# coding: utf-8
from ..module import *


class Network(object):
    def __init__(self, params, input_size=784, hidden_size=100, output_size=10):
        self.params = {}
        self.params['W1'] = params['W1']
        self.params['b1'] = params['b1']
        self.params['W2'] = params['W2']
        self.params['b2'] = params['b2']

    def initParams(self, input_size=784, hidden_size=100, output_size=10, weight_init_std=0.01):
        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
        self.params['b1'] = np.zeros(hidden_size)
        self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
        self.params['b2'] = np.zeros(output_size)

    def predict(self, x):
        W1, W2 = self.params['W1'], self.params['W2']
        b1, b2 = self.params['b1'], self.params['b2']

        a1 = np.dot(x, W1) + b1
        z1 = sigmoid(a1)
        a2 = np.dot(z1, W2) + b2
        y = softmax(a2)
        return y

    def judge(self, x):
        return np.argmax(self.predict(x))

    def loss(self, x, t):
        y = self.predict(x)
        return cross_entropy_error(y, t)

    def accuracy(self, x, t):
        y = predict(x)
        y = np.argmax(y, axis=1)
        t = np.argmax(t, axis=1)
        accuracy = np.sum(y == t) / float(x.shape[0])
        return accuracy

    def numerical_gradient(self, x, t):
        loss_W = lambda W: self.loss(x, t)

        grads = {}
        grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
        grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
        grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
        grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
        return grads
