dwf

"""Display Theano functions in the IPython notebook with pydotprint."""

__author__ = "David Warde-Farley"
__copyright__ = "Copyright 2012, Universite de Montreal"
__credits__ = ["David Warde-Farley"]
__license__ = "3-clause BSD"
__email__ = "wardefar@iro"
__maintainer__ = "David Warde-Farley"

dwf

dwf

dwf

class KeyAwareDefaultDict(dict):
    """
    Like a standard library defaultdict, but pass the key
    to the default factory.
    """
    def __init__(self, default_factory=None):
        self.default_factory = default_factory

    def __getitem__(self, key):
        if key not in self and self.default_factory is not None:

dwf

"""
A matplotlib-based image viewer utility function.
"""
import matplotlib.pyplot as plt

def image_view(arr, fig=None, frame_on=True, *args, **kwargs):
    """
    Sensibly display an image in a plot window.

    Parameters

dwf

import theano
import functools


def cached_theano_function(fn):
    @functools.wraps(fn)
    def wrapped(self):
        if not hasattr(self, '_function_cache'):
            self._function_cache = {}
        if fn.func_name not in self._function_cache:

dwf

"""
Parallelized k-means module.

By David Warde-Farley, February 2012. Licensed under the 3-clause BSD.
"""
cimport cython
from cython.parallel import prange
import numpy as np
cimport numpy as np
from numpy.random import normal

dwf

"""
Example of how to read a partial NumPy array stored in NPY
format off of disk.
"""
__author__ = "David Warde-Farley"
__copyright__ = "Copyright (c) 2012 by " + __author__
__license__ = "3-clause BSD"
__email__ = "[email protected]"

import struct

dwf

#!/usr/bin/env python

# Copyright (c) 2009, David Warde-Farley
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.

dwf

"""
L1-penalized minimization using the feature sign search algorithm.
"""

import logging
import numpy as np
log = logging.getLogger("feature_sign")
log.setLevel(logging.INFO)