larswaechter

/**
 * TicTacToe bitboard
 */
class Board(
    val map: LongArray = longArrayOf(
        0b000_000_000, // X = player 1
        0b000_000_000  // O = player -1
    )
) {
    companion object {

larswaechter

# Medium Article: https://larswaechter.medium.com/a-decent-introduction-to-gradient-descent-in-python-846be2e41592

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# The model's output (prediction)
def predict(X, w, b):
    return X * w + b

larswaechter

int main(int argc, char *argv[])
{
  int *arr;
  arr = malloc(5);

  arr[0] = 1;
  arr[1] = 10;
  arr[2] = 20;
  arr[3] = 30;
  arr[4] = 40;

larswaechter

public class Node {
	int value;
	Node next;

	Node(int value) {
		this.value = value;
	}

	public int get(int index) {
		assert index >= 0 && index < this.length();

larswaechter

import kotlin.math.pow
import kotlin.random.Random

// Article: https://medium.com/@larswaechter/zobrist-hashing-305c6c3c54d0

/*
 * Build the Zobrist table with random numbers.
 * For 9 cells and 2 players we need 18 random numbers.
 */
fun buildZobristTable(): Array<Array<Long>> {

larswaechter

/**
 * Interface for implementing Minimax algorithm in two-player zero-sum games
 * <T> => Move data type
 */
interface Minimax<T> {
    // 1 or -1
    var currentPlayer: Int

    /**
     * Evaluate game state for current player.

larswaechter

class Stack<T> implements Stackable<T> {
	private Stack<T> previous;
	private T value;

	Stack() {
	}

	Stack(T value) {
		this.value = value;
	}