Lea 2.2.0 is now released!
What is Lea?
------------
Lea is a Python package aiming at working with discrete probability
distributions in an intuitive way. It allows you to model a broad range of
random phenomenons, like dice throwing, coin tossing, gambling, finance,
weather, etc. It offers high-level modeling features for probabilistic
programming and bayesian inference. Lea has several original features: the
storage of probabilities as integer weights, an inference algorithm that
produces *exact* results and a strong emphasis on ease-of-use. Lea is
lightweight, open-source (LGPL) and pure Python, with support of versions 2
and 3). See project page below for installation, tutorials, examples, etc.
What's new in Lea 2.2.0?
------------------------
Compared to latest version (2.1.2), many things have been made to improve
ease-of-use and overall performance. Maybe one of the most notable feature
is that you can now get individual probabilities very easily, as a fraction
or float, thanks to the new 'P' and 'Pf' functions. Here are some examples
that you can type in your Python console:
>>> P(dice <= 5)
5/18
>>> Pf(dice <= 5)
0.2777777777777778
>>> P(rain.given(grassWet))
891/2491
>>> Pf(rain.given(grassWet))
0.3576876756322762
Other new features include:
- build joint probability distributions from CSV files or Pandas dataframes
- pmf histograms using matplotlib
- Monte-Carlo sampling estimation
- multi-arguments 'given' method (ANDing of evidences)
- likelihood ratio
- extended 'draw' method: with/without sorting, with/without replacement
- machine learning (experimental)
- built-in functions and distributions for games
- various optimizations
Most of the new features are documented in a new tutorial on Lea's wiki
(http://bitbucket.org/piedenis/lea/wiki/LeaPyTutorial3).
Credits
-------
Thanks to all of you for this large bunch of feedbacks, encouragements and
suggestions! In particular, the present version owes much to Paul Moore, who
made important contributions; among other things, Paul fixed the
installation procedure, set up a test suite using the Tox tool and created
an efficient algorithm for calculating probability distribution resulting
from a drawing process. Thanks Paul for making the package more mature!
Lea project page
----------------
http://bitbucket.org/piedenis/lea
Download Lea (PyPI)
-------------------
http://pypi.python.org/pypi/lea
With the hope that Lea can make your joy less random,
Pierre Denis
--
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http://www.python.org/psf/donations/
