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Artificial Neural Networks made easy with the FANN library

, 28 Aug 2013 CPOL
Neural networks are typically associated with specialised applications, developed only by select groups of experts. This misconception has had a highly negative effect on its popularity. Hopefully, the FANN library will help fill this gap.
fann-1_2_0.zip
fann-1.2.0
debian
changelog
compat
control
copyright
docs
libfann1-dev.dirs
libfann1-dev.examples
libfann1-dev.files
libfann1-dev.install
libfann1.dirs
libfann1.files
libfann1.install
rules
doc
fann_doc_complete_1.0.pdf
Makefile
html
src
include
Makefile.in
Makefile.am
Makefile.in
COPYING
Makefile.am
win32_dll
examples
makefile
README
Makefile.in
configure
AUTHORS
COPYING
ChangeLog
INSTALL
Makefile.am
NEWS
TODO
aclocal.m4
config.guess
config.sub
configure.in
depcomp
fann.pc.in
fann.spec.in
install-sh
ltmain.sh
missing
mkinstalldirs
benchmarks
datasets
building.test
building.train
diabetes.test
diabetes.train
gene.test
gene.train
mushroom.test
mushroom.train
robot.test
robot.train
soybean.test
soybean.train
thyroid.test
thyroid.train
two-spiral.train
pumadyn-32fm.test
pumadyn-32fm.train
two-spiral.test
parity8.train
parity8.test
parity13.test
parity13.train
Makefile
README
benchmark.sh
benchmarks.pdf
gnuplot
performance.cc
quality.cc
.cvsignore
examples
Makefile
xor.data
python
README
examples
libfann.i
makefile.gnu
makefile.msvc
libfann.pyc
MSVC++
libfann.dsp
all.dsw
simple_test.dsp
simple_train.dsp
steepness_train.dsp
xor_test.dsp
xor_train.dsp
config.in
fann_win32_dll-1_2_0.zip
changelog
compat
control
copyright
docs
libfann1-dev.dirs
libfann1-dev.examples
libfann1-dev.files
libfann1-dev.install
libfann1.dirs
libfann1.files
libfann1.install
rules
fann_doc_complete_1.0.pdf
Makefile
Makefile.in
Makefile.am
Makefile.in
COPYING
Makefile.am
makefile
README
Makefile.in
configure
AUTHORS
COPYING
ChangeLog
INSTALL
Makefile.am
NEWS
TODO
aclocal.m4
config.guess
config.sub
configure.in
depcomp
fann.pc.in
fann.spec.in
install-sh
ltmain.sh
missing
mkinstalldirs
building.test
building.train
diabetes.test
diabetes.train
gene.test
gene.train
mushroom.test
mushroom.train
robot.test
robot.train
soybean.test
soybean.train
thyroid.test
thyroid.train
two-spiral.train
pumadyn-32fm.test
pumadyn-32fm.train
two-spiral.test
parity8.train
parity8.test
parity13.test
parity13.train
Makefile
README
benchmark.sh
benchmarks.pdf
gnuplot
performance.cc
quality.cc
.cvsignore
Makefile
xor.data
README
libfann.i
makefile.gnu
makefile.msvc
libfann.pyc
libfann.dsp
all.dsw
simple_test.dsp
simple_train.dsp
steepness_train.dsp
xor_test.dsp
xor_train.dsp
config.in
bin
fanndoubled.dll
fanndoubled.lib
fanndoubleMTd.dll
fanndoubleMTd.lib
fannfixedd.dll
fannfixedd.lib
fannfixedMTd.dll
fannfixedMTd.lib
fannfloatd.dll
fannfloatd.lib
fannfloatMTd.dll
fannfloatMTd.lib
fanndouble.dll
fanndouble.lib
fanndoubleMT.dll
fanndoubleMT.lib
fannfixed.dll
fannfixed.lib
fannfixedMT.dll
fannfixedMT.lib
fannfloat.dll
fannfloat.lib
fannfloatMT.dll
fannfloatMT.lib
vs_net2003.zip
VS.NET2003
from distutils.core import setup, Extension
from distutils.command.install_data import install_data
from compiler.pycodegen import compileFile
import glob
import distutils
import distutils.sysconfig
import distutils.core
import os
import py2exe

VERSION='1.2.0'

LONG_DESCRIPTION="""\
Fast Artificial Neural Network Library implements multilayer
artificial neural networks with support for both fully connected
and sparsely connected networks. It includes a framework for easy 
handling of training data sets. It is easy to use, versatile, well 
documented, and fast. 
"""

class smart_install_data(install_data):
    """
    override default distutils install_data, so we can copy
    files directly, without splitting into modules, scripts,
    packages, and extensions."
    """
    def run(self):
        # need to change self.install_dir to the actual library dir

        install_cmd = self.get_finalized_command('install')
        self.install_dir = getattr(install_cmd, 'install_lib')
        return install_data.run(self)

def hunt_files(root, which):
    return glob.glob(os.path.join(root, which))

data_files = []

# add sources
data_files = data_files + [['', ['fann.py', '__init__.py']]]

# add dll and swig output
compileFile('libfann.py')
data_files = data_files + [['', ['libfann.pyc', '_libfann.pyd']]]

# add examples
data_files = data_files + [['examples', hunt_files('examples', '*.py')]]

# add examples datasets
data_files = data_files + [['examples/datasets', hunt_files('../benchmarks/datasets', 'mushroom*')]]
data_files = data_files + [['examples/datasets', hunt_files('../examples', 'xor.data')]]

setup(
    name='pyfann',
    description='Fast Artificial Neural Network Library (fann)',
    long_description=LONG_DESCRIPTION,
    version=VERSION,
    author='Steffen Nissen',
    author_email='lukesky@diku.dk',
    maintainer='Gil Megidish',
    maintainer_email='gil@megidish.net',
    url='http://sourceforge.net/projects/fann/',
    platforms='WIN32',
    license='GNU LESSER GENERAL PUBLIC LICENSE (LGPL)',
    data_files=data_files,
    cmdclass={'install_data': smart_install_data},
    extra_path='pyfann'
)

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This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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