old_contents: #!/usr/bin/env python
  
  from sklearn import datasets
  from random import shuffle
  
  # This example loads the IRIS dataset and classifies
  # using our neural network implementation.
  # The results are visualized in a 2D-plot.
  
  def main():
      iris = datasets.load_iris()
      X = iris.data
      Y = iris.target
      # Randomize (shuffle) the indexes
      # Shuffle randomizes idx in place
      idx = range(len(X))
      shuffle(idx)
      # Split the shuffled indexes into half, to training and test
      # The int conversion is needed in python 3, I think (for odd number of indexes)
      train_idx = idx[:int(len(idx)/2)]
      test_idx = idx[int(len(X)/2):]
  
  if __name__=='__main__':
      main()
  
new_contents: #!/usr/bin/env python
  
  from sklearn import datasets
  from random import shuffle
  import numpy as np
  from neuralnet import NeuralNet
  
  # This example loads the IRIS dataset and classifies
  # using our neural network implementation.
  # The results are visualized in a 2D-plot.
  
  def main():
      iris = datasets.load_iris()
      X = iris.data
      Y = iris.target
      # Randomize (shuffle) the indexes
      # Shuffle randomizes idx in place
      idx = range(len(X))
      shuffle(idx)
      # Split the shuffled indexes into half, to training and test
      # The int conversion is needed in python 3, I think (for odd number of indexes)
      train_idx = idx[:int(len(idx)/2)]
      test_idx = idx[int(len(X)/2):]
      # Initialize zero matrix for outputs in binary form
      Y_bin = np.zeros((len(Y),3),dtype=np.int)
      # Convert output from int to binary representation for neural network
      for i in range(len(Y)):
          Y_bin[i][Y[i]] = 1
  
  if __name__=='__main__':
      main()
  
subject: Convert output values from int to binary for neural network