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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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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 os
import struct
import numpy as np

"""
Loosely inspired by http://abel.ee.ucla.edu/cvxopt/_downloads/mnist.py
which is GPL licensed.
"""

def read(dataset = "training", path = "."):

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from enums import *
import random
import tensorflow as tf
import numpy as np

class DeepGambler:
    def __init__(self, learning_rate=0.1, discount=0.95, exploration_rate=1.0, iterations=10000):
        self.learning_rate = learning_rate
        self.discount = discount # How much we appreciate future reward over current
        self.exploration_rate = 1.0 # Initial exploration rate

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'''Implementation and  Demostration of Dynamic Time Warping
Requires : python 2.7.x, Numpy 1.7.1, Matplotlib, 1.2.1'''

from math import *
import numpy as np
import sys

def DTW(A, B, window = sys.maxint, d = lambda x,y: abs(x-y)):
    # create the cost matrix
    A, B = np.array(A), np.array(B)