| (ns socket.http | |
| (:require | |
| [clojure.java.io :as io] | |
| [clojure.string :as str] | |
| [clojure.tools.logging :as log]) | |
| (:import [java.net ServerSocket] | |
| [java.net SocketException] | |
| [java.nio.file Files] | |
| [java.io File] | |
| [java.io InputStream OutputStream])) |
I recently discovered a relatively obscure algorithm for calculating the digits of pi: https://en.wikipedia.org/wiki/Gauss–Legendre_algorithm.
Well, at least obscure compared to Chudnovsky's. Wikipedia notes that it is "memory-intensive" but is it really?
Let's compare to the MPFR pi function:
function gauss_legendre(prec)
setprecision(BigFloat, prec, base=10)
GC.enable(false)
| #!/usr/bin/gawk -f | |
| BEGIN{ | |
| # Printing header! | |
| print "<!DOCTYPE html>\n\ | |
| <html xmlns=\"http://www.w3.org/1999/xhtml\" lang=\"\" xml:lang=\"\">\n\ | |
| <head>\n\ | |
| <meta charset=\"utf-8\" />\n\ | |
| <style type=\"text/css\">\n\ | |
| body{\n\ |
| (require '[clojure.pprint :as pp] | |
| '[clojure.edn :as edn]) | |
| (def collection-to-print {:a 1 | |
| :b 2 | |
| :c 3 | |
| :d [1 2 3 4 5]}) | |
| (defn pretty-spit | |
| [file-name collection] | |
| (spit (java.io.File. file-name) |
WARNING: Article moved to separate repo to allow users contributions: https://github.com/raysan5/custom_game_engines
A couple of weeks ago I played (and finished) A Plague Tale, a game by Asobo Studio. I was really captivated by the game, not only by the beautiful graphics but also by the story and the locations in the game. I decided to investigate a bit about the game tech and I was surprised to see it was developed with a custom engine by a relatively small studio. I know there are some companies using custom engines but it's very difficult to find a detailed market study with that kind of information curated and updated. So this article.
Nowadays lots of companies choose engines like [Unreal](https:
| function partition(array, partitionSize) { | |
| if (partitionSize >= array.length) | |
| return array; | |
| var result = []; | |
| var partition = []; | |
| for (var i = 0; i < array.length; i++) { | |
| if (partition.length < partitionSize) { | |
| partition.push(array[i]); | |
| console.log(partition); | |
| } |
| (defn maze [n] | |
| (->> (repeatedly (* n n) #(rand-nth "╱╲")) | |
| (partition n) | |
| (transduce | |
| (comp | |
| (map #(apply str %)) | |
| (interpose "\n")) | |
| str))) |
| #!/usr/bin/env bash | |
| a=$(ifconfig en0 | grep ether | sed -e 's/^[[:space:]]*ether//') | |
| echo "Current MAC address:$a" | |
| r=$(openssl rand -hex 6 | sed 's/\(..\)/\1:/g; s/.$//') | |
| echo "New MAC address: $r" | |
| echo $r | xargs sudo ifconfig en0 ether |
| import numpy as np | |
| class NeuralNetwork: | |
| def __init__(self, x, y): | |
| self.input = x | |
| self.weights1 = np.random.rand(self.input.shape[1],4) | |
| self.weights2 = np.random.rand(4,1) | |
| self.y = y | |
| self.output = np.zeros(self.y.shape) |
| ### JHW 2018 | |
| import numpy as np | |
| import umap | |
| # This code from the excellent module at: | |
| # https://stackoverflow.com/questions/4643647/fast-prime-factorization-module | |
| import random |