seanmor5

<!-- livebook:{"persist_outputs":true} -->

# zip create benchmark

```elixir
Mix.install([
  {:benchee, "~> 1.0"}
])
```

seanmor5

defmodule Client do
  def main(pid) do
    cmd = IO.gets("Enter a command: ") |> String.trim()

    case String.split(cmd) do
      ["show"] ->
        key = self()

        Task.async(fn ->
          send(pid, {:list, key, self()})

seanmor5

main = fn ->
  cmd = IO.gets("Enter a command: ") |> String.trim()

  case String.split(cmd) do
    ["show"] ->
      todos = Process.get(:todos) || []
      IO.puts(Enum.join(todos, "\n"))

    ["create" | rest] ->
      todos = Process.get(:todos) || []

seanmor5

main = fn ->
  cmd = IO.gets("Enter a command: ") |> String.trim()

  case cmd do
    "1" ->
      todos = Process.get(:todos) || []
      IO.puts(Enum.join(todos, "\n"))

    "2" ->
      todos = Process.get(:todos) || []

seanmor5

{"name": "Chateau de Landiras 2018", "url": "https://www.wine.com//product/chateau-de-landiras-2018/677595", "varietal": "Bordeaux Red Blends", "location": "Graves, Bordeaux, France", "alcohol_volume": "750ML", "alcohol_percent": "0", "price": "$19", "notes": "Blend: 75% Merlot, 25% Cabernet Sauvignon", "reviews": [{"author": "Decanter", "review": "Grand generosity, showcasing deep, vigorous dark forest fruit and an appetising blackcurrant freshness within juicy and fabulously-integrated tannins. Lovely, boisterous and rich in fruit. Bravo!", "rating": "95"}]}
{"name": "Silver Oak Napa Valley Cabernet Sauvignon 2016", "url": "https://www.wine.com//product/silver-oak-napa-valley-cabernet-sauvignon-2016/683756", "varietal": "Cabernet Sauvignon", "location": "Napa Valley, California", "alcohol_volume": "750ML", "alcohol_percent": "14.2", "price": "$144", "notes": "Deep ruby in color, the 2016 Napa Valley Cabernet Sauvignon is an opulent wine with notes of cassis, ripe raspberry, toasted coconut and shiitake mush

seanmor5

defmodule Day4 do
  import Nx.Defn

  def part1() do
    File.read!("aoc/4.txt")
    |> parse_input()
    |> play_bingo()
    |> find_winning_board()
  end

seanmor5

defmodule Day3 do
  # Since my last answer wasn't purely Nx, I'm going
  # to try to stick to Nx as much as is possible, but
  # we don't have string manipulation stuff so that will
  # have to be done in Elixir
  import Nx.Defn

  def part1 do
    File.read!("aoc/3.txt")
    |> parse_input()

seanmor5

# part 1
File.read!('aoc/2.txt')
|> String.replace("\r", "") # i am on windows right now :(
|> String.split("\n")
|> Enum.map(fn line ->
  [direction, amt] = String.split(line)
  amt_int = String.to_integer(amt)

  case direction do
    "forward" ->

seanmor5

# Part 1
File.read!('aoc/1.txt')
|> String.replace("\r", "") # i am on windows right now :(
|> String.split("\n")
|> Enum.map(&String.to_integer/1)
|> Nx.tensor()
|> then(fn x ->
  Nx.greater(Nx.slice_axis(x, 1, Nx.size(x) - 1, 0), Nx.slice_axis(x, 0, Nx.size(x) - 1, 0))
end)
|> Nx.sum()

seanmor5

Genetic Algorithm in Nx

Preface

After writing Genetic Algorithms in Elixir, I had no real hopes or expectations that numerical computing would become a focus for Elixir. However, to my surprise, the Nx project developed rather quickly, and proved that Elixir could be used for practical machine learning and numerical computing applications. While I don't have any plans (yet) of reworking the examples in my book to take advantage of the acceleration enabled by Nx, EXLA, and other projects, I feel it's necessary to show how you could go about rewriting your genetic algorithms to take advantage of Elixir's new numerical computing libraries.

Introduction

Nx is a numerical computing library for Elixir which supports the creation and manipulation of multi-dimensional arrays (called tensors in the API), automatic differentiation, and just-in-time (JIT) compilation to CPU, GPU, and other accelerators via pluggable backends and compilers. Nx opens up a realm of possibilities for Elixir dev