ltbringer ltbringer

I hereby claim:

To claim this, I am signing this object:

	use std::thread;
	use std::sync::atomic::{Ordering, AtomicIsize};
	use std::sync::Arc;
	use std::time::{Instant};


	fn parallel_search(sstables: &Vec<u8>, k: u8, n_threads: usize) -> isize {
	let n_sstables = sstables.len();
	let mut handles = vec![];
	let early_exit = Arc::new(AtomicIsize::new(-1));

	from typing import Any, Callable


	a1 = Callable[[Any], Any]


	class Compose:
	def __init__(self, f: a1) -> None:
	self.f = f

	from typing import Any, Callable


	a1 = Callable[[Any], Any]


	class Compose:
	def __init__(self, f: a1) -> None:
	self.f = f

	import json
	import string
	import random
	import colorsys
	import operator as op

	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt

	;; =============================================================
	;; A file simulator is used to create an example problem
	;; that helps demonstrate the use of macros in common lisp
	;; =============================================================
	;; Interfaces to file I/Os expose methods like
	;; `open`, `close`, `read`
	;; not closing a file can lead to issues where the machine runs
	;; out of memory, or unreliable state of the file
	;; (due to unwanted side-effects)
	;;

	'use strict';

	/*
	* given a string of bits such as 100100, calculate the number of steps required to convert
	* the string to 000000 using the following rules:
	* a bit may be flipped only if it is following immediately by a one, followed only by zeroes
	* the far-right bit may be toggled freely
	*
	* examples: 111 -> 110 -> 010 -> 011 -> 001 -> 000 (score 5)
	* 1101 -> 1100 -> 0100 -> 0101 -> 0111 -> 0100 -> 0010 -> 0011 -> 0001 -> 0000 (score 9)

	bot1_sym = 'O'
	bot2_sym = 'X'

	def optimize_bot(game, bot1, bot2):
	"""
	Punish or Reward the bot with respect to the agent that wins the game
	"""
	if game.winner == bot1_sym:
	bot1.on_reward(1)
	# reward

	class Agent(object):
	def __init__(self, exploration_rate=0.33, learning_rate=0.5, discount_factor=0.01):
	"""
	An agent is a problem solver.
	It should perform actions like:
	- plotting a symbol on the tic-tac-toe board if it is vacant.
	- Remember which states are more profitable than the others.
	- Explore better states
	- Exploit for maximum profit

	import numpy as np
	import pandas as pd
	from matplotlib import pyplot as plt
	import seaborn as sns

	class Board(object):
	"""
	The environment for the reinforcement learning project.

	It should: