) \end{description} \end{description} \section*{Tips on practical use} \begin{itemize} \item Note that SVMs may be very sensitive to the proper choice of parameters, so allways check a range of parameter combinations, at least on a reasonable subset of your data. \item For classification tasks, you will most likely use $C$-classification with the RBF kernel (default), because of its good general performance and the few number of parameters (only two: $C$ and $\gamma$). The authors of \pkg{libsvm} suggest to try small and large values for $C$---like 1 to 1000---first, then to decide which are better for the data by cross validation, and finally to try several $\gamma$'s for the better $C$'s. \item However, better results are obtained by using a grid search over all parameters. For this, we recommend to use the \texttt{tune.svm()} function in \pkg{e1071}. \item Be careful with large datasets as training times may increase rather fast. \item Scaling of the data usually drastically improves the results. Therefore, \texttt{svm()} scales the data by default. \end{itemize} \section*{Model Formulations and Kernels} Dual representation of models implemented: \begin{itemize} \item $C$-classification:\\ \begin{eqnarray} \min_\alpha&&\frac{1}{2}\alpha^\top \mathbf{Q} \alpha-\mathbf{e}^\top\alpha \nonumber\\ \mbox{s.t.} &&0\le\alpha_i\le C,~i=1,\ldots,l,\\ &&\mathbf{y}^\top\alpha=0~, \nonumber \end{eqnarray} where $\mathbf{e}$ is the unity vector, $C$ is the upper bound, $\mathbf{Q}$ is an $l$ by $l$ positive semidefinite matrix, $Q_{ij} \equiv y_i y_j K(x_i, x_j)$, and $K(x_i, x_j) \equiv \phi(x_i)^\top\phi(x_j)$ is the kernel. \item $\nu$-classification:\\ \begin{eqnarray} \min_\alpha&&\frac{1}{2}\alpha^\top \mathbf{Q} \alpha \nonumber\\ \mbox{s.t.}&&0\le\alpha_i\le 1/l,~i=1,\ldots,l,\\ &&\mathbf{e}^\top \alpha \ge \nu, \nonumber\\ &&\mathbf{y}^\top\alpha=0~. \nonumber \end{eqnarray} where $\nu \in (0,1]$. \item one-class classification:\\ \begin{eqnarray} \min_\alpha&&\frac{1}{2}\alpha^\top \mathbf{Q} \alpha \nonumber\\ \mbox{s.t.} &&0\le\alpha_i\le 1/(\nu l),~i=1,\ldots,l,\\ &&\mathbf{e}^\top\alpha=1~,\nonumber \end{eqnarray} \item $\epsilon$-regression:\\ \begin{eqnarray} \min_{\alpha, \alpha^*}&&\frac{1}{2}(\alpha-\alpha^*)^\top \mathbf{Q} (\alpha-\alpha^*) + \nonumber\\ &&\epsilon\sum_{i=1}^{l}(\alpha_i+\alpha_i^*) + \sum_{i=1}^{l}y_i(\alpha_i-\alpha_i^*) \nonumber\\ \mbox{s.t.} &&0\le\alpha_i, \alpha_i^*\le C,~i=1,\ldots,l,\\ &&\sum_{i=1}^{l}(\alpha_i-\alpha_i^*)=0~.\nonumber \end{eqnarray} \item $\nu$-regression:\\ \begin{eqnarray} \min_{\alpha, \alpha^*}&&\frac{1}{2}(\alpha-\alpha^*)^\top \mathbf{Q} (\alpha-\alpha^*) + \mathbf{z}^\top(\alpha_i-\alpha_i^*) \nonumber\\ \mbox{s.t.} &&0\le\alpha_i, \alpha_i^*\le C,~i=1,\ldots,l,\\ &&\mathbf{e}^\top(\alpha-\alpha^*)=0\nonumber\\ &&\mathbf{e}^\top(\alpha+\alpha^*)=C\nu~.\nonumber \end{eqnarray} \end{itemize} \noindent Available kernels:\\ \\ \noindent \begin{table}[h] \centering \begin{tabular}{|l|l|l|} \hline kernel & formula & parameters \\ \hline \hline linear & $\bf u^\top v$& (none) \\ polynomial & $(\gamma \mathbf{u^\top v}+c_0)^d$ & $\gamma, d, c_0$\\ radial basis fct. & $\exp\{-\gamma|\mathbf{u-v}|^2\}$&$\gamma$\\ sigmoid & $\tanh\{\gamma \mathbf{u^\top v}+c_0\}$ &$\gamma, c_0$\\ \hline \end{tabular} \end{table} \section*{Conclusion} We hope that \texttt{svm} provides an easy-to-use interface to the world of SVMs, which nowadays have become a popular technique in flexible modelling. There are some drawbacks, though: SVMs scale rather badly with the data size due to the quadratic optimization algorithm and the kernel transformation. Furthermore, the correct choice of kernel parameters is crucial for obtaining good results, which practically means that an extensive search must be conducted on the parameter space before results can be trusted, and this often complicates the task (the authors of \texttt{libsvm} currently conduct some work on methods of efficient automatic parameter selection). Finally, the current implementation is optimized for the radial basis function kernel only, which clearly might be suboptimal for your data. \begin{thebibliography}{5} \bibitem[Bennett \& Campbell(2000)]{svm:bennett+campbell:2000} Bennett, K.~P. \& Campbell, C. (2000). \newblock Support vector machines: Hype or hallelujah? \newblock \emph{SIGKDD Explorations}, \textbf{2}(2). \newblock \url{http://www.acm.org/sigs/sigkdd/explorations/issue2-2/bennett.pdf}. \bibitem[Chang \& Lin(2001)]{svm:chang+lin:2001} Chang, C.-C. \& Lin, C.-J. (2001). \newblock {LIBSVM}: a library for support vector machines. \newblock Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}, detailed documentation (algorithms, formulae, \dots) can be found in \url{http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz} \bibitem[Cortes \& Vapnik(1995)]{svm:cortes+vapnik:1995} Cortes, C. \& Vapnik, V. (1995). \newblock Support-vector network. \newblock \emph{Machine Learning}, \textbf{20}, 1--25. \bibitem[Sch\"olkopf et~al.(2000)Sch\"olkopf, Smola, Williamson, \& Bartlett]{svm:scholkopf+smola+williamson:2000} Sch\"olkopf, B., Smola, A., Williamson, R.~C., \& Bartlett, P. (2000). \newblock New support vector algorithms. \newblock \emph{Neural Computation}, \textbf{12}, 1207--1245. \bibitem[Vapnik(1998)]{svm:vapnik:1998} Vapnik, V. (1998). \newblock \emph{Statistical learning theory}. \newblock New York: Wiley. \end{thebibliography} \end{document} e1071/inst/NEWS.Rd0000655000175100001440000003743013475426026013067 0ustar hornikusers\name{NEWS} \title{News for Package \pkg{e1071}} \newcommand{\cpkg}{\href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}} \section{Changes in version 1.7-2}{ \itemize{ \item Change license to GPL-2 OR GPL-3 \item add coef() method for SVMs with linear kernel } } \section{Changes in version 1.7-1}{ \itemize{ \item add warning in \code{predict.naiveBayes()} if the variable type (numeric/factor) does not match for training and new data. \item Fix bug in tune when parameter space is sampled \item Fix formula interface for NaiveBayes to account for variable removal } } \section{Changes in version 1.7-0}{ \itemize{ \item Bug fix in \code{lca()} \item The \code{class.weights} argument of \code{svm()} now accepts \code{"inverse"}, setting the weights inversely proportional to the class distribution \item \code{predict.naiveBayes} now fixes the factor levels of \code{newdata} to be identical with the training data. \item{libsvm updated to version 3.23} } } \section{Changes in version 1.6-8}{ \itemize{ \item add and use native symbols for C-code \item \code{naiveBayes()} now supports logical variables } } \section{Changes in version 1.6-7}{ \itemize{ \item fix some bug in handling weights in \code{svm.default()} } } \section{Changes in version 1.6-6}{ \itemize{ \item fix numeric issue in \code{classAgreement()} \item add functions from recommended packages to NAMESPACE \item fix bug in svm.default (incorrect handling of subset= argument) \item fix bug in predict.svm (new data with NA in response got removed) \item residuals are now correctly computed for regression in case of scaled data } } \section{Changes in version 1.6-5}{ \itemize{ \item \code{hamming.distance()} no longer converts input to binary \item \code{tune()} now uses \code{mean()} to aggregate error measures from cross-fold replications } } \section{Changes in version 1.6-4}{ \itemize{ \item remove library("SparseM") statements in code, use namespace semantics instead \item Fix memory leak and uninitialized read error in \code{write.svm()} \item add warning in \code{predict.svm()} if probabilities should be predicted, but the model was not trained with \code{probability = TRUE} \item add \code{eps} to laplace smoothing in \code{predict.naiveBayes()} to account for close-zero probabilities also. \item use R's random number generator for cross-validation and probability computation instead of the system one. } } \section{Changes in version 1.6-3}{ \itemize{ \item remove require() statements and dependency on stats } } \section{Changes in version 1.6-2}{ \itemize{ \item vignettes moved to \code{vignettes} folder. \item libsvm upgrade to version 3.17, getting rid of stdout and stderr } } \section{Changes in version 1.6-1}{ \itemize{ \item \code{write.matrix.csr()} now accepts a \code{fac} argument similar to \code{read.matrix.csr()}, writing factor levels instead of the numeric codes. \item \code{naiveBayes()} uses a numerically more stable formula for calculating the a-posterior probabilities. \item \code{predict.naiveBayes()} now accepts data with predictors in an order different from the training data, and also ignores variables not in the model (especially the response variable). \item \code{svm()} checks whether parameters which are passed to the C-code are set to NULL to avoid segfaults. } } \section{Changes in version 1.6}{ \itemize{ \item bug fix in tune with sparse matrices \item version bump of libsvm to 3.1 \item Fixed partial argument matching in several places \item NEWS file changed to .Rd format and moved to \file{inst/} } } \section{Changes in version 1.5-28}{ \itemize{ \item bug fix in svm cross validation } } \section{Changes in version 1.5-27}{ \itemize{ \item \code{svm()} now accepts to set the random seed for libsvm. } } \section{Changes in version 1.5-26}{ \itemize{ \item \code{tune()} now allows user-specified error functionals. } } \section{Changes in version 1.5-25}{ \itemize{ \item add auto-coercion from Matrix and simple_triplet_matrix objects to \code{predict.svm()} \item Bug fix in \code{tune.svm()}: when a data frame was provided as validation sample, the response variable was not correctly extracted } } \section{Changes in version 1.5-24}{ \itemize{ \item Cosmetics: use \code{sQuote()} instead of hard-coded quotes in warnings and error messages in several places \item Bug fix in labeling of decision values \item add \code{decision.values} of fitted values to a svm object } } \section{Changes in version 1.5-23}{ \itemize{ \item Bug fix in \code{svm()}: Error messages returned by the C function have not been correctly handled, causing segfaults. } } \section{Changes in version 1.5-22}{ \itemize{ \item minor fix } } \section{Changes in version 1.5-21}{ \itemize{ \item Allow sparse_triplet_matrix objects for \code{svm()} } } \section{Changes in version 1.5-20}{ \itemize{ \item More flexible interface to \code{naiveBayes()} \item Fix bugs in docs for \code{kurtosis()} } } \section{Changes in version 1.5-19}{ \itemize{ \item Fix bugs in \code{read.matrix.csr()} and \code{write.matrix.csr()} \item Allow Matrix objects for \code{svm()} \item Version bump of libsvm to 2.88 } } \section{Changes in version 1.5-18}{ \itemize{ \item Improve \file{DESCRIPTION} install metadata } } \section{Changes in version 1.5-17}{ \itemize{ \item \code{tune()} now also returns a dispersion measure of all training samples. \item Bootstrap is done \emph{with} replacement. \item \code{tune.svm()} now also accepts the \code{epsilon} parameter. } } \section{Changes in version 1.5-16}{ \itemize{ \item \code{write.svm()} now also stores the scaling information for the dependent variable. \item data sets Glass, HouseVotes84, and Ozone removed (are in package \cpkg{mlbench}) \item merged help pages for \code{naiveBayes()} and \code{predict.naiveBayes()} } } \section{Changes in version 1.5-15}{ \itemize{ \item Bug in \file{NAMESPACE} file fixed (conditional import from \pkg{utils} failed in R 2.3.1) } } \section{Changes in version 1.5-14}{ \itemize{ \item \code{predict.naiveBayes()} sped up \item Bug fix in \code{plot.svm()} (error in case of training categories without predictions) \item \pkg{methods} now added to \samp{Suggests}, and \pkg{grDevices} to \samp{Imports} } } \section{Changes in version 1.5-13}{ \itemize{ \item Bug fix: sparse handling was broken since 1.5-9 } } \section{Changes in version 1.5-12}{ \itemize{ \item update to libsvm 2.81 \item laplace smoothing added to \code{naiveBayes()} } } \section{Changes in version 1.5-11}{ \itemize{ \item \code{tune()}: allow list of vectors as tune parameter range so that class.weights in svm-models can be tuned \item better default color palette for \code{plot.tune()} \item New function \code{probplot()} for probability plots } } \section{Changes in version 1.5-10}{ \itemize{ \item Bug fix: class probability prediction was broken since 1.5-9 } } \section{Changes in version 1.5-9}{ \itemize{ \item \code{tune()} now returns the split indices into training/validation set. Information added about cross validation \item \code{plot.svm()}: wrong labeling order in levels fixed \item \code{predict.svm()} now adds row numbers to predictions, and correctly handles the \code{na.action} argument using \code{napredict()}. } } \section{Changes in version 1.5-8}{ \itemize{ \item Update to libsvm 2.8 (uses a faster optimization algorithm) } } \section{Changes in version 1.5-7}{ \itemize{ \item \code{read.matrix.csr()} did not work correctly with matrix-only objects. \item \code{svm()}: Fixed wrong labeling for predicted decision values and probabilities in case of a Class factor created from a non-ordered character vector } } \section{Changes in version 1.5-6}{ \itemize{ \item \code{cmeans()} is substantially enhanced, with a complete rewrite of the underlying C code. It is now possible to specify case weights and the relative convergence tolerance. For Manhattan distances, centers are correctly computed as suitably weighted medians (rather than means) of the observations. The print method for fclust objects is now more in parallel with related methods, and registered in the name space. } } \section{Changes in version 1.5-5}{ \itemize{ \item \code{read.octave()} is now deprecated in favor of a substantially enhanced version in package \pkg{foreign} for reading in files in Octave text data format. } } \section{Changes in version 1.5-4}{ \itemize{ \item Use lazy loading } } \section{Changes in version 1.5-3}{ \itemize{ \item New arguments in \code{plot.svm()} for customizing plot symbols and colors \item Fix of broken code in \code{plot.svm()} for the \code{fill = FALSE} (non-default) case } } \section{Changes in version 1.5-2}{ \itemize{ \item Fixed memory leak in \code{svm()} } } \section{Changes in version 1.5-1}{ \itemize{ \item Fixed C++ style comments } } \section{Changes in version 1.5-0}{ \itemize{ \item Example for weighting added in \code{svm()} help page \item upgrade to libsvm 2.6: support for probabilities added } } \section{Changes in version 1.4-1}{ \itemize{ \item \code{NaiveBayes()} is more accurate for small probabilities \item call is more sensible in \code{tune()}, \code{tune.foo()}, and \code{best.foo()} objects. \item \code{control} parameter of \code{tune()} changed to \code{tunecontrol} to solve name space conflict with training methods using \code{control} themselves \item new function \code{matchControls()} \item fixed a bug in \code{bclust()} triggered when a cluster had only one center } } \section{Changes in version 1.4-0}{ \itemize{ \item adjusted to restructering of R base packages \item added a \file{NAMESPACE} file \item Function \code{write.svm()} now also creates a file with scaling information } } \section{Changes in version 1.3.16}{ \itemize{ \item Small bug fixes in \code{predict.svm()} and \code{plot.svm()} \item Function \code{write.svm()} added which saves models created with \code{svm()} in the format libsvm can read. } } \section{Changes in version 1.3.15}{ \itemize{ \item Bug fix in \code{plot.svm()}: non-SVs had wrong colors \item data sets Ozone and Glass added } } \section{Changes in version 1.3.14}{ \itemize{ \item Several Docu bug fixes (for functions \code{plot.bclust()}, \code{impute()}, \code{stft()}, \code{svm.formula()}, \code{svm.default()}) \item upgrade to libsvm 2.5. New feature: \code{predict.svm()} optionally returns decision values for multi-class classification \item svm-vignette gave warnings due to rank deficiency in Ozone data \item \code{naiveBayes()} now also supports metric predictors, and the standard interface. } } \section{Changes in version 1.3.13}{ \itemize{ \item Bug fixes in svm: \itemize{ \item Prediction of 1 single observation gave an error \item Only \eqn{k} instead of \eqn{k*(k-1)/2} \eqn{\rho} coefficients have been returned by svm (\eqn{k} number of classes), having caused nonsensical results for \eqn{k > 3}. } \item The \file{svmdoc} file in \file{inst/doc} now is a vignette. } } \section{Changes in version 1.3-12}{ \itemize{ \item The \code{x} argument of \code{cmeans()} and \code{bclust()} is now automatically coerced to a matrix. \item Started \file{tests} directory \item New method: \code{naiveBayes()} classifier for categorical predictors \item optimization of \code{read.matrix.csr()} which used to be rather slow \item Bug fixes for the \code{svm()} interface: when the data included categorical predictors, the scaling procedure did not only affect the metric variables, but also the binary variables in the model matrix. \item Function \code{scaclust()} removed. Bug has to be fixed. } } \section{Changes in version 1.3-10}{ \itemize{ \item Now supports libsvm 2.4 } } \section{Changes in version 1.3-9}{ \itemize{ \item \code{rdiscrete()} is now simply a wrapper for \code{sample()} and provided for backwards compatibility only. \item Minor bug fixes in \code{svm()} and \code{tune()} (mostly interface issues). New plot function for objects of class \code{svm} working for the 2d-classification case. } } \section{Changes in version 1.3-7}{ \itemize{ \item \code{svm()} now supports the matrix.csr format, as handled by the \cpkg{SparseM} package. Predictors and response variable (if numeric) are scaled per default. \item A new \code{plot()} function for \code{svm()} objects visualizes classification models by plotting data and support vectors in the data input space, along with the class borders. \item A new generic \code{tune()} function allows parameter tuning of arbitrary functions using, e.g., boot strapping, or cross validation. Several convenience wrappers (e.g., for \code{svm()}, \code{nnet()}, and \code{rpart()}) do exist. } } \section{Changes in version 1.3-3}{ \itemize{ \item Bug fixes in various bclust routines: \code{stop()} if required packages are not found \item \code{svm()} now interfaces LIBSVM 2.35 which is a bug fix release. A call with invalid parameters now no longer causes R to be terminated, and the C(++) code became completely silent. \item Bugs fixed in \code{fclustIndex()} function and \code{print.fclust()}. } } \section{Changes in version 1.3-1}{ \itemize{ \item Functions \code{rmvnorm()} and \code{dmvnorm()} for multivariate normal distributions have been moved to package \cpkg{mvtnorm}. \item Bug fixes in \code{print.fclust()} and \code{fclustIndex()}. \item fixed \file{floyd.c} (ANSI C pedantic warnings) } } \section{Changes in version 1.2-1}{ \itemize{ \item Bug fixes in \file{cmeans.c}, \file{cshell.c} and \file{scaclust.c} (R header files included and unused variables removed) \item Bug fixes in \file{Rsvm.c} and \file{svm.R} (incomplete list of returned Support Vectors). \item Encapsulate kmeans call in \code{bclust()} in a \code{try()} construct, because kmeans gives an error when a cluster becomes empty (which can happen for almost every data set from time to time). } } \section{Changes in version 1.2-0}{ \itemize{ \item Added functions for bagged clustering, see help(bclust). \item \code{read.pnm()} and \code{write.pgm()} have been removed from \cpkg{e1071}, much improved versions can now be found in the new packagepixmap. \item Lots of documentation updates and bugfixes. \item Support Vector Machine interface now upgraded to libsvm V. 2.31 featuring: \itemize{ \item Multi-Class Classification \item weighting of classes for C-classification (for asymmetric sample sizes) \item \eqn{\nu}-regression \item Formula Interface \item \eqn{k}-fold cross-validation } In addition, an introductory article is provided in directory \file{docs/} (\file{svmdoc.pdf}). \item \code{classAgreement()} now features an option to match factor levels \item updated API design for the fuzzy clustering functions (\code{cmeans()}, \code{cshell()}, \code{scaclust()}). Documentation updates and function name changes (\code{cmeanscl()} to \code{cmeans()}, \code{validity.measures()} to \code{fclustIndex()}) } } e1071/cleanup0000755000175100001440000000011213567004332012377 0ustar hornikusers#!/bin/sh rm config.* -f rm \#*\# -rf rm .\#* -rf rm autom4te.cache -rf e1071/configure0000755000175100001440000015540313567004332012747 0ustar hornikusers#! /bin/sh # Guess values for system-dependent variables and create Makefiles. # Generated by GNU Autoconf 2.59. # # Copyright (C) 2003 Free Software Foundation, Inc. # This configure script is free software; the Free Software Foundation # gives unlimited permission to copy, distribute and modify it. ## --------------------- ## ## M4sh Initialization. ## ## --------------------- ## # Be Bourne compatible if test -n "${ZSH_VERSION+set}" && (emulate sh) >/dev/null 2>&1; then emulate sh NULLCMD=: # Zsh 3.x and 4.x performs word splitting on ${1+"$@"}, which # is contrary to our usage. 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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_cxx_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 $ac_status); }; }; then ac_cv_prog_cxx_g=yes else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 ac_cv_prog_cxx_g=no fi rm -f conftest.err conftest.$ac_objext conftest.$ac_ext fi echo "$as_me:$LINENO: result: $ac_cv_prog_cxx_g" >&5 echo "${ECHO_T}$ac_cv_prog_cxx_g" >&6 if test "$ac_test_CXXFLAGS" = set; then CXXFLAGS=$ac_save_CXXFLAGS elif test $ac_cv_prog_cxx_g = yes; then if test "$GXX" = yes; then CXXFLAGS="-g -O2" else CXXFLAGS="-g" fi else if test "$GXX" = yes; then CXXFLAGS="-O2" else CXXFLAGS= fi fi for ac_declaration in \ '' \ 'extern "C" void std::exit (int) throw (); using std::exit;' \ 'extern "C" void std::exit (int); using std::exit;' \ 'extern "C" void exit (int) throw ();' \ 'extern "C" void exit (int);' \ 'void exit (int);' do cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ $ac_declaration #include int main () { exit (42); ; return 0; } _ACEOF rm -f conftest.$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_cxx_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 $ac_status); }; }; then : else echo "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 continue fi rm -f conftest.err conftest.$ac_objext conftest.$ac_ext cat >conftest.$ac_ext <<_ACEOF /* confdefs.h. */ _ACEOF cat confdefs.h >>conftest.$ac_ext cat >>conftest.$ac_ext <<_ACEOF /* end confdefs.h. */ $ac_declaration int main () { exit (42); ; return 0; } _ACEOF rm -f conftest.$ac_objext if { (eval echo "$as_me:$LINENO: \"$ac_compile\"") >&5 (eval $ac_compile) 2>conftest.er1 ac_status=$? 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