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):

dsparks

# Cross-matrix distances and different measurement options, with "proxy"
doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("proxy", "MASS", "Zelig")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Invent two data frames
voterIdealPoints <- data.frame(matrix(rnorm(26*2), ncol = 2))
rownames(voterIdealPoints) <- letters