text: import numpy as np
  from sklearn import neighbors,linear_model
  from sklearn.neighbors import KNeighborsClassifier 
  from sklearn import svm
  from sklearn.metrics import accuracy_score
  from sklearn.naive_bayes import GaussianNB 
  
  imageSet = []
  labelSet = []
  
  imageSet = np.load('info.npy')
  labelSet = np.load('lb.npy')
  
  sample_i = len(imageSet)
  sample_l = len(labelSet)
  
  imageSet_train = imageSet[:int(0.8*sample_i)] 
  imageSet_test = imageSet[int(0.8*sample_i):]
  
  labelSet_train = labelSet[:int(0.8*sample_l)]
  labelSet_test = labelSet[int(0.8*sample_l):]
  
  kNN = neighbors.KNeighborsClassifier()
  Logistic = linear_model.LogisticRegression()
  
  print('KNN : %f' % kNN.fit(imageSet_train, labelSet_train).score(imageSet_test, labelSet_test))
  print('LogisticRegression : %f' % Logistic.fit(imageSet_train, labelSet_train).score(imageSet_train, labelSet_train))
  
  #print ("[+] processing...")
  classify = svm.SVC(kernel='rbf', C=1.0)
  classify.fit(imageSet_train,labelSet_train)
  pred = classify.predict(imageSet_test)
  acurracy = accuracy_score(labelSet_test, pred)
  print "Linear SVM accuracy :",acurracy
  
  classify1 = GaussianNB()
  classify1.fit(imageSet_train,labelSet_train)
  pred1 = classify1.predict(imageSet_test)
  accuracy1 = accuracy_score(labelSet_test, pred1)
  print "Naive bayes : ",accuracy1