old_contents: from numpy import *
  from sigmoid import sigmoid
  
  def predict(Theta, X):
      # Takes as input a number of instances and the network learned variables
      # Returns a vector of the predicted labels
      
      # Useful values
      m = X.shape[0]
      num_labels = Theta[-1].shape[0]
      num_layers = len(Theta) + 1
  
      # ================================ TODO ================================
      # You need to return the following variables correctly
      p = zeros((1,m))
   
      
      return p
  
  
new_contents: from numpy import *
  from sigmoid import sigmoid
  
  def predict(Theta, X):
      # Takes as input a number of instances and the network learned variables
      # Returns a vector of the predicted labels
  
      # Useful values
      m = X.shape[0]
      num_labels = Theta[-1].shape[0]
      num_layers = len(Theta) + 1
  
      # You need to return the following variables correctly
      p = zeros((1,m))
  
      for t in range(0,m):
          a = X[t]
          print a
  
          for i in range(0,num_layers - 1):
              a = insert(a,0,values=1,axis=0)
              print Theta[i]
              z = a.dot(transpose(Theta[i]))
              a = sigmoid(z)
  
          p[0][t] = argmax(a)
  
      return p
  
subject: Add predict