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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Pierre Denis