Commit graph

9 commits

Author SHA1 Message Date
wiz
7f172acfd2 Fix typo in comment. 2014-01-26 10:28:53 +00:00
gdt
a51b1207d5 Add newline to separate decls, but really to provoke a commit.
Commit message that should have been in previous commit follows:

Version 2.2.0 is backwards compatible and adds the following new
features:

    Added Sarprop training
    Added fann_create_train for creating an empty training data struct
    Added fann_copy for copying an ANN
    Added cascade_min_out_epochs and cascade_min_cand_epochs to
    improve cascade training
    Added extra checks when training, to ensure that data and network
    input and output sizes matches
    Added Visual Studio 2010 solution
    Added support for 64bit architecture
    Cleanup in sources
    Moved source from CVS to GIT
2014-01-22 01:49:21 +00:00
gdt
f9350b45eb MSG 2014-01-22 01:17:27 +00:00
asau
e1ab7079b6 Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days. 2012-10-31 11:16:30 +00:00
joerg
bacea7cad5 Remove @dirrm entries from PLISTs 2009-06-14 17:48:39 +00:00
joerg
2d1ba244e9 Simply and speed up buildlink3.mk files and processing.
This changes the buildlink3.mk files to use an include guard for the
recursive include. The use of BUILDLINK_DEPTH, BUILDLINK_DEPENDS,
BUILDLINK_PACKAGES and BUILDLINK_ORDER is handled by a single new
variable BUILDLINK_TREE. Each buildlink3.mk file adds a pair of
enter/exit marker, which can be used to reconstruct the tree and
to determine first level includes. Avoiding := for large variables
(BUILDLINK_ORDER) speeds up parse time as += has linear complexity.
The include guard reduces system time by avoiding reading files over and
over again. For complex packages this reduces both %user and %sys time to
half of the former time.
2009-03-20 19:23:50 +00:00
gdt
2bbc285339 user-destdir 2009-02-11 00:33:54 +00:00
gdt
b5edca5632 split Makefile into Makefile.common for impending py-fann package.
Add bl3.
2006-10-05 14:55:21 +00:00
gdt
4039b51e3f Fast Artificial Neural Network Library implements multilayer
artificial neural networks in C with support for both fully connected
and sparsely connected networks. Cross-platform execution in both
fixed and floating point are supported. It includes a framework for
easy handling of training data sets. It is easy to use, versatile,
well documented, and fast. Bindings to other programming languages
and a GUI are also available.
2006-10-04 20:12:34 +00:00