R语言中使用支持向量机
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R语言中使用支持向量机
Including the SVM package
The SVM package is in a package called "e1071." Firt you need to set the path to include the directory where the e1071 package is. For example, if e1071 is in the subdirectory R-packages of your home directory:> export R_LIB=~/R-packagesThen you have to install and include it
> install.packages("e1071")> library("e1071")The usage
You can use> ?svmto see the help information of the interface.
A simple example: the XOR problem
Assign four points to a four by two matrix called x> x <- array(data = c(0,0,1,1,0,1,0,1),dim=c(4,2))Assign labels of four data
> y <- factor(c(1,-1,-1,1))Training the problem using support vector classification
> model <- svm(x,y,type="C-classification")Test the original data again using the output model
> predict(model,x)
Three useful examples
In the following there are three more complicated examples which are from the document of this interface:- Support vector classification (with the famous iris dataset)
- Support vector regression (approximate the log function)
- Density estimation
> example("svm")svm> data(iris)svm> attach(iris)svm> model <- svm(Species ~ ., data = iris)*optimization finished, #iter = 96nu = 0.046487obj = -2.403411, rho = 0.041035nSV = 11, nBSV = 2*optimization finished, #iter = 36nu = 0.038898obj = -1.945146, rho = 0.167765nSV = 10, nBSV = 0*optimization finished, #iter = 63nu = 0.293571obj = -21.377493, rho = 0.143712nSV = 33, nBSV = 26Total nSV = 45svm> x <- subset(iris, select = -Species)svm> y <- Speciessvm> model <- svm(x, y)*optimization finished, #iter = 96nu = 0.046487obj = -2.403411, rho = 0.041035nSV = 11, nBSV = 2*optimization finished, #iter = 36nu = 0.038898obj = -1.945146, rho = 0.167765nSV = 10, nBSV = 0*optimization finished, #iter = 63nu = 0.293571obj = -21.377493, rho = 0.143712nSV = 33, nBSV = 26Total nSV = 45svm> print(model)Call: svm.default(x = x, y = y) Parameters: SVM-Type: C-classification SVM-Kernel: radial cost: 1 degree: 3 gamma: 0.25 coef.0: 0 nu: 0.5 epsilon: 0.5 cost: 1 Number of Support Vectors: 45 ( 7 19 19 )Number of Classes: 3 Levels: setosa versicolor virginica Rho: 0.04103499 0.1677649 0.1437119 svm> summary(model)Call: svm.default(x = x, y = y) Parameters: SVM-Type: C-classification SVM-Kernel: radial cost: 1 degree: 3 gamma: 0.25 coef.0: 0 nu: 0.5 epsilon: 0.5 cost: 1 Number of Support Vectors: 45 ( 7 19 19 )Number of Classes: 3 Levels: setosa versicolor virginica Rho: 0.04103499 0.1677649 0.1437119 Support Vectors: Sepal.Length Sepal.Width Petal.Length Petal.Width14 4.3 3.0 1.1 0.116 5.7 4.4 1.5 0.419 5.7 3.8 1.7 0.324 5.1 3.3 1.7 0.525 4.8 3.4 1.9 0.242 4.5 2.3 1.3 0.345 5.1 3.8 1.9 0.451 7.0 3.2 4.7 1.453 6.9 3.1 4.9 1.555 6.5 2.8 4.6 1.557 6.3 3.3 4.7 1.658 4.9 2.4 3.3 1.061 5.0 2.0 3.5 1.064 6.1 2.9 4.7 1.467 5.6 3.0 4.5 1.569 6.2 2.2 4.5 1.571 5.9 3.2 4.8 1.873 6.3 2.5 4.9 1.577 6.8 2.8 4.8 1.478 6.7 3.0 5.0 1.779 6.0 2.9 4.5 1.584 6.0 2.7 5.1 1.685 5.4 3.0 4.5 1.586 6.0 3.4 4.5 1.687 6.7 3.1 4.7 1.599 5.1 2.5 3.0 1.1101 6.3 3.3 6.0 2.5102 5.8 2.7 5.1 1.9107 4.9 2.5 4.5 1.7111 6.5 3.2 5.1 2.0119 7.7 2.6 6.9 2.3120 6.0 2.2 5.0 1.5122 5.6 2.8 4.9 2.0124 6.3 2.7 4.9 1.8127 6.2 2.8 4.8 1.8128 6.1 3.0 4.9 1.8130 7.2 3.0 5.8 1.6132 7.9 3.8 6.4 2.0134 6.3 2.8 5.1 1.5135 6.1 2.6 5.6 1.4139 6.0 3.0 4.8 1.8142 6.9 3.1 5.1 2.3147 6.3 2.5 5.0 1.9148 6.5 3.0 5.2 2.0150 5.9 3.0 5.1 1.8Coefficiants: [,1] [,2] [1,] 0.000000000 0.01017126 [2,] 0.434080723 0.95574579 [3,] 0.063057430 0.00000000 [4,] 0.005863633 0.00000000 [5,] 0.341475825 0.01149015 [6,] 1.000000000 0.96747404 [7,] 0.479896333 0.00000000 [8,] -0.567881290 0.00000000 [9,] 0.000000000 1.00000000[10,] 0.000000000 1.00000000[11,] 0.000000000 1.00000000[12,] -0.541206839 0.00000000[13,] 0.000000000 0.40490500[14,] 0.000000000 1.00000000[15,] 0.000000000 1.00000000[16,] 0.000000000 1.00000000[17,] 0.000000000 1.00000000[18,] 0.000000000 1.00000000[19,] 0.000000000 1.00000000[20,] -0.141083347 1.00000000[21,] 0.000000000 1.00000000[22,] -0.074202468 1.00000000[23,] 0.000000000 1.00000000[24,] 0.000000000 0.27363722[25,] 0.000000000 1.00000000[26,] -1.000000000 0.00000000[27,] -0.118961508 0.00000000[28,] 0.000000000 -0.31890447[29,] -0.731975036 -1.00000000[30,] 0.000000000 -1.00000000[31,] -0.427405462 0.00000000[32,] -0.101852870 -1.00000000[33,] 0.000000000 -1.00000000[34,] 0.000000000 -1.00000000[35,] 0.000000000 -1.00000000[36,] 0.000000000 -1.00000000[37,] 0.000000000 -0.82788343[38,] -0.405831306 -0.13176137[39,] 0.000000000 -1.00000000[40,] 0.000000000 -0.62555212[41,] 0.000000000 -1.00000000[42,] -0.158855069 -0.77444084[43,] 0.000000000 -1.00000000[44,] 0.000000000 -1.00000000[45,] 0.000000000 -1.00000000svm> pred <- predict(model, x)svm> table(pred, y) ypred setosa versicolor virginica setosa 50 0 0 versicolor 0 48 0 virginica 0 2 50svm> x <- seq(0.1, 5, by = 0.05)svm> y <- log(x) + rnorm(x, sd = 0.2)svm> m <- svm(x, y)*optimization finished, #iter = 12nu = 0.040404obj = -2.511836, rho = -0.359518nSV = 6, nBSV = 2svm> new <- predict(m, x)svm> plot(x, y)svm> points(x, log(x), col = 2)svm> points(x, new, col = 4)svm> X <- data.frame(a = rnorm(1000), b = rnorm(1000))svm> attach(X)svm> m <- svm(X)*optimization finished, #iter = 266obj = 26836.184233, rho = 128.900207nSV = 503, nBSV = 498svm> m <- svm(~a + b)*optimization finished, #iter = 266obj = 26836.184233, rho = 128.900207nSV = 503, nBSV = 498svm> m <- svm(~., data = X)*optimization finished, #iter = 266obj = 26836.184233, rho = 128.900207nSV = 503, nBSV = 498svm> predict(m, t(c(0, 0)))[1] FALSEsvm> predict(m, t(c(4, 4)))[1] FALSEsvm> plot(X)svm> points(X[m$index, ], col = 2)> 0 0
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