karpathy

""" 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

Dante83

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import fetch_olivetti_faces
from sklearn.utils.validation import check_random_state
from scipy.ndimage import imread

from sklearn.ensemble import ExtraTreesRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression

liyu1981

#!/usr/bin/python
# Copyright (C) 2010 Michael Ligh
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of

jorng

#! /usr/bin/python

def downloadFileWithProgress(url, downloadFolder='./', sha1=None, chunk_size=8192):
  import requests, sys
  """
  Function to download a file from the specified URL.
  Outputs a basic progress bar and download stats to the CLI.

  Optionally downloads to a specified download folder, and checks the SHA1 checksum of the file.
  Chunk size can also be specified, to control the download.

stober

#! /usr/bin/env python
"""
Author: Jeremy M. Stober
Program: SOFTMAX.PY
Date: Wednesday, February 29 2012
Description: Simple softmax function.
"""

import numpy as np
npa = np.array

jtriley

#!/usr/bin/env python
import os
import shlex
import struct
import platform
import subprocess
 
 
def get_terminal_size():
    """ getTerminalSize()