ilovejs

# Examples from http://andreypopp.com/posts/2012-10-30-callbacks-to-promises.html
# using IcedCoffeeScript http://maxtaco.github.com/coffee-script/ :
search = (engine, q, cb) ->
  $.ajax(url: engine, success: cb)
await
  search 'google', 'js', defer(googleSearched)
  search 'bing', 'js', defer(bingSearched)
processResults(bingSearched, googleSearched)

await

ilovejs

http://devstreaming.apple.com/videos/wwdc/2014/403xxksrj0qs8c0/403/403_hd_intermediate_swift.mov?dl=1
http://devstreaming.apple.com/videos/wwdc/2014/403xxksrj0qs8c0/403/403_sd_intermediate_swift.mov?dl=1
http://devstreaming.apple.com/videos/wwdc/2014/403xxksrj0qs8c0/403/403_intermediate_swift.pdf?dl=1
http://devstreaming.apple.com/videos/wwdc/2014/419xxli6f60a6bs/419/419_hd_advanced_graphics_and_animation_performance.mov?dl=1
http://devstreaming.apple.com/videos/wwdc/2014/419xxli6f60a6bs/419/419_sd_advanced_graphics_and_animation_performance.mov?dl=1
http://devstreaming.apple.com/videos/wwdc/2014/419xxli6f60a6bs/419/419_advanced_graphics_and_animation_performance.pdf?dl=1
http://devstreaming.apple.com/videos/wwdc/2014/101xx36lr6smzjo/101/101_hd.mov?dl=1
http://devstreaming.apple.com/videos/wwdc/2014/101xx36lr6smzjo/101/101_sd.mov?dl=1
http://devstreaming.apple.com/videos/wwdc/2014/236xxwk3fv82sx2/236/236_hd_building_interruptible_and_responsive_interactions.mov?dl=1
http://devstreaming.apple.com/videos/wwdc/2

ilovejs

Array.prototype.mergeSort=function(){
    var merge=function(left,right){
        var final=[];
        while (left.length  && right.length) {
            final.push(left[0] <= right[0] ? left.shift() : right.shift() );
        }
        return final.concat(left.concat(right));
    }
    // 递归结束
    if (this.length < 2) {

ilovejs

<#@ template language="C#" debug="true" hostspecific="true" #>
<#@ output extension=".d.ts" #>
<#@ assembly name="System.Core" #>
<#@ assembly name="Microsoft.VisualStudio.Shell.Interop.8.0" #>
<#@ assembly name="EnvDTE" #>
<#@ assembly name="EnvDTE80" #>
<#@ import namespace="System.Collections.Generic" #>
<#@ import namespace="System.Linq" #>
<#@ import namespace="System.Text" #>
<#@ import namespace="EnvDTE" #>

ilovejs

Movie Recommendations with k-Nearest Neighbors and Cosine Similarity

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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.

ilovejs

# This method finds related articles using Jaccard index (optimized for PostgreSQL).
# More info: http://en.wikipedia.org/wiki/Jaccard_index

class Article < ActiveRecord::Base

  def related(limit=10)
    Article.find_by_sql(%Q{
      SELECT
      	a.*,
      	( SELECT array_agg(t.name) FROM taggings tg, tags t

ilovejs

#!/usr/bin/env ruby

# Please read http://otobrglez.opalab.com for more information about this code.

class Book < Struct.new(:title)

  def words
    @words ||= self.title.gsub(/[a-zA-Z]{3,}/).map(&:downcase).uniq.sort
  end

ilovejs

library(shiny)
library(ggplot2)
library(RCurl)
library(reshape2)

ilovejs

#
# Get appraised value of car from Edmunds.com using the developer API with R
# Reference: http://developer.edmunds.com/docs/read/The_Vehicle_API
#

# set working dir
setwd('~/R/carvalue')

#load libraries
library(RJSONIO)

ilovejs

Frontend Development

Attention: the list was moved to

https://github.com/dypsilon/frontend-dev-bookmarks

This page is not maintained anymore, please update your bookmarks.

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