release can be found at http://library.gnome.org/misc/release-notes/2.32/
This will be the last release of the GNOME 2.x series, mainly a bugfix and
bridge release to the first release of the GNOME 3.x series.
This release features commits by avl, marcus, mezz and myself.
The FreeBSD GNOME Team would like to thank the following contributors and
testers for there help with this release:
Zane C.B. <vvelox@vvelox.net>
romain@
Olaf Seibert <O.Seibert@cs.ru.nl>
DomiX
Bapt <baptiste.daroussin@gmail.com>
jsa@
miwi@
Sergio de Almeida Lenzi <lenzi.sergio@gmail.com>
Maxim Samsonov <xors@mne.ru>
Kris Moore
And pav@ for 2 exp-runs
PR: ports/152255
ports/143260
ports/141033
ports/149629
ports/150350
ports/151523
With hat: gnome@
on DCDFLIB for more information.
Functions are available for 7 continuous distributions (Beta,
Chi-square, F, Gamma, Normal, Poisson and T-distribution) and for two
discrete distributions (Binomial and Negative Binomial). Optional
non-centrality parameters are available for the Chi-square, F and
T-distributions. Cumulative probabilities are available for all 9
distributions and quantile functions are available for the 7
continuous distributions.
WWW: http://search.cpan.org/dist/Math-CDF/
PR: ports/152204
Submitted by: Gea-Suan Lin <gslin at gslin.org>
localization. Even in Chinese digits should be displayed as Arabic numerals but
not in localized forms.
PR: ports/151143
Submitted by: "Denise H. G." <darcsis@gmail.com>
With Hat: gnome@
be generated or checked, and will be silently ignored for now. Also,
generalize the MD5_FILE macro to DISTINFO_FILO.
PR: 149657
Submitted by: rene
Approved by: portmgr
Tested on: pointyhat i386 7-exp
with some machine learning features. The main features are as follows:
* Directed, undirected and multigraphs designed under a hierarchical
class structure
* Sparse and Dense graph structures using numpy and scipy for fast linear
algebra computations
* Many operations on graphs such as subgraphs, search, Floyd-Warshall,
Dijkstras algorithm
* Erdos-Renyi, Small-World and Albert-Barabasi random graphs
* Write to Pajek, and simple CSV files
* Some machine learning features - data preprocessing, kernels, PCA, KCCA,
wrappers for LibSVM, and some mlpy learning algorithms
* Unit tested using the Python unittest framework
WWW: http://packages.python.org/apgl/
