import numpy as np
import random

def mat_mult(A,B):
    return [[sum([A[i][m]*B[m][j] for m in range(len(A[0]))]) for j in range(len(B[0]))] for i in range(len(A))]

class Neural_Network(object):
    # inspired from https://enlight.nyc/projects/neural-network/
    def __init__(self, W1=None, W2=None):
        #parameters
        self.inputSize = 3
        self.outputSize = 2
        self.hiddenSize = 3
        self.fitness = 0 

        #weights
        if not W1 :
            self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # weights from input to hidden layer
        if not W2 :
            self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # weights from hidden to output layer
        # self.w1 = [[random.random() for i in range(self.hiddenSize)] for i in range(self.inputSize)]
        # self.w2 = [[random.random() for i in range(self.outputSize)] for i in range(self.hiddenSize)]

    def predict(self, X):
        #forward propagation through our network
        self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
        self.z2 = self.sigmoid(self.z) # activation function
        self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights
        o = self.sigmoid(self.z3) # final activation function
        # self.z = mat_mult(X, self.w1) # dot product of X (input) and first set of 3x2 weights
        # self.z2 = self.sigmoid(self.z) # activation function
        # self.z3 = mat_mult(self.z2, self.w2) # dot product of hidden layer (z2) and second set of 3x1 weights
        # o = self.sigmoid(self.z3) # final activation function
        return o

    def sigmoid(self, s):
        # activation function
        return 1/(1+np.exp(-s)) -0.5