painters
skladowe_painters<-painters[, 1:4]
print("skladowe glowne")
skladowe_glowne=prcomp(skladowe_painters)
skladowe_glowne
plot(skladowe_glowne)
summary(skladowe_glowne)
biplot(skladowe_glowne)
print("wartosci srednia")
print("composition: ")
mean(painters[,1])
print("drawing: ")
mean(painters[,2])
print("color: ")
mean(painters[,3])
print("expression ")
mean(painters[,4])
print("odchylenie standardowe composition")
sd(painters[,1])
print("odchylenie standardowe drawing")
sd(painters[,2])
print("odchylenie standardowe color")
sd(painters[,3])
print("odchylenie standardowe expression")
sd(painters[,4])
print("histogram:")
summary(painters)
print("composition: ")
histogram_composition<-subset(painters, painters$Composition<20)
summary(histogram_composition$Composition)
attach(histogram_composition)
summary(Composition)
bin_comp=seq(min(Composition),max(Composition)+2,2)
hist(Composition, main="Histogram composition", ylab="Czestotliwosc Composition", xlab="Composition", col="grey", breaks=54)
print("DLA 'DRAWING'")
hist_draw<-subset(painters, painters$Drawing<20)
summary(hist_draw$Drawing)
attach(hist_draw)
summary(Drawing)
bin_draw=seq(min(Drawing),max(Drawing)+2,2)
hist(Drawing, main="Histogram of 'Drawing'", ylab="Czestotliwosc Drawing'", xlab="Drawing", col="blue", breaks=54)
print("color: ")
hist_col<-subset(painters, painters$Colour<20)
summary(hist_col$Colour)
attach(hist_col)
summary(Colour)
bin_col=seq(min(Colour),max(Colour)+2,2)
hist(Colour, main="Histogram color", ylab="Czestotliwosc color", xlab="color", col="red", breaks=54)
print("expression: ")
hist_exp<-subset(painters, painters$Expression<20)
summary(hist_exp$Expression)
attach(hist_exp)
summary(Expression)
bin_exp=seq(min(Expression),max(Expression)+2,2)
hist(Expression, main="Histogram expression", ylab="Czestotliwosc expression", xlab="expression", col="yellow", breaks=54)
print("macierz korelacji")
pairs(painters[,1:4])
cor(painters[,1:4]) #Pearson
cor(painters[,1:4], method="spearman")
cor(painters[,1:4], method="kendall")
print("klasyfikator LDA")
painters.lda=lda(School~., data=painters)
painters.pred=predict(painters.lda, newdata=painters)
print(table_lda<-table(painters$School, painters.pred$class))
print(procent<-100*sum(diag(table_lda))/sum(table_lda))
print("klasyfikator QDA")
painters.qda1=qda(School~Composition+Drawing, data=painters)
painters.pred_qda1=predict(painters.qda1, newdata=painters)
print(table_qda1<-table(painters$School, painters.pred_qda1$class))
print(procent<-100*sum(diag(table_qda1))/sum(table_qda1))
painters.qda2=qda(School~Colour+Expression, data=painters)
painters.pred_qda2=predict(painters.qda2, newdata=painters)
print(table_qda2<-table(painters$School, painters.pred_qda2$class))
print(procent<-100*sum(diag(table_qda2))/sum(table_qda2))
print("metoda krokowa")
set.seed(4578)
painters.step_forward=stepclass(School~., data=painters, method="lda", direction="forward", improvement=0.0001)
painters.step_backward=stepclass(School~., data=painters, method="lda", direction="backward", improvement=0.0001)
print("metoda bayesa")
painters.bayes<-NaiveBayes(School~., data=painters, userkernel=TRUE)
painters.bayes_predicted<-predict(painters.bayes, painters)
print(table_bayes<-table(painters$School, painters.bayes_predicted$class))
print(procent<-100.0*sum(diag(table_bayes))/sum(table_bayes))
print("metoda najblizszych sasiadow")
data(painters)
str(painters)
table(painters$School)
head(painters)
set.seed(9850)
painters.grp<-runif(nrow(painters))
painters<-painters[order(painters.grp),]
str(painters)
summary(painters[,c(1,2,3,4)])
normalizacja<-function(x){ return( (x-min(x))/(max(x)-min(x)) ) }
painters_norm<-as.data.frame(lapply(painters[,c(1,2,3,4)], normalizacja))
str(painters_norm)
summary(painters_norm)
painters_trening<-painters_norm[1:33,]
painters_test<-painters_norm[34:54,]
painters_trening_cel<-painters[1:33,5]
painters_test_cel<-painters[34:54,5]
require(class)
model1<-knn(train=painters_trening, test=painters_test, cl=painters_trening_cel, k=8)
model1
table_knn=table(painters_test_cel, model1)
table_knn
print(procent<-100*sum(diag(table_knn))/sum(table_knn))