yusong-shen

## littlechecker.py
import os, filecmp

codes = {200:'success',404:'file not found',400:'error',408:'timeout'}

def compile(file,lang):
    if lang == 'java':
        class_file = file[:-4]+"class"
    elif lang == 'c':
        class_file = file[:-2]
    elif lang=='cpp':

## Git_mergetool_tutorial_with_Vim.md

      
              1 file
            
          
              129 forks
            
          
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              545 stars
            
          
                karenyyng
                / Git_mergetool_tutorial_with_Vim.md
            
            
              Last active
              October 15, 2025 11:54
            
              
                How to use `git mergetool` to resolve conflicts in Vim / NeoVim
              
          
    Table Of Content

Skip to the relevant sections if needed.

2-min tutorial to do it the quick-and-dirty-way
Concepts for resolving Git conflicts
Setting up different editors / tool for using git mergetool

Finding out what mergetool editors are supported


mergetool simple code example for vimdiff

Resolving conflict from a Git pull


Other great references and tutorials


## hifreqwords.scala
val f = sc.textFile("sample.txt")
// word count
val wc = f.flatMap(l => l.split(" ")).map(word => (word,1)).reduceByKey(_ + _)
// swap k,v to v,k to sort by word frequency
val wc_swap = wc.map(_.swap)
// sort keys by ascending=false (descending)
val hifreq_words = wc_swap.sortByKey(false,1)
hifreq_words.saveAsTextFile("hifreq_words")
// get an array of top 20 frequent words
val top20 = hifreq_words.take(20)

## pmf-and-modified-bpmf-pymc.py
"""
Implementations of:

    Probabilistic Matrix Factorization (PMF) [1],
    Bayesian PMF (BPMF) [2],
    Modified BPFM (mBPMF)

using `pymc3`. mBPMF is, to my knowledge, my own creation. It is an attempt
to circumvent the limitations of `pymc3` w/regards to the Wishart distribution:

## busted-output.txt
●●
2 successes / 0 failures / 0 errors / 0 pending : 0.008108 seconds

## MemeMaker-Solution.html
<!DOCTYPE html>

<html>
<head>
  <title>MemeMaker-Simple</title>

  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0">
  <meta name="mobile-web-app-capable" content="yes">
  <meta name="apple-mobile-web-app-capable" content="yes">
  <style>

## scaladays2014.md

      
              1 file
            
          
              16 forks
            
          
                14 comments
              
            
              116 stars
            
          
                kevinwright
                / scaladays2014.md
            
            
              Last active
              November 16, 2024 17:40
            
              
                Scaladays 2014 slides
              
          
    As compiled by Kevin Wright a.k.a @thecoda
(executive producer of the movie, and I didn't even know it... clever huh?)
please, please, please - If you know of any slides/code/whatever not on here, then ping me on twitter or comment this Gist!
This gist will be updated as and when I find new information.  So it's probably best not to fork it, or you'll miss the updates!
Monday June 16th

  
## TicTacToe.java

import java.util.logging.Level;
import java.util.logging.Logger;


public class TicTacToe extends javax.swing.JFrame {
    static int winComb[][] = {{1,2,3},{4,5,6},{7,8,9},{1,4,7},{2,5,8},{3,6,9},{1,5,9},{3,5,7}};
    public static int[][] state = {{0,0,0},{0,0,0},{0,0,0}};
    Player pl1 = new Human();
    Player pl2 = new Computer("mind\\layer");

## GitHub-Forking.md

      
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              790 forks
            
          
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              2885 stars
            
          
                Chaser324
                / GitHub-Forking.md
            
            
              Last active
              October 3, 2025 10:01
            
              
                GitHub Standard Fork & Pull Request Workflow
              
          
    Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.
In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.
Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

  
## gist:9640856


原文地址：http://in.sdo.com/?p=1707
收集和整理了目前互联网上能找到的开源推荐系统，并附上了个人的一些简单点评（未必全面准确），这个列表是目前为止比较全面的了，希望对大家了解掌握推荐系统有帮助（文/陈运文）
SVDFeature
由上海交大的同学开发，采用C++语言，代码质量很高。去年我们参加KDD竞赛时用过，很好很方便，而且出自咱们国人之手，所以置顶推荐！
项目地址：
http://svdfeature.apexlab.org/wiki/Main_Page
SVDFeature包含一个很灵活的Matrix Factorization推荐框架，能方便的实现SVD、SVD++等方法, 是单模型推荐算法中精度最高的一种。SVDFeature代码精炼，可以用相对较少的内存实现较大规模的单机版矩阵分解运算。
另外含有Logistic regression的model，可以很方便的用来进行ensemble运算
	import os, filecmp

	codes = {200:'success',404:'file not found',400:'error',408:'timeout'}

	def compile(file,lang):
	if lang == 'java':
	class_file = file[:-4]+"class"
	elif lang == 'c':
	class_file = file[:-2]
	elif lang=='cpp':
	val f = sc.textFile("sample.txt")
	// word count
	val wc = f.flatMap(l => l.split(" ")).map(word => (word,1)).reduceByKey(_ + _)
	// swap k,v to v,k to sort by word frequency
	val wc_swap = wc.map(_.swap)
	// sort keys by ascending=false (descending)
	val hifreq_words = wc_swap.sortByKey(false,1)
	hifreq_words.saveAsTextFile("hifreq_words")
	// get an array of top 20 frequent words
	val top20 = hifreq_words.take(20)
	"""
	Implementations of:

	Probabilistic Matrix Factorization (PMF) [1],
	Bayesian PMF (BPMF) [2],
	Modified BPFM (mBPMF)

	using `pymc3`. mBPMF is, to my knowledge, my own creation. It is an attempt
	to circumvent the limitations of `pymc3` w/regards to the Wishart distribution:
	●●
	2 successes / 0 failures / 0 errors / 0 pending : 0.008108 seconds
	<!DOCTYPE html>

	<html>
	<head>
	<title>MemeMaker-Simple</title>

	<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0">
	<meta name="mobile-web-app-capable" content="yes">
	<meta name="apple-mobile-web-app-capable" content="yes">
	<style>

	import java.util.logging.Level;
	import java.util.logging.Logger;


	public class TicTacToe extends javax.swing.JFrame {
	static int winComb[][] = {{1,2,3},{4,5,6},{7,8,9},{1,4,7},{2,5,8},{3,6,9},{1,5,9},{3,5,7}};
	public static int[][] state = {{0,0,0},{0,0,0},{0,0,0}};
	Player pl1 = new Human();
	Player pl2 = new Computer("mind\\layer");


	原文地址：http://in.sdo.com/?p=1707
	收集和整理了目前互联网上能找到的开源推荐系统，并附上了个人的一些简单点评（未必全面准确），这个列表是目前为止比较全面的了，希望对大家了解掌握推荐系统有帮助（文/陈运文）
	SVDFeature
	由上海交大的同学开发，采用C++语言，代码质量很高。去年我们参加KDD竞赛时用过，很好很方便，而且出自咱们国人之手，所以置顶推荐！
	项目地址：
	http://svdfeature.apexlab.org/wiki/Main_Page
	SVDFeature包含一个很灵活的Matrix Factorization推荐框架，能方便的实现SVD、SVD++等方法, 是单模型推荐算法中精度最高的一种。SVDFeature代码精炼，可以用相对较少的内存实现较大规模的单机版矩阵分解运算。
	另外含有Logistic regression的model，可以很方便的用来进行ensemble运算