svm.py

import PIL
import Image
from sklearn import svm
import matplotlib.pyplot as plot
from sklearn.decomposition import PCA
from sklearn import cross_validation
import numpy as np
from sknn.mlp import Regressor, Layer
import matplotlib.pyplot as plt

def getAccuracy(test, predictions):
	correct = 0
	for x in range(len(test)):
		if test[x] == predictions[x]:
			correct = correct + 1
	percentage =  (correct/float(len(test))) * 100.0
	return percentage



def splitList(someList, testing, list1, list2):
	counter = 0
	for something in someList:
		if(counter < testing ):
			list2.append(something)
			counter = counter + 1
		else:
			list1.append(something)

def mainworker(limit1,limit2):
	N=10
	l=[]
	w1=[] # +1 class
	w2=[]#-1 class
	temp=[]
	classlist=[]
	f=open("pdata.txt")
	for line in f:
        	x=(line.strip("\n")).split(",")
        	temp=[]
        	for i in xrange(len(x)):
			x[i]=int(x[i])
			temp.append(x[i])
        	clas=temp.pop()
		temp=temp[:limit1]+temp[limit2+1:]
        	l.append(temp)
       		classlist.append(clas)
       		"""if(temp[-1]==-1):
                	w2.append(temp)
       		else:
                	w1.append(temp)"""
	f.close()
	X=np.array(l)
	y=np.array(classlist)

	X=np.array(l)
	y=np.array(classlist)
	karray=[2,3,4,5]
	for k in karray:
		kf = cross_validation.KFold(11054, n_folds=k)
		averager=[]
		for train_index,test_index in kf:
		#print("TRAIN:", train_index, "TEST:", test_index)
	   		X_train, X_test = X[train_index], X[test_index]
	   		y_train, y_test = y[train_index], y[test_index]
		#print X_train, len(X_test), len(y_train), len(y_test)
			train_data=[]
	        	test_data=[]
        		train_label=[]
       			test_label=[]
			X1 = X_train#train_data
			Y1 = y_train#train_label	
			clf = svm.SVC()
			clf.fit(X1,Y1)
			Z = X_test#test_data
			predicted = clf.predict(Z)
			accuracy = getAccuracy(predicted, y_test)#test_label)
			averager.append(accuracy)
		answer=np.mean(averager)
		print "The mean for",k,"fold is:"
		print answer
			
print "Address bar features removed----------"
mainworker(0,11)
print "Abnormal features removed------"
mainworker(12,17)
print "HTML Javascript features removed--------"
mainworker(18,22)
print "Domain based features removed--------"
mainworker(23,29)