from __future__ import division
import numpy as np

def tanh(x):
    return np.tanh(x)

def tanh_deriv(x):
    return 1.0 - np.tanh(x)**2

def logistic(x):
    return 1/(1 + np.exp(-x))

def logistic_derivative(x):
    return logistic(x)*(1-logistic(x))

def _scale_to_binary(e, minV, maxV):
    result = ((e-minV)/(maxV-minV))*(1-0)+0
    return result

def rescale_from_binary(e, minV, maxV):
    result = e*(maxV-minV) + minV
    return result


class NeuralNetwork:
    def __init__(self, layers, activation='tanh'):
        """
        :param layers: A list containing the number of units in each layer.
        Should be at least two values
        :param activation: The activation function to be used. Can be
        "logistic" or "tanh"
        """
        np.random.seed(0)
        if activation == 'logistic':
            self.activation = logistic
            self.activation_deriv = logistic_derivative
        elif activation == 'tanh':
            self.activation = tanh
            self.activation_deriv = tanh_deriv

        self.weights = []
        for i in range(1, len(layers) - 1):
            self.weights.append((2*np.random.random((layers[i - 1] + 1, layers[i]
                                + 1))-1)*2.0)



        self.weights.append((2*np.random.random((layers[i] + 1, layers[i +
                            1]))-1)*2.0)

        #print self.weights

    def fit(self, X, y, learning_rate=2, epochs=50000):
        #Set initial temperature to be 27
        X = np.atleast_2d(X)
        #temp = np.full([X.shape[0], X.shape[1]+1],27)#Initial temperature is 27
        temp = np.ones([X.shape[0], X.shape[1]+1])
        temp[:, 0:-1] = X  # adding the bias unit to the input layer
        X = temp #Create a new X but with an extra bias item
        y = np.array(y)

        for k in range(epochs):
            i = np.random.randint(X.shape[0])
            a = [X[i]]

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

            for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last layer
                deltas.append(deltas[-1].dot(self.weights[l].T)*self.activation_deriv(a[l]))
            deltas.reverse()
            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 predict(self, x):
        x = np.array(x)
        #temp = np.full([x.shape[0]+1],27)
        temp = np.ones(x.shape[0]+1)
        temp[0:-1] = x
        a = temp
        #print a
        for l in range(0, len(self.weights)):
            #print a
            a = self.activation(np.dot(a, self.weights[l]))
        return a
