old_contents: from sklearn import svm
  x= [[0,0],[1,2]]
  y = [0,1]
  clf = svm.SVC()
  clf.fit(x,y)
  clf.predict([[2,2]])
new_contents: from sklearn import svm
  import numpy as nu
  class SVM:
  	x=[]
  	y=[]
  	clf = None
  	def __init__(self,filename):
  		with open(filename) as f:
  			for line in f:
  				if(line[0]>='0' and line[0]<='9'):
  					#print(line)
  					tmp = line.strip('\n').split(',')
  					tmp = map(lambda x:float(x),tmp)
  					self.x.append(tmp[:-1])
  					self.y.append(tmp[-1])
  		self.clf = svm.SVC()
  	def train(self):
  		self.clf.fit(self.x,self.y)
  	def predict(self,input):
  		return self.clf.predict(input)
  if __name__ == "__main__":
  	svm = SVM("../decisionTree/trainingSet/train_500.txt")
  	svm.train()
  	print(svm.x[0])
  	print(svm.predict(svm.x[0]))
  
subject: add SVM