Coverage for src/lsqfitgp/copula/__init__.py: 100%

1# lsqfitgp/copula/__init__.py 

2# 

3# Copyright (c) 2023, 2024, Giacomo Petrillo 

4# 

5# This file is part of lsqfitgp. 

6# 

7# lsqfitgp is free software: you can redistribute it and/or modify 

8# it under the terms of the GNU General Public License as published by 

9# the Free Software Foundation, either version 3 of the License, or 

10# (at your option) any later version. 

11# 

12# lsqfitgp is distributed in the hope that it will be useful, 

13# but WITHOUT ANY WARRANTY; without even the implied warranty of 

14# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

15# GNU General Public License for more details. 

16# 

17# You should have received a copy of the GNU General Public License 

18# along with lsqfitgp. If not, see <http://www.gnu.org/licenses/>. 

19 

20""" Reparametrize probability distributions as Normal """ 

21 

22from ._base import DistrBase 6 ctx1abcdef

23from ._distr import Distr, distribution 6 ctx1abcdef

24from ._copula import Copula 6 ctx1abcdef

25from ._makedict import makedict 6 ctx1abcdef

26from ._copulas import ( 6 ctx1abcdef

27 beta, 

28 dirichlet, 

29 gamma, 

30 loggamma, 

31 invgamma, 

32 halfcauchy, 

33 halfnorm, 

34 uniform, 

35 lognorm, 

36) 

37 

38# TODO I could try to drop BufferDict altogether. It adds more complexity than 

39# necessary: won't keep track of dependencies between keys, structure fixed to a 

40# dictionary, and global unique transformation names. 

41# 

42# I need something analogous to carry around both the distribution definition 

43# and specific values. Copula.concretize(x) -> ConcreteCopula object. Method 

44# .values() returns pytree of transformed values (container-copied), 

45# .input_values() the flat input array (readonly view). Prints as: 

46# Copula({ 

47# 'x': beta(1, 2), 

48# 'y': gamma(1, <x>), 

49# }) 

50# ConcreteCopula({ 

51# 'x': 0.1234, 

52# 'y': 0.1314, 

53# }) 

54# But there is a description method to list the original values: 

55# ConcreteCopula({ 

56# 'x': 0.1234 <- 0.1442, 

57# 'y': 0.1314 <- 0.4124, <x> 

58# }) 

59# or to do comparisons: 

60# ConcreteCopula({ 

61# 'x': 0.1234 | 0.12344 <- 0.1442 | 0.7777, 

62# 'y': 0.1314 | 0.4144 <- 0.4124 | 0.1341, <x> 

63# }) 

64# 

65# Make empbayes_fit take Copula_like as input, and output ConcreteCopula for 

66# the posterior. Attribute .p would be ConcreteCopula.values(), new attribute 

67# post would contain everything. 

68# 

69# I could maintain the BufferDict functionality, listed under a bottom section 

70# in the reference.