Lea 3.0 final is now released! ---> http://pypi.org/project/lea/3.0.0 What is Lea? ------------ Lea is a Python module aiming at working with discrete probability distributions in an intuitive way. It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols, . Each probability distribution is modeled as a plain object, which can be named, displayed, queried or processed to produce new probability distributions. Lea also provides advanced functions and Probabilistic Programming (PP) features; these include conditional probabilities, Bayesian networks, joint probability distributions, Markov chains and symbolic computation. Lea can be used for AI, machine learning, education, ... LGPL - Python 2.6+ / Python 3 supported What's new in Lea 3? -------------------- Compared to latest version (2.3.5), many things have changed to extend the usability and openness of the library. To name a few: * ability to choose between different probability representations: floats, fractions and decimals * symbolic computation: Lea can now calculate probability *formula* using the SymPy library (http://www.sympy.org) * simpler API and compliance with PEP8 naming convention * revamped tutorials and examples -> http://bitbucket.org/piedenis/lea/wiki/Home * paper on the "Statues" algorithm used in Lea -> http://arxiv.org/abs/1806.09997 Here is a short sample. A biased coins is flipped with 1/4 chance to be 'head'. Suppose that this coin is thrown 6 times. What is the probability to get no more than two 'heads'? Here is how you could make this calculation in Lea, using successively float, fraction and symbolic representations: print (P(lea.binom(6,1/4) <= 2)) # -> 0.83056640625 print (P(lea.binom(6,'1/4') <= 2)) # -> 1701/2048 print (P(lea.binom(6,'p') <= 2)) # -> (p - 1)**4*(10*p**2 + 4*p + 1)) print (P(lea.binom(6,'p') <= 2).subs('p',1/4)) # -> 0.830566406250000 To learn more... ---------------- Lea 3 on PyPI -> http://pypi.org/project/lea/3.0.0 Lea project page -> http://bitbucket.org/piedenis/lea Documentation -> http://bitbucket.org/piedenis/lea/wiki/Home Statues algorithm -> http://arxiv.org/abs/1806.09997 With the hope that Lea can make the Universe less hazardous, Pierre Denis