Path: csiph.com!fu-berlin.de!uni-berlin.de!not-for-mail From: Pierre Denis Newsgroups: comp.lang.python.announce Subject: ANN: Lea 2.2.0-beta.4 Date: Tue, 22 Dec 2015 17:25:56 -0500 (EST) Lines: 61 Approved: python-announce-list@python.org Message-ID: Reply-To: Pierre Denis Mime-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable X-Trace: news.uni-berlin.de v+lln9zgp5tVSdx5yQnyLw8UaCVTk9sEV2PWFDUO5SXA== Return-Path: X-Original-To: python-announce-list@python.org Delivered-To: python-announce-list@mail.python.org X-Spam-Status: OK 0.004 X-Spam-Evidence: '*H*': 0.99; '*S*': 0.00; 'url:pypi': 0.03; 'wiki': 0.03; '-----------': 0.04; 'open-source': 0.04; 'url:bitbucket': 0.05; '------------': 0.07; 'subject:ANN': 0.07; 'any.': 0.09; 'csv': 0.09; 'high-level': 0.09; 'tutorials,': 0.09; 'python': 0.10; '2.2.0': 0.16; 'bug)': 0.16; 'i\xe2\x80\x99m': 0.16; 'lea': 0.16; 'received:io': 0.16; 'received:psf.io': 0.16; 'documented': 0.18; 'runs': 0.18; '>>>': 0.20; 'fairly': 0.22; 'fraction': 0.22; 'feature': 0.24; 'etc).': 0.29; 'random': 0.29; 'tutorial': 0.29; 'allows': 0.30; 'url:wiki': 0.30; 'compared': 0.30; 'e.g.': 0.30; 'received:be': 0.30; 'version,': 0.30; 'announce': 0.32; 'maybe': 0.33; 'problem': 0.33; 'url:python': 0.33; 'programming,': 0.33; 'skip:- 10': 0.34; 'file': 0.34; 'stable': 0.35; 'should': 0.36; 'project': 0.36; 'url:org': 0.36; 'evaluation': 0.36; 'subject:: ': 0.37; 'thanks': 0.37; 'things': 0.38; 'version': 0.38; 'several': 0.38; 'skip:p 20': 0.38; 'subject:-': 0.39; 'build': 0.40; 'to:addr:python.org': 0.40; 'hope': 0.61; 'default': 0.61; 'more': 0.63; 'information': 0.63; 'latest': 0.64; 'header:Reply- To:1': 0.67; 'overall': 0.72; 'all!': 0.84; 'denis': 0.84; 'discrete': 0.84; 'estimation': 0.84; 'float,': 0.84; 'received:195.238': 0.84; 'notable': 0.91; 'modelling': 0.93 X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: A2DMBQBTzXlW/9EU7sNegzoiMG6MUKlwiSAXCoVmBIE2PBABAQEBAQEBfwuENgQkayACGA4CXySIFgwKnVyHXogShlWLcIEBhVWMOAwuE4E3BYdchlKEWoN5hTuJbEmHIoUvAo42OSuBVoJMIIVcAQEB X-Priority: 3 Importance: Medium X-Mailer: Open-Xchange Mailer v7.2.2-Rev27 X-Mailman-Approved-At: Wed, 23 Dec 2015 08:22:06 -0500 X-BeenThere: python-announce-list@python.org X-Mailman-Version: 2.1.20+ Precedence: list List-Id: Announcement-only list for the Python programming language List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Xref: csiph.com comp.lang.python.announce:1964 Hi all! For those of you interested in probabilities and probabilistic programming,= I=E2=80=99m happy to announce that Lea 2.2.0 is now under beta-test. 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, weather, et= c. It offers several high-level modelling features for probabilistic programming, including bayesian inference and Markov chains. Lea is open-source (LGPL) a= nd runs on Python 2 or 3. See project page below for more information (installation, tutorials, examples, etc). What=E2=80=99s new? ----------- Compared to latest version (2.1.2), many things have been made in 2.2.0 to improve ease-of-use and overall performance, without breaking backward compatibility. Maybe one of the most notable feature is that you can now ge= t individual probability very easily, as a fraction or float, thanks to the n= ew 'P' and 'Pf' functions, e.g. >>> P(dice <=3D 5) 5/18 >>> Pf(dice <=3D 5) 0.2777777777777778 >>> P(rain.given(grassWet)) 891/2491 >>> Pf(rain.given(grassWet)) 0.3576876756322762 New methods allow you to read a CSV file or Pandas dataframe, then build th= e corresponding joint probability distribution. Also, Monte-Carlo sampling estimation is now available, should Lea=E2=80=99s default exact evaluation = is intractable. Most of the new features are documented in a new tutorial on L= ea's wiki (https://bitbucket.org/piedenis/lea/wiki/LeaPyTutorial3). The latest version, Lea 2.2.0-beta.4, is fairly stable (no known bug) so yo= u can start to use it and report any problem or dislike, if any. Lea project page ---------------- https://bitbucket.org/piedenis/lea Download Lea (PyPI) ------------------- http://pypi.python.org/pypi/lea With the hope that Lea can make the Force less uncertain, Pierre Denis