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package com.agilogy.spray.cors

import spray.http.{HttpMethods, HttpMethod, HttpResponse, AllOrigins}
import spray.http.HttpHeaders._
import spray.http.HttpMethods._
import spray.routing._

// see also https://developer.mozilla.org/en-US/docs/Web/HTTP/Access_control_CORS
trait CORSSupport {
  this: HttpService =>

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plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,bias=T,wts.only=F,rel.rsc=5,
                    circle.cex=5,node.labs=T,var.labs=T,x.lab=NULL,y.lab=NULL,
                    line.stag=NULL,struct=NULL,cex.val=1,alpha.val=1,
                    circle.col='lightblue',pos.col='black',neg.col='grey',
                    bord.col='lightblue', max.sp = F,...){

  require(scales)
  
  #sanity checks
  if('mlp' %in% class(mod.in)) warning('Bias layer not applicable for rsnns object')

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import numpy as np
from scipy.spatial.distance import cdist

def nearest_neighbor(samples, targets, samples_to_classify, metric='euclidean'):
    return targets[np.argmin(cdist(samples_to_classify, samples, metric=metric), axis=1)]

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import Image, ImageDraw

def get_colors(infile, outfile, numcolors=10, swatchsize=20, resize=150):

    image = Image.open(infile)
    image = image.resize((resize, resize))
    result = image.convert('P', palette=Image.ADAPTIVE, colors=numcolors)
    result.putalpha(0)
    colors = result.getcolors(resize*resize)

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The unreasonable effectiveness of Character-level Language Models\n",
    "## (and why RNNs are still cool)\n",
    "\n",
    "###[Yoav Goldberg](http://www.cs.biu.ac.il/~yogo)\n",

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{
    "tests": {
        "buttoncolortst" : {
            "description" : "Test which button color has a higher click-through.",
            "version" : 1,
            "rule" : null,
            "constants" : {
            },
            "salt" : "buttoncolortst",
            "buckets" : [ {

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import sys

import numpy
from nltk.cluster import KMeansClusterer, GAAClusterer, euclidean_distance
import nltk.corpus
from nltk import decorators
import nltk.stem

stemmer_func = nltk.stem.EnglishStemmer().stem
stopwords = set(nltk.corpus.stopwords.words('english'))

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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ADD JAR /opt/cloudera/parcels/CDH/lib/hive/lib/hive-contrib.jar;

DROP TABLE raw_log;

CREATE EXTERNAL TABLE raw_log(
	IP		STRING,
	timestamp	STRING,
	URL		STRING,
	referrer	STRING,
	user_agent	STRING)