ltbringer

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));

ltbringer

from typing import Any, Callable


a1 = Callable[[Any], Any]


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

ltbringer

from typing import Any, Callable


a1 = Callable[[Any], Any]


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

ltbringer

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

ltbringer

Keybase proof

I hereby claim:

• I am amreshvenugopal on github.
• I am ltbringer (https://keybase.io/ltbringer) on keybase.
• I have a public key ASAYY7A_gJS3mDSJ7qG48aK-_vRooqrCT4V7QDpJ3Punvgo

To claim this, I am signing this object:

ltbringer

;; =============================================================
;; 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)
;;

ltbringer

'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)

ltbringer

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

ltbringer

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
        

ltbringer

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: