Tim Sainburg timsainb

## AdjustedMutualInformation.scaja
/*
 * Copyright (c) 2011, Lawrence Livermore National Security, LLC. Produced at
 * the Lawrence Livermore National Laboratory. Written by Keith Stevens,
 * kstevens@cs.ucla.edu OCEC-10-073 All rights reserved.
 *
 * This file is part of the S-Space package and is covered under the terms and
 * conditions therein.
 *
 * The S-Space package is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License version 2 as published

## unsupervised_alt.py
""" Unsupervised evaluation metrics. """

# License: BSD Style.

from itertools import combinations
import numpy as np

from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import distance_metrics
from sklearn.metrics.pairwise import pairwise_distances

## mutual_info.py
'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

This code is maintained at https://github.com/mutualinfo/mutual_info
Please download the latest code there, to have improvements and
bug fixes.

## audio_tools.py
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

## rgb_to_hsv_np.py
#
# Copyright (c) 2016, Nikolay Polyarnyi
# All rights reserved.
#

import numpy as np


def rgb_to_hsv(rgb):
    """

## Attention.py

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

## blog_tensorflow_variational_auto_encoder.py
# Full example for my blog post at:
# https://danijar.com/building-variational-auto-encoders-in-tensorflow/

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

tfd = tf.contrib.distributions

## Signal reconstruction from spectrograms.ipynb

      
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                kastnerkyle
                / Signal reconstruction from spectrograms.ipynb
            
            
              Created
              May 31, 2018 18:17
                — forked from carlthome/Signal reconstruction from spectrograms.ipynb
            
              
                Try to recover audio from filtered magnitudes when phase information has been lost.
              
          
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## unsupervisedpcfg.py
import numpy as np
import pandas as pd

from random import shuffle, choice
from scipy.stats import dirichlet
from scipy.misc import logsumexp
from nltk.tree import Tree

class UnsupervisedPCFG(object):
    '''unsupervised pcfg induction with EM

## dtw_mse.py
# based on https://github.com/kylerbrown/ezdtw
# with modifications to be fully njit-able

import numpy as np
from numba import njit

@njit
def sqeuclidean(a, b):
    return np.sum((a - b)**2)
	/*
	* Copyright (c) 2011, Lawrence Livermore National Security, LLC. Produced at
	* the Lawrence Livermore National Laboratory. Written by Keith Stevens,
	* kstevens@cs.ucla.edu OCEC-10-073 All rights reserved.
	*
	* This file is part of the S-Space package and is covered under the terms and
	* conditions therein.
	*
	* The S-Space package is free software: you can redistribute it and/or modify
	* it under the terms of the GNU General Public License version 2 as published
	""" Unsupervised evaluation metrics. """

	# License: BSD Style.

	from itertools import combinations
	import numpy as np

	from sklearn.utils import check_random_state
	from sklearn.metrics.pairwise import distance_metrics
	from sklearn.metrics.pairwise import pairwise_distances
	'''
	Non-parametric computation of entropy and mutual-information

	Adapted by G Varoquaux for code created by R Brette, itself
	from several papers (see in the code).

	This code is maintained at https://github.com/mutualinfo/mutual_info
	Please download the latest code there, to have improvements and
	bug fixes.
	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
	#
	# Copyright (c) 2016, Nikolay Polyarnyi
	# All rights reserved.
	#

	import numpy as np


	def rgb_to_hsv(rgb):
	"""

	from keras import backend as K, initializers, regularizers, constraints
	from keras.engine.topology import Layer


	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	# Full example for my blog post at:
	# https://danijar.com/building-variational-auto-encoders-in-tensorflow/

	import numpy as np
	import matplotlib.pyplot as plt
	import tensorflow as tf
	from tensorflow.examples.tutorials.mnist import input_data

	tfd = tf.contrib.distributions
	import numpy as np
	import pandas as pd

	from random import shuffle, choice
	from scipy.stats import dirichlet
	from scipy.misc import logsumexp
	from nltk.tree import Tree

	class UnsupervisedPCFG(object):
	'''unsupervised pcfg induction with EM
	# based on https://github.com/kylerbrown/ezdtw
	# with modifications to be fully njit-able

	import numpy as np
	from numba import njit

	@njit
	def sqeuclidean(a, b):
	return np.sum((a - b)**2)