omaraflak

#include <iostream>
#include <vector>
#include <assert.h>
#include <cmath>
#include <png++/png.hpp>

using namespace std;

typedef vector<double> Array;
typedef vector<Array> Matrix;

omaraflak

all:
	g++ main.cpp -o main -std=c++11 `sdl2-config --cflags --libs`
	./main

omaraflak

import sys
import getch
from collections import defaultdict


class Oracle:
    def __init__(self, window_size: int):
        self.window_size = window_size
        self.window: str = ''
        self.patterns: defaultdict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))

omaraflak

<canvas id="canvas"></canvas>
<script>
const WIDTH = 1500
const HEIGHT = 800

const canvas = document.getElementById("canvas")
const ctx = canvas.getContext("2d")

canvas.width = WIDTH
canvas.height = HEIGHT

omaraflak

def print_cells(cells: list[int]):
    print(''.join('■' if i == 1 else ' ' for i in cells))


def next_step(cells: list[int], rules: list[int]) -> list[int]:
    new_cells: list[int] = [False] * len(cells)
    for i in range(len(cells)):
        left = cells[(i - 1) % len(cells)]
        right = cells[(i + 1) % len(cells)]
        idx = int(''.join([str(left), str(cells[i]), str(right)]), 2)

omaraflak

import numpy as np
import matplotlib.pyplot as plt

width = 300
height = 200

camera = np.array([0, 0, 1])
ratio = float(width) / height
screen = (-1, 1 / ratio, 1, -1 / ratio) # left, top, right, bottom

omaraflak

import cv2
import numpy as np

size = 28
drawing = False
win_name = "Draw digit"

def draw(event, x, y, flags, *param):
    global drawing
    if event == cv2.EVENT_LBUTTONDOWN:

omaraflak

from layer import Layer
import numpy as np

# inherit from base class Layer
class FCLayer(Layer):
    # input_size = number of input neurons
    # output_size = number of output neurons
    def __init__(self, input_size, output_size):
        self.weights = np.random.rand(input_size, output_size) - 0.5
        self.bias = np.random.rand(1, output_size) - 0.5

omaraflak

class Network:
    def __init__(self):
        self.layers = []
        self.loss = None
        self.loss_prime = None

    # add layer to network
    def add(self, layer):
        self.layers.append(layer)

omaraflak

import numpy as np

from network import Network
from fc_layer import FCLayer
from activation_layer import ActivationLayer
from activations import tanh, tanh_prime
from losses import mse, mse_prime

# training data
x_train = np.array([[[0,0]], [[0,1]], [[1,0]], [[1,1]]])