evanthebouncy

E = {
    "A": [],
    "B": ["A", "E"],
    "C": ["D", "H"],
    "D": ["B"],
    "E": ["F"],
    "F": [],
    "G": ["A"],
    "H": ["B", "G"],
}

evanthebouncy

E = {
    "A": [],
    "B": ["A", "E"],
    "C": ["D", "H"],
    "D": ["B"],
    "E": ["F"],
    "F": [],
    "G": ["A"],
    "H": ["B", "G"],
}

evanthebouncy

E = { "A": ["E"], 
      "B": ["A", "C", "G"], 
      "C": ["A"], 
      "D": ["B", "G"], 
      "E": ["F"], 
      "F": [], 
      "G": ["A", "F"], 
    }

evanthebouncy

N = 8

def neighbors(board):
    occupied = set(board)
    for r in range(N):
        for c in range(N):
            if (r, c) not in occupied:
                yield board + ((r, c),)

def solve_8q_one():

evanthebouncy

import random
import numpy as np

def simulate_T(n):
    if n <= 1:
        return 0
    # the pivot may end up randomly anywhere from 0 to n-1
    pivot_index = random.randint(0, n - 1)

    first_half = simulate_T(pivot_index)

evanthebouncy

import numpy as np
import matplotlib.pyplot as plt

# roll out of a particular game, start at position n, and roll a coin of some prob to move up or down. 
# if it reaches 0, you lose
def simulate_game_log_space(start_n, coin_prob):
    trajectory = []
    n = start_n
    for i in range(1000):
        trajectory.append(n)

evanthebouncy

import numpy as np
import matplotlib.pyplot as plt
import json
import cv2
import math

# open the ./data_train.json
GRID = 64
IMG_W = GRID * 20

evanthebouncy

# a self play version of 20 quetsions with gpt playing against itself
from langchain.llms import OpenAI
# from langchain.chat_models import ChatOpenAI
llm = OpenAI(model_name="gpt-3.5-turbo-0301")

# an oracle
def get_oracle(concept):
    def give_answer(question):
        prompt = f"For a {concept}, \
        {question}. Answer with a single word [YES, NO]"

evanthebouncy

# get all possible next states
def get_next_state(tuple_state):
    ret = []
    # for all index of tuple_state, subtract any number of stones 1 through maximum possible
    for i in range(len(tuple_state)):
        for j in range(1, tuple_state[i]+1):
            # create new state
            new_state = list(tuple_state)
            new_state[i] -= j

evanthebouncy

from rectangle import is_inside, is_correct, inside, outside, W
import random
import string 

# for the purpose of showing this is a "language model", all programs here are 
# written as STRINGS, you need to call eval(prog) on them to get the actual program

def writer1():
    return ''.join(random.choice(string.printable) for i in range(9))