VClassifier <-
function(Face) {
Dat <- data.matrix(Face)
cat("Major Feature Description", "\n")
cat(">>>>>>>>>>>>>>>> 20% finished >>>>>>>>>>>>>>>>>>", "\n")
M_0 <- prcomp(Dat[,-1])
Score_1 <- M_0$x[,1]
Score_2 <- M_0$x[,2]
dev.new()
plot(Score_1 ~ Score_2, pch=19, col=as.numeric(Dat[,1]+1))
cat("Implement LDA and QDA", "\n")
cat(">>>>>>>>>>>>>>>> 40% finished >>>>>>>>>>>>>>>>>>", "\n")
M_1 <- lda(Target ~ ., data=as.data.frame(Dat), CV=TRUE)
(table(M_1$class, Dat[,1]))
(sum(diag(table(M_1$class, Dat[,1])))/sum(table(M_1$class, Dat[,1])))
# M_2 <- qda(V1 ~ ., data=Dat, CV=TRUE)
# (table(M_2$class, Dat[,1]))
# (sum(diag(table(M_1$class, Dat[,1])))/sum(table(M_1$class, Dat[,1])))
#
# # M_1_train <- lda(Target ~., data=as.data.frame(Train))
# # pred <- predict(M_1_train, as.data.frame(Test[,-1]))
# # (Tab <- table(pred$class, Test[,1]))
# # sum(diag(Tab))/sum(Tab)
# #
# # M_2_train <- qda(Target ~., data=as.data.frame(Train))
# # pred <- predict(M_1_train, as.data.frame(Test[,-1]))
# # (Tab <- table(pred$class, Test[,1]))
# # sum(diag(Tab))/sum(Tab)
#
# cat("Implement GLM LASSO", "\n")
# cat(">>>>>>>>>>>>>>>> 60% finished >>>>>>>>>>>>>>>>>>")
#
# M_3 <- cv.glmnet(as.matrix(Dat[,-1]), as.matrix(Dat[,1]),
# family = "binomial", type="class", nfolds=10)
# fit <- predict(M_3, as.matrix(Dat[,-1]), type="class", s="lambda.min")
# dev.new()
# plot(M_3)
# dev.off()
# (table(fit, Dat[,1]))
# (sum(diag(table(fit, Dat[,1])))/sum(table(fit, Dat[,1])))
#
# cat("Implement naive bayes classifier", "\n")
# cat(">>>>>>>>>>>>>>>> 80% finished >>>>>>>>>>>>>>>>>>")
#
# M_4 <- naiveBayes(as.matrix(Dat[,-1]), as.factor(Dat[,1]))
# (table(predict(M_4, as.matrix(Dat[,-1])), as.factor(Dat[,1])))
#
# cat("Analysis remains unfinished, further knowledge needed", "\n")
}
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