import numpy as np
import random


class Network(object):
    def __init__(self, sizes):
        self.sizes = sizes
        self.num_layers = len(sizes)
        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]
        self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]

    def feed_forward(self, a):
        for b, w in zip(self.biases, self.weights):
            a = Network.sigmoid(b + np.dot(w, a))
        return a

    def stochatic_gradient_descent(self, training_data, epochs, mini_batch_size, eta, test_data=None):
        m = len(training_data)
        for j in range(epochs):
            random.shuffle(training_data)
            mini_batches = [training_data[k:k+mini_batch_size] for k in range(0, m, mini_batch_size)]
            for mini_batch in mini_batches:
                self.update_mini_batch(mini_batch, eta)

            if test_data:
                print "Epoch {0}: {1} / {2}".format(j, self.evaluate(test_data), len(test_data))
            else:
                print "Epoch {0} complete".format(j)

    def update_mini_batch(self, mini_batch, eta):
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        for x, y in mini_batch:
            delta_nabla_b, delta_nabla_w = self.backprop(x, y)
            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
        self.biases = [b - (eta / len(mini_batch)) * nb for b, nb in zip(self.biases, nabla_b)]
        self.weights = [w - (eta / len(mini_batch)) * nw for w, nw in zip(self.weights, nabla_w)]

    def backprop(self, x, y):
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        activation = x
        activations = [x]
        zs = []

        for b, w in zip(self.biases, self.weights):
            z = b + np.dot(w, activation)
            zs.append(z)
            activation = Network.sigmoid(z)
            activations.append(activation)

        delta = Network.cost_derivative(activations[-1], y) * self.sigmoid_prime(zs[-1])
        nabla_b[-1] = delta
        nabla_w[-1] = np.dot(delta, activations[-2].T)

        for l in range(2, self.num_layers):
            delta = np.dot(self.weights[-l+1].T, delta) * Network.sigmoid_prime(zs[-l])
            nabla_b[-l] = delta
            nabla_w[-l] = np.dot(delta, activations[-l-1].T)

        return nabla_b, nabla_w

    def evaluate(self, test_data):
        test_results = [(np.argmax(self.feed_forward(x)), y) for x, y in test_data]
        return sum(int(x == y) for x, y in test_results)

    @staticmethod
    def cost_derivative(output_activations, y):
        return output_activations - y

    @staticmethod
    def sigmoid(z):
        return 1.0 / (1.0 + np.exp(-z))

    @staticmethod
    def sigmoid_prime(z):
        sig = Network.sigmoid(z)
        return sig * (1 - sig)
