text: import sys
  from learning.algorithms.prep_terrain_data import makeTerrainData
  from learning.algorithms.class_vis import prettyPicture, output_image
  
  import matplotlib.pyplot as plt
  import copy
  import numpy as np
  import pylab as pl
  
  features_train, labels_train, features_test, labels_test = makeTerrainData()
  
  ########################## SVM #################################
  ### we handle the import statement and SVC creation for you here
  from sklearn.svm import SVC
  
  clf = SVC(kernel="linear", gamma=1.0)
  
  #### now your job is to fit the classifier
  #### using the training features/labels, and to
  #### make a set of predictions on the test data
  clf.fit(features_train, labels_train)
  
  #### store your predictions in a list named pred
  pred = clf.predict(features_test)
  
  from sklearn.metrics import accuracy_score
  
  acc = accuracy_score(pred, labels_test)
  
  
  def submitAccuracy():
      prettyPicture(clf, features_test, labels_test)
      output_image("test.png", "png", open("test.png", "rb").read())
      return acc
  
  
  print submitAccuracy()
  