sawantuday

<?php
function crawl_page($url, $depth = 5)
{
    static $seen = array();
    if (isset($seen[$url]) || $depth === 0) {
        return;
    }

    $seen[$url] = true;

sawantuday

/* bling.js */

window.$ = document.querySelectorAll.bind(document);

Node.prototype.on = window.on = function (name, fn) {
  this.addEventListener(name, fn);
}

NodeList.prototype.__proto__ = Array.prototype;

sawantuday

# Output to a jpeg file
set terminal jpeg size 1280,720

# Set the aspect ratio of the graph
set size 1, 1

# The file to write to
set output "timeseries.jpg"

# The graph title

sawantuday

auth_mechanisms = plain login digest-md5 cram-md5 ntlm apop
!include auth-sql.conf.ext

sawantuday

// Just before switching jobs:
// Add one of these.
// Preferably into the same commit where you do a large merge.
//
// This started as a tweet with a joke of "C++ pro-tip: #define private public",
// and then it quickly escalated into more and more evil suggestions.
// I've tried to capture interesting suggestions here.
//
// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_,
// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant,

sawantuday

<?php
/*
Plugin Name: On-Demand image resizer
Plugin URI: http://www.monkeyman.be
Description: Create and store images in different sizes on demand
Version: 1.0
Author: Jan Fabry

This plugins monitors 404 requests to the uploads directory and created new images of a requested size. They are saved as new files in the upload directory, not cached somewhere, so future requests are served directly by the server. This allows you to eliminate the creation of intermediate image sizes [see monkeyman-virtual-intermediate-images] or resize images based on the size used in the editor [see monkeyman-resize-image-tags].

sawantuday

import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseAccumulation;
import org.nd4j.linalg.api.ops.impl.accum.distances.EuclideanDistance;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.factory.Nd4j;

/**
 * Requires RC1<br/>
 * This is a sample implementation of K-Nearest Neighbor. KNN doesn't create a latent/hidden model, nor does it train. It simply uses it's dataset as a look up table. 
 * Then grabs the closest k data point/records. From there it is able to look at the labels for the K nearest neighbors and make a decision on what label to predict the incoming 

sawantuday

#!/bin/bash

# --------------------------------
# Totally Simple Web Server (TSWS)
# --------------------------------
#
#  (c) 2015 Dave Fletcher
#  All Rights Reserved
#
#  This is free and unencumbered software released into the public domain.

sawantuday

#!/bin/sh

# script for batch running sysbench I/O tests for mysql workload
# http://www.mysqlperformanceblog.com/2009/08/06/ec2ebs-single-and-raid-volumes-io-bencmark/

set -u
set -x
set -e
    
for size in 256M 16G; do

sawantuday

var PRINT = false;
var LOG = 1;

var SEL = ".pin";

var COND = function($node) {
	var ret = true;

	ret = checkNum($node, ".pinPrice", ">=", 10);
	if (!ret) { return false; }