atr000

#!/bin/bash

# iching.sh
# casts an i ching 
# use: ching [number]
#      with number looks up that hexagram, otherwise casts 
      
function cast {
 l=0
 until [ $l == 6 ]

mbostock

license: gpl-3.0

staticfloat

Julia Debugging Procedures

So you managed to break Julia. Congratulations! Collected here are some general procedures you can undergo for common symptoms encountered when something goes awry. Including the information from these debugging steps can greatly help the maintainers when tracking down a segfault or trying to figure out why your script is running slower than expected.

If you've been directed to this page, find the symptom that best matches what you're experiencing and follow the instructions to generate the debugging information requested. Table of symptoms:

• Segfaults during bootstrap (sysimg.jl)

• Segfaults when running a script

reflection.swift

import Foundation


struct Point {
    var x: Int = 200
    var y: Int! = 2
    var z: Int? = nil
}

class CPoint {

skariel

module VoronoiDelaunay

# VoronoiDelaunay_v0.2.9
#
# Fast, robust 2D Voronoi/Delaunay tesselation
# Implementation follows algorithms described in http://arxiv.org/abs/0901.4107
# and used (for e.g.) in the Illustris Simulation
# http://www.illustris-project.org/
#
# Author: Ariel Keselman (skariel@gmail.com)

skariel

[JULIA] Faster than CGAL Delaunay

TL;DR

DelaunayJL is an incremental 2D Delaunay triangulation algorithm implemented in Julia, it is robust and ~20% faster than CGAL, the C++ de-facto industry standard. And it's MIT Licensed! all links to code are below

Show me the data!

The figure below shows how much time it takes to run a benchmark on my computer, an Intel Core i7-4800MQ CPU @ 2.7Ghz, 16GB RAM, Win8 64bit. The benchmark consists of inserting a number of points uniformly distributed. The benchmark is run 5 times for each number of points once for CGAL and once for Julia. The numbers of points used are 10K, 100K, 1M, and 10M. CGAL v4.4 was compiled with VS2013 64bit release mode, Julia is of version 0.3.0 Commit 7681878 (2014-08-20 20:43 UTC) x86_64-w64-mingw32 the delaunay code is here (see other gists of mine for complementing files... I'll compile this all into a library when I have the time)

radex

import Foundation

struct Stream {
    let string: NSString
    var position: Int
    
    var matchingRange: NSRange {
        return NSRange(location: position, length: string.length - position)
    }
}

ocornut

// Creating a node graph editor for Dear ImGui
// Quick sample, not production code! 
// This is quick demo I crafted in a few hours in 2015 showcasing how to use Dear ImGui to create custom stuff,
// which ended up feeding a thread full of better experiments.
// See https://github.com/ocornut/imgui/issues/306 for details

// Fast forward to 2023, see e.g. https://github.com/ocornut/imgui/wiki/Useful-Extensions#node-editors

// Changelog
// - v0.05 (2023-03): fixed for renamed api: AddBezierCurve()->AddBezierCubic().

haje01

텐서플로우 시작하기

글쓴이: 김정주(haje01@gmail.com)

이 문서는 텐서플로우 공식 페이지 내용을 바탕으로 만들어졌습니다.

---

소개

텐서플로우(TensorFlow)는 기계 학습과 딥러닝을 위해 구글에서 만든 오픈소스 라이브러리입니다. 데이터 플로우 그래프(Data Flow Graph) 방식을 사용하였습니다.

eungju

#!/usr/bin/env elixir
{d, _} = Integer.parse(Enum.at(System.argv(), 0))
IO.stream(:stdio, :line)
|> Stream.map(&Regex.replace(~r/<SYNC Start=(\d+)>/i, &1, fn _, g1 -> {t, _} = Integer.parse(g1); "<SYNC Start=#{t + d}>" end))
|> Stream.each(&IO.write/1)
|> Stream.run