import numpy as np


class Neural_Network(object):
    # inspired from https://enlight.nyc/projects/neural-network/
    def __init__(self):
        #parameters
        self.inputSize = 3
        self.outputSize = 2
        self.hiddenSize = 3

        #weights
        self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # weights from input to hidden layer
        self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # weights from hidden to output layer

    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
        return o

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