AI-NNFlex

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CHANGES  view on Meta::CPAN

<activation function>_slope implemented, which returns the
slope. I haven't implemented a slope function in sigmoid, for
the simple reason that I need to learn how to integrate the
function to find out what the slope is! 

This version introduces a new, friendlier constructor. Instead
of the pair of anonymous hashes previously required, the 
constructor now takes fairly normal looking parameters. Layers
are added to a network using the add_layer method. This combines
a more intuitive approach with more flexibility.
The original constructor is not deprecated. Parameter names with
spaces in on the other hand ('activation function') are. They
should be replaced by the same parameter name without any spaces.
The new constructor will convert old format parameter names, but
its at the users risk.
See xor.pl or the perldoc & readme for examples.

Cleaned up the perldoc some more. Commented out all the method
perldocs, so there is just the single block defining the 
distributions documentation, as advocated by perlmonks. Method
perldocs in importable modules have not been commented out.

lib/AI/NNFlex.pm  view on Meta::CPAN

use strict;
use vars qw ($VERSION);
#use warnings;
###############################################################################
# NNFlex - Neural Network (flexible) - a heavily custom NN simulator
# 
# Sept 2004 - CW Colbourn
#
# This was developed from the abortive nnseq package originally intended
# for real time neural networks.
# The basis of the approach for this version is a very flexible, modular
# set of packages. This package constitutes the base, allowing the modeller
# to create meshes, apply input, and read output ONLY!
#
# Separate modules are to be written to perform feedback adjustments,
# various activation functions, text/gui front ends etc
#
###############################################################################
# Version Control

lib/AI/NNFlex.pm  view on Meta::CPAN

None. NNFlex should run OK on any version of Perl 5 >. 


=head1 ACKNOWLEDGEMENTS

Phil Brierley, for his excellent free java code, that solved my backprop problem

Dr Martin Le Voi, for help with concepts of NN in the early stages

Dr David Plaut, for help with the project that this code was originally intended for.

Graciliano M.Passos for suggestions & improved code (see SEE ALSO).

Dr Scott Fahlman, whose very readable paper 'An empirical study of learning speed in backpropagation networks' (1988) has driven many of the improvements made so far.

=head1 SEE ALSO

 AI::NNFlex::Backprop
 AI::NNFlex::Feedforward
 AI::NNFlex::Mathlib

lib/AI/NNFlex/Backprop.pm  view on Meta::CPAN

None. NNFlex::Backprop should run OK on any version of Perl 5 >. 


=head1 ACKNOWLEDGEMENTS

Phil Brierley, for his excellent free java code, that solved my backprop problem

Dr Martin Le Voi, for help with concepts of NN in the early stages

Dr David Plaut, for help with the project that this code was originally intended for.

Graciliano M.Passos for suggestions & improved code (see SEE ALSO).

Dr Scott Fahlman, whose very readable paper 'An empirical study of learning speed in backpropagation networks' (1988) has driven many of the improvements made so far.

=head1 SEE ALSO

 AI::NNFlex

 AI::NNEasy - Developed by Graciliano M.Passos 

lib/AI/NNFlex/Reinforce.pm  view on Meta::CPAN

None. NNFlex::Reinforce should run OK on any version of Perl 5 >. 


=head1 ACKNOWLEDGEMENTS

Phil Brierley, for his excellent free java code, that solved my backprop problem

Dr Martin Le Voi, for help with concepts of NN in the early stages

Dr David Plaut, for help with the project that this code was originally intended for.

Graciliano M.Passos for suggestions & improved code (see SEE ALSO).

Dr Scott Fahlman, whose very readable paper 'An empirical study of learning speed in backpropagation networks' (1988) has driven many of the improvements made so far.

=head1 SEE ALSO

 AI::NNFlex
 AI::NNFlex::Backprop
 AI::NNFlex::Dataset

t/Dataset.t  view on Meta::CPAN

ok ($result);

# test empty dataset
my $dataset2 = AI::NNFlex::Dataset->new();
ok($dataset);

# test load
$result = $dataset2->load(filename=>'test.pat');
ok($result);

#  compare original & loaded dataset
my $comparison;
if (scalar @{$dataset->{'data'}} == scalar @{$dataset2->{'data'}}){$comparison=1}
ok($comparison);

# delete a pair from the dataset
$result = $dataset->delete([4,5]);
ok($result);

# Test a learning pass
my $err = $dataset->learn($network);