import numpy as np

class NN():
    score = 0

    def __init__(self, *args):
        self.layers = args
        self.synapses = []
        for i in range(len(self.layers)-1):
            self.synapses.append(np.random.normal(scale=1,size=(self.layers[i], self.layers[i+1])))

    def calculateOutput(self, input):
        layerInput = input

        for synapseLayer in self.synapses:
            layerInput = np.dot(layerInput, synapseLayer)
            layerInput = self.sigmoid(layerInput)

        return layerInput

    def sigmoid(self, s):
        #print("Arr:", arr)
        #s = np.array(arr)
        # activation function
        res = 1 / (1 + np.exp(-s))
        #print("Res:", res)
        return res

    def getSynapses(self):
        return self.synapses

    def setSynapses(self, synapses):
        self.synapses = synapses

    def mutate(self, val):
        mut_arr = []
        for i in range(len(self.layers)-1):
            mut_arr.append(np.random.normal(scale=val,size=(self.layers[i], self.layers[i+1])))
        self.synapses = np.add(self.synapses, mut_arr)
        return mut_arr