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bob
bob.learn.em
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2950575c
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2950575c
authored
2 years ago
by
Yannick DAYER
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meta [readme]: Switch the readme.rst to markdown.
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meta [readme]: Switch the readme.rst to markdown
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include LICENSE README.
rst
include LICENSE README.
md
recursive-include doc conf.py *.rst
recursive-include src/bob/learn/em/data *.*
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rst
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md
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.. vim: set fileencoding=utf-8 :
.. Mon 15 Aug 2016 09:48:28 CEST
.. image:: https://img.shields.io/badge/docs-latest-orange.svg
:target: https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/sphinx/index.html
.. image:: https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/pipeline.svg
:target: https://gitlab.idiap.ch/bob/bob.learn.em/commits/master
.. image:: https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/coverage.svg
:target: https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/coverage
.. image:: https://img.shields.io/badge/gitlab-project-0000c0.svg
:target: https://gitlab.idiap.ch/bob/bob.learn.em
[

](https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/sphinx/index.html)
[

](https://gitlab.idiap.ch/bob/bob.learn.em/commits/master)
[

](https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/coverage)
[

](https://gitlab.idiap.ch/bob/bob.learn.em)
=================================================
Expectation Maximization Machine Learning Tools
=================================================
# Expectation Maximization Machine Learning Tools
This package is part of the signal-processing and machine learning toolbox
Bob
_. It contains routines for learning probabilistic models via Expectation
Maximization (EM).
[
Bob
](
https://www.idiap.ch/software/bob
)
. It contains routines for learning
probabilistic models via Expectation
Maximization (EM).
The EM algorithm is an iterative method that estimates parameters for
statistical models, where the model depends on unobserved latent variables. The
...
...
@@ -28,7 +19,8 @@ computes parameters maximizing the expected log-likelihood found on the E step.
These parameter-estimates are then used to determine the distribution of the
latent variables in the next E step.
The package includes the machine definition per se and a selection of different trainers for specialized purposes:
The package includes the machine definition per se and a selection of different
trainers for specialized purposes:
-
Maximum Likelihood (ML)
-
Maximum a Posteriori (MAP)
...
...
@@ -40,23 +32,17 @@ The package includes the machine definition per se and a selection of different
-
EM Principal Component Analysis (EM-PCA)
Installation
------------
## Installation
Complete Bob's
`
installation
`_ instructions. Then, to install this package,
run
:
:
Complete Bob's
[
installation
](
https://www.idiap.ch/software/bob/install
)
instructions. Then, to install this package,
run:
$ conda install bob.learn.em
```
sh
conda
install
bob.learn.em
```
Contact
-------
## Contact
For questions or reporting issues to this software package, contact our
development `mailing list`_.
.. Place your references here:
.. _bob: https://www.idiap.ch/software/bob
.. _installation: https://www.idiap.ch/software/bob/install
.. _mailing list: https://www.idiap.ch/software/bob/discuss
development
[
mailing list
](
https://www.idiap.ch/software/bob/discuss
)
.
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pyproject.toml
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...
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@@ -58,7 +58,7 @@
package-dir
=
{
""
=
"src"
}
[tool.setuptools.dynamic]
readme
=
{
file
=
"README.
rst
"
}
readme
=
{
file
=
"README.
md
"
}
[tool.distutils.bdist_wheel]
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