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""" From:  http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """
from keras.models import Sequential  
from keras.layers.core import TimeDistributedDense, Activation, Dropout  
from keras.layers.recurrent import GRU
import numpy as np

def _load_data(data, steps = 40):  
    docX, docY = [], []
    for i in range(0, data.shape[0]/steps-1):
        docX.append(data[i*steps:(i+1)*steps,:])

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# Time series forecasting based on multiple time series, including the original one
# This script is based on the following examples and discussions:
# https://gist.github.com/lukovkin/1aefa4509e066690b892
# https://groups.google.com/forum/#!topic/keras-users/9GsDwkSdqBg

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
import theano

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import pandas as pd  
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100
pdata = pd.DataFrame({"a":flow, "b":flow})  
pdata.b = pdata.b.shift(9)  
data = pdata.iloc[10:] * random()  # some noise  

import numpy as np

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#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
import numpy as np
import random
from random import shuffle
import tensorflow as tf

from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn

NUM_EXAMPLES = 10000

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upstream myapp {
    server 127.0.0.1:8081;
}

limit_req_zone $binary_remote_addr zone=login:10m rate=1r/s;

server {
    listen 443 ssl spdy;
    server_name _;
  

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""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

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// set-up a connection between the client and the server
var socket = io.connect();

// let's assume that the client page, once rendered, knows what room it wants to join
var room = "abc123";

socket.on('connect', function() {
   // Connected, let's sign-up for to receive messages for this room
   socket.emit('room', room);
});

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#!/usr/bin/env/python
# 
# More of a reference of using jinaj2 without actual template files.
# This is great for a simple output transformation to standard out.
#
# Of course you will need to "sudo pip install jinja2" first!
#
# I like to refer to the following to remember how to use jinja2 :)
# http://jinja.pocoo.org/docs/templates/
#

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from ib.opt import Connection, message
from ib.ext.Contract import Contract
from ib.ext.Order import Order

from random import randint
import time

def error_handler(msg):
    print ("Server Error: %s" % msg)