diff options
| author | Sunpoet Po-Chuan Hsieh <sunpoet@FreeBSD.org> | 2019-01-17 19:21:19 +0000 |
|---|---|---|
| committer | Sunpoet Po-Chuan Hsieh <sunpoet@FreeBSD.org> | 2019-01-17 19:21:19 +0000 |
| commit | bde795881fddbfdb05256917237ec01ffebd5bf4 (patch) | |
| tree | 1245c41d8a6e6a4979df50ed6b0fc149c0d147ac /math/py-cma | |
| parent | b93cbf234246c7a852c0bfda1dc7cd8610519ada (diff) | |
Notes
Diffstat (limited to 'math/py-cma')
| -rw-r--r-- | math/py-cma/Makefile | 23 | ||||
| -rw-r--r-- | math/py-cma/distinfo | 3 | ||||
| -rw-r--r-- | math/py-cma/pkg-descr | 9 |
3 files changed, 35 insertions, 0 deletions
diff --git a/math/py-cma/Makefile b/math/py-cma/Makefile new file mode 100644 index 000000000000..97110eadd4f8 --- /dev/null +++ b/math/py-cma/Makefile @@ -0,0 +1,23 @@ +# Created by: Po-Chuan Hsieh <sunpoet@FreeBSD.org> +# $FreeBSD$ + +PORTNAME= cma +PORTVERSION= 2.6.0 +CATEGORIES= math python +MASTER_SITES= CHEESESHOP +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} + +MAINTAINER= sunpoet@FreeBSD.org +COMMENT= CMA-ES for non-linear numerical optimization in Python + +LICENSE= BSD3CLAUSE +LICENSE_FILE= ${WRKSRC}/LICENSE + +RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}numpy>=0:math/py-numpy@${PY_FLAVOR} + +USES= python +USE_PYTHON= autoplist concurrent distutils + +NO_ARCH= yes + +.include <bsd.port.mk> diff --git a/math/py-cma/distinfo b/math/py-cma/distinfo new file mode 100644 index 000000000000..2b0e45441f18 --- /dev/null +++ b/math/py-cma/distinfo @@ -0,0 +1,3 @@ +TIMESTAMP = 1547723709 +SHA256 (cma-2.6.0.tar.gz) = ef9e21fd821485518341fe1b96be4d127f8b78e60aeb6a286d602dc98f25eab6 +SIZE (cma-2.6.0.tar.gz) = 213116 diff --git a/math/py-cma/pkg-descr b/math/py-cma/pkg-descr new file mode 100644 index 000000000000..15f9f9e77b10 --- /dev/null +++ b/math/py-cma/pkg-descr @@ -0,0 +1,9 @@ +pycma is a Python implementation of CMA-ES and a few related numerical +optimization tools. + +The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic +derivative-free numerical optimization algorithm for difficult (non-convex, +ill-conditioned, multi-modal, rugged, noisy) optimization problems in continuous +search spaces. + +WWW: https://github.com/CMA-ES/pycma |