DataWraith

#!/usr/bin/env python3

"""

ll-cma.py: Solve LunarLander-v2 using CMA-ES.

This code solves the LunarLander-v2 environment from the OpenAI gym using CMA-ES.
You need to pip-install the packages numpy, gym[box2d] and cma in order for it to work.

It doesn't seem to solve the environment in every run, but if it does, it

DataWraith

WraithBot

This is a high-level description of my Halite III bot. At the time of writing, a few days before the end of the competition, it has a rating of slightly over 74, which puts it at rank ~115.

Bot description

The bot is based on Value Iteration and Uniform Cost Search for mining halite and returning it to the nearest dropoff. Dropoffs are not pre-planned -- a ship

DataWraith

defmodule FizzBuzz do
  @moduledoc """
  FizzBuzz outputs integers interspersed with configurable words
  at configurable intervals.
  """

  def fizzbuzz(count, words \\ [{3, "Fizz"}, {5, "Buzz"}]) do
    words
    |> Enum.map(&make_word_stream/1)
    |> Stream.zip

DataWraith

(ns clj-fizzbuzz.core
  (:gen-class))

(defn hitword
  [word n]
  (cycle (cons word (repeat (dec n) nil))))

(defn fizzbuzz
  [x]
  (let [fizzes (hitword "Fizz" 3)

DataWraith

{-
   "dependencies": {
        "eeue56/elm-flat-matrix": "3.0.2 <= v < 4.0.0",
        "elm-lang/core": "5.1.1 <= v < 6.0.0",
        "elm-lang/html": "2.0.0 <= v < 3.0.0",
        "elm-lang/svg": "2.0.0 <= v < 3.0.0"
    },
    "elm-version": "0.18.0 <= v < 0.19.0"
-}

DataWraith

import math
import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim
from scipy import misc

class IMR(object):
    def __init__(self, sess, channels = 3):
        self.sess = sess
        self.num_channels= channels

DataWraith

import gym
import numpy as np

# This is a straightforwad implementation of SARSA for the FrozenLake OpenAI
# Gym testbed. I wrote it mostly to make myself familiar with the OpenAI gym;
# the SARSA algorithm was implemented pretty much from the Wikipedia page alone.

env = gym.make("FrozenLake-v0")

def choose_action(observation):

DataWraith

package main

// This program takes a .zim-file and dumps all contained articles below the
// current directory. I tested it with a Wikipedia snapshot; that did not
// contain deleted articles or LinkTargetEntrys, so I'm unsure how to handle
// those, for now I'm ignoring them.
//
// Redirects are handled by simply writing out the page pointed to by the
// redirect. IPFS deduplication should take care of it, so I think this is the
// most economical solution, even better than writing out small HTML files with

DataWraith

#!/usr/bin/env ruby

require 'securerandom'

# Generates a random password made up of NUM_SYLLABLES koremutake syllables.
# See http://shorl.com/koremutake for more on koremutake.

NUM_SYLLABLES=14

SYLLABLES = %w{

DataWraith

# This entry is based on Prediction by Partial Matching, a method that is used
# in data compression.

import random
from collections import defaultdict

CONTEXT_SIZE=32

if input == "":
    # Initialize data structure