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#データの読み込み | |
leaf=read.csv("leaf.csv",header = F, | |
colClasses=c("factor",rep("numeric",15)), | |
col.names=c("Class","Specimen Number","Eccentricity","Aspect Ratio","Elongation","Solidity","Stochastic Convexity","Isoperimetric Factor","Maximal Indentation Depth","Lobedness","Average Intensity","Average Contrast","Smoothness","Third moment","Uniformity","Entropy")) | |
leaf=leaf[,-2] | |
y=leaf$Class | |
x=leaf[,-1] | |
train <- seq.int(1, 340, by=2) | |
test <- setdiff(1:340, train) | |
#線形判別分析 | |
library(MASS) | |
leaf.lda=lda(x[train,],y[train]) | |
test.lda=predict(leaf.lda, x[test,]) | |
lda.tab=table(y[test],test.lda$class) | |
lda.tab | |
sum(lda.tab[row(lda.tab)==col(lda.tab)])/sum(lda.tab) #識別率 | |
#線形SVM | |
library(e1071) | |
leaf.svm=svm(Class~.,leaf[train,],method = "C-classification", | |
kernel = "linear", | |
cost = 2.5 ) | |
test.svm=predict(leaf.svm, x[test,]) | |
svm.tab=table(y[test],test.svm) | |
svm.tab | |
sum(svm.tab[row(svm.tab)==col(svm.tab)])/sum(svm.tab) #識別率 | |
#RBFカーネルを用いたSVM | |
library(e1071) | |
leaf.rbf_svm=svm(Class~.,leaf[train,],method = "C-classification", | |
kernel = "radial", | |
cost = 20 ) | |
test.rbf_svm=predict(leaf.rbf_svm, x[test,]) | |
rbf_svm.tab=table(y[test],test.rbf_svm) | |
rbf_svm.tab | |
sum(rbf_svm.tab[row(rbf_svm.tab)==col(rbf_svm.tab)])/sum(rbf_svm.tab) #識別率 | |
#randomforest | |
library(randomForest) | |
tuneRF(x[train,],y[train],doBest=T) | |
leaf.rf=randomForest(x[train,],y[train],mtry=6) | |
test.rf = predict(leaf.rf, x[test,]) | |
rf.tab=table(y[test],test.rf) | |
sum(rf.tab[row(rf.tab)==col(rf.tab)])/sum(rf.tab) #識別率 | |
#kNN | |
library(class) | |
p2=knn(x[train,],x[test,],y[train],k=2) | |
kNN.tab=table(y[test],p2) | |
kNN.tab | |
sum(kNN.tab[row(kNN.tab)==col(kNN.tab)])/sum(kNN.tab) #識別率 | |
p5=knn(x[train,],x[test,],y[train],k=5) | |
kNN.tab=table(y[test],p5) | |
kNN.tab | |
sum(kNN.tab[row(kNN.tab)==col(kNN.tab)])/sum(kNN.tab) #識別率 | |
#naive bayes | |
library(e1071) | |
leaf.nb=naiveBayes(Class~.,data=leaf[train,]) | |
test.nb=predict(leaf.nb,x[test,]) | |
nb.tab=table(y[test],test.nb) | |
nb.tab | |
sum(nb.tab[row(nb.tab)==col(nb.tab)])/sum(nb.tab) #識別率 | |
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