dwf

"""Extract reference documentation from the NumPy source tree.

"""

import inspect
import textwrap
import re
import sys
import types

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# These hyperparameters, identified by David Warde-Farley via random search,
# obtain 1.08% on MNIST, nearly matching the 1.05% in Nitish Srivastava's
# Master's thesis.
#
# An important difference is that this uses only the first 50,000 examples
# and does early stopping on a validation set of the last 10,000 training
# points, whereas Nitish Srivastava's results trained on the entire training
# set for a given number of epochs. It is quite possible that re-training
# with these hyperparameters on the entire 60,000 using some alternate stopping
# criterion (matching the best training set likelihood of this job, for instance,

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from functools import wraps
import sys


# Goddamnit Python 2/3 differences.
str_type = str if sys.version_info[0] >= '3' else basestring


def filename_or_file_like(mode):
    """Decorator that checks if the first argument to a function is

dwf

try:
    from queue import Queue, Empty
except ImportError:
    from Queue import Queue, Empty
import threading
import numpy
from numpy.lib.format import read_magic, read_array_header_1_0


class BufferedChunkedNPY(object):

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# Copyright (c) 2015 David Warde-Farley.
# 
# Permission is granted to use this code under the MIT license: 
# http://opensource.org/licenses/mit-license.php

import logging
import progressbar
import time

dwf

class MobileShellMousefix < Formula
  homepage "http://mosh.mit.edu/"
  revision 2

  head do
    url "https://github.com/lpkruger/mosh.git", :revision => "d5f75ec54a"

    depends_on "autoconf" => :build
    depends_on "automake" => :build
  end

dwf

"""Very simple PUSH-PULL reusable producer-consumer with ZeroMQ."""

# By David Warde-Farley. Released under the 3-clause BSD license.

import time
from multiprocessing import Process
import zmq


def _producer_wrapper(f, port, addr='tcp://127.0.0.1'):

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"""
A demonstration of how to deal with object-specific random number
generator states. One application is if you want two objects to
work with the same pseudorandom sequence but don't particularly
want to generate them in advance, or to replicate results after
serializing and de-serializing an object.

By David Warde-Farley, dwf at cs.toronto.edu, January 2010.
Released under BSD license.
"""

dwf

import numpy as np

def sample_pf_ball(p,b):
    """
    Samples N binary variables using the product form distribution given in
    the vector b (of length N), where b[n] is the probability that s[n]=1.
    However, only K variables may on at a time, where K is the length of
    vector b minus 1, and b[k] is a distribution that biases the number of
    variables set to 1.
    %

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#!/usr/bin/env python
"""
NumPy implementation of the classic 'four regions' benchmark.
By David Warde-Farley -- user AT cs dot toronto dot edu (user = dwf)

Redistributable under the terms of the 3-clause BSD license 
(see http://www.opensource.org/licenses/bsd-license.php for details)
"""

import numpy as np