./wip/py-lea, Discrete probability distributions in Python

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Branch: CURRENT, Version: 2.1.2, Package name: py311-lea-2.1.2, Maintainer: jihbed.research

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, cards hands, gambling,
lottery, ? with fair or unfair characteristics More generally, Lea may be used
for any finite set of discrete values having known probability: numbers,
boolean variables (true/false), date/times, symbols, ... Each distribution
is modelled as a plain object, which can be named, displayed, queried or
processed to produce new distribution objects


Required to run:
[devel/py-readline] [lang/python37]

Required to build:
[pkgtools/cwrappers]

Master sites:

RMD160: 972bea8bd223a0a5200a316570e07e183f2f5f5f
Filesize: 45.46 KB

Version history: (Expand)


CVS history: (Expand)


   2015-08-11 00:35:56 by Kamel Ibn Aziz Derouiche | Files touched by this commit (2)
Log message:

	o ADDED gnu-gpl-v3 LICENSE
	o ADDED dependencey: py-readline
   2015-03-07 23:18:24 by Kamel Ibn Aziz Derouiche | Files touched by this commit (3) | Package updated
Log message:

added keyword 'probabilistic programming 	
updated doc
added 2015 in copyright
redefine dict in Python2 for using efficient iterator methods
added mode method
aca03eebf080 		
fix bug on CPT with context-specific independence; add getAleaLeavesSet and 
_getLeaChildren methods; refactor reset method
corrected withProb method
added Lea FOSDEM15 presentation
Added tag 2.0.0 for changeset 613c68acdfa6
version Lea 2.0.0
updated API doc
updated poisson method, added binom, bernoulli, interval methods
Added tag 2.0.0-beta.6 for changeset e7d5ec60e411
version Lea 2.0.0-beta.6
optimize random generator; memoize cumul functions; improve means for Python 
2/3 portability; split Alea _vps attribute into _vs and _ps
fix mean of deltatime in Python 2.x
Added tag 2.0.0-beta.5 for changeset 66362ef7ae4b
updated HTML doc
rename 'integral' by 'cumul'
version Lea 2.0.0-beta.5
added histogram + refactoring string representation methods
cleanup markov
Added tag 2.0.0-beta.4 for changeset 8273fdffa6f3
updated HTML doc

  
   2015-02-01 00:26:31 by Kamel Ibn Aziz Derouiche | Files touched by this commit (2)
Log message:

	Update package, 
	for ChangeLog please see: https://code.google.com/p/lea/source/list
   2015-01-19 22:17:35 by Kamel Ibn Aziz Derouiche | Files touched by this commit (1) | Package updated
Log message:

	Added tag 2.0.0-beta.5 for changeset
Jan 11, 2015	
updated HTML doc
Jan 11, 2015	
rename 'integral' by 'cumul'
Jan 11, 2015	
version Lea 2.0.0-beta.5
Jan 10, 2015	
added histogram + refactoring string representation methods
Jan 10, 2015	
cleanup markov
Jan 10, 2015	
Added tag 2.0.0-beta.4 for changeset 8273fdffa6f3
Jan 2, 2015	
updated HTML doc
Jan 2, 2015	
micro-optimization of coerce method
Jan 2, 2015	
fix bug on fastMin, fastMax + refactoring
Jan 2, 2015	
Added tag 2.0.0-beta.3 for changeset 09b6cc99ad18
Jan 1, 2015	
updated HTML doc
Jan 1, 2015	
version Lea 2.0.0-beta.3
Dec 31, 2014	
misc refactoring + new min, max functions
Dec 31, 2014		
cleanup
Dec 31, 2014	
Refactor markov module + bug fix + new fromSeq method
Dec 31, 2014	
Added tag Lea, 2.0.0-beta.2 for changeset 47ab33997cda
Dec 25, 2014	
version Lea 2.0.0-beta.2
Dec 25, 2014	
clean-up
Dec 25, 2014	
added timesTuple method
Dec 25, 2014	
correct bug on pmf
Dec 22, 2014	
Added tag 2.0.0-beta.1 for changeset ef6cf4ac2f16
Dec 22, 2014	
new Lea 2 logo
Dec 19, 2014	
added keywords in setup.py
Dec 19, 2014	
updated API HTML
   2015-01-11 19:10:16 by Kamel Ibn Aziz Derouiche | Files touched by this commit (3)
Log message:
Update Makefile

   2014-05-04 02:27:26 by Kamel Derouiche | Files touched by this commit (5)
Log message:
Import py27-lea-1.2 as wip/py-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, cards hands, gambling,
lottery, ? with fair or unfair characteristics More generally, Lea may be used
for any finite set of discrete values having known probability: numbers,
boolean variables (true/false), date/times, symbols, ... Each distribution
is modelled as a plain object, which can be named, displayed, queried or
processed to produce new distribution objects