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from pyspark import SparkContext

import numpy as np

from sklearn.cross_validation import train_test_split, Bootstrap
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier

def run(sc):

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curl -s https://api.github.com/orgs/twitter/repos?per_page=200 | ruby -rubygems -e 'require "json"; JSON.load(STDIN.read).each { |repo| %x[git clone #{repo["ssh_url"]} ]}'

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from pattern.en import sentiment
import colors

items = sentiment.viewitems()

# some text
text = ("Then, the module's variables, functions, and classes will be available to the caller through "
    "the module’s namespace, a central concept in programming that is particularly helpful and "
    "powerful in Python. Thanks to the way imports and modules are handled in Python, it is "
    "relatively easy to structure a Python project. Easy, here, means that you do not have many "

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import requests

API_KEY = 'API_KEY'
IDEN = 'DEVICE_ID'

# get the list of available devices
resp = requests.get('https://api.pushbullet.com/v2/devices',auth=(API_KEY,''))
print resp.json()

data = {