DataWraith

Task

We’re going to write a “read it later”-tool (ril) in Go, and test it using the BATS (Bash Automated Testing System). Its purpose is to save URLs to an SQLite database for later viewing.

Functionality

Adding URLs

Each URL is assigned an integer id. IDs are formatted right-aligned in fields of width 4. The ID used in the examples below is 17.

A newly added URL is retrieved and the <title> element is parsed (e.g. https://www.example.com -> Example Domain). Use a User-Agent string containing “Read-it-later”. If the URL does not resolve with a 200 status, use “[Unknown Title]” as the title.

New URL

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{