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ffnet-0.7.win32-python2.7.exe 2011-12-17 387.0 kB 11 weekly downloads
README 2011-08-08 3.5 kB 11 weekly downloads
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This is ffnet-0.7 README file. Distributed under the terms of the GNU General Public License (GPL) http://www.gnu.org/copyleft/gpl.html Copyright (C) 2011 by Marek Wojciechowski <mwojc@p.lodz.pl> Overview -------- ffnet is a fast and easy-to-use feed-forward neural network training solution for python. For usage examples go to http://ffnet.sourceforge.net or browse source distribution of the software. Requirements ------------ ffnet needs at least: * python-2.6 (or 2.4, 2.5 + multiprocessing package) * numpy-1.4 * scipy-0.8 * networkx-1.3 For plots (which appear in examples) you'll need also the matplotlib package (http://matplotlib.sourceforge.net/). If you're going to compile ffnet from sources you'll need also python header files and C and Fortran 77 compiler. Installation of those depends on your operating system. Download -------- Go to http://ffnet.sourceforge.net to download latest release version of ffnet. You can also download development version from ffnet subversion repository (you need subversion installed on your system): svn co https://ffnet.svn.sourceforge.net/svnroot/ffnet/trunk ffnet In order to grab the latest development snapshot via your web browser go to http://ffnet.svn.sourceforge.net/viewvc/ffnet/trunk/ and download the tarball. Be aware that development versions are not guaranteed to be fully functional. Installation ------------ For building from sources you can try: easy_install ffnet If this doesn't work for you (for example you don't have setuptools installed) you can try the options below: 1. Building from source on Linux/Unix. Unpack ffnet to the directory of your choice, enter this directory and run as root: python setup.py install ffnet uses numpy.distutils and f2py tool to compile Fortran parts of the program. The above will work if you are running Linux/Unix system with gcc and g77 (gfortran). If you need to use another compiler run: f2py -c --help-fcompiler to see a list of supported compilers. For example, installing with Intel Fortran Compiler on 32-bit machine looks like: python setup.py install --fcompiler=intel 2. Binary packages for Linux/Unix You are welcome to produce binary packages for your Linux distribution. 3. Building from source on Windows (32-bit). If you have mingw compilers installed, run: python setup.py build --compiler=mingw32 python setup.py install --skip-build 4. Binary installers for Windows: You are welcome to produce binary packages for Windows. Testing ------- Installation can be tested with: from ffnet._tests import runtest runtest() Execute also ffnet examples. They all should work. Basic usage ----------- >>> from ffnet import ffnet, mlgraph, savenet, loadnet, exportnet >>> conec = mlgraph( (2,2,1) ) >>> net = ffnet(conec) >>> input = [ [0.,0.], [0.,1.], [1.,0.], [1.,1.] ] >>> target = [ [1.], [0.], [0.], [1.] ] >>> net.train_tnc(input, target, maxfun = 1000) >>> net.test(input, target, iprint = 2) >>> savenet(net, "xor.net") >>> exportnet(net, "xor.f") >>> net = loadnet("xor.net") >>> answer = net( [ 0., 0. ] ) >>> partial_derivatives = net.derivative( [ 0., 0. ] ) Notes ----- Windows users might be interested in installing Enthought Python Distribution: http://www.enthought.com/products/getepd.php which reaches all ffnet requirements and is free for non-commercial use. It is also very convenient to use ffnet interactively with ipython, an enhanced python shell. See http://ipython.scipy.org/moin.
Source: README, updated 2011-08-08