morgankenyon

module App

open Elmish
open Elmish.React
open Feliz

type State =
    { Username: string
      Password: string
      LoggedIn: bool }

morgankenyon

> Executing task: C:\Users\morga\.platformio\penv\Scripts\platformio.exe run <

Processing uno (platform: atmelavr; board: uno; framework: arduino)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Verbose mode can be enabled via `-v, --verbose` option
CONFIGURATION: https://docs.platformio.org/page/boards/atmelavr/uno.html
PLATFORM: Atmel AVR (3.4.0) > Arduino Uno
HARDWARE: ATMEGA328P 16MHz, 2KB RAM, 31.50KB Flash
DEBUG: Current (avr-stub) On-board (avr-stub, simavr)
PACKAGES:

morgankenyon

//Compiled on 9/17/2021 7:46:51 PM
#include <inttypes.h>
#include <stdbool.h>

#ifndef JUNIPER_H
#define JUNIPER_H

#include <stdlib.h>

namespace juniper

morgankenyon

import copy

class Choice():
    def __init__(self, move, value, depth):
        self.move = move
        self.value = value
        self.depth = depth

    def __str__(self):
        return (str(self.move) + ": " + str(self.value))

morgankenyon

import copy

class Choice():
    def __init__(self, move, value, depth):
        self.move = move
        self.value = value
        self.depth = depth

    def __str__(self):
        return (str(self.move) + ": " + str(self.value))

morgankenyon

# https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#Pseudocode
# gist is saved as a .py file, but that's just for highlighting
# this isn't valid python code.
function alphabeta(node, depth, α, β, maximizingPlayer) is
    if depth = 0 or node is a terminal node then
        return the heuristic value of node
    if maximizingPlayer then
        value := −∞
        for each child of node do
            value := max(value, alphabeta(child, depth − 1, α, β, FALSE))

morgankenyon

from ttt.player import Player
from ttt.game import Game
from ttt.random_bot import RandomBot
from ttt.invincibot import InvinciBot
from datetime import datetime

def main():
    dateTimeObj = datetime.now()
    print(dateTimeObj)
    xbot = InvinciBot(Player.x)

morgankenyon

import copy

class Choice():
    def __init__(self, move, value, depth):
        self.move = move
        self.value = value
        self.depth = depth

    def __str__(self):
        return (str(self.move) + ": " + str(self.value))

morgankenyon

from ttt.player import Player
from ttt.game import Game
from ttt.random_bot import RandomBot
from ttt.two_layer_bot import TwoLayerBot

def main():
    xbot = RandomBot(Player.x)
    obot = TwoLayerBot(Player.o)
    game = Game(1000)
    print ("randombot (x) vs twolayerbot (o)")

morgankenyon

import random
import copy

class TwoLayerBot():
    def __init__(self, player):
        self.player = player

    def select_move(self, board):
        losing_move = None
        first_candidates = board.get_legal_moves()