steveburkett

#include <iostream>
using namespace std;
int main() {
    int i, n;
    cin >> n;
    for (i=0; i<n; i++) {
        cout << "hello world\n";
    }
    return 0;
}

clayadavis

nodes = [{'name': n, 'group': G.node[n]['question_id'], 'size': G.node[n]['count']} for n in G]
l = G.edges()
edges = [{'source': l.index(s), 'target': l.index(t), 'value': G[s][t]['weight']} for s,t in itertools.product(l, l) if s in G and t in G[s]]
json.dump({'nodes': nodes, 'links': edges}, open('filename.json', 'w'))

ceteri

$ ./bin/spark-shell
14/04/18 15:23:49 INFO spark.HttpServer: Starting HTTP Server
14/04/18 15:23:49 INFO server.Server: jetty-7.x.y-SNAPSHOT
14/04/18 15:23:49 INFO server.AbstractConnector: Started [email protected]:49861
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 0.9.1
      /_/

ceteri

ceteri

// load error messages from a log into memory
// then interactively search for various patterns

// base RDD
val lines = sc.textFile("log.txt")

// transformed RDDs
val errors = lines.filter(_.startsWith("ERROR"))
val messages = errors.map(_.split("\t")).map(r => r(1))
messages.cache()

rbnvrw

from igraph import *
import numpy as np

# Create the graph
vertices = [i for i in range(7)]
edges = [(0,2),(0,1),(0,3),(1,0),(1,2),(1,3),(2,0),(2,1),(2,3),(3,0),(3,1),(3,2),(2,4),(4,5),(4,6),(5,4),(5,6),(6,4),(6,5)]

g = Graph(vertex_attrs={"label":vertices}, edges=edges, directed=True)

visual_style = {}

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

wagnerjgoncalves

What's in Big Data Applications and Systems?

Introduction

So we will start by introducing you to where big data comes from and what kinds of things you can do with it.

We'll also provide an overview of some of the key characteristics of big data and a short summary of the data science process to get value out of big data.

aneesh-joshi

import tensorflow as tf
import numpy as np

corpus_raw = 'He is the king . The king is royal . She is the royal  queen '

# convert to lower case
corpus_raw = corpus_raw.lower()

words = []
for word in corpus_raw.split():

jonathanoheix

main_url = "http://books.toscrape.com/index.html"

import requests
result = requests.get(main_url)

result.text[:1000]