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Recurrent neural network package for problems of time-series prediction and generation

Copyright (c) 2009-2011, Jun Namikawa License: ISC license

This package implements a gradient-based learning algorithm for recurrent neural networks. The package supports (1) both fully connected and sparsely connected networks, (2) both discrete-time neural networks and continuous-time neural networks, (3) training examples of both symbolic data and floating point numbers, (4) multi-threading, and (5) analyzing some characteristics (ex: Lyapunov spectrum, Kullback-Leibler divergence).

=== Installation ===

First, type ./' in the current directory to create configure file. Next, type./configure' and when it finishes, type make'. This will creaternn-learn', `rnn-generate' and other utility programs.

Run them with the argument `-h' to show the usages of them.

If you wish to install the programs, type make install'. By default, this will install all the files in/usr/local/bin' or /usr/local/lib'. You can change the install path with the--prefix' option of the configure script, for instance --prefix=$HOME' (use./configure --help' for other options).

=== Requirements ===

Building this package requires a C compiler supporting C99 and Autotools (GNU Autoconf, Automake and Libtool).

In addition, utility scripts in the src/python' directory require python version 2.5 or later (but not python-3.x). Gnuplot is also needed to runrnn-plot-log' script.

=== Example ===

Here is a sample session.

cd examples echo "import gen_target gen_target.print_sin_curve(500, 20)" | python > target.txt rnn-learn -e 5000 target.txt rnn-generate -n 1000 rnn.dat