mlbench/0000755000175100001440000000000015145276141011706 5ustar hornikusersmlbench/README0000644000175100001440000000067414614461434012576 0ustar hornikusersThis package contains a collection of real-world datasets and functions for creating artificial datasets that work as benchmarks for machine learning methods. Most datasets have been taken from the UCI Repository of Machine Learning Databases. See the corresponding help files for original data sources. 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hornikusersmlbench/R/mlbench-class.R0000644000175100001440000003117214734524431014752 0ustar hornikusers# # Copyright (C) 1997-2010 Friedrich Leisch # $Id: mlbench-class.R 5293 2024-12-30 14:07:21Z hornik $ # mlbench.xor <- function(n, d=2){ x <- matrix(runif(n*d,-1,1),ncol=d,nrow=n) if((d != as.integer(d)) || (d<2)) stop("d must be an integer >=2") z <- rep_len(0, n) for(k in 1:n){ if(x[k,1]>=0){ tmp <- (x[k,2:d] >=0) z[k] <- 1+sum(tmp*2^(0:(d-2))) } else { tmp <- !(x[k,2:d] >=0) z[k] <- 1 + sum(tmp*2^(0:(d-2))) } } retval <- list(x=x, classes=factor(z)) class(retval) <- c("mlbench.xor", "mlbench") retval } mlbench.circle <- function(n, d=2){ x <- matrix(runif(n*d,-1,1),ncol=d,nrow=n) if((d != as.integer(d)) || (d<2)) stop("d must be an integer >=2") z <- rep_len(1, n) r <- (2^(d-1) * gamma(1+d/2) / (pi^(d/2)))^(1/d) z[apply(x, 1, function(x) sum(x^2)) > r^2] <- 2 retval <- list(x=x, classes=factor(z)) class(retval) <- c("mlbench.circle", "mlbench") retval } mlbench.2dnormals <- function(n, cl=2, r=sqrt(cl), sd=1){ e <- sample(0:(cl-1), size=n, replace=TRUE) m <- r * cbind(cos(pi/4 + e*2*pi/cl), sin(pi/4 + e*2*pi/cl)) x <- matrix(rnorm(2*n, sd=sd), ncol=2) + m retval <- list(x=x, classes=factor(e+1)) class(retval) <- c("mlbench.2dnormals", "mlbench") retval } mlbench.1spiral <- function(n, cycles=1, sd=0) { w <- seq(0, by=cycles/n, length.out=n) x <- matrix(0, nrow=n, ncol=2) x[,1] <- (2*w+1)*cos(2*pi*w)/3; x[,2] <- (2*w+1)*sin(2*pi*w)/3; if(sd>0){ e <- rnorm(n, sd=sd) xs <- cos(2*pi*w)-pi*(2*w+1)*sin(2*pi*w) ys <- sin(2*pi*w)+pi*(2*w+1)*cos(2*pi*w) nrm <- sqrt(xs^2+ys^2) x[,1] <- x[,1] + e*ys/nrm x[,2] <- x[,2] - e*xs/nrm } x } mlbench.spirals <- function(n, cycles=1, sd=0) { x <- matrix(0, nrow=n, ncol=2) c2 <- sample(1:n, size=n/2, replace=FALSE) cl <- factor(rep_len(1, n), levels=as.character(1:2)) cl[c2] <- 2 x[-c2,] <- mlbench.1spiral(n=n-length(c2), cycles=cycles, sd=sd) x[c2,] <- - mlbench.1spiral(n=length(c2), cycles=cycles, sd=sd) retval <- list(x=x, classes=cl) class(retval) <- c("mlbench.spirals", "mlbench") retval } mlbench.ringnorm <- function(n, d=20) { x <- matrix(0, nrow=n, ncol=d) c2 <- sample(1:n, size=n/2, replace=FALSE) cl <- factor(rep_len(1, n), levels=as.character(1:2)) cl[c2] <- 2 a <- 1/sqrt(d) x[-c2,] <- matrix(rnorm(n=d*(n-length(c2)), sd=2), ncol=d) x[c2,] <- matrix(rnorm(n=d*length(c2), mean=a), ncol=d) retval <- list(x=x, classes=cl) class(retval) <- c("mlbench.ringnorm", "mlbench") retval } mlbench.twonorm <- function (n, d = 20) { x <- matrix(0, nrow = n, ncol = d) c2 <- sample(1:n, size = n/2, replace = FALSE) cl <- factor(rep_len(1, n), levels = as.character(1:2)) cl[c2] <- 2 a <- 2/sqrt(d) x[-c2, ] <- matrix(rnorm(n = d * (n - length(c2)), mean = a, sd = 1), ncol = d) x[c2, ] <- matrix(rnorm(n = d * length(c2), mean = -a), ncol = d) retval <- list(x = x, classes = cl) class(retval) <- c("mlbench.twonorm", "mlbench") retval } mlbench.threenorm <- function (n, d = 20) { x <- matrix(0, nrow = n, ncol = d) c2 <- sample(1:n, size = n/2, replace = FALSE) cl <- factor(rep_len(1, n), levels = as.character(1:2)) cl[c2] <- 2 c1 <- (1:n)[-c2] a <- 2/sqrt(d) for (i in c1) { distr <- as.logical(round(runif(1,0,1))) if ( distr ) x[i, ] <- rnorm(n = d, mean = a) else x[i, ] <- rnorm(n = d, mean = -a) } m <- rep(c(a, -a), d/2) if ((d %% 2)==1) m <- c(m, a) x[c2, ] <- matrix(rnorm(n = d * length(c2), mean = m), ncol = d, byrow=TRUE) retval <- list(x = x, classes = cl) class(retval) <- c("mlbench.threenorm", "mlbench") retval } mlbench.waveform <- function (n) { Rnuminstances <- n retval <- .C("waveform", Rnuminstances = as.integer(Rnuminstances), x = double(21*n), type = integer(n), PACKAGE = "mlbench") x <- matrix (retval$x, ncol=21, byrow = TRUE) retval <- list (x=x, classes=as.factor(retval$type+1)) class(retval) <- c("mlbench.waveform","mlbench") return(retval) } mlbench.cassini <- function(n,relsize=c(2,2,1)) { cassinib <- function(x, a, c) { y <- numeric(2) y[1] <- -sqrt(-c^2 - x^2 + sqrt(a^4 + 4*c^2*x^2)) y[2] <- sqrt(-c^2 - x^2 + sqrt(a^4 + 4*c^2*x^2)) y } circle <- function(x, r) sqrt(r^2-x^2) big1<-relsize[1] big2<-relsize[2] small<-relsize[3] parts<-big1+big2+small npiece<-n/parts n1<-round(big1*npiece) n2<-round(big2*npiece) n3<-round(small*npiece) if ((n1+n2+n3)!=n) n3<-n3+1 a<-1 C<-0.97 Cell<-sqrt((1+C^2)/3) aell <- Cell*sqrt(2) transl <- 1.1 r <- 0.6 tmima1<-matrix(0,ncol=2,nrow=n1) tmima2<-matrix(0,ncol=2,nrow=n2) tmima3<-matrix(0,ncol=2,nrow=n3) n1found <- 0 while(n1found < n1) { x1 <- runif(1,min=-sqrt(a^2+C^2),max=sqrt(a^2+C^2)) y1 <- runif(1,min=-transl-1,max=-transl+0.6) if ((y1 < cassinib(x1,a,C)[2]-transl) && (y1 > cassinib(x1,aell,Cell)[1]-transl)) { n1found <- n1found +1 tmima1[n1found,]<-c(x1,y1) } } n2found <- 0 while(n2found < n2) { x2 <- runif(1,min=-sqrt(a^2+C^2),max=sqrt(a^2+C^2)) y2 <- runif(1,max= transl+1,min=transl-0.6) if ((y2 > cassinib(x2,a,C)[1]+transl) && (y2 < cassinib(x2,aell,Cell)[2]+transl)) { n2found <- n2found +1 tmima2[n2found,]<-c(x2,y2) } } n3found <- 0 while(n3found < n3) { x3<-runif(1,min=-r,max=r) y3<-runif(1,min=-r,max=r) if ((y3 > -circle(x3,r)) && (y3 < circle(x3,r))) { n3found <- n3found +1 tmima3[n3found,]<-c(x3,y3) } } teliko <- rbind(tmima1,tmima2,tmima3) cl <- factor(c(rep(1,n1),rep(2,n2),rep(3,n3))) retval<-list(x=teliko,classes=cl) class(retval) <- c("mlbench.cassini","mlbench") retval } mlbench.cuboids <- function (n, relsize=c(2,2,2,1)) { big1 <- relsize[1] big2 <- relsize[2] big3 <- relsize[3] small <- relsize[4] parts<-big1+big2++big3+small npiece<-n/parts n1<-round(big1*npiece) n2<-round(big2*npiece) n3<-round(big3*npiece) n4<-round(small*npiece) if ((n1+n2+n3+n4)!=n) n4<-n4+1 x1 <- cbind(runif(n1,min=0,max=1),runif(n1,min=0.75,max=1.0),runif(n1,min=0.75,max=1)) x2 <- cbind(runif(n2,min=0.75,max=1.0),runif(n2,min=0,max=0.25),runif(n2,min=0,max=1)) x3 <- cbind(runif(n3,min=0.0,max=0.25),runif(n3,min=0.0,max=1),runif(n3,min=0,max=0.25)) x4 <- cbind(runif(n4,min=0.4,max=0.6),runif(n4,min=0.4,max=0.6),runif(n4,min=0.4,max=0.6)) x<-rbind(x1,x2,x3,x4) retval <-list(x=x,classes=factor(c(rep(1,n1),rep(2,n2), rep(3,n3),rep(4,n4)))) class(retval) <- c("mlbench.cuboids","mlbench") return(retval) } mlbench.smiley <- function(n=500, sd1=.1, sd2=.05) { n1 <- round(n/6) n2 <- round(n/4) n3 <- n - 2 * n1 - n2 x1 <- cbind(rnorm(n1, -.8, sd1), rnorm(n1, 1, sd1)) x2 <- cbind(rnorm(n1, .8, sd1), rnorm(n1, 1, sd1)) x3 <- cbind(runif(n2, -.2, .2), runif(n2, 0, .75)) x3[,1] <- x3[,1]*(1-x3[,2]) x4 <- runif(n3, -1, 1) x4 <- cbind(x4, x4^2 - 1 + rnorm(n3, 0, sd2)) x <- retval <- list(x = rbind(x1, x2, x3, x4), classes=factor(c(rep(1,n1),rep(2,n1),rep(3,n2),rep(4,n3)))) class(retval) <- c("mlbench.smiley", "mlbench") retval } mlbench.shapes <- function(n=500) { n1 <- round(n/4) n2 <- n-3*n1 x1 <- cbind(rnorm(n1, -1, .2), rnorm(n1, 1.5, .2)) x2 <- cbind(runif(n1, -1.5, -0.5), runif(n1, -2, 0)) x3 <- cbind(runif(n1, -1, 1), runif(n1, 1, 2)) x3[,1] <- x3[,1]*(2-x3[,2])+1 x4 <- runif(n2, 0.5, 2) x4 <- cbind(x4, cos(4*x4)-x4+runif(n2,-.2,.2)) retval <- list(x = rbind(x1, x2, x3, x4), classes=factor(c(rep(1,n1),rep(2,n1),rep(3,n1),rep(4,n2)))) class(retval) <- c("mlbench.shapes", "mlbench") retval } ###********************************************************** ## Original ist bincombinations in e1071 hypercube <- function(d) { retval <- matrix(0, nrow=2^d, ncol=d) for(n in 1:d){ retval[,n] <- rep_len(c(rep(0, (2^d/2^n)), rep(1, (2^d/2^n))), 2^d) } retval } mlbench.hypercube <- function(n=800, d=3, sides=rep(1,d), sd=0.1) { m <- hypercube(d) n1 <- round(n/2^d) sides <- rep_len(sides, d) z <- NULL for(k in 1:nrow(m)) { m[k,] <- m[k,]*sides z1 <- matrix(rnorm(d*n1, sd=sd), ncol=d) z1 <- sweep(z1, 2, m[k,], "+") z <- rbind(z, z1) } retval <- list(x=z, classes=factor(rep(1:nrow(m), rep(n1, nrow(m))))) class(retval) <- c("mlbench.hypercube", "mlbench") retval } ## for backwards compatibility mlbench.corners <- function(...) mlbench.hypercube(...) ###********************************************************** simplex <- function(d, sides, center = TRUE) { m <- matrix(0, d+1, d) cent <- rep(0,d) m[2,1] <- sides cent[1] <- sides/2 b <- sides/2 if(d>=2) { for(i in 2:d) { m[i+1,] <- cent m[i+1,i] <- sqrt(sides^2-b^2) cent[i] <- 1/(i+1)* m[i+1,i] b <- (1- 1/(i+1)) * m[i+1,i] } } if(center) m <- t(t(m) - cent) m } mlbench.simplex <- function (n = 800, d = 3, sides = 1, sd = 0.1, center=TRUE) { m <- simplex(d=d , sides=sides, center=center) n1 <- round(n/2^d) z <- NULL for (k in 1:nrow(m)) { z1 <- matrix(rnorm(d * n1, sd = sd), ncol = d) z1 <- sweep(z1, 2, m[k, ], "+") z <- rbind(z, z1) } retval <- list(x = z, classes = factor(rep(1:nrow(m), rep(n1, nrow(m))))) class(retval) <- c("mlbench.simplex", "mlbench") retval } ###********************************************************** bayesclass <- function(z) UseMethod("bayesclass") bayesclass.noerr <- function(z) z$classes bayesclass.mlbench.xor <- bayesclass.noerr bayesclass.mlbench.circle <- bayesclass.noerr bayesclass.mlbench.cassini <- bayesclass.noerr bayesclass.mlbench.cuboids <- bayesclass.noerr bayesclass.mlbench.2dnormals <- function(z){ ncl <- length(levels(z$classes)) z <- z$x for(k in 1:nrow(z)){ z[k,] <- z[k,] / sqrt(sum(z[k,]^2)) } winkel <- acos(z[,1] * sign(z[,2])) + pi * (z[,2]<0) winkel <- winkel - pi/ncl - pi/4 winkel[winkel < 0] <- winkel[winkel<0] + 2*pi retval <- (winkel)%/%(2 * pi/ncl) factor((retval+1)%%ncl+1) } bayesclass.mlbench.ringnorm <- function (z) { z <- z$x ndata <- dim(z)[1] ndim <- dim(z)[2] a <- 1/sqrt(ndim) center1 <- rep(0,ndim) center2 <- rep(a,ndim) m1 <- mahalanobis(z, center1, (4*diag(ndim)), inverted=FALSE) + ndim*log(4) m2 <- mahalanobis(z, center2, diag(ndim), inverted=FALSE) as.factor ((m1 > m2) +1) } bayesclass.mlbench.twonorm <- function (z) { z <- z$x ndata <- dim(z)[1] bayesclass <- integer(ndata) ndim <- dim(z)[2] a <- 2/sqrt(ndim) center1 <- rep(a,ndim) center2 <- rep(-a,ndim) for (i in 1:ndata) { dist1 <- sum((z[i, ] - center1) ^2) dist2 <- sum((z[i, ] - center2) ^2) bayesclass[i] <- (dist1 > dist2) +1 } as.factor(bayesclass) } ## Code by Julia Schiffner bayesclass.mlbench.threenorm <- function(z) { z <- z$x ndim <- dim(z)[2] a <- 2/sqrt(ndim) center1a <- rep(a, ndim) center1b <- rep(-a, ndim) center2 <- rep(c(a, -a), ndim/2) if ((ndim%%2) == 1) center2 <- c(center2, a) m1 <- 0.5 * exp(-0.5 * mahalanobis(z, center1a, diag(ndim), inverted = FALSE)) + 0.5 * exp(-0.5 * mahalanobis(z, center1b, diag(ndim), inverted = FALSE)) m2 <- exp(-0.5 * mahalanobis(z, center2, diag(ndim), inverted = FALSE)) as.factor((m1 < m2) + 1) } ###********************************************************** as.data.frame.mlbench <- function(x, row.names=NULL, optional=FALSE, ...) { data.frame(x=x$x, classes=x$classes) } plot.mlbench <- function(x, xlab="", ylab="", ...) { if(ncol(x$x)>2){ pairs(x$x, col=as.integer(x$classes), ...) } else{ plot(x$x, col=as.integer(x$classes), xlab=xlab, ylab=ylab, ...) } } mlbench/R/mlbench-regression.R0000644000175100001440000000253611453575462016034 0ustar hornikusers# # Copyright (C) 1997-2010 Friedrich Leisch # $Id: mlbench-regression.R 4612 2010-10-08 09:51:20Z leisch $ # mlbench.friedman1 <- function(n, sd=1){ x <- matrix(runif(10*n),ncol=10) y <- 10 * sin(pi * x[,1] * x[,2]) y <- y + 20 * ( x[,3] - 0.5)^2 + 10 * x[,4] + 5 * x[,5] if(sd>0){ y <- y + rnorm(n, sd=sd) } list(x=x, y=y) } mlbench.friedman2 <- function(n, sd=125){ x <- cbind(runif(n,min=0,max=100), runif(n,min=40*pi,max=560*pi), runif(n,min=0,max=1), runif(n,min=1,max=11)) y <- sqrt(x[,1]^2 + (x[,2]*x[,3] - 1/(x[,2]*x[,4]))^2) if(sd>0){ y <- y + rnorm(n, sd=sd) } list(x=x, y=y) } mlbench.friedman3 <- function(n, sd=0.1){ x <- cbind(runif(n,min=0,max=100), runif(n,min=40*pi,max=560*pi), runif(n,min=0,max=1), runif(n,min=1,max=11)) y <- atan( (x[,2]*x[,3] - 1/(x[,2]*x[,4])) / x[,1] ) if(sd>0){ y <- y + rnorm(n, sd=sd) } list(x=x, y=y) } mlbench.peak <- function(n, d=20) { metro <- numeric(n) y <- numeric(n) x <- matrix(0, nrow=n, ncol=d) for (ndata in 1:n) { radius <- runif(1, min=0, max=3) x[ndata,] <- rnorm(d) metro[ndata] <- sqrt(sum(x[ndata,]^2)) x[ndata,] <- radius * (x[ndata,]/metro[ndata]) y[ndata] <- 25 * exp(-0.5* radius^2) } list(x=x, y=y) } mlbench/NEWS0000644000175100001440000000507415145272054012412 0ustar hornikusersChanges in Version 2.1-7 o Generate bibliographic citations and references from bibentries. Have full bibliographic information for all references. Changes in Version 2.1-6 o Improve URLs. Changes in Version 2.1-5 o Clarify source information: the old UCI URLs no longer work. Changes in Version 2.1-4 o fixed warnings in waveform.c Changes in Version 2.1-2 o Moved Data-Administration.R to inst/notes o fixed NAMESPACE problems o added registration of C functions Changes in Version 2.1-1 o Added a NAMESPACE. Changes in Version 2.1-0 o Removed dependencies on e1071 and scatterplot3d (the latter replaced by cloud() from lattice). o mlbench.corners() has been renamed to mlbench.hypercube(). o New function mlbench.simplex() by Manuel Eugster and Sebastian Kaiser. o Bugfixes in the bayesclass() methods for ringnorm and threenorm submitted by Julia Schiffer. Changes in Version 2.0-0 o Changed license of complete package to GPL-2. o Recompressed all data sets using xz. o Added examples to all help pages. o Added info about UCI repository to mlbench.waveform.Rd. Changes in Version 1.1-6 o Improve CITATION. Changes in Version 1.1-5 o Fixed Rd bug and renamed README to LICENSE. Changes in Version 1.1-4 o Fixed documentation of S3 objects. Changes in Version 1.1-3 o Bugfix: data sets that are loaded using R code did not close the corresponding connections. Changes in Version 1.1-2 o New data set Zoo. Changes in Version 1.1-2 o Converted the 'chas' columns of BostonHousing2 to a factor. o Added a corrected version of PimaIndiansDiabetes as PimaIndiansDiabetes2. o Added correct citation for UCI repository in various places. Changes in Version 1.1-1 o Add '...' argument to as.data.frame() methods for consistency with R 2.4.0 or later. Changes in Version 1.1-0 o New artificial classification problems mlbench.shapes() and mlbench.corners() o Added corrected version of Boston housing data (thanks to John Maindonald). Changes in Version 1.0-1 o Minor fixes in BostonHousing.Rd. Changes in Version 1.0-1 o Fixed a Bug in the waveform function (thanks to Karsten Lbke). o Added a "ZipData: No" to the DESCRIPTION file. o All data files are now in compressed format. Changes in Version 1.0-0 o New function mlbench.smiley(). o Version 1.0-0 is not really different from the previous release (version 0.5-10), but we decided that it is stupid to have a version number smaller than 1.0 for a package that is stable and useful. mlbench/data/0000755000175100001440000000000012213262742012612 5ustar hornikusersmlbench/data/BostonHousing2.rda0000644000175100001440000004043415145272263016200 0ustar hornikusers7zXZi"6!X# @])TW"nRʟ%!},"Z cyZ?V0HȘS!˰[olQGsX3i_eGSwl-_j8"! t] Rt&CLb  ?ύ%YLTIg; Yag~wM+o4e6F߮d\Q]> ĀvR/o%%\.:RNj1u~=xM--! fcquAy Xm,ܧ?ƋSM^Ӓū O̱mhOL,)L\s^PhS/qBX$s| C[B^a4ܘ5x9O8ǨrgX,`y#ITlQ]Y=q%xr?rܲǠEE?~޽}:k*tzW~0 u&Ј}{k$+(h4݄."+]R^XVpqd"rab+d"7HO Q|GkH,(Js0BH%WSL YYmƫmc|`tcH7`ae}Qs -Jg :)B0Y#R\tX#ZnJu1s7'Iޞ9_ຏAGHZ$?<آMZm=1?_0|lXBi( z6siaA7aej)؏f̠}X2Ćh)-gU!CxkoCoLHlTV`{RW|xht| )N :rd&׈KԒ Sx*)dQڟu,oՉ>cC8QWQSm@43TA=WhÌJJmaMFlȫk[j^^cnQ/aج?{?EkՐKy]aEG#BV8=^:L5;t})츛*`n!GA^_gr^ +LQ9&5ҵ]\n@otqM;%OF t#8X{F1>,@4+Y6CuƍaD}ʆ;nxZNv%;^U4.YdeF`K-V y}nj |fԙWdL^׼#ڿ<sO/>]Aٙ6>{[G\Wb RbW:hJ!VĨ{xlK=p S0o]/ߍW5 U֐ ,`kڅD`pK5uD"BE:Bi%/Y*_X{dQX?׫La^AVW!{|v*WmE,WwBBBI6ulRTi术0AdF^1sMP0tN^kFu))ffC3=\3+̔ZIn,o5QLK m'4,m4lh@@P>9v}vWpjAև %tZ)BU”Fb P5[ =G lj+%sC}^ZP霎y)X\&QPܼ?9EwkBvB r8RAbya9lN5 qWo4Mkԏ$W;0:6UQ̸IԆrAWdĢs|sKQ`u;1|Jq^W O@3)ap|0 7v5 Y5X@(cKaʆAoQs%ZD <ۺon-А`(Ȋ(24w'v0-Cc(yЍq>n]4k^uv=UjTbT8* 7mk[+_Z.wo^8Cxfg'Qt֩m`GN>d=$>]m%Ͷi^.xAr@:Pj5_܌&f@2zbt윱D1pLJ\:H _zJˮwr [΁U c=ƀ-5'Gk" 6w;RɲTZro_dF—"Ҫ8۪`o~>$tj 廁(`ɝhP)0+oY>[ l؍K5(BQ4+lcNL>1@b>Fȶ'8~[@t-Z0.4/ g%l퍫SAֿ/+9ѱUx)~RC 127 ,"? kngL\+SZF"S[57nI|oYcϤ;WoG# b̀mս9C}rD_L \Sl3)^Xz KSh%(淅ҸgA / 2Fs/GibL:fsf}&/]s@&rSv2ĉ%Jw3TJ1D3asܕJ]Sx\Tej] "SDY-+0Qn|)ָ kr* ;U{2$!QysQZz7'uN,^wZ9Q۟?%ݸ0 R፽=`=g w4db5VO4YHx 3aDGeiYA5pn4tJ'ݕ0tfb;5;TX{bgxqgSI ٢!Ks(ax@ӼWA?9ka#tf068 qnɰPDKJ9L0Cx5Q`y/;H3|pY*~*^O՚N&"S:#{vKyZn%"r'-OGl^'r]u{PmydD>ƯGSgs["D d3 LJ^0ՇS)%(3v,O cOQ#ԃJ1Q#V44Wg->h! #Ҏx7sn _14<;Vg~E`Ldz7EzWϗ; =R]Ȫ_9lzߊ "|mj@3=.fۦ7uAd.KWL8o?\G2xxVC[K-col6({l𲐤0 [-/k);L :b}9& R]QZd`ĖK'5;Y`e2߰(&م5b&xiZBϹ7EUO.a&?XIFJ;LDvޯ Q#UU¨ezTxmY?)ݑ2D08X3;SE;h C[,NdWh1W>bHx̓tZ$U*?"fSJxupşr7y?U0A 㱋lfP kA6݈PDž٩%(ýK<:S.RU:̂FS3Ov *Rϼ#~Xk1vqy 9g"D6FHkih$2C5}d*w>Ǘ:lo L]j&auZa8-)'j 9)h/ r 侱]0(c .aϢo`6WܵO2/&69NS6x|:?d_vii'H4,c*kƞ~E[#$K5:+"Fe 5lL$ZjKHC\?v˙[ +J&rL0$>sRK kl zD(xB"㦇)ih"Gk"ٯ \f\ⰖTүK*Xs@wE7k_).ezֳnc$ė[\)WBDz^۬@nn/$#@};yY2~ z- ҁϱG)e֮k:4OSÿ}JG&sb"F3֎|JMׂ'?qXI$ |69V]G1ZL>ɠcpZeyjբϴ7u c -96jD/V7Ya]E)ٿͬm{6ZB'.ǣ#<$1!vq~?snIXlw4ΊϦ%9f}Os8` n˯C(K f51?u4-$Q `c9 )FUsLW\8i[_\>ݒR4z6^tYV/HjIQ*O8t ,R~qyF}vR $:@- UZϮE`Syx54%+jggin[O9DŽ+#_o?`4d?d C] _+5vƏjtHYC Q],Z5~sw lr@++nQeT!/XsH*©AuUMrT%d2Q⹔o,ָ%}CٚL*1F?pX(tHs _SFhJdDRj\QOikW o%'%yǣ=gz\<̫>34MLrˡi%' $Oo_hHUX=e }(g}-GjaM,&vt f!Llep0?):95Nu$X39 Hg*oU4c|hgC 7 tI&-Fw+ک!|#jK SS@3dK53TL 1vNGBp!=iQcyi B[855D<~ l 19yin*Tz5?۬`"pW&^VijSF>2nn%{h9&D+D.I#H!;*~@q̑D~Qu~QQ)ԇ0F2xSYOp..ELO8e>JGJhϗ i_!~R9|V2Ho.TXG(_=qO5!t~=ć`1a*/޼J;i1,6LߋvƊOħ)R 5[Ť\[uͶD`\2O؋$P!6iJö#\"cC y|s&v\dzQx6?!Pb0o!;0?0Jر3d)Ł~u=6^AZz6_xcm.vZ@ryGU@/> q,zXp5Q%%kUAݵHuL:])?RaÐS!C *`hd5?H.+>kg.>nJvv26m< e$V(=\bhXrĎ@2ms&F52l4Ƴo<8iI^!N-<ADk l2?>_Հ81>Ͳ-\I~$j@yVc` wh.ŔQn!o;>aG>bifQcJ<AtMI>qwb$bR@{d\O .͵kʻ A4Ģb/<xE UAe7QٗT/kvnɁ77;vWğEL]ϥSE!^70Xjmj+U=⒗] n,wMc=%ܒA]ZyoJGPW^V}Y545RCJ @ym*T'a lkrz' I1!Hsz|&ӚdЖK,t;d#F|r^R'2d3;O5h|!)N5[W P,k4҂8^P<.M`Gc81ք] "jKUKGs>ِ:[.6l;9i14KϏI~4 ]S~YGD##W(Oނ)_50/In5BڣO5NCN^j6Ce;٠| -4zxhN (MP#s )GY*N[7BsïMs{TW1ŧt}j\T!Y|9U¼[."^R<zYLY8R)6S"?  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Requires use of the UNIXSTAT tool named "probdist". modified by Friedrich Leisch on 2000/12/11 to use R's random number generator ===================================================================== */ #include #include #include #include #define NUMBER_OF_ATTRIBUTES 21 #define NUMBER_OF_CLASSES 3 int num_instances; double h[NUMBER_OF_CLASSES][NUMBER_OF_ATTRIBUTES]; /* ===================================================================== Main Function ===================================================================== */ void waveform(int *R_num_instances, double *x, int *type) { void execute(double *x, int *type); void initialize(void); num_instances = *R_num_instances; GetRNGstate(); initialize(); execute(x, type); PutRNGstate(); } /* ===================================================================== Initializes the algorithm. ==================================================================== */ void initialize(void) { int i,j; /*==== Setup for waveform of types 1 through 3 ====*/ for(i=0; i<3; i++) for(j=0; j<21; j++) h[i][j] = 0.0; /*==== Waveform 1 ====*/ for(i=1; i<=6; i++) h[0][i] = (double)i; j=1; for(i=11; i>=7; i--) { h[0][i] = (double)j; j++; } /*==== Waveform 2 ====*/ j = 1; for(i=9; i<=14; i++) { h[1][i] = (double)j; j++; } j=1; for(i=19; i>=15; i--) { h[1][i] = (double)j; j++; } /*==== Waveform 3 ====*/ j = 1; for(i=5; i<=10; i++) { h[2][i] = (double)j; j++; } j=1; for(i=15; i>=11; i--) { h[2][i] = (double)j; j++; } } /* ===================================================================== Executes the algorithm. ===================================================================== */ void execute(double *x, int *type) { int num_instance, num_attribute; int waveform_type, choice[2]; double random_attribute_value, multiplier[2]; for(num_instance=0; num_instance #include #define CDEF(name, n) {#name, (DL_FUNC) &name, n} void waveform(int *R_num_instances, double *x, int *type); static const R_CMethodDef cMethods[] = { CDEF(waveform, 3), {NULL, NULL, 0} }; void R_init_mlbench(DllInfo *dll) { R_useDynamicSymbols(dll, FALSE); R_registerRoutines(dll, cMethods, NULL, NULL, NULL); } mlbench/NAMESPACE0000644000175100001440000000104114612764410013120 0ustar hornikusersuseDynLib(mlbench) importFrom("graphics", "pairs") importFrom("stats", "mahalanobis", "rnorm", "runif") export(bayesclass) exportPattern("^mlbench.*") S3method(as.data.frame, mlbench) S3method(plot, mlbench) S3method(bayesclass, noerr) S3method(bayesclass, mlbench.2dnormals) S3method(bayesclass, mlbench.circle) S3method(bayesclass, mlbench.xor) S3method(bayesclass, mlbench.cassini) S3method(bayesclass, mlbench.cuboids) S3method(bayesclass, mlbench.twonorm) S3method(bayesclass, mlbench.threenorm) S3method(bayesclass, mlbench.ringnorm) mlbench/inst/0000755000175100001440000000000015144410112012646 5ustar hornikusersmlbench/inst/notes/0000755000175100001440000000000014612764410014012 5ustar hornikusersmlbench/inst/notes/Data-Administration.R0000644000175100001440000000566714612764410020007 0ustar hornikusers## This file keeps record of reading the data into R and ## transformations (if any) that have been applied. All ## transformations are indicated in the respective help pages. ###********************************************************** LetterRecognition <- scan(file="LetterRecognition.data") LetterRecognition <- matrix(LetterRecognition,ncol=17,byrow=TRUE) LetterRecognition <- as.data.frame(LetterRecognition) colnames(LetterRecognition) <- c("lettr", "x.box", "y.box", "width", "high", "onpix", "x.bar", "y.bar", "x2bar", "y2bar", "xybar", "x2ybr", "xy2br", "x.ege", "xegvy", "y.ege", "yegvx") LetterRecognition$lettr <- factor(LetterRecognition$lettr, labels=LETTERS) save(LetterRecognition, file="data/LetterRecognition.rda") ###********************************************************** ### PimaIndiansDiabetes2 load("data/PimaIndiansDiabetes.rda") PimaIndiansDiabetes2 = PimaIndiansDiabetes for(n in c("glucose", "pressure","triceps", "insulin", "mass")){ PimaIndiansDiabetes2[[n]][PimaIndiansDiabetes[[n]]==0] <- NA } save(PimaIndiansDiabetes2, file="data/PimaIndiansDiabetes2.rda") ###********************************************************** Satellite <- scan("Satellite.data") Satellite <- matrix(Satellite,ncol=37,byrow=TRUE) Satellite <- data.frame(x=Satellite[,1:36], classes=factor(Satellite[,37])) levels(Satellite$classes) <- c("red soil", "cotton crop", "grey soil", "damp grey soil", "vegetation stubble", "very damp grey soil") save(Satellite, file="data/Satellite.rda") ###********************************************************** ### Zoo ## download zoo.data from UCI repository (2007-02-02) ## edit zoo.data from UCI repository: two rows have name "frog" ## -> frog.1 and frog.2 Zoo <- read.csv("zoo.data", header=FALSE, row.names=1) colnames(Zoo) <- c("hair", "feathers", "eggs", "milk", "airborne", "aquatic", "predator", "toothed", "backbone", "breathes", "venomous", "fins", "legs", "tail", "domestic", "catsize", "type") Zoo[,1:12] <- lapply(Zoo[,1:12], as.logical) Zoo[,14:16] <- lapply(Zoo[,14:16], as.logical) Zoo[,17] <- factor(Zoo[,17], labels=c("mammal","bird","reptile","fish", "amphibian","insect","mollusc.et.al")) save(Zoo, file="Zoo.rda") ###********************************************************** ## change compression type for(f in list.files("data")){ n <- sub(".rda", "", f) load(file.path("data", f)) save(list=n, file=f, compress="xz") } mlbench/inst/CITATION0000644000175100001440000000207314614627346014031 0ustar hornikusersif(!exists("meta") || is.null(meta)) meta <- packageDescription("mlbench") year <- sub("-.*", "", meta$Date) note <- sprintf("R package version %s", meta$Version) bibentry("Manual", title = "mlbench: Machine Learning Benchmark Problems", author = c(as.person("Friedrich Leisch"), as.person("Evgenia Dimitriadou")), year = year, note = note, url = "https://CRAN.R-project.org/package=mlbench", header = "When using mlbench in publications, please cite both the mlbench package and, if applicable, the 1998 UCI Machine Learning Repository itself:" ) bibentry("Manual", title = "UCI Repository of Machine Learning Databases", author = c(as.person("Catherine L. Blake"), as.person("Christopher J. Merz")), year = "1998", organization = "University of California, Irvine, Department of Information and Computer Sciences", address = "Irvine, CA", note = "Formerly available from \\url{http://www.ics.uci.edu/~mlearn/MLRepository.html}", header = "To cite the 1998 UCI Machine Learning Repository, please use:" ) mlbench/inst/REFERENCES.R0000644000175100001440000004043115144410112014414 0ustar hornikusersbibentry(bibtype = "Misc", key = "Blake+Merz:1998", author = c(person(given = c("C.", "L."), family = "Blake"), person(given = c("C.", "J."), family = "Merz")), title = "{UCI} Repository of Machine Learning Databases", year = "1998", note = "University of California, Irvine, Department of Information and Computer Science. 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"Using Weighted Networks to Represent Classification Knowledge in Noisy Domains", editor = person(given = "John", family = "Laird"), booktitle = "Machine Learning Proceedings 1988", publisher = "Morgan Kaufmann", address = "San Francisco (CA)", pages = "121-134", year = "1988", isbn = "978-0-934613-64-4", doi = "10.1016/B978-0-934613-64-4.50018-9", author = c(person(given = "Ming", family = "Tan"), person(given = "Larry", family = "Eshelman"))) bibentry(bibtype = "PhdThesis", key = "Towell:1991", author = person(given = c("Geoffrey", "G."), family = "Towell"), title = "Symbolic Knowledge and Neural Networks: Insertion, Refinement, and Extraction", school = "University of Wisconsin Madison", year = "1991") bibentry(bibtype = "InCollection", key = "Towell+Craven+Shavlik:1991", title = "Constructive Induction in Knowledge-Based Neural Networks", editor = c(person(given = c("Lawrence", "A."), family = "Birnbaum"), person(given = c("Gregg", "C."), family = "Collins")), booktitle = "Machine Learning Proceedings 1991", publisher = "Morgan Kaufmann", address = "San Francisco (CA)", pages = "213--217", year = "1991", isbn = "978-1-55860-200-7", doi = "10.1016/B978-1-55860-200-7.50046-5", author = c(person(given = c("Geoffrey", "G."), family = "Towell"), person(given = c("Mark", "W."), family = "Craven"), person(given = c("Jude", "W."), family = "Shavlik"))) bibentry(bibtype = "InProceedings", key = "Towell+Shavlik:1991", author = c(person(given = c("Geoffrey", "G."), family = "Towell"), person(given = c("Jude", "W."), family = "Shavlik")), title = "Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules", year = "1991", isbn = "1558602224", publisher = "Morgan Kaufmann Publishers Inc.", address = "San Francisco, CA, USA", booktitle = "Proceedings of the 5th International Conference on Neural Information Processing Systems", pages = "977--984", numpages = "8", location = "Denver, Colorado", series = "NIPS'91") 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ILJ9PU#I9 Bjkfh#]RęB2)%XB,Sv!=HHl%mqm(;]9[,F_.$/wpHw|WZ;,gUAd@$}oV#J_,Bl)ggyT:KT8M3:ڰM['4 FeCKD^Tw WU'{G/b1gL3#;cdҀ^dl!2e- *Eגbf;:r杍+ͬXmqwJ+s9;΀E{~!-b(l2΀ϱ\bD4ytɏ4P #m-W?OQO~@@pKhGUܷ}R+8$6t\ӰuO[(wueivn:7m t‚~QSWnyw\ #Y\n|'Z:yz5,Ys_6# q=]PYN Z5I~x ~C"8v:j vW4B)i $ Eف%EG'B>U!&o#XH]'獆]֏[e8ҭy}&;@6cV)(e/OX"ݾtt#G&Q+I,Izi)ba%1m,l5x= X6Аk 99*/*_J;¶#dwO=.@@zR[d:d ֍"P SdJP,VI)Y[᱄Y>{g S$bvD!w3ldb ?X3m46ͺ/K 1 0 0\cr \tab C -> 0 1 0\cr \tab G -> 0 0 1\cr \tab T -> 0 0 0\cr } Hint. Much better performance is generally observed if attributes closest to the junction are used. In the StatLog version, this means using attributes A61 to A120 only. } \source{ \itemize{ \item Source:\cr - all examples taken from Genbank 64.1 (ftp site: genbank.bio.net)\cr - categories "ei" and "ie" include every "split-gene" for primates in Genbank 64.1\cr - non-splice examples taken from sequences known not to include a splicing site\cr \item Donor: G. Towell, M. Noordewier, and J. Shavlik, {towell,shavlik}@cs.wisc.edu, noordewi@cs.rutgers.edu } These data have been taken from: \itemize{ \item ftp.stams.strath.ac.uk/pub/Statlog } and were converted to R format by Evgenia Dimitriadou. } \references{ \bibshow{Noordewier+Towell+Shavlik:1990, Towell:1991, Towell+Craven+Shavlik:1991, Towell+Shavlik:1991} } \keyword{datasets} \examples{ data("DNA", package = "mlbench") summary(DNA) } mlbench/man/mlbench.ringnorm.Rd0000644000175100001440000000163111304021261016173 0ustar hornikusers\name{mlbench.ringnorm} \alias{mlbench.ringnorm} \title{Ringnorm Benchmark Problem} \usage{ mlbench.ringnorm(n, d=20) } \arguments{ \item{n}{number of patterns to create} \item{d}{dimension of the ringnorm problem} } \value{Returns an object of class \code{"mlbench.ringnorm"} with components \item{x}{input values} \item{classes}{factor vector of length \code{n} with target classes} } \description{ The inputs of the ringnorm problem are points from two Gaussian distributions. Class 1 is multivariate normal with mean 0 and covariance 4 times the identity matrix. Class 2 has unit covariance and mean \eqn{(a,a,\ldots,a)}, \eqn{a=d^{-0.5}}. } \references{ Breiman, L. (1996). Bias, variance, and arcing classifiers. Tech. Rep. 460, Statistics Department, University of California, Berkeley, CA, USA. } \examples{ p<-mlbench.ringnorm(1000, d=2) plot(p) } \keyword{datagen} mlbench/man/LetterRecognition.Rd0000644000175100001440000000571715144410334016413 0ustar hornikusers\name{LetterRecognition} \title{Letter Image Recognition Data} \usage{ data("LetterRecognition", package = "mlbench") } \alias{LetterRecognition} \format{A data frame with 20,000 observations on 17 variables, the first is a factor with levels A-Z, the remaining 16 are numeric. \tabular{rll}{ [,1] \tab lettr \tab capital letter\cr [,2] \tab x.box \tab horizontal position of box\cr [,3] \tab y.box \tab vertical position of box\cr [,4] \tab width \tab width of box\cr [,5] \tab high \tab height of box\cr [,6] \tab onpix \tab total number of on pixels\cr [,7] \tab x.bar \tab mean x of on pixels in box\cr [,8] \tab y.bar \tab mean y of on pixels in box\cr [,9] \tab x2bar \tab mean x variance\cr [,10] \tab y2bar \tab mean y variance\cr [,11] \tab xybar \tab mean x y correlation\cr [,12] \tab x2ybr \tab mean of \eqn{x^2 y} \cr [,13] \tab xy2br \tab mean of \eqn{x y^2} \cr [,14] \tab x.ege \tab mean edge count left to right\cr [,15] \tab xegvy \tab correlation of x.ege with y\cr [,16] \tab y.ege \tab mean edge count bottom to top\cr [,17] \tab yegvx \tab correlation of y.ege with x\cr } } \description{ The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. We typically train on the first 16000 items and then use the resulting model to predict the letter category for the remaining 4000. See the article cited below for more details. } \source{ \itemize{ \item Creator: David J. Slate \item Odesta Corporation; 1890 Maple Ave; Suite 115; Evanston, IL 60201 \item Donor: David J. Slate (dave@math.nwu.edu) (708) 491-3867 } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Friedrich Leisch in the late 1990s. The current version of the UC Irvine Machine Learning Repository Letter Recognition data set is available from \doi{10.24432/C5ZP40}. } \references{ \bibshow{Frey+Slate:1991} The research for this article investigated the ability of several variations of Holland-style adaptive classifier systems to learn to correctly guess the letter categories associated with vectors of 16 integer attributes extracted from raster scan images of the letters. The best accuracy obtained was a little over 80\%. It would be interesting to see how well other methods do with the same data. \bibshow{Blake+Merz:1998} } \keyword{datasets} \examples{ data("LetterRecognition", package = "mlbench") summary(LetterRecognition) } mlbench/man/Vowel.Rd0000644000175100001440000000246515144410420014040 0ustar hornikusers\name{Vowel} \alias{Vowel} \title{Vowel Recognition (Deterding data)} \usage{ data("Vowel", package = "mlbench") } \keyword{datasets} \format{ A data frame with 990 observations on 10 independent variables, one nominal and the other numerical, and 1 as the target class.} \description{Speaker independent recognition of the eleven steady state vowels of British English using a specified training set of lpc derived log area ratios. The vowels are indexed by integers 0-10. For each utterance, there are ten floating-point input values, with array indices 0-9. The vowels are the following: hid, hId, hEd, hAd, hYd, had, hOd, hod, hUd, hud, hed. } \source{ \itemize{ \item Creator: Tony Robinson \item Maintainer: Scott E. Fahlman, CMU } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Evgenia Dimitriadou in the late 1990s. The current version of the UC Irvine Machine Learning Repository Connectionist Bench Vowel Recognition Deterding Data data set is available from \doi{10.24432/C58P4S}. } \references{ \bibshow{Deterding:1990, Niranjan+Fallside:1990, Renals+Rohwer:1989, Blake+Merz:1998} } \examples{ data("Vowel", package = "mlbench") summary(Vowel) } mlbench/man/bayesclass.Rd0000644000175100001440000000207211304021261015062 0ustar hornikusers\name{bayesclass} \alias{bayesclass} \alias{bayesclass.noerr} \alias{bayesclass.mlbench.2dnormals} \alias{bayesclass.mlbench.circle} \alias{bayesclass.mlbench.xor} \alias{bayesclass.mlbench.cassini} \alias{bayesclass.mlbench.cuboids} \alias{bayesclass.mlbench.twonorm} \alias{bayesclass.mlbench.threenorm} \alias{bayesclass.mlbench.ringnorm} \title{Bayes classifier} \usage{ bayesclass(z) } \arguments{ \item{z}{An object of class \code{"mlbench"}.} } \description{ Returns the decision of the (optimal) Bayes classifier for a given data set. This is a generic function, i.e., there are different methods for the various mlbench problems. If the classes of the problem do not overlap, then the Bayes decision is identical to the true classification, which is implemented as the dummy function \code{bayesclass.noerr} (which simply returns \code{z$classes} and is used for all problems with disjunct classes). } \examples{ # 6 overlapping classes p <- mlbench.2dnormals(500,6) plot(p) plot(p$x, col=as.numeric(bayesclass(p))) } \keyword{classif} mlbench/man/BostonHousing.Rd0000644000175100001440000000544515144410263015553 0ustar hornikusers\name{BostonHousing} \alias{BostonHousing} \alias{BostonHousing2} \title{Boston Housing Data} \usage{ data("BostonHousing", package = "mlbench") data("BostonHousing2", package = "mlbench") } \description{Housing data for 506 census tracts of Boston from the 1970 census. The data frame \code{BostonHousing} contains the original data by \bibcitet{Harrison+Rubinfeld:1978}, the dataf rame \code{BostonHousing2} the corrected version with additional spatial information (see references below). } \format{The original data are 506 observations on 14 variables, \code{medv} being the target variable: \tabular{ll}{ crim \tab per capita crime rate by town \cr zn \tab proportion of residential land zoned for lots over 25,000 sq.ft \cr indus \tab proportion of non-retail business acres per town \cr chas \tab Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) \cr nox \tab nitric oxides concentration (parts per 10 million) \cr rm \tab average number of rooms per dwelling \cr age \tab proportion of owner-occupied units built prior to 1940 \cr dis \tab weighted distances to five Boston employment centres \cr rad \tab index of accessibility to radial highways \cr tax \tab full-value property-tax rate per USD 10,000 \cr ptratio \tab pupil-teacher ratio by town \cr b \tab \eqn{1000(B - 0.63)^2} where \eqn{B} is the proportion of blacks by town\cr lstat \tab percentage of lower status of the population \cr medv \tab median value of owner-occupied homes in USD 1000's } The corrected data set has the following additional columns: \tabular{ll}{ cmedv \tab corrected median value of owner-occupied homes in USD 1000's \cr town \tab name of town \cr tract \tab census tract \cr lon \tab longitude of census tract \cr lat \tab latitude of census tract \cr } } \source{ The original data were taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and no longer seem to be available from the UC Irvine Machine Learning Repository (now at \url{https://archive.ics.uci.edu/}). The corrected data were taken from Statlib at \url{https://lib.stat.cmu.edu/datasets/}. See Statlib and references there for details on the corrections. Both were converted to R format by Friedrich Leisch. } \references{ \bibinfo{Gilley+Pace:1996}{note}{[Provided corrections and examined censoring.]} \bibinfo{Pace+Gilley:1997}{note}{[Added georeferencing and spatial estimation.]} \bibshow{Blake+Merz:1998, Gilley+Pace:1996, Harrison+Rubinfeld:1978, Pace+Gilley:1997} } \keyword{datasets} \examples{ data("BostonHousing", package = "mlbench") summary(BostonHousing) data("BostonHousing2", package = "mlbench") summary(BostonHousing2) } mlbench/man/mlbench.xor.Rd0000644000175100001440000000135011304021261015146 0ustar hornikusers\name{mlbench.xor} \alias{mlbench.xor} \title{Continuous XOR Benchmark Problem} \usage{ mlbench.xor(n, d=2) } \arguments{ \item{n}{number of patterns to create} \item{d}{dimension of the XOR problem} } \value{Returns an object of class \code{"mlbench.xor"} with components \item{x}{input values} \item{classes}{factor vector of length \code{n} with target classes} } \description{ The inputs of the XOR problem are uniformly distributed on the \code{d}-dimensional cube with corners \eqn{\{\pm 1\}}{\{+-1\}}. Each pair of opposite corners form one class, hence the total number of classes is \eqn{2^(d-1)} } \examples{ # 2d example p<-mlbench.xor(300,2) plot(p) # # 3d example p<-mlbench.xor(300,3) plot(p) } \keyword{datagen} mlbench/man/mlbench.twonorm.Rd0000644000175100001440000000161211453575462016072 0ustar hornikusers\name{mlbench.twonorm} \alias{mlbench.twonorm} \title{Twonorm Benchmark Problem} \usage{ mlbench.twonorm(n, d=20) } \arguments{ \item{n}{number of patterns to create} \item{d}{dimension of the twonorm problem} } \value{Returns an object of class \code{"mlbench.twonorm"} with components \item{x}{input values} \item{classes}{factor vector of length \code{n} with target classes} } \description{ The inputs of the twonorm problem are points from two Gaussian distributions with unit covariance matrix. Class 1 is multivariate normal with mean \eqn{(a,a,\ldots,a)} and class 2 with mean \eqn{(-a,-a,\ldots,-a)}, \eqn{a=2/d^{0.5}}. } \references{ Breiman, L. (1996). Bias, variance, and arcing classifiers. Tech. Rep. 460, Statistics Department, University of California, Berkeley, CA, USA. } \examples{ p<-mlbench.twonorm(1000, d=2) plot(p) } \keyword{datagen} mlbench/man/PimaIndiansDiabetes.Rd0000644000175100001440000000412315144410344016577 0ustar hornikusers\name{PimaIndiansDiabetes} \alias{PimaIndiansDiabetes} \alias{PimaIndiansDiabetes2} \title{Pima Indians Diabetes Database} \usage{ data("PimaIndiansDiabetes", package = "mlbench") data("PimaIndiansDiabetes2", package = "mlbench") } \keyword{datasets} \description{ A data frame with 768 observations on 9 variables.} \format{ \tabular{rl}{ pregnant \tab Number of times pregnant\cr glucose \tab Plasma glucose concentration (glucose tolerance test)\cr pressure \tab Diastolic blood pressure (mm Hg)\cr triceps \tab Triceps skin fold thickness (mm)\cr insulin \tab 2-Hour serum insulin (mu U/ml)\cr mass \tab Body mass index\cr pedigree \tab Diabetes pedigree function\cr age \tab Age (years)\cr diabetes \tab Class variable (test for diabetes)\cr } } \details{The data set \code{PimaIndiansDiabetes2} contains a corrected version of the original data set. While the UCI repository index claims that there are no missing values, closer inspection of the data shows several physical impossibilities, e.g., blood pressure or body mass index of 0. In \code{PimaIndiansDiabetes2}, all zero values of \code{glucose}, \code{pressure}, \code{triceps}, \code{insulin} and \code{mass} have been set to \code{NA}, see also \bibcitet{Wahba+Gu+Wang:1995} and \bibcitet{Ripley:1996}. } \source{ \itemize{ \item Original owners: National Institute of Diabetes and Digestive and Kidney Diseases \item Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu) } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Friedrich Leisch in the late 1990s. The data no longer seems to be available from the UC Irvine Machine Learning Repository (now at \url{https://archive.ics.uci.edu/}). } \references{ \bibshow{Blake+Merz:1998, Ripley:1996, Wahba+Gu+Wang:1995} } \examples{ data("PimaIndiansDiabetes", package = "mlbench") summary(PimaIndiansDiabetes) data("PimaIndiansDiabetes2", package = "mlbench") summary(PimaIndiansDiabetes2) } mlbench/man/mlbench.cuboids.Rd0000644000175100001440000000146411304021261015774 0ustar hornikusers\name{mlbench.cuboids} \alias{mlbench.cuboids} \title{Cuboids: A 3 Dimensional Problem} \usage{ mlbench.cuboids(n, relsize=c(2,2,2,1)) } \arguments{ \item{n}{number of patterns to create} \item{relsize}{relative size of the classes (vector of length 4)} } \value{Returns an object of class \code{"mlbench.cuboids"} with components \item{x}{input values} \item{classes}{vector of length \code{n} with target classes} } \description{ The inputs of the cuboids problem are uniformly distributed on a \code{3}-dimensional space within 3 cuboids and a small cube in the middle of them. } \author{Evgenia Dimitriadou, and Andreas Weingessel} \examples{ p <- mlbench.cuboids(7000) plot(p) \dontrun{ library(Rggobi) g <- ggobi(p$x) g$setColors(p$class) g$setMode("2D Tour") }} \keyword{datagen} mlbench/man/Ozone.Rd0000644000175100001440000000225515144403613014041 0ustar hornikusers\name{Ozone} \alias{Ozone} \title{Los Angeles ozone pollution data, 1976} \usage{ data("Ozone", package = "mlbench") } \keyword{datasets} \description{A data frame with 366 observations on 13 variables, each observation is one day} \format{ \tabular{rl}{ 1 \tab Month: 1 = January, ..., 12 = December\cr 2 \tab Day of month\cr 3 \tab Day of week: 1 = Monday, ..., 7 = Sunday\cr 4 \tab Daily maximum one-hour-average ozone reading\cr 5 \tab 500 millibar pressure height (m) measured at Vandenberg AFB\cr 6 \tab Wind speed (mph) at Los Angeles International Airport (LAX)\cr 7 \tab Humidity (\%) at LAX\cr 8 \tab Temperature (degrees F) measured at Sandburg, CA\cr 9 \tab Temperature (degrees F) measured at El Monte, CA\cr 10 \tab Inversion base height (feet) at LAX\cr 11 \tab Pressure gradient (mm Hg) from LAX to Daggett, CA\cr 12 \tab Inversion base temperature (degrees F) at LAX\cr 13 \tab Visibility (miles) measured at LAX\cr } } \details{ The problem is to predict the daily maximum one-hour-average ozone reading (V4). } \source{ Data used in \bibshow{Breiman+Friedman:1985} } \examples{ data("Ozone", package = "mlbench") summary(Ozone) } mlbench/man/mlbench.hypercube.Rd0000644000175100001440000000173511453575462016361 0ustar hornikusers\name{mlbench.hypercube} \alias{mlbench.corners} \alias{mlbench.hypercube} \alias{hypercube} \title{Corners of Hypercube} \usage{ mlbench.hypercube(n=800, d=3, sides=rep(1,d), sd=0.1) hypercube(d) } \arguments{ \item{n}{number of patterns to create} \item{d}{dimensionality of hypercube, default is 3} \item{sides}{lengths of the sides of the hypercube, default is to create a unit hypercube} \item{sd}{standard deviation} } \value{Returns an object of class \code{"mlbench.hypercube"} with components \item{x}{input values} \item{classes}{factor of length \code{n} with target classes} } \description{ The created data are \code{d}-dimensional spherical Gaussians with standard deviation \code{sd} and means at the corners of a \code{d}-dimensional hypercube. The number of classes is \eqn{2^d}. } \examples{ p <- mlbench.hypercube() plot(p) library("lattice") cloud(x.3~x.1+x.2, groups=classes, data=as.data.frame(p)) } \keyword{datagen} mlbench/man/plot.mlbench.Rd0000644000175100001440000000115611453334300015326 0ustar hornikusers\name{plot.mlbench} \alias{plot.mlbench} \title{Plot mlbench objects} \usage{ \S3method{plot}{mlbench}(x, xlab="", ylab="", ...) } \arguments{ \item{x}{Object of class \code{"mlbench"}.} \item{xlab}{Label for x-axis.} \item{ylab}{Label for y-axis.} \item{\dots}{Further plotting options.} } \description{ Plots the data of an mlbench object using different colors for each class. If the dimension of the input space is larger that 2, a scatter plot matrix is used. } \examples{ # 6 normal classes p <- mlbench.2dnormals(500,6) plot(p) # 4-dimensiona XOR p <- mlbench.xor(500,4) plot(p) } \keyword{hplot} mlbench/man/Vehicle.Rd0000644000175100001440000000546015144410412014322 0ustar hornikusers\name{Vehicle} \alias{Vehicle} \title{Vehicle Silhouettes} \usage{ data("Vehicle", package = "mlbench") } \keyword{datasets} \format{ A data frame with 846 observations on 19 variables, all numerical and one nominal defining the class of the objects. \tabular{cll}{ [,1] \tab Comp \tab Compactness\cr [,2] \tab Circ \tab Circularity\cr [,3] \tab D.Circ \tab Distance Circularity\cr [,4] \tab Rad.Ra \tab Radius ratio\cr [,5] \tab Pr.Axis.Ra \tab pr.axis aspect ratio\cr [,6] \tab Max.L.Ra \tab max.length aspect ratio\cr [,7] \tab Scat.Ra \tab scatter ratio\cr [,8] \tab Elong \tab elongatedness\cr [,9] \tab Pr.Axis.Rect \tab pr.axis rectangularity\cr [,10] \tab Max.L.Rect \tab max.length rectangularity\cr [,11] \tab Sc.Var.Maxis \tab scaled variance along major axis\cr [,12] \tab Sc.Var.maxis \tab scaled variance along minor axis\cr [,13] \tab Ra.Gyr \tab scaled radius of gyration\cr [,14] \tab Skew.Maxis \tab skewness about major axis\cr [,15] \tab Skew.maxis \tab skewness about minor axis\cr [,16] \tab Kurt.maxis \tab kurtosis about minor axis\cr [,17] \tab Kurt.Maxis \tab kurtosis about major axis\cr [,18] \tab Holl.Ra \tab hollows ratio\cr [,19] \tab Class \tab type } } \description{ The purpose is to classify a given silhouette as one of four types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles. The features were extracted from the silhouettes by the HIPS (Hierarchical Image Processing System) extension BINATTS, which extracts a combination of scale independent features utilising both classical moments based measures such as scaled variance, skewness and kurtosis about the major/minor axes and heuristic measures such as hollows, circularity, rectangularity and compactness. Four "Corgie" model vehicles were used for the experiment: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400. This particular combination of vehicles was chosen with the expectation that the bus, van and either one of the cars would be readily distinguishable, but it would be more difficult to distinguish between the cars. } \source{ \itemize{ \item Creator: Drs.\sspace{}Pete Mowforth and Barry Shepherd, Turing Institute, Glasgow, Scotland. } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Evgenia Dimitriadou in the late 1990s. The current version of the UC Irvine Machine Learning Repository Statlog Vehicle Silhouettes data set is available from \doi{10.24432/C5HG6N}. } \references{ \bibshow{Siebert:1987, Blake+Merz:1998} } \examples{ data("Vehicle", package = "mlbench") summary(Vehicle) } mlbench/man/HouseVotes84.Rd0000644000175100001440000000433315144410320015217 0ustar hornikusers\name{HouseVotes84} \alias{HouseVotes84} \title{United States Congressional Voting Records 1984} \usage{ data("HouseVotes84", package = "mlbench") } \description{ This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired for, and announced for (these three simplified to yea), voted against, paired against, and announced against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known (these three simplified to an unknown disposition). } \keyword{datasets} \format{ A data frame with 435 observations on 17 variables: \tabular{rl}{ 1 \tab Class Name: 2 (democrat, republican)\cr 2 \tab handicapped-infants: 2 (y,n)\cr 3 \tab water-project-cost-sharing: 2 (y,n)\cr 4 \tab adoption-of-the-budget-resolution: 2 (y,n)\cr 5 \tab physician-fee-freeze: 2 (y,n)\cr 6 \tab el-salvador-aid: 2 (y,n)\cr 7 \tab religious-groups-in-schools: 2 (y,n)\cr 8 \tab anti-satellite-test-ban: 2 (y,n)\cr 9 \tab aid-to-nicaraguan-contras: 2 (y,n)\cr 10 \tab mx-missile: 2 (y,n)\cr 11 \tab immigration: 2 (y,n)\cr 12 \tab synfuels-corporation-cutback: 2 (y,n)\cr 13 \tab education-spending: 2 (y,n)\cr 14 \tab superfund-right-to-sue: 2 (y,n)\cr 15 \tab crime: 2 (y,n)\cr 16 \tab duty-free-exports: 2 (y,n)\cr 17 \tab export-administration-act-south-africa: 2 (y,n)\cr } } \source{ \itemize{ \item Source: Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional Quarterly Inc., ington, D.C., 1985 \item Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Friedrich Leisch in the late 1990s. The current version of the UC Irvine Machine Learning Repository Congressional Voting Records data set is available from \doi{10.24432/C5C01P}. } \references{ \bibshow{Blake+Merz:1998} } \examples{ data("HouseVotes84", package = "mlbench") summary(HouseVotes84) } mlbench/man/mlbench.waveform.Rd0000644000175100001440000000172615144410436016207 0ustar hornikusers\name{mlbench.waveform} \alias{mlbench.waveform} \title{Waveform Database Generator} \usage{ mlbench.waveform(n) } \arguments{ \item{n}{number of patterns to create} } \value{ Returns an object of class \code{"mlbench.waveform"} with components \item{x}{input values} \item{classes}{factor vector of length \code{n} with target classes} } \description{ The generated data set consists of 21 attributes with continuous values and a variable showing the 3 classes (33\% for each of 3 classes). Each class is generated from a combination of 2 of 3 "base" waves. } \source{ The original C code for the waveform generator was taken from the UCI Repository of Machine Learning Databases \bibcitep{Blake+Merz:1998}. The C code was modified to use R's random number generator by Friedrich Leisch, who also wrote the R interface. } \references{ \bibshow{Breiman:1996b, Blake+Merz:1998} } \examples{ p <- mlbench.waveform(100) plot(p) } \keyword{datagen} mlbench/man/Soybean.Rd0000644000175100001440000000744215144410404014346 0ustar hornikusers\name{Soybean} \title{Soybean Database} \usage{ data("Soybean", package = "mlbench") } \alias{Soybean} \format{A data frame with 683 observations on 36 variables. There are 35 categorical attributes, all numerical and a nominal denoting the class. \tabular{cll}{ [,1] \tab Class \tab the 19 classes\cr [,2] \tab date \tab apr(0),may(1),june(2),july(3),aug(4),sept(5),oct(6).\cr [,3] \tab plant.stand \tab normal(0),lt-normal(1).\cr [,4] \tab precip \tab lt-norm(0),norm(1),gt-norm(2).\cr [,5] \tab temp \tab lt-norm(0),norm(1),gt-norm(2).\cr [,6] \tab hail \tab yes(0),no(1).\cr [,7] \tab crop.hist \tab dif-lst-yr(0),s-l-y(1),s-l-2-y(2), s-l-7-y(3).\cr [,8] \tab area.dam \tab scatter(0),low-area(1),upper-ar(2),whole-field(3).\cr [,9] \tab sever \tab minor(0),pot-severe(1),severe(2).\cr [,10] \tab seed.tmt \tab none(0),fungicide(1),other(2).\cr [,11] \tab germ \tab 90-100\%(0),80-89\%(1),lt-80\%(2).\cr [,12] \tab plant.growth \tab norm(0),abnorm(1).\cr [,13] \tab leaves \tab norm(0),abnorm(1).\cr [,14] \tab leaf.halo \tab absent(0),yellow-halos(1),no-yellow-halos(2).\cr [,15] \tab leaf.marg \tab w-s-marg(0),no-w-s-marg(1),dna(2).\cr [,16] \tab leaf.size \tab lt-1/8(0),gt-1/8(1),dna(2).\cr [,17] \tab leaf.shread \tab absent(0),present(1).\cr [,18] \tab leaf.malf \tab absent(0),present(1).\cr [,19] \tab leaf.mild \tab absent(0),upper-surf(1),lower-surf(2).\cr [,20] \tab stem \tab norm(0),abnorm(1).\cr [,21] \tab lodging \tab yes(0),no(1).\cr [,22] \tab stem.cankers \tab absent(0),below-soil(1),above-s(2),ab-sec-nde(3).\cr [,23] \tab canker.lesion \tab dna(0),brown(1),dk-brown-blk(2),tan(3).\cr [,24] \tab fruiting.bodies \tab absent(0),present(1).\cr [,25] \tab ext.decay \tab absent(0),firm-and-dry(1),watery(2).\cr [,26] \tab mycelium \tab absent(0),present(1).\cr [,27] \tab int.discolor \tab none(0),brown(1),black(2).\cr [,28] \tab sclerotia \tab absent(0),present(1).\cr [,29] \tab fruit.pods \tab norm(0),diseased(1),few-present(2),dna(3).\cr [,30] \tab fruit.spots \tab absent(0),col(1),br-w/blk-speck(2),distort(3),dna(4).\cr [,31] \tab seed \tab norm(0),abnorm(1).\cr [,32] \tab mold.growth \tab absent(0),present(1).\cr [,33] \tab seed.discolor \tab absent(0),present(1).\cr [,34] \tab seed.size \tab norm(0),lt-norm(1).\cr [,35] \tab shriveling \tab absent(0),present(1).\cr [,36] \tab roots \tab norm(0),rotted(1),galls-cysts(2). } } \description{ There are 19 classes, only the first 15 of which have been used in prior work. The folklore seems to be that the last four classes are unjustified by the data since they have so few examples. There are 35 categorical attributes, some nominal and some ordered. The value ``dna'' means does not apply. The values for attributes are encoded numerically, with the first value encoded as ``0,'' the second as ``1,'' and so forth. } \source{ \itemize{ \item Source: \bibcitet{Michalski+Chilausky:1980} \item Donor: Ming Tan & Jeff Schlimmer (Jeff.Schlimmer\%cs.cmu.edu) } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Evgenia Dimitriadou in the late 1990s. The current version of the UC Irvine Machine Learning Repository Soybean (Large) data set is available from \doi{10.24432/C5JG6Z}. } \references{ \bibshow{Michalski+Chilausky:1980} \bibshow{Tan+Eshelman:1988}\cr -- IWN recorded a 97.1\% classification accuracy \cr -- 290 training and 340 test instances \bibshow{Fisher+Schlimmer:1988}\cr -- Notes why this database is highly predictable \bibshow{Blake+Merz:1998} } \keyword{datasets} \examples{ data("Soybean", package = "mlbench") summary(Soybean) } mlbench/man/mlbench.peak.Rd0000644000175100001440000000120011304021261015250 0ustar hornikusers\name{mlbench.peak} \alias{mlbench.peak} \title{Peak Benchmark Problem} \usage{ mlbench.peak(n, d=20) } \arguments{ \item{n}{number of patterns to create} \item{d}{dimension of the problem} } \description{ Let \eqn{r=3u} where u is uniform on [0,1]. Take x to be uniformly distributed on the d-dimensional sphere of radius r. Let \eqn{y=25exp(-.5r^2)}. This data set is not a classification problem but a regression problem where y is the dependent variable. } \value{Returns a list with components \item{x}{input values (independent variables)} \item{y}{output values (dependent variable)} } \keyword{datagen} mlbench/man/Ionosphere.Rd0000644000175100001440000000623715144410326015065 0ustar hornikusers\name{Ionosphere} \title{Johns Hopkins University Ionosphere database} \usage{ data("Ionosphere", package = "mlbench") } \alias{Ionosphere} \format{A data frame with 351 observations on 35 independent variables, some numerical and 2 nominal, and one last defining the class.} \description{ This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts. See the paper for more details. The targets were free electrons in the ionosphere. "good" radar returns are those showing evidence of some type of structure in the ionosphere. "bad" returns are those that do not; their signals pass through the ionosphere. Received signals were processed using an autocorrelation function whose arguments are the time of a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Instances in this databse are described by 2 attributes per pulse number, corresponding to the complex values returned by the function resulting from the complex electromagnetic signal. See cited below for more details.} \source{ \itemize{ \item Source: Space Physics Group; Applied Physics Laboratory; Johns Hopkins University; Johns Hopkins Road; Laurel; MD 20723 \item Donor: Vince Sigillito (vgs@aplcen.apl.jhu.edu) } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Evgenia Dimitriadou in the late 1990s. The current version of the UC Irvine Machine Learning Repository Ionosphere data set is available from \doi{10.24432/C5W01B}. } \references{ \bibshow{Sigillito+Wing+Hutton:1989} They investigated using backprop and the perceptron training algorithm on this database. Using the first 200 instances for training, which were carefully split almost 50\% positive and 50\% negative, they found that a "linear" perceptron attained 90.7\%, a "non-linear" perceptron attained 92\%, and backprop an average of over 96\% accuracy on the remaining 150 test instances, consisting of 123 "good" and only 24 "bad" instances. (There was a counting error or some mistake somewhere; there are a total of 351 rather than 350 instances in this domain.) Accuracy on "good" instances was much higher than for "bad" instances. Backprop was tested with several different numbers of hidden units (in [0,15]) and incremental results were also reported (corresponding to how well the different variants of backprop did after a periodic number of epochs). David Aha (aha@ics.uci.edu) briefly investigated this database. He found that nearest neighbor attains an accuracy of 92.1\%, that Ross Quinlan's C4 algorithm attains 94.0\% (no windowing), and that IB3 (Aha and Kibler, IJCAI-1989) attained 96.7\% (parameter settings: 70\% and 80\% for acceptance and dropping respectively). \bibshow{Blake+Merz:1998} } \keyword{datasets} \examples{ data("Ionosphere", package = "mlbench") summary(Ionosphere) } mlbench/man/mlbench.threenorm.Rd0000644000175100001440000000166615144403343016366 0ustar hornikusers\name{mlbench.threenorm} \alias{mlbench.threenorm} \title{Threenorm Benchmark Problem} \usage{ mlbench.threenorm(n, d=20) } \arguments{ \item{n}{number of patterns to create} \item{d}{dimension of the threenorm problem} } \value{Returns an object of class \code{"mlbench.threenorm"} with components \item{x}{input values} \item{classes}{factor vector of length \code{n} with target classes} } \description{ The inputs of the threenorm problem are points from two Gaussian distributions with unit covariance matrix. Class 1 is drawn with equal probability from a unit multivariate normal with mean \eqn{(a,a,\ldots,a)} and from a unit multivariate normal with mean \eqn{(-a,-a,\ldots,-a)}. Class 2 is drawn from a multivariate normal with mean at \eqn{(a,-a,a, \ldots,-a)}, \eqn{a=2/d^{0.5}}. } \references{ \bibshow{Breiman:1996b} } \examples{ p<-mlbench.threenorm(1000, d=2) plot(p) } \keyword{datagen} mlbench/man/mlbench.friedman1.Rd0000644000175100001440000000154515144407667016242 0ustar hornikusers\name{mlbench.friedman1} \alias{mlbench.friedman1} \title{Benchmark Problem Friedman 1} \usage{ mlbench.friedman1(n, sd=1) } \arguments{ \item{n}{number of patterns to create} \item{sd}{Standard deviation of noise} } \description{ The regression problem Friedman 1 as described in \bibcitet{Friedman:1991} and \bibcitet{Breiman:1996}. Inputs are 10 independent variables uniformly distributed on the interval \eqn{[0,1]}, only 5 out of these 10 are actually used. Outputs are created according to the formula \deqn{y = 10 \sin(\pi x1 x2) + 20 (x3 - 0.5)^2 + 10 x4 + 5 x5 + e}{ y = 10 sin(\pi x1 x2) + 20 (x3 - 0.5)^2 + 10 x4 + 5 x5 + e} where e is N(0,sd). } \value{Returns a list with components \item{x}{input values (independent variables)} \item{y}{output values (dependent variable)} } \references{ \bibshow{Breiman:1996, Friedman:1991} } \keyword{datagen} mlbench/man/mlbench.spirals.Rd0000644000175100001440000000163311304021261016017 0ustar hornikusers\name{mlbench.spirals} \alias{mlbench.spirals} \alias{mlbench.1spiral} \title{Two Spirals Benchmark Problem} \usage{ mlbench.spirals(n, cycles=1, sd=0) mlbench.1spiral(n, cycles=1, sd=0) } \arguments{ \item{n}{number of patterns to create} \item{cycles}{the number of cycles each spiral makes} \item{sd}{standard deviation of data points around the spirals} } \value{Returns an object of class \code{"mlbench.spirals"} with components \item{x}{input values} \item{classes}{factor vector of length \code{n} with target classes} } \description{ The inputs of the spirals problem are points on two entangled spirals. If \code{sd>0}, then Gaussian noise is added to each data point. \code{mlbench.1spiral} creates a single spiral. } \examples{ # 1 cycle each, no noise p<-mlbench.spirals(300) plot(p) # # 1.5 cycles each, with noise p<-mlbench.spirals(300,1.5,0.05) plot(p) } \keyword{datagen} mlbench/man/Shuttle.Rd0000644000175100001440000000234515144410371014376 0ustar hornikusers\name{Shuttle} \title{Shuttle Dataset (Statlog version)} \usage{ data("Shuttle", package = "mlbench") } \alias{Shuttle} \format{A data frame with 58,000 observations on 9 numerical independent variables and 1 target class.} \description{The shuttle dataset contains 9 attributes all of which are numerical with the first one being time. The last column is the class with the following 7 levels: Rad.Flow, Fpv.Close, Fpv.Open, High, Bypass, Bpv.Close, Bpv.Open. Approximately 80\% of the data belongs to class 1. Therefore the default accuracy is about 80\%. The aim here is to obtain an accuracy of 99 - 99.9\%. } \source{ \itemize{ \item Source: Jason Catlett of Basser Department of Computer Science; University of Sydney; N.S.W.; Australia. } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Evgenia Dimitriadou in the late 1990s. The current version of the UC Irvine Machine Learning Repository Statlog Shuttle data set is available from \doi{10.24432/C5WS31}. } \keyword{datasets} \references{ \bibshow{Blake+Merz:1998} } \examples{ data("Shuttle", package = "mlbench") summary(Shuttle) } mlbench/man/mlbench.cassini.Rd0000644000175100001440000000147311304021261015775 0ustar hornikusers\name{mlbench.cassini} \alias{mlbench.cassini} \title{Cassini: A 2 Dimensional Problem} \usage{ mlbench.cassini(n, relsize=c(2,2,1)) } \arguments{ \item{n}{number of patterns to create} \item{relsize}{relative size of the classes (vector of length 3)} } \value{Returns an object of class \code{"mlbench.cassini"} with components \item{x}{input values} \item{classes}{vector of length \code{n} with target classes} } \description{ The inputs of the cassini problem are uniformly distributed on a \code{2}-dimensional space within 3 structures. The 2 external structures (classes) are banana-shaped structures and in between them, the middle structure (class) is a circle. } \author{Evgenia Dimitriadou and Andreas Weingessel} \examples{ p <- mlbench.cassini(5000) plot(p) } \keyword{datagen} mlbench/man/Glass.Rd0000644000175100001440000000321315144410304014006 0ustar hornikusers\name{Glass} \alias{Glass} \title{Glass Identification Database} \usage{ data("Glass", package = "mlbench") } \keyword{datasets} \description{A data frame with 214 observation containing examples of the chemical analysis of 7 different types of glass. The problem is to forecast the type of class on basis of the chemical analysis. The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence (if it is correctly identified!). } \format{ A data frame with 214 observations on 10 variables: \tabular{cll}{ [,1] \tab RI \tab refractive index\cr [,2] \tab Na \tab Sodium\cr [,3] \tab Mg \tab Magnesium\cr [,4] \tab Al \tab Aluminum\cr [,5] \tab Si \tab Silicon\cr [,6] \tab K \tab Potassium\cr [,7] \tab Ca \tab Calcium\cr [,8] \tab Ba \tab Barium\cr [,9] \tab Fe \tab Iron \cr [,10] \tab Type \tab Type of glass (class attribute) \cr } } \source{ \itemize{ \item Creator: B. German, Central Research Establishment, Home Office Forensic Science Service, Aldermaston, Reading, Berkshire RG7 4PN \item Donor: Vina Spiehler, Ph.D., DABFT, Diagnostic Products Corporation } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Friedrich Leisch in the late 1990s. The current version of the UC Irvine Machine Learning Repository Glass Identification data set is available from \doi{10.24432/C5WW2P}. } \references{ \bibshow{Blake+Merz:1998} } \examples{ data("Glass", package = "mlbench") summary(Glass) } mlbench/man/as.data.frame.mlbench.Rd0000644000175100001440000000070611304021261016746 0ustar hornikusers\name{as.data.frame.mlbench} \alias{as.data.frame.mlbench} \title{Convert an mlbench object to a dataframe} \description{ Converts \code{x} (which is basically a list) to a dataframe. } \usage{ \method{as.data.frame}{mlbench}(x, row.names=NULL, optional=FALSE, \dots) } \arguments{ \item{x}{Object of class \code{"mlbench"}.} \item{row.names,optional,\dots}{currently ignored.} } \examples{ p <- mlbench.xor(5) p as.data.frame(p) } \keyword{manip} mlbench/man/mlbench.smiley.Rd0000644000175100001440000000117411304021261015644 0ustar hornikusers\name{mlbench.smiley} \alias{mlbench.smiley} \title{The Smiley} \usage{ mlbench.smiley(n=500, sd1 = 0.1, sd2 = 0.05) } \arguments{ \item{n}{number of patterns to create} \item{sd1}{standard deviation for eyes} \item{sd2}{standard deviation for mouth} } \value{Returns an object of class \code{"mlbench.smiley"} with components \item{x}{input values} \item{classes}{factor vector of length \code{n} with target classes} } \description{ The smiley consists of 2 Gaussian eyes, a trapezoid nose and a parabula mouth (with vertical Gaussian noise). } \examples{ p<-mlbench.smiley() plot(p) } \keyword{datagen} mlbench/man/mlbench.shapes.Rd0000644000175100001440000000070011304021261015617 0ustar hornikusers\name{mlbench.shapes} \alias{mlbench.shapes} \title{Shapes in 2d} \usage{ mlbench.shapes(n=500) } \arguments{ \item{n}{number of patterns to create} } \value{Returns an object of class \code{"mlbench.shapes"} with components \item{x}{input values} \item{classes}{factor of length \code{n} with target classes} } \description{A Gaussian, square, triangle and wave in 2 dimensions.} \examples{ p<-mlbench.shapes() plot(p) } \keyword{datagen} mlbench/man/mlbench.simplex.Rd0000644000175100001440000000233411453575462016050 0ustar hornikusers\name{mlbench.simplex} \alias{mlbench.simplex} \alias{simplex} \title{Corners of d-dimensional Simplex} \usage{ mlbench.simplex(n = 800, d = 3, sides = 1, sd = 0.1, center=TRUE) simplex(d, sides, center=TRUE) } \arguments{ \item{n}{number of patterns to create} \item{d}{dimensionality of simplex, default is 3} \item{sides}{lengths of the sides of the simplex, default is to create a unit simplex} \item{sd}{standard deviation} \item{center}{If \code{TRUE}, the origin is the center of gravity of the simplex. If \code{FALSE}, the origin is a corner of the simplex and all coordinates of the simplex are positive.} } \value{Returns an object of class \code{"mlbench.simplex"} with components \item{x}{input values} \item{classes}{factor of length \code{n} with target classes} } \description{ The created data are \code{d}-dimensional spherical Gaussians with standard deviation \code{sd} and means at the corners of a \code{d}-dimensional simplex. The number of classes is \code{d+1}. } \author{ Manuel Eugster and Sebastian Kaiser } \examples{ p <- mlbench.simplex() plot(p) library("lattice") cloud(x.3~x.1+x.2, groups=classes, data=as.data.frame(p)) } \keyword{datagen} mlbench/man/BreastCancer.Rd0000644000175100001440000000617215144410274015306 0ustar hornikusers\name{BreastCancer} \title{Wisconsin Breast Cancer Database} \usage{ data("BreastCancer", package = "mlbench") } \alias{BreastCancer} \format{A data frame with 699 observations on 11 variables, one being a character variable, 9 being ordered or nominal, and 1 target class. \tabular{cll}{ [,1] \tab Id \tab Sample code number\cr [,2] \tab Cl.thickness \tab Clump Thickness\cr [,3] \tab Cell.size \tab Uniformity of Cell Size\cr [,4] \tab Cell.shape \tab Uniformity of Cell Shape\cr [,5] \tab Marg.adhesion \tab Marginal Adhesion\cr [,6] \tab Epith.c.size \tab Single Epithelial Cell Size\cr [,7] \tab Bare.nuclei \tab Bare Nuclei\cr [,8] \tab Bl.cromatin \tab Bland Chromatin\cr [,9] \tab Normal.nucleoli \tab Normal Nucleoli\cr [,10] \tab Mitoses \tab Mitoses\cr [,11] \tab Class \tab Class } } \description{ The objective is to identify each of a number of benign or malignant classes. Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself. Each variable except for the first was converted into 11 primitive numerical attributes with values ranging from 0 through 10. There are 16 missing attribute values. See cited below for more details.} \source{ \itemize{ \item Creator: Dr. WIlliam H. Wolberg (physician); University of Wisconsin Hospital ;Madison; Wisconsin; USA \item Donor: Olvi Mangasarian (mangasarian@cs.wisc.edu) \item Received: David W. Aha (aha@cs.jhu.edu) } These data have been taken from the UCI Repository Of Machine Learning Databases \bibcitep{Blake+Merz:1998} and were converted to R format by Evgenia Dimitriadou in the late 1990s. The current version of the UC Irvine Machine Learning Repository Breast Cancer Wisconsin (Original) data set is available from \doi{10.24432/C5HP4Z}. } \references{ 1. \bibshow{Wolberg+Mangasarian:1990}\cr - Size of data set: only 369 instances (at that point in time)\cr - Collected classification results: 1 trial only\cr - Two pairs of parallel hyperplanes were found to be consistent with 50\% of the data\cr - Accuracy on remaining 50\% of dataset: 93.5\%\cr - Three pairs of parallel hyperplanes were found to be consistent with 67\% of data\cr - Accuracy on remaining 33\% of dataset: 95.9\% 2. \bibshow{Zhang:1992}\cr - Size of data set: only 369 instances (at that point in time)\cr - Applied 4 instance-based learning algorithms\cr - Collected classification results averaged over 10 trials\cr - Best accuracy result: \cr - 1-nearest neighbor: 93.7\%\cr - trained on 200 instances, tested on the other 169\cr - Also of interest:\cr - Using only typical instances: 92.2\% (storing only 23.1 instances)\cr - trained on 200 instances, tested on the other 169 \bibshow{Blake+Merz:1998} } \keyword{datasets} \examples{ data("BreastCancer", package = "mlbench") summary(BreastCancer) } mlbench/DESCRIPTION0000644000175100001440000000210515145276141013412 0ustar hornikusersPackage: mlbench Version: 2.1-7 Title: Machine Learning Benchmark Problems Date: 2026-02-18 Authors@R: c(person("Friedrich", "Leisch", role = "aut", comment = c(ORCID = "0000-0001-7278-1983", "maintainer up to 2024")), person("Evgenia", "Dimitriadou", role = "aut"), person("Kurt", "Hornik", role = "cre", email = "Kurt.Hornik@R-project.org", comment = c(ORCID = "0000-0003-4198-9911"))) Description: A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository. Depends: R (>= 2.10) License: GPL-2 Suggests: lattice ZipData: No NeedsCompilation: yes Packaged: 2026-02-18 08:13:39 UTC; hornik Author: Friedrich Leisch [aut] (ORCID: , maintainer up to 2024), Evgenia Dimitriadou [aut], Kurt Hornik [cre] (ORCID: ) Maintainer: Kurt Hornik Repository: CRAN Date/Publication: 2026-02-18 08:46:25 UTC