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Jan Melchior authored and Jan Melchior committed Apr 19, 2017
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8 changes: 8 additions & 0 deletions .gitignore
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.pyc
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140 changes: 102 additions & 38 deletions README.md
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# PyDeep

NEWS: In the following weeks there will be a lot of updates, see list of features below

PyDeep is a machine learning / deep learning library with focus on unsupervised learning especially Restricted Boltzmann machines.

So why another ML lib?

- First of all, the library will contain code I produced during my research that allows to reproduce the results in corresponding publications.
- If you simply want to use standard algorithms, use one of the big ML libs having GPU support, symbolic differentiation, etc. , this library is not meant to compete with those libraries!
- The focus is on well documented and modular code that allows you to understand the functionality and thus modify the code easily.

Feature list:
- Centered Binary-Binary RBMs
- CD and PCD sampling
- Centered gradient
- Calculate true partition function
- Calculate Log-likelihood
- Calculate reconstruction error
- Visualization, profiling, misc. tools

Being prepared for commit:
- Centered Gaussian-Binary RBMs
- Centered Gaussian-Binary RBMs with trainable variance
- Centered Binary-Rectifier RBMs
- Centered Rectifier-Binary RBMs
- Centered RectRect RBM (RR-RBM)
- Centered GaussianRectVariance RBM (GRV-RBM)
- Centered RBMs with additional label units
- Centered Softmax RBMs
- unit tests
- PCA
- ZCA
- Fast ICA
- Annealed inportant sampling
- Reverse annealed important sampling
- MDP wrapper
- Auto encoder
Documentation: http://pydeep.readthedocs.io/en/latest/index.html


Welcome

PyDeep is a machine learning / deep learning library with focus on unsupervised learning.
The library has a modular design, is well documented and purely written in Python/Numpy.
This allows you to understand, use, modify, and debug the code easily. Furthermore,
its extensive use of unittests assures a high level of reliability and correctness.

News

- The documentation is updated to restructured text
- Documentation hosted
- Next the unit tests will be added
- Upcoming: Tutorials will be added
- Upcoming: Auto encoders will be added
- Upcoming: MDP integration will be added
- Upcoming: Deep Boltzmann machines will be added
- Upcoming: Feed Forward neural networks will be added

Features

- Principal Component Analysis (PCA)

- Zero Phase Component Analysis (ZCA)

- Independent Component Analysis (ICA)

- centered BinaryBinary RBM (BB-RBM)

- centered GaussianBinary RBM (GB-RBM) with fixed variance

- centered GaussianBinaryVariance RBM (GB-RBM) with trainable variance

- centered BinaryBinaryLabel RBM (BBL-RBM)

- centered GaussianBinaryLabel RBM (GBL-RBM)

- centered BinaryRect RBM (BR-RBM)

- centered RectBinary RBM (RB-RBM)

- centered RectRect RBM (RR-RBM)

- centered GaussianRect RBM (GR-RBM)

- centered GaussianRectVariance RBM (GRV-RBM)

- Gibbs Sampling

- Persistent Gibbs Sampling

- Parallel Tempering Sampling

- Independent Parallel Tempering Sampling

- Annealed Importance Sampling (AIS)

- reverse Annealed Importance Sampling (AIS)

- Contrastive Divergence (CD)

- Persistent Contrastive Divergence (PCD)

- Tempering Sampling Contrastive Divergence (PT)

- Independent Tempering Sampling Contrastive Divergence (IPT)

- Exact Gradient (GD)


Scientific use

The library contains code I have written during my PhD research allowing you to reproduce
the results described in the following publications.

- `Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. Melchior, J., Wang, N., & Wiskott, L.. (2017). PLOS ONE, 12(2), 1–24. <http://doi.org/10.1371/journal.pone.0171015>`_

- `How to Center Deep Boltzmann Machines. Melchior, J., Fischer, A., & Wiskott, L.. (2016). Journal of Machine Learning Research, 17(99), 1–61. <http://jmlr.org/papers/v17/14-237.html>`_

- `Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics Wang, N., Melchior, J., & Wiskott, L.. (2014). (Vol. 1401.5900). arXiv.org e-Print archive. <http://arxiv.org/abs/1401.5900>`_

- `How to Center Binary Restricted Boltzmann Machines (Vol. 1311.1354). Melchior, J., Fischer, A., Wang, N., & Wiskott, L.. (2013). arXiv.org e-Print archive. <http://arxiv.org/pdf/1311.1354.pdf>`_

- `An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images. Wang, N., Melchior, J., & Wiskott, L.. (2012). In Proc. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 25–27, Bruges, Belgium (pp. 287–292). <https://www.ini.rub.de/PEOPLE/wiskott/Reprints/WangMelchiorEtAl-2012a-ProcESANN-RBMImages.pdf>`_

- `Learning Natural Image Statistics with Gaussian-Binary Restricted Boltzmann Machines. Melchior, J, 29.05.2012. Master’s thesis, Applied Computer Science, Univ. of Bochum, Germany. <https://www.ini.rub.de/PEOPLE/wiskott/Reprints/Melchior-2012-MasterThesis-RBMs.pdf>`_

IF you want to use PyDeep in your publication, you can cite it as follows.

.. code-block:: latex

@misc{melchior2017pydeep,
title={PyDeep},
author={Melchior, Jan},
year={2017},
publisher={GitHub},
howpublished={\url{https://github.com/MelJan/PyDeep.git}},
}

Contact:

`Jan Melchior <https://www.ini.rub.de/the_institute/people/jan-melchior/>`_
20 changes: 20 additions & 0 deletions docs/Makefile
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# Minimal makefile for Sphinx documentation
#

# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SPHINXPROJ = PyDeep
SOURCEDIR = .
BUILDDIR = _build

# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

.PHONY: help Makefile

# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
165 changes: 165 additions & 0 deletions docs/conf.py
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# -*- coding: utf-8 -*-
#
# PyDeep documentation build configuration file, created by
# sphinx-quickstart on Wed Apr 5 20:57:40 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
sys.path.insert(0, os.path.abspath('../'))


# -- General configuration ------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.intersphinx','sphinx.ext.autosectionlabel']#,'autoapi.extension']

# Document Python Code
#autoapi_type = 'python'
#autoapi_dirs = ['../pydeep']
#autoapi_file_pattern = '*.py'
#autoapi_options = ['members', 'private-members', 'special-members']

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']

# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'

# The master toctree document.
master_doc = 'index'

# General information about the project.
project = u'PyDeep'
copyright = u'2017, Jan Melchior'
author = u'Jan Melchior'

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = u'1.1.0'
# The full version, including alpha/beta/rc tags.
release = u'1.1.0'

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
# language = None

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = ['_build']

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'

# If true, `todo` and `todoList` produce output, else they produce nothing.
# todo_include_todos = False


# -- Options for HTML output ----------------------------------------------

# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'default'

# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']


# -- Options for HTMLHelp output ------------------------------------------

# Output file base name for HTML help builder.
htmlhelp_basename = 'PyDeepdoc'


# -- Options for LaTeX output ---------------------------------------------

latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',

# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',

# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',

# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'PyDeep.tex', u'PyDeep Documentation',
u'Jan Melchior', 'manual'),
]


# -- Options for manual page output ---------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'pydeep', u'PyDeep Documentation',
[author], 1)
]


# -- Options for Texinfo output -------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'PyDeep', u'PyDeep Documentation',
author, 'PyDeep', 'One line description of project.',
'Miscellaneous'),
]




# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {'https://docs.python.org/': None}

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