tarex

# Node.js app Docker file

FROM ubuntu:14.04
MAINTAINER Thom Nichols "[email protected]"

ENV DEBIAN_FRONTEND noninteractive

RUN apt-get update
RUN apt-get -qq update
RUN apt-get install -y nodejs npm

tarex

#!/bin/bash
set -eu
 
main() {
  local args=("$@")
  local inFile=$(pwd)/docker-compose.yml
  local outFile=$(pwd)/$1
    
  if [[ $# -lt 2 ]]; then
  	usage

tarex

/**
 * @file A WordPress-like hook system for JavaScript.
 *
 * This file demonstrates a simple hook system for JavaScript based on the hook
 * system in WordPress. The purpose of this is to make your code extensible and
 * allowing other developers to hook into your code with their own callbacks.
 *
 * There are other ways to do this, but this will feel right at home for
 * WordPress developers.
 *

tarex

##Recommendation Engine Services

####IREUS Recommendation Engine for stores as SaaS

http://www.ireus.net/

####Plista

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<?php
	class comment_walker extends Walker_Comment {
		var $tree_type = 'comment';
		var $db_fields = array( 'parent' => 'comment_parent', 'id' => 'comment_ID' );
 
		// constructor – wrapper for the comments list
		function __construct() { ?>

			<section class="comments-list">

tarex

First of all you need to be able to run MAMP in port 80. This is a "heat check" if you don't have any process jamming http ports. You can check it like this:

sudo lsof | grep LISTEN

If you do happen to have any process with something like this *:http (LISTEN), you are in trouble. Before with adventure check if it isn't MAMP itself (yeah, you should close that beforehand)

ps <pid of that process>

If you don't see MAMP, you are in good hands, I have just the thing for you:

tarex

show_icons() {
  defaults write com.apple.finder CreateDesktop true
  killall Finder
}

hide_icons() {
  defaults write com.apple.finder CreateDesktop false
  killall Finder
}

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daemon off;

worker_processes 4;

events {
  use epoll;
  accept_mutex on;
  multi_accept on;
  worker_connections 1024;
}

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Movie Recommendations with k-Nearest Neighbors and Cosine Similarity

---

Introduction

The k-nearest neighbors (k-NN) algorithm is among the simplest algorithms in the data mining field. Distances / similarities are calculated between each element in the data set using some distance / similarity metric ^^[1]^ that the researcher chooses (there are many distance / similarity metrics), where the distance / similarity between any two elements is calculated based on the two elements' attributes. A data element’s k-NN are the k closest data elements according to this distance / similarity.

---

1. A distance metric measures distance; the higher the distance the further apart the neighbors. A similarity metric measures similarity; the higher the similarity the closer the neighbors.

tarex

import express from "express";

/**
 * Takes a route handling function and returns a function
 * that wraps it after first checking that the strings in
 * `reserved` are not part of `req.body`. Used for ensuring
 * create and update requests do not overwrite server-generated
 * values.
 */
function checkReservedParams(routeHandler, ...reserved) {