from numpy import exp,array, random, dot


class NeuralNetwork():
     def __init__(self):
         # Seed the random number generator, so it generates the same numbers
         # every time the program runs.
         random.seed(1)

         # We model a single neuron, with 3 input connections and 1 output connection.
         # We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1
         # and mean 0.
         self.synaptic_weight = 2*random.random((3,1)) - 1


     # The Sigmoid function, which describes an S shaped curve.
     # We pass the weighted sum of the inputs through this function to
     # normalise them between 0 and 1.
     def _sigmoid_(self,x):

         return 1 / (1 +  exp(-x))

     # The derivative of the Sigmoid function.
     # This is the gradient of the Sigmoid curve.
     # It indicates how confident we are about the existing weight
     def _sigmoid_derivative(self,x):

         return  x * (1 - x)

     # We train the neural network through a process of trial and error.
     # Adjusting the synaptic weights each time.

     def train(self , training_set_inputs , training_set_outputs , number_of_training_iterations):
         for iteration in xrange(number_of_training_iterations):
              # Pass the training set through our neural network (a single neuron).
              output = self.think(training_set_inputs)
             # Calculate the error (The difference between the desired output
             # and the predicted output).
              error = (training_set_outputs - output)
         # Multiply the error by the input and again by the gradient of the Sigmoid curve.
         # This means less confident weights are adjusted more.
         # This means inputs, which are zero, do not cause changes to the weights.


              adjustment = dot(training_set_inputs.T , error * self._sigmoid_derivative(output))

          # Adjust the weights.

         self.synaptic_weights += adjustment

# The neural network thinks.
     def think(self, inputs):
        # Pass inputs through our neural network (our single neuron).
        return self.__sigmoid(dot(inputs, self.synaptic_weights))


if __name__ == "__main__":

    neural_network =  NeuralNetwork()

    print ('Random starting synaptic weights: ')
    print  (neural_network.synaptic_weights)


    training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
    training_set_outputs = array([[0, 1, 1, 0]]).T


    neural_network.train(training_set_inputs,training_set_outputs,10000)

    print ("New synaptic weights after training: ")
    print (neural_network.synaptic_weights)

    print ("Considering new situation [1, 0, 0] -> ?: ")
    print (neural_network.think(array([1, 0, 0])))











from numpy import exp,array, random, dot


class NeuralNetwork():
     def __init__(self):
         # Seed the random number generator, so it generates the same numbers
         # every time the program runs.
         random.seed(1)

         # We model a single neuron, with 3 input connections and 1 output connection.
         # We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1
         # and mean 0.
         self.synaptic_weight = 2*random.random((3,1)) - 1


     # The Sigmoid function, which describes an S shaped curve.
     # We pass the weighted sum of the inputs through this function to
     # normalise them between 0 and 1.
     def _sigmoid_(self,x):

         return 1 / (1 +  exp(-x))

     # The derivative of the Sigmoid function.
     # This is the gradient of the Sigmoid curve.
     # It indicates how confident we are about the existing weight
     def _sigmoid_derivative(self,x):

         return  x * (1 - x)

     # We train the neural network through a process of trial and error.
     # Adjusting the synaptic weights each time.

     def train(self , training_set_inputs , training_set_outputs , number_of_training_iterations):
         for iteration in xrange(number_of_training_iterations):
              # Pass the training set through our neural network (a single neuron).
              output = self.think(training_set_inputs)
             # Calculate the error (The difference between the desired output
             # and the predicted output).
              error = (training_set_outputs - output)
         # Multiply the error by the input and again by the gradient of the Sigmoid curve.
         # This means less confident weights are adjusted more.
         # This means inputs, which are zero, do not cause changes to the weights.


              adjustment = dot(training_set_inputs.T , error * self._sigmoid_derivative(output))

          # Adjust the weights.

         self.synaptic_weights += adjustment

# The neural network thinks.
     def think(self, inputs):
        # Pass inputs through our neural network (our single neuron).
        return self.__sigmoid(dot(inputs, self.synaptic_weights))


if __name__ == "__main__":

    neural_network =  NeuralNetwork()

    print ('Random starting synaptic weights: ')
    print  (neural_network.synaptic_weights)


    training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
    training_set_outputs = array([[0, 1, 1, 0]]).T


    neural_network.train(training_set_inputs,training_set_outputs,10000)

    print ("New synaptic weights after training: ")
    print (neural_network.synaptic_weights)

    print ("Considering new situation [1, 0, 0] -> ?: ")
    print (neural_network.think(array([1, 0, 0])))