#!/usr/bin/env python
import numpy as np
from lib.func import AIlib as ai


class rgb(object):
    def __init__(self, loadedWeights: np.matrix = None, loadedBias: np.matrix = None):

        if(not loadedWeights or not loadedBias):  # if one is null (None) then just generate new ones
            print("Generating weights and biases...")
            self.weights = [ai.genRandomMatrix(3, 8), ai.genRandomMatrix(
                8, 8), ai.genRandomMatrix(8, 3)]  # array of matrices of weights
            # 3 input neurons -> 8 hidden neurons -> 8 hidden neurons -> 3 output neurons

            # Generate the biases
            self.bias = [ai.genRandomMatrix(1, 8), ai.genRandomMatrix(
                1, 8), ai.genRandomMatrix(1, 3)]
            # This doesn't look very good, but it works so...

            self.learningrate = 0.01  # the learning rate of this ai

            print(self.weights)
            print(self.bias)

        else:  # if we want to load our progress from before then this would do it
            self.weights = loadedWeights
            self.bias = loadedBias

    def calcError(self, inp: np.array, out: np.array):
        cost = ai.calcCost(inp, out)
        # Cost needs to get to 0, we can figure out this with backpropagation
        return cost

    def learn(self):
        ai.learn(3, 0.0001, self, 0.001)

    def think(self, inp: np.array):
        print("\n-Input-")
        print(inp)

        res = ai.think(inp, self)

        print("\n-Output-")
        print(res)
        return res


def init():
    bot = rgb()
    bot.learn()

    inpArr = np.asarray([1.0, 1.0, 1.0])
    res = bot.think(inpArr)
    err = bot.calcError(inpArr, res)
    print(err)


init()