Path: csiph.com!fu-berlin.de!uni-berlin.de!not-for-mail From: "Pierre Denis" Newsgroups: comp.lang.python.announce Subject: ANN: Lea 3.1.0 released Date: Mon, 25 Mar 2019 22:11:27 +0100 Lines: 52 Approved: python-announce-list@python.org Message-ID: References: <001e01d4e34f$4f5a5a70$ee0f0f50$@denis@skynet.be> Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: 7bit X-Trace: news.uni-berlin.de 2voNXR2dwTrb1koXIXF0PQRA+P8dGIqR97zvaPCPrlEg== Return-Path: X-Original-To: python-announce-list@python.org Delivered-To: python-announce-list@mail.python.org X-Spam-Status: OK 0.000 X-Spam-Evidence: '*H*': 1.00; '*S*': 0.00; 'wiki': 0.03; 'subject:ANN': 0.05; 'subject:released': 0.05; 'pypi': 0.07; '------------': 0.09; 'aiming': 0.09; 'conditional': 0.09; 'fixes': 0.09; 'functions,': 0.09; 'generally,': 0.09; 'intuitive': 0.09; 'object,': 0.09; 'url:project': 0.09; 'url:pypi': 0.09; 'python': 0.13; 'values': 0.14; 'bug': 0.16; 'random': 0.16; 'ai,': 0.16; 'chains': 0.16; 'denis': 0.16; 'finite': 0.16; 'pierre': 0.16; 'received:adsl- dyn.isp.belgacom.be': 0.16; 'received:belgacom.be': 0.16; 'received:isp.belgacom.be': 0.16; 'url-ip:18.205.93.0/32': 0.16; 'url-ip:18.205.93.1/32': 0.16; 'url-ip:18.205.93.2/32': 0.16; 'url-ip:18.205.93/24': 0.16; 'url-ip:18.205/16': 0.16; 'url:bitbucket': 0.16; 'algorithm': 0.18; 'essentially': 0.18; 'url:wiki': 0.18; 'module': 0.19; 'api': 0.19; 'tutorials': 0.19; 'cc:2**0': 0.22; 'programming': 0.23; 'url-ip:151.101.0.223/32': 0.23; 'url-ip:151.101.128.223/32': 0.23; 'url- ip:151.101.192.223/32': 0.23; 'url-ip:151.101.64.223/32': 0.23; 'paul': 0.23; 'url:home': 0.23; 'url-ip:151.101.192/24': 0.24; 'functions': 0.24; 'received:be': 0.24; 'url-ip:151.101.0/24': 0.24; 'url-ip:151.101.128/24': 0.24; 'url-ip:151.101.64/24': 0.24; '...': 0.25; 'url-ip:18/8': 0.28; 'url-ip:151.101/16': 0.29; 'url- ip:151/8': 0.29; 'to:addr:python-announce-list': 0.29; 'cc:no real name:2**0': 0.33; 'function': 0.34; 'subject:: ': 0.35; 'especially': 0.36; 'working': 0.36; 'pfxlen:0': 0.36; '....': 0.36; 'broad': 0.37; 'symbolic': 0.37; 'url:3': 0.38; 'are:': 0.39; 'less': 0.60; 'finance,': 0.61; 'more...': 0.61; 'plain': 0.62; 'range': 0.62; 'particular': 0.63; 'known': 0.65; 'hope': 0.65; 'skip:- 20': 0.65; 'numbers,': 0.66; 'education,': 0.67; 'modeling': 0.67; 'networks,': 0.67; 'chain': 0.68; 'gave': 0.68; 'optimization': 0.68; 'produce': 0.68; 'feedbacks': 0.84; 'statues': 0.84 X-Belgacom-Dynamic: yes X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: =?us-ascii?q?A2AiBQDYQplc/zcK9VFkGwEBAQEDAQE?= =?us-ascii?q?BBwMBAQGBZQKCDycBHSNxEieIQIRJjQUBjg2LcjIIAYQ2CoUTIzkFDAEBAwE?= =?us-ascii?q?BCQEDAm0cDIVZHwESHCMNBQYSUCAfAQQeC4MNgXkLr0kCMYQwAYV+gS8BjQg?= =?us-ascii?q?/gRGDUIURhRQDijyHPZMdBwICgkqFF4tWGYJdkSABiCOIfo8yIIFWbYNACBY?= =?us-ascii?q?gik2FQEEwAYEfCBUBjm0BAQ?= X-IPAS-Result: =?us-ascii?q?A2AiBQDYQplc/zcK9VFkGwEBAQEDAQEBBwMBAQGBZQKCD?= =?us-ascii?q?ycBHSNxEieIQIRJjQUBjg2LcjIIAYQ2CoUTIzkFDAEBAwEBCQEDAm0cDIVZH?= =?us-ascii?q?wESHCMNBQYSUCAfAQQeC4MNgXkLr0kCMYQwAYV+gS8BjQg/gRGDUIURhRQDi?= =?us-ascii?q?jyHPZMdBwICgkqFF4tWGYJdkSABiCOIfo8yIIFWbYNACBYgik2FQEEwAYEfC?= =?us-ascii?q?BUBjm0BAQ?= X-Mailer: Microsoft Office Outlook 12.0 Content-Language: en-us Thread-Index: AdTjT08pL5QRLx60QW6Igs4/LVn4kA== X-Mailman-Approved-At: Wed, 27 Mar 2019 08:03:20 -0400 X-BeenThere: python-announce-list@python.org X-Mailman-Version: 2.1.29 Precedence: list List-Id: Announcement-only list for the Python programming language List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-Mailman-Original-Message-ID: <001e01d4e34f$4f5a5a70$ee0f0f50$@denis@skynet.be> Xref: csiph.com comp.lang.python.announce:3079 Lea 3.1.0 final is now released! ---> http://pypi.org/project/lea/3.1.0 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, PP, gambling, education, ... LGPL - Python 2.6+ / Python 3 supported What's new in Lea 3.1.0? ------------------------ The present version essentially consolidates the previous one (3.0.1), adding just few new features. The main changes are: - new switch_func method, for defining CPT by a function (far less memory-consuming for models like noisy-or, noisy-max) - improvements on Markov chain functions, including calculation of absorbing MC - optimization of lea.max_of, lea.min_of functions - bug fixes for symbolic calculation (in particular for Python 2.7) - numerous addings/improvements on wiki tutorials and documentation, especially on Lea API Many thanks... -------------- ... to Paul Moore, Jens Finkhaeuser and Rasmus Bonnevie who provided proposals, contributions, feedbacks that gave the impetus for the present version. To learn more... ---------------- Lea 3 on PyPI -> http://pypi.org/project/lea/3.1.0 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 this Universe less hazardous, Pierre Denis