c93614

<?php

  # http://darklaunch.com/2009/08/07/base58-encode-and-decode-using-php-with-example-base58-encode-base58-decode

	#################################################################

	function base58_encode($num) {
		$alphabet = '123456789abcdefghijkmnopqrstuvwxyzABCDEFGHJKLMNPQRSTUVWXYZ';
		$base_count = strlen($alphabet);
		$encoded = '';

c93614

#!/bin/sh
#
# sphinx searchd Free open-source SQL full-text search engine
#
# chkconfig:   - 20 80
# description: Starts and stops the sphinx searchd daemon that handles \
#          all search requests.

### BEGIN INIT INFO
# Provides: searchd

c93614

<?php

class base58
{
	static public $alphabet = "123456789abcdefghijkmnopqrstuvwxyzABCDEFGHJKLMNPQRSTUVWXYZ";

	public static function encode($int) {
		$base58_string = "";
		$base = strlen(self::$alphabet);
		while($int >= $base) {

c93614

# -*- coding=utf-8 -*-
# 修改了原gist里space tab混乱的情况
import feedparser
import re
import collections
import math

def info_entropy(words):
    result = 0
    total = sum([val for _, val in words.iteritems()])

c93614

<?php
session_start();

// ref: http://expressjs.com/guide.html#users-online
function get_online_user($session_id) {
    if (false === ($online_users = apc_fetch("online/users"))) {
        $online_users = [];
    }

    $online_users[$session_id] = time();

c93614

These weights are often combined into a tf-idf value, simply by multiplying them together. The best scoring words under tf-idf are uncommon ones which are repeated many times in the text, which lead early web search engines to be vulnerable to pages being stuffed with repeated terms to trick the search engines into ranking them highly for those keywords. For that reason, more complex weighting schemes are generally used, but tf-idf is still a good first step, especially for systems where no one is trying to game the system.

There are a lot of variations on the basic tf-idf idea, but a straightforward implementation might look like:

    <?php
    $tfidf = $term_frequency *  // tf
        log( $total_document_count / $documents_with_term, 2); // idf
    ?>

It's worth repeating that the IDF is the total document count over the count of the ones containing the term. So, if there were 50 documents in the collection, and two of them contained the term in question, the IDF would be 50/2 = 25. To be accurate, we s

c93614

func callA() string {
	time.Sleep(time.Millisecond * 300)
	return "Hello A"
}

func callB() string {
	time.Sleep(time.Millisecond * 100)
	return "Hello B"
}

c93614

分支管理

最少有三个长期分支

• master: 用于生产环境部署
• testing: 用于测试环境测试
• dev: 用于日常开发

有一些临时分支

c93614

// requires
var utils = require('utils');
var casper = require('casper').create()
var casper = require('casper').create({
    verbose: true,
    logLevel: "debug"
});

// setup globals 
var email = casper.cli.options['email'] || 'REPLACE THIS EMAIL';

c93614

<?php

date_default_timezone_set( 'Europe/Moscow' );
ini_set( 'memory_limit', '1024M' );

$ch = curl_init();
curl_setopt( $ch, CURLOPT_URL, 'https://email.us-east-1.amazonaws.com' );
curl_setopt( $ch, CURLOPT_POST, true );
curl_setopt( $ch, CURLOPT_RETURNTRANSFER, 1 );
curl_setopt( $ch, CURLOPT_HEADER, true );