--- a/accuracy.R	Fri Oct 13 10:09:29 2017 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,48 +0,0 @@
-require(caret, quietly = TRUE)
-
-args <- commandArgs(trailingOnly = TRUE)
-
-input = args[1]
-p = args[2]   
-output1 = args[3] 
-output2 = args[4] 
-
-dataset <- read.csv(input, header=TRUE)
-
-validation_index <- createDataPartition(dataset$Species, p=p, list=FALSE)
-
-validation <- dataset[-validation_index,]
-
-validdataset <- dataset[validation_index,]
-
-percentage <- prop.table(table(validdataset$Species)) * 100
-cbind(freq=table(validdataset$Species), percentage=percentage)
-
-output_summary <- summary(validdataset) 
-write.csv(output_summary,output1)
-
-control <- trainControl(method="cv", number=10)
-metric <- "Accuracy"
-
-# a) linear algorithms
-set.seed(7)
-fit.lda <- train(Species~., data=validdataset, method="lda", metric=metric, trControl=control)
-# b) nonlinear algorithms
-# CART
-set.seed(7)
-fit.cart <- train(Species~., data=validdataset, method="rpart", metric=metric, trControl=control)
-# kNN
-set.seed(7)
-fit.knn <- train(Species~., data=validdataset, method="knn", metric=metric, trControl=control)
-# c) advanced algorithms
-# SVM
-set.seed(7)
-fit.svm <- train(Species~., data=validdataset, method="svmRadial", metric=metric, trControl=control)
-# Random Forest
-set.seed(7)
-fit.rf <- train(Species~., data=validdataset, method="rf", metric=metric, trControl=control)
-
-results <- resamples(list(lda=fit.lda, cart=fit.cart, knn=fit.knn, svm=fit.svm, rf=fit.rf))
-output_results <- summary(results) 
-
-write.csv(as.matrix(output_results),output2)