michaelfeng

""" 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

michaelfeng

"""adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state"""
import tensorflow as tf, numpy as np
RNNCell = tf.nn.rnn_cell.RNNCell

class BNLSTMCell(RNNCell):
    '''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
    def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95):
        """
        * max bn steps is the maximum number of steps for which to store separate population stats
        """

michaelfeng

import glob, os
for filename in glob.iglob(os.path.join('.', '*')):
    os.rename(filename, filename + '.jpg')

michaelfeng

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

michaelfeng

#!/usr/bin/env python2
'''Serving dynamic images with Pandas and matplotlib (using flask).'''

import matplotlib
matplotlib.use('Agg')

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from cStringIO import StringIO

michaelfeng

import os

import falcon
import jinja2


def load_template(name):
    path = os.path.join('templates', name)
    with open(os.path.abspath(path), 'r') as fp:
        return jinja2.Template(fp.read())

michaelfeng

def karatsuba(x,y):
	"""Function to multiply 2 numbers in a more efficient manner than the grade school algorithm"""
	if len(str(x)) == 1 or len(str(y)) == 1:
		return x*y
	else:
		n = max(len(str(x)),len(str(y)))
		nby2 = n / 2
		
		a = x / 10**(nby2)
		b = x % 10**(nby2)

michaelfeng

import numpy as np
import cv2

def getSobel (channel):

    sobelx = cv2.Sobel(channel, cv2.CV_16S, 1, 0, borderType=cv2.BORDER_REPLICATE)
    sobely = cv2.Sobel(channel, cv2.CV_16S, 0, 1, borderType=cv2.BORDER_REPLICATE)
    sobel = np.hypot(sobelx, sobely)

    return sobel;

michaelfeng

import numpy as np
import cv2


class BackGroundSubtractor:
	# When constructing background subtractor, we
	# take in two arguments:
	# 1) alpha: The background learning factor, its value should
	# be between 0 and 1. The higher the value, the more quickly
	# your program learns the changes in the background. Therefore, 

michaelfeng

"""
tiny script to convert a pandas data frame into a JSON object
"""
 
import json as json

def df_to_json(df, filename=''):
    x = df.reset_index().T.to_dict().values()
    if filename:
        with open(filename, 'w+') as f: f.write(json.dumps(x))