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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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from argparse import ArgumentParser

def get_options():
  parser = ArgumentParser()
  parser.add_argument('--MAX_SIZE', type=int, default=100,
                     help='max number os size')
  parser.add_argument('--GAMMA', type=float, defatult=0.9,
                      help='disount factor')
  
  options = parser.parse_args()

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import h5py
import numpy as np

with h5py.File('mydatafile.mat','r') as hf:
  print('List of arrays in this file:{}'.format(hf.keys()))
  data = hf.get('dataset_1')
  np_data = np.array(data)
  print('Shape of the array dataset_1:{}.format(np_data.shape))

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#build model many to one
embedding_vector_length = 32
model = Sequential()
model.add(Embedding(input_dim=top_words, 
                    output_dim=embedding_vector_length, 
                    input_length=max_review_length))
model.add(LSTM(100, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(100, return_sequences=True))
model.add(Dropout(0.2))

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"""
Based from : https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/cnn_mnist_simple.ipynb
"""

import tensorflow as tf
import os
import sys
import time
from tensorflow.examples.tutorials.mnist import input_data

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from __future__ import print_function
'''
Basic Multi GPU computation example using TensorFlow library.
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

'''
This tutorial requires your machine to have 2 GPUs
"/cpu:0": The CPU of your machine.

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typedef struct CAN_msgTag
{
  uint8_t Sig_1 : 8;
  uint8_t Sig_2 : 8;
  uint8_t Sig_3 : 8;
  uint8_t Sig_4 : 8;
  uint8_t Sig_5 : 8;
  uint8_t Sig_6 : 8;
  uint8_t Sig_7 : 8;
  uint8_t Sig_8 : 8;

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template <typename Target, size_t Length>
// https://www.codeproject.com/Tips/1079637/Twos-Complement-for-Unusual-Integer-Sizes
class TwosComplementUpscaler
{
  Target bitfield : Length;
  
  TwosComplementUpscaler(Target value) : bitfield(value){}
  TwosComplementUpscaler();
  
public:

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import time
import threading

ser = 0
start_time = time.time()

def callback():
    global ser, start_time

    if ser == 1:

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# ref from : https://stackoverflow.com/questions/46506850/how-can-i-get-input-from-an-xbox-one-controller-in-python
from inputs import get_gamepad
import math
import threading

class XboxController(object):
    MAX_TRIG_VAL = math.pow(2, 8)
    MAX_JOY_VAL = math.pow(2, 15)

    def __init__(self):