munk

import csv
filename = '/full/path/tofile/'
with open(filename, 'rb') as csvfile:
    reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
    result = [row for row in reader if 'N/A' in reader]    
    print result

munk

import itertools as it
import random

def cards(n, t):
    nt = n*t
    images = range(nt)
    return [[j for j in images[i:i + t]] for i in range(0, n*t, t)]

def edges(cards, link_count):
    ct = min(len(cards), link_count) # we can't have more edges than cards for prop4

munk

(ns interactive)

(defn my-fancy-function
  [x y z]
  (* x y z)) 

(comment
  (= 1 (my-fancy-function 1 1 1)) ; eval here, move to a test ns when you're done and want a regression suite
)

munk

; HTTP Client for OpenHDS-REST

(ns ohds.client
  (:require [clojure.edn :as edn]
            [clojure.tools.logging :as log]
            [org.httpkit.client :as http]
            [clojure.data.json :as json]
            [schema.core :as s])
  (:import (java.time LocalDateTime)))

munk

(ns framework
    (:require [reagent.core :as reagent]))
(def isClient (not (nil? (try (.-document js/window)
                              (catch js/Object e nil)))))

(def rflush reagent/flush)

(defn add-test-div [name]
  (let [doc     js/document
        body    (.-body js/document)

munk

#!/bin/bash

function hacstest {
    name=$1
    result=$2
    expected=$3
    if [ $result = $expected ]
    then
        echo -n "."
    else

munk

#!/bin/bash

set -eu
shopt -s nullglob

readonly base_dir=/var/lib/docker/registry
readonly output_dir=$(mktemp -d -t trace-images-XXXX)
readonly jq=/usr/bin/jq

readonly repository_dir=$base_dir/repositories

munk

# Python 3.4
 
import requests
import bs4 
 
def get_stock_price(name):
    url = "https://www.google.com/finance"
    data = requests.get(url, params={'q': name})
    bs = bs4.BeautifulSoup(data.text)
    div_price = bs.find(attrs={"id": "price-panel"})

munk

import json
import httplib
import pymongo
from time import sleep

url = 'http://citibikenyc.com/stations/json'
connection = pymongo.Connection()
db = connection['citibike']
collection = db['station_status']

munk

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