#!/usr/bin/env python3
#  coding: utf-8
import numpy as np


# 计算tanh的值，激活函数
def tanh(x):
    return np.tanh(x)


# 求tanh的导数
def tanh_deriv(x):
    return 1.0 - np.tanh(x)*np.tanh(x)


# 逻辑函数
def logistic(x):
    return 1.0/(1.0 + np.exp(-x))


# 逻辑函数求导
def logistic_deriv(x):
    return logistic(x)*(1.0 - logistic(x))


class NeuralNetwork:
    def __init__(self, layers, activation='tanh'):
        """
        :param layers: A list
        """
        if activation == 'logistic':
            self.activation = logistic
            self.activation_deriv = logistic_deriv
        elif activation == 'tanh':
            self.activation = tanh
            self.activation_deriv = tanh_deriv

        self.weights = []
        for i in range(1, len(layers) - 1):
            # 初始化 权值范围 [-0.25,0.25)
            # [0,1) * 2 - 1 => [-1,1) => * 0.25 => [-0.25,0.25)
            self.weights.append((2*np.random.random((layers[i - 1] + 1, layers[i] + 1))-1)*0.25)
            self.weights.append((2*np.random.random((layers[i] + 1, layers[i+1]))-1)*0.25)

    def fit(self, x, y, learning_rate=0.2, epochs=10000):
        x = np.atleast_2d(x)
        temp = np.ones([x.shape[0], x.shape[1] + 1])
        temp[:, 0:-1] = x
        x = temp
        y = np.array(y)
        for k in range(epochs):
            i = np.random.randint(x.shape[0])
            a = [x[i]]

            # 正向计算
            for l in range(len(self.weights)):
                a.append(self.activation(np.dot(a[l], self.weights[l])))
            # 反向传播
            error = y[i] - a[-1]
            deltas = [error*self.activation_deriv(a[-1])]

            # 开始反向计算,从倒数第二层开始计算
            for j in range(len(a)-2, 0, -1):
                deltas.append(deltas[-1].dot(self.weights[j].T)*self.activation_deriv(a[j]))
            deltas.reverse()

            # 更新权值
            for i in range(len(self.weights)):
                layer = np.atleast_2d(a[i])
                delta = np.atleast_2d(deltas[i])
                self.weights[i] += learning_rate * layer.T.dot(delta)

    def predit(self, x):
        x = np.array(x)
        temp = np.ones(x.shape[0] + 1)
        temp[0:-1] = x
        a = temp
        for l in range(0, len(self.weights)):
            a = self.activation(np.dot(a, self.weights[l]))
        return a