нейро_гавно

import numpy as np

x = np.array([
    [
        0, 0, 0, 0, 0,
        0, 0, 1, 0, 0,
        0, 1, 0, 1, 0,
        1, 1, 1, 1, 1,
        0, 0, 0, 0, 0,
    ],
    [
        0, 0, 0, 0, 0,
        0, 1, 1, 1, 0,
        0, 1, 0, 1, 0,
        0, 1, 1, 1, 0,
        0, 0, 0, 0, 0,
    ],
    [
        0, 0, 0, 0, 0,
        0, 0, 1, 0, 0,
        0, 1, 0, 1, 0,
        0, 0, 1, 0, 0,
        0, 0, 0, 0, 0,
    ],
    [
        0, 0, 1, 0, 0,
        0, 1, 0, 1, 0,
        1, 0, 0, 0, 1,
        0, 1, 0, 1, 0,
        0, 0, 1, 0, 0,
    ]
])

y = np.array([
    np.array([[1, 0, 0, 0]]).T,
    np.array([[0, 1, 0, 0]]).T,
    np.array([[0, 0, 1, 0]]).T,
    np.array([[0, 0, 0, 1]]).T
])


def sigmoid(x):
    return 1 / (1 + np.exp(-x))


def study(tr_stroka):
    sin_ves = 2 * np.random.random((25, 1)) - 1

    for i in range(10000):
        x1 = x
        y1 = sigmoid(np.dot(x1, sin_ves))
        error = tr_stroka - y1
        raspr = np.dot(x1.T, error * y1 * (1 - y1))
        sin_ves += raspr
    return sin_ves


sin_ves = np.array([
    2 * np.random.random((25, 1)) - 1,
    2 * np.random.random((25, 1)) - 1,
    2 * np.random.random((25, 1)) - 1,
    2 * np.random.random((25, 1)) - 1
])

for i in range(4):
    sin_ves[i] = study(y[i])


def check(x0):
    chance = 0

    y0 = -1
    for i in range(4):
        proz = sigmoid(np.dot(x0, sin_ves[i]))
        if proz > chance:
            chance = proz
            y0 = i
    return y0


new_x = np.array([
    [
        0, 0, 0, 0, 0,
        0, 0, 1, 0, 0,
        0, 1, 0, 1, 0,
        0, 0, 1, 1, 0,
        0, 0, 0, 0, 0,
    ],
    [
        0, 0, 0, 0, 0,
        0, 1, 1, 1, 0,
        0, 1, 0, 1, 0,
        0, 1, 1, 1, 0,
        0, 0, 0, 0, 0,
    ],
    [
        0, 0, 1, 0, 0,
        0, 1, 0, 1, 0,
        1, 1, 0, 0, 1,
        0, 1, 0, 1, 0,
        0, 0, 1, 0, 0,
    ],
    [
        0, 0, 0, 0, 0,
        0, 0, 1, 0, 0,
        0, 1, 0, 1, 1,
        1, 1, 1, 1, 1,
        0, 0, 0, 0, 0,
    ]
])


def symbol(a):
    if a == 0:
        return '∆'
    if a == 1:
        return '□'
    if a == 2:
        return '0'
    if a == 3:
        return '◊'


for i in range(4):
    print(i, 'RESULT=', symbol(check(new_x[i])))