ilovejs

# Back-Propagation Neural Networks
# another way: solve it as a Regression Problem
# Written in Python.  See http://www.python.org/
# Modified by JSun to solve the problem here: http://www.weibo.com/1497035431/ynPEvC78V
# Neil Schemenauer <nas@arctrix.com>

import math
import random
import string

ilovejs

package main

import (
	"net/http"
	"net/url"
	"log"
	"io/ioutil"
	"regexp"
	"fmt"
	//"net/http/httputil"

ilovejs

import numpy as np
import pylab as pl
import pandas as pd

from sklearn import svm
from sklearn import linear_model
from sklearn import tree

from sklearn.metrics import confusion_matrix

ilovejs

if (!require(quantmod)) {
  stop("This app requires the quantmod package. To install it, run 'install.packages(\"quantmod\")'.\n")
}

# Download data for a stock if needed, and return the data
require_symbol <- function(symbol, envir = parent.frame()) {
  if (is.null(envir[[symbol]])) {
    envir[[symbol]] <- getSymbols(symbol, auto.assign = FALSE)
  }

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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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

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

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

# 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

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.

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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.