--- a
+++ b/bin/oner_class.r
@@ -0,0 +1,65 @@
+setwd(".")
+options(stringsAsFactors = FALSE)
+# library("clusterSim")
+
+library("OneR");
+library(class)
+library(gmodels)
+source("./confusion_matrix_rates.r")
+
+threshold <- 0.5
+
+fileName <- "../data/LungCancerDataset_AllRecords_NORM_27reduced_features.csv"
+prc_data_norm <- read.csv(file=fileName, head=TRUE,sep=",",stringsAsFactors=FALSE)
+
+cat("fileName: ", fileName, sep="")
+
+prc_data_norm <- prc_data_norm[sample(nrow(prc_data_norm)),] # shuffle the rows
+
+target_index <- dim(prc_data_norm)[2]
+
+training_set_perce = 80
+cat("training_set_perce = ", training_set_perce, "\n", sep="")
+
+# the training set is the first 60% of the whole dataset
+training_set_first_index <- 1 # NEW
+training_set_last_index <- round(dim(prc_data_norm)[1]*training_set_perce/100) # NEW
+
+# the test set is the last 40% of the whole dataset
+test_set_first_index <- training_set_last_index+1 # NEW
+test_set_last_index <- dim(prc_data_norm)[1] # NEW
+
+cat("[Creating the subsets for the values]\n")
+prc_data_train <- prc_data_norm[training_set_first_index:training_set_last_index, 1:(target_index)] # NEW
+prc_data_test <- prc_data_norm[test_set_first_index:test_set_last_index, 1:(target_index)] # NEW
+
+prc_data_test_labels  <- prc_data_norm[test_set_first_index:test_set_last_index, target_index]   # NEW
+
+
+print("dim(prc_data_train)")
+print(dim(prc_data_train))
+
+print("dim(prc_data_test)")
+print(dim(prc_data_test))
+
+
+# #rf_new <- randomForest(Metastasis ~ ., data=prc_data_train, importance=TRUE, proximity=TRUE)
+
+
+# Original application of One Rule with all the dataset
+prc_model_train <- OneR(prc_data_train, verbose = TRUE)
+
+# Generation of the CART model
+# prc_model_train <- OneR(Metastasis ~ keep.side + platelet.count..PLT., method="class", data=prc_data_train);
+
+summary(prc_model_train)
+prediction <- predict(prc_model_train, prc_data_test)
+# eval_model(prediction, prc_data_test)
+
+prediction_binary <- as.numeric(prediction) -1
+prc_data_test_PRED_binary <- data.frame(prediction)
+
+confusion_matrix_rates(prc_data_test_labels, prediction_binary, "@@@ Test set @@@")
+
+
+