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# this script has been tested and worked in a freshly installed Ubuntu 16.04 and 16.10
# it assumes that you are running airflow in a private netowrk and no need to be worry about outside access
# if that's not the case, the lines for PostgreSQL and Redis in this script need to be updated accordingly
# run as root
sudo su

# initial system updates and installs
apt-get update && apt-get upgrade -y && apt-get autoremove && apt-get autoclean

apt-get -y install build-essential binutils gcc make git htop nethogs tmux

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from random import random

# function to optimize: takes in a list of decision variables, returns an objective value
# this is the Rosenbrock function: http://en.wikipedia.org/wiki/Rosenbrock_function
# the global minimum is located at x = (1,1) where f(x) = 0
def my_function(x):
  return (1-x[0])**2 + 100*(x[1] - x[0]**2)**2

# function to perform (a very crude, stupid) optimization
# bounds = lower and upper bounds for each decision variable (2D list)

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from __future__ import print_function

from itertools import starmap

import tensorflow as tf
import random
from tensorflow.python.ops import rnn
import math

flags = tf.flags

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#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

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#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

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import operator
import threading
from functools import reduce

import keras
import keras.backend as K
from keras.engine import Model
import numpy as np
import tensorflow as tf
import time

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import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim

from torch.autograd import Variable

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var cluster = require('cluster');

if (cluster.isWorker) {

  console.log('Worker ' + process.pid + ' has started.');

  // Send message to master process.
  process.send({msgFromWorker: 'This is from worker ' + process.pid + '.'})

  // Receive messages from the master process.

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#!/usr/bin/env python
# -*- coding: UTF-8 -*-

import json
import oauth2 # https://raw.github.com/brosner/python-oauth2/master/oauth2/__init__.py
from pprint import pprint
import urllib2


stream_url = "https://stream.twitter.com/1/statuses/filter.json"