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New port: math/py-ssm: Bayesian learning and inference for state space models

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This commit is contained in:
Yuri Victorovich 2020-10-12 23:15:19 +00:00
parent 3480167573
commit 7bd3e97799
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=552155
4 changed files with 57 additions and 0 deletions
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1
math/Makefile
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@ -837,6 +837,7 @@
SUBDIR += py-snuggs
SUBDIR += py-spectral
SUBDIR += py-spot
SUBDIR += py-ssm
SUBDIR += py-statsmodels
SUBDIR += py-statsmodels010
SUBDIR += py-svgmath

32
math/py-ssm/Makefile Normal file
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@ -0,0 +1,32 @@
# $FreeBSD$
PORTNAME= ssm
DISTVERSION= 0.0.1
CATEGORIES= math python
MASTER_SITES= CHEESESHOP
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
MAINTAINER= yuri@FreeBSD.org
COMMENT= Bayesian learning and inference for state space models
LICENSE= MIT
PY_DEPENDS= ${PYNUMPY} \
${PYTHON_PKGNAMEPREFIX}autograd>0:math/py-autograd@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}future>0:devel/py-future@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}matplotlib>0:math/py-matplotlib@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}numba>0:devel/py-numba@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}scipy>0:science/py-scipy@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}scikit-learn>0:science/py-scikit-learn@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}seaborn>0:math/py-seaborn@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}tqdm>0:misc/py-tqdm@${PY_FLAVOR}
BUILD_DEPENDS= ${PY_DEPENDS}
RUN_DEPENDS= ${PY_DEPENDS}
USES= python
USE_PYTHON= distutils cython concurrent autoplist
post-install:
${STRIP_CMD} ${STAGEDIR}${PYTHONPREFIX_SITELIBDIR}/${PORTNAME}/*.so
.include <bsd.port.mk>

3
math/py-ssm/distinfo Normal file
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TIMESTAMP = 1602537377
SHA256 (ssm-0.0.1.tar.gz) = b3eca53d3049306097de5977bb5c663f0c5f11db14e77ad7515c2902067f0458
SIZE (ssm-0.0.1.tar.gz) = 309185

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math/py-ssm/pkg-descr Normal file
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This package has fast and flexible code for simulating, learning, and performing
inference in a variety of state space models. Currently, it supports:
* Hidden Markov Models (HMM)
* Auto-regressive HMMs (ARHMM)
* Input-output HMMs (IOHMM)
* Hidden Semi-Markov Models (HSMM)
* Linear Dynamical Systems (LDS)
* Switching Linear Dynamical Systems (SLDS)
* Recurrent SLDS (rSLDS)
* Hierarchical extensions of the above
* Partial observations and missing data
It supports the following observation models:
* Gaussian
* Student's
* Bernoulli
* Poisson
* Categorical
* Von Mises
WWW: https://github.com/lindermanlab/ssm