import numpy as np



class Neural_Network(object):
  def __init__(self):
    #parameters
    self.inputSize = 2
    self.outputSize = 1
    self.hiddenSize = 3

    #weights
    self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # (3x2) weight matrix from input to hidden layer
    self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # (3x1) weight matrix from hidden to output layer

  def forward(self, X):
    #forward propagation through our network
    self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
    self.z2 = self.sigmoid(self.z) # activation function
    self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights
    o = self.sigmoid(self.z3) # final activation function
    return o

  def sigmoid(self, s):
    # activation function
    return 1/(1+np.exp(-s))

  def sigmoidPrime(self, s):
    #derivative of sigmoid
    return s * (1 - s)

  def backward(self, X, y, o):
    # backward propgate through the network
    self.o_error = y - o # error in output
    self.o_delta = self.o_error*self.sigmoidPrime(o) # applying derivative of sigmoid to error

    self.z2_error = self.o_delta.dot(self.W2.T) # z2 error: how much our hidden layer weights contributed to output error
    self.z2_delta = self.z2_error*self.sigmoidPrime(self.z2) # applying derivative of sigmoid to z2 error

    self.W1 += X.T.dot(self.z2_delta) # adjusting first set (input --> hidden) weights
    self.W2 += self.z2.T.dot(self.o_delta) # adjusting second set (hidden --> output) weights

  def train (self, X, y):
    o = self.forward(X)
    self.backward(X, y, o)



if __name__ == '__main__':
    from sklearn import datasets

    X = np.matrix([[0,0], [0,1], [1,0], [1,1]])
    y = np.matrix([[0], [1], [1], [0]])

    NN = Neural_Network()
    for i in range(1000): # trains the NN 1,000 times
      print ("Input: \n" + str(X))
      print ("Actual Output: \n" + str(y) )
      print ("Predicted Output: \n" + str(NN.forward(X)) )
      print ("Loss: \n" + str(np.mean(np.square(y - NN.forward(X))))) # mean sum squared loss
      print ("\n")
      NN.train(X, y)