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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 doc conf.py *.rst
recursive-include src/bob/learn/em/data *.* recursive-include src/bob/learn/em/data *.*
.. vim: set fileencoding=utf-8 :
.. Mon 15 Aug 2016 09:48:28 CEST
.. image:: https://img.shields.io/badge/docs-latest-orange.svg [![badge latest doc](https://img.shields.io/badge/docs-latest-orange.svg)](https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/sphinx/index.html)
:target: https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/sphinx/index.html [![badge pipeline](https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/pipeline.svg)](https://gitlab.idiap.ch/bob/bob.learn.em/commits/master)
.. image:: https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/pipeline.svg [![badge coverage](https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/coverage.svg)](https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/coverage)
:target: https://gitlab.idiap.ch/bob/bob.learn.em/commits/master [![badge gitlab](https://img.shields.io/badge/gitlab-project-0000c0.svg)](https://gitlab.idiap.ch/bob/bob.learn.em)
.. 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
# Expectation Maximization Machine Learning Tools
=================================================
Expectation Maximization Machine Learning Tools
=================================================
This package is part of the signal-processing and machine learning toolbox This package is part of the signal-processing and machine learning toolbox
Bob_. It contains routines for learning probabilistic models via Expectation [Bob](https://www.idiap.ch/software/bob). It contains routines for learning
Maximization (EM). probabilistic models via Expectation Maximization (EM).
The EM algorithm is an iterative method that estimates parameters for The EM algorithm is an iterative method that estimates parameters for
statistical models, where the model depends on unobserved latent variables. The 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. ...@@ -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 These parameter-estimates are then used to determine the distribution of the
latent variables in the next E step. 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 Likelihood (ML)
- Maximum a Posteriori (MAP) - Maximum a Posteriori (MAP)
...@@ -40,23 +32,17 @@ The package includes the machine definition per se and a selection of different ...@@ -40,23 +32,17 @@ The package includes the machine definition per se and a selection of different
- EM Principal Component Analysis (EM-PCA) - EM Principal Component Analysis (EM-PCA)
Installation ## Installation
------------
Complete Bob's `installation`_ instructions. Then, to install this package, Complete Bob's [installation](https://www.idiap.ch/software/bob/install)
run:: 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 For questions or reporting issues to this software package, contact our
development `mailing list`_. development [mailing list](https://www.idiap.ch/software/bob/discuss).
.. 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
...@@ -58,7 +58,7 @@ ...@@ -58,7 +58,7 @@
package-dir = {"" = "src"} package-dir = {"" = "src"}
[tool.setuptools.dynamic] [tool.setuptools.dynamic]
readme = {file = "README.rst"} readme = {file = "README.md"}
[tool.distutils.bdist_wheel] [tool.distutils.bdist_wheel]
......
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