debasishg

General Background and Overview

1. 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.
2. Models and Issues in Data Stream Systems
3. 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
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

azymnis

import com.twitter.scalding._
import com.twitter.algebird.{ MinHasher, MinHasher32, MinHashSignature }

/**
 * Computes similar items (with a string itemId), based on approximate
 * Jaccard similarity, using LSH.
 *
 * Assumes an input data TSV file of the following format:
 *
 *    itemId   userId

fawda123

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')

kaja47

// genrate all combinations of integers in range from 0 to `len`-1
// fast as fuck
def combIdxs(len: Int, k: Int): Iterator[Array[Int]] = {
  val arr = Array.range(0, k)
  arr(k-1) -= 1
  val end = k-1
  
  Iterator.continually {
    arr(end) += 1
    if (arr(end) >= len) {

piotrbelina

import cascading.tuple.{Fields, TupleEntry}
import com.twitter.scalding._
import java.net.URLDecoder
import scala.util.matching.Regex

class BoomerangLogJob(args: Args) extends Job(args) {
  val input = TextLine(args("input"))
  val output = TextLine(args("output"))
  val trap = Tsv(args("trap"))

bmarcot

def knapsack_aux(x: (Int, Int), is: List[Int]): List[Int] = {
  for {
    w <- is.zip(is.take(x._1) ::: is.take(is.size - x._1).map(_ + x._2))
  } yield math.max(w._1, w._2)
}

def knapsack_rec(xs: List[(Int, Int)], is: List[Int]): List[List[Int]] = {
  xs match {
    case x :: xs => knapsack_aux(x, is) :: knapsack_rec(xs, knapsack_aux(x, is))
    case _ => Nil

steipete

// Taken from the commercial iOS PDF framework http://pspdfkit.com.
// Copyright (c) 2014 Peter Steinberger, PSPDFKit GmbH. All rights reserved.
// Licensed under MIT (http://opensource.org/licenses/MIT)
//
// You should only use this in debug builds. It doesn't use private API, but I wouldn't ship it.

// PLEASE DUPE rdar://27192338 (https://openradar.appspot.com/27192338) if you would like to see this in UIKit.

#import <objc/runtime.h>
#import <objc/message.h>

Yangqing

from mincepie import mapreducer, launcher
import gflags
import glob
import leargist
import numpy as np
import os
from PIL import Image
import uuid
 
# constant value

dsturnbull

You can use cURL to upload packet captures to Packetloop. We created a simple script that shows how to login, list capture points, create capture points, upload and also check processing status.

## variables
PL_ENDPOINT=https://www.packetloop.com
PL_USERNAME=...		# your packetloop email address
PL_PASSWORD=...		# your packetloop password

## logging in
PL_TOKEN=$(curl -3 -s -b cookies.jar -c cookies.jar -X GET "$PL_ENDPOINT/init")
curl -3 -s -H "X-CSRF-Token: $PL_TOKEN" -H "Content-Type: application/json" -H "Accept: application/json" -b cookies.jar -c cookies.jar -X POST "$PL_ENDPOINT/users/sign_in.json?pretty=true" -d "{ \"user\": { \"email\": \"$PL_USERNAME\", \"password\": \"$PL_PASSWORD\" } }"

codeinthehole

from celery import chain

from django.core.management.base import BaseCommand

from . import tasks


class Command(BaseCommand):

    def handle(self, *args, **kwargs):