new_contents: #!/usr/bin/env pythonw
  
  import numpy as np
  from sklearn import svm
  import matplotlib.pyplot as plt
  
  X_train = np.load('input/X_train.npy')
  y_train = np.load('input/y_train.npy')
  
  order = list(range(X_train.shape[0]))
  np.random.shuffle(order)
  
  X_train = X_train[order]
  y_train = y_train[order]
  
  X_test = np.load('input/X_test.npy')
  y_test = np.load('input/y_test.npy')
  
  order = list(range(X_test.shape[0]))
  np.random.shuffle(order)
  
  X_test = X_test[order]
  y_test = y_test[order]
  
  X_validate = X_test[:1000]
  X_test = X_test[1000:]
  
  y_validate = y_test[:1000]
  y_test = y_test[1000:]
  
  input_layer_size = X_train.shape[1]
  
  X_train /= 255.
  X_test /= 255.
  
  def accuracy(y_test, y_pred):
      return np.sum(y_test==y_pred)/len(y_test)
  
  clf = svm.SVC()
  clf.fit(X_train, y_train)
  
  y_pred = clf.predict(X_validate)
  
  print(accuracy(y_validate, y_pred)) 
  print(clf.support_vectors)
subject: Support Vector Machine