kastnerkyle

from scipy.linalg import svd
from scipy.io import wavfile
from scipy.cluster.vq import vq
from scipy.signal import firwin
import matplotlib.pyplot as plt
import numpy as np
import zipfile
import tarfile
from numpy.lib.stride_tricks import as_strided
import theano

kastnerkyle

from scipy.linalg import svd
from scipy.io import wavfile
from scipy.cluster.vq import vq
from scipy.signal import firwin
import matplotlib.pyplot as plt
import numpy as np
import zipfile
import tarfile
from numpy.lib.stride_tricks import as_strided
import theano

kastnerkyle

class SPPLayer(lasagne.layers.Layer):
    def __init__(self, incoming, **kwargs):
        super(SPPLayer, self).__init__(incoming, **kwargs)

        # divide by 4 gives 16 patches
        self.win1 = (int(np.floor(incoming.output_shape[2]/4.0)), int(np.floor(incoming.output_shape[3]/4.0)))
        self.str1 = (int(np.ceil(incoming.output_shape[2]/4.0)), int(np.ceil(incoming.output_shape[3]/4.0)))

        # divide by 2 gives 4 patches
        self.win2 = (int(np.floor(incoming.output_shape[2]/2.0)), int(np.floor(incoming.output_shape[3]/2.0)))

kastnerkyle

 def gaussian_density_batch(x, mean, stddev, correlation, compute_derivatives=False): 
  """
  Compute the Gaussian density at x for a 2D normal distribution with parameters mean, stddev, correlation.
  
  This works simultaneously on a batch of inputs.  The inputs should have dimensions: 
    x.shape = (n, 1, 2)
    mean.shape = stddev.shape = (n, m, 2)
    correlation.shape = (n, m, 1)
  where n*m is the number of different Gaussian density functions that we want to evaluate, on n input points x.  
  So the same input x is plugged into the density for m Gaussian pdfs.  (This is convenient for evaluating a 

kastnerkyle

"""
bitmap utils from Shawn Tan
"""
# Author: Kyle Kastner
# License: BSD 3-clause
# Equation 46 from http://arxiv.org/abs/1308.0850
from theano import tensor
from scipy import linalg
import theano
import numpy as np

kastnerkyle

raise ValueError("DEPRECATED/FROZEN - see https://github.com/kastnerkyle/tools for the latest")

# License: BSD 3-clause
# Authors: Kyle Kastner
# Harvest, Cheaptrick, D4C, WORLD routines based on MATLAB code from M. Morise
# http://ml.cs.yamanashi.ac.jp/world/english/
# MGC code based on r9y9 (Ryuichi Yamamoto) MelGeneralizedCepstrums.jl
# Pieces also adapted from SPTK
from __future__ import division
import numpy as np

kastnerkyle

# code from Stack Overflow with minor Python 3 tweaks
# Thanks to Andrew from SO for making such great code!
# http://stackoverflow.com/questions/14416130/dynamically-create-a-list-of-shared-arrays-using-python-multiprocessing
# For a reference on parallel processing in Python see this tutorial by David Beazley
# http://www.slideshare.net/dabeaz/an-introduction-to-python-concurrency
import time
import ctypes
import logging
try:
    import Queue

kastnerkyle

# Author: Kyle Kastner
# License: BSD 3-Clause
# For a reference on parallel processing in Python see tutorial by David Beazley
# http://www.slideshare.net/dabeaz/an-introduction-to-python-concurrency
# Loosely based on IBM example
# http://www.ibm.com/developerworks/aix/library/au-threadingpython/
try:
    import Queue
except ImportError:
    import queue as Queue

kastnerkyle

# Example from J.F. Sebastien on SO
# http://stackoverflow.com/questions/7894791/use-numpy-array-in-shared-memory-for-multiprocessing/7908612#7908612
import ctypes
import logging
import multiprocessing as mp

from contextlib import closing

import numpy as np

kastnerkyle

# Authors: Kyle Kastner, Francesco Visin
# License: BSD 3-Clause
try:
    import Queue
except ImportError:
    import queue as Queue
import threading
import time
from matplotlib.path import Path
from PIL import Image