test.py

from neuralNetwork import *
import math
import random
import copy

popSize = 200
inputSize = 2
hiddenSize = 2
outputSize = 1



class xor():
    def __init__(self):
        self.nn = NeuralNetwork(inputSize,hiddenSize,outputSize)
        self.score = 0


input = [[[1,0],1],
         [[0,1],1],
         [[1,1],0],
         [[0,0],0],
]
arr = []
for i in range(popSize):
    arr.append(xor())

bestScore = 0
while True:
    for i in arr:
        for j in range(4):
            predict = i.nn.Predict2(input[j][0],False)
            res = abs(predict[0] - input[j][1])
            i.score +=  1 - res

    index = 0
    arrBest = []
    arrBest.append(arr[0])
    for i in range(popSize-1):
        if arr[index].score < arr[i+1].score:
            index = i+1
            arrBest.append(arr[i+1])
        elif arr[index].score == arr[i+1].score:
            arrBest.append(arr[i+1])

    best = random.choice(arrBest)

    if bestScore < best.score:
        bestScore = best.score
        print(bestScore)
    if arr[index].score >= 4.0:
        break
    newArr = []
    for i in range(popSize):
        r = xor()
        r.nn.mutation(best.nn,0.1)
        newArr.append(r)
    arr = newArr

print("done")