second commit

Author: kartikeyakirar

Compare.R

@@ -1,11 +1,11 @@
-Compare<-function(r1,r2,inelastic)
+Compare<-function(r1,r2,strech)
 {
   if (is.numeric(r1)||is.numeric(r2)) {
     write('Can\'t compare Rhythms before calling done()');
   }
 
   lengthofarr<-length(r1)
-  if (!inelastic) {
+  if (!strech) {
     dur1 <-sum(r1,na.rm = FALSE)
     dur2 <-sum(r2,na.rm = FALSE)
 

data

@@ -0,0 +1,3 @@
+#section contain url of training and test set
+dataurl1=""
+dataurl2=""

data.R

@@ -1,3 +1,5 @@
+## Getting and cleaning data according to classifire
+
 gettingAndCleaning<-function(X)
 {
   Train <- as.matrix( subset( X, subset = TRUE,select =-c(USER)) );

gettingAndCleaning.R

@@ -1,22 +1,30 @@
 source("Classifier.R")
 source("readFrom.R")
 source("gettingAndCleaning.R")
-
-train<- '/home/kartikeya/Downloads/keystroke-dynamics (1).csv'
-test <-  '/home/kartikeya/Downloads/ksdtest.csv'
+source("data.R")
 
 datatrain<-readFrom(train,"csv");
 datatest<-readFrom(test,"csv");
 
 YTrain <- gettingAndCleaning(datatrain);
 YTest <-  gettingAndCleaning(datatest);
+score<-c(
+euclideanScore(euclideanTrain(YTrain),YTest),
+mahalanobisScore(mahalanobisTrain(YTrain),YTest),
+manhattanScore(manhattanTrain(YTrain),YTest),
+KMeansScore(KMeansTrain(YTrain),YTest),
+OutlierCountScore(OutlierCountTrain(YTrain),YTest),
+SVMScore(SVMTrain(YTrain),YTest),
+KNNScore(KNNTrain(YTrain),YTest),
+ScaledManhattanScore(ScaledManhattanTrain(YTrain),YTest),
+MahalanobisKNNScore(MahalanobisKNNTrain(YTrain),YTest))
+
+mulFactore<-c(50000,8.1,1000,5,2.6,0.1,30,35,10)
+scaledScore<-score*mulFactore
+probNear<-Compare(YTrain,YTest,FALSE)
+perProb<-probNear*100
 
-euclideanScore(euclideanTrain(YTrain),YTest)
-mahalanobisScore(mahalanobisTrain(YTrain),YTest)
-manhattanScore(manhattanTrain(YTrain),YTest)
-KMeansScore(KMeansTrain(YTrain),YTest)
-OutlierCountScore(OutlierCountTrain(YTrain),YTest)
-SVMScore(SVMTrain(YTrain),YTest)
-KNNScore(KNNTrain(YTrain),YTest)
-ScaledManhattanScore(ScaledManhattanTrain(YTrain),YTest)
-MahalanobisKNNScore(MahalanobisKNNTrain(YTrain),YTest)
+if(perProb>80 || (scaledScore <=__ ||scaledScore >= __ )
+   write(TRUE)
+   else
+     write(FALSE)

main.R

@@ -1,8 +1,8 @@
-source(Classifier.R)
-dataurl1<-"/home/kartikeya/Downloads/DSL-StrongPasswordData.csv"
-dataurl2<-"/home/kartikeya/Downloads/DSL-StrongPasswordData1.csv"
-trainset<-read.csv(dataurl1,header = TRUE)
-traintest<-read.csv(dataurl2,header = TRUE)
+source("Classifier.R")
+source("readFrom.R")
+source("data.R")
+trainset<-readFrom(dataurl1,"csv")
+traintest<-readFrom(dataurl2,"csv")
 
 subjectlist<-function(data , subject){
   slist<- sort(levels(data$USER))