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R版本：3.6.1
setwd('G:\\R语言\\大三下半年\\数据挖掘：R语言实战\\')
> library("e1071", lib.loc="H:/Program Files/R/R-3.6.1/library")
Warning message:
程辑包‘e1071’是用R版本3.6.2 来建造的
#############模拟线性可分下的SVM
> set.seed(12345)
> x<-matrix(rnorm(n=40*2,mean=0,sd=1),ncol=2,byrow=TRUE)
> y<-c(rep(-1,20),rep(1,20))
> x[y==1,]<-x[y==1,]+1.5
> data_train<-data.frame(Fx1=x[,1],Fx2=x[,2],Fy=as.factor(y)) #生成训练样本集
> x<-matrix(rnorm(n=20,mean=0,sd=1),ncol=2,byrow=TRUE)
> y<-sample(x=c(-1,1),size=10,replace=TRUE)
> x[y==1,]<-x[y==1,]+1.5
> data_test<-data.frame(Fx1=x[,1],Fx2=x[,2],Fy=as.factor(y)) #生成测试样本集
> plot(data_train[,2:1],col=as.integer(as.vector(data_train[,3]))+2,pch=8,cex=0.7,main="训练样本集-1和+1类散点图")
> SvmFit<-svm(Fy~.,data=data_train,type="C-classification",kernel="linear",cost=10,scale=FALSE)
> summary(SvmFit)
Call:
svm(formula = Fy ~ ., data = data_train, type = "C-classification",
kernel = "linear", cost = 10, scale = FALSE)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 10
Number of Support Vectors: 16
( 8 8 )
Number of Classes: 2
Levels:
-1 1
> SvmFit$index
[1] 1 6 7 10 11 16 17 20 22 24 28 31 33 35 36 37
> plot(x=SvmFit,data=data_train,formula=Fx1~Fx2,svSymbol="#",dataSymbol="*",grid=100)
> SvmFit<-svm(Fy~.,data=data_train,type="C-classification",kernel="linear",cost=0.1,scale=FALSE)
> summary(SvmFit)
Call:
svm(formula = Fy ~ ., data = data_train, type = "C-classification",
kernel = "linear", cost = 0.1, scale = FALSE)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 0.1
Number of Support Vectors: 25
( 12 13 )
Number of Classes: 2
Levels:
-1 1
##############10折交叉验证选取损失惩罚参数C
> set.seed(12345)
> tObj<-tune.svm(Fy~.,data=data_train,type="C-classification",kernel="linear",
+ cost=c(0.001,0.01,0.1,1,5,10,100,1000),scale=FALSE)
> summary(tObj)
Parameter tuning of ‘svm’:
- sampling method: 10-fold cross validation
- best parameters:
cost
5
- best performance: 0.175
- Detailed performance results:
cost error dispersion
1 1e-03 0.675 0.3129164
2 1e-02 0.375 0.3584302
3 1e-01 0.225 0.2486072
4 1e+00 0.200 0.2297341
5 5e+00 0.175 0.2371708
6 1e+01 0.175 0.2371708
7 1e+02 0.175 0.2371708
8 1e+03 0.175 0.2371708
> BestSvm<-tObj$best.model
> summary(BestSvm)
Call:
best.svm(x = Fy ~ ., data = data_train, cost = c(0.001, 0.01,
0.1, 1, 5, 10, 100, 1000), type = "C-classification", kernel = "linear",
scale = FALSE)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 5
Number of Support Vectors: 16
( 8 8 )
Number of Classes: 2
Levels:
-1 1
> yPred<-predict(BestSvm,data_test)
> (ConfM<-table(yPred,data_test$Fy))
yPred -1 1
-1 6 0
1 1 3
> (Err<-(sum(ConfM)-sum(diag(ConfM)))/sum(ConfM))
[1] 0.1
训练样本40个观测。不同颜色代表不同类别。
当损失惩罚参数C=10时，一共16个向量。
利用tune.svm函数尝试不同的惩罚参数。
> ##############模拟线性不可分下的SVM
> set.seed(12345)
> x<-matrix(rnorm(n=400,mean=0,sd=1),ncol=2,byrow=TRUE)
> x[1:100,]<-x[1:100,]+2
> x[101:150,]<-x[101:150,]-2
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