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package AI::Nerl::Network;
use Moose 'has', inner => { -as => 'moose_inner' };
use PDL;
use PDL::NiceSlice;
use PDL::Constants 'E';

# ABSTRACT: 3-layer Neural network on PDL with backpropagation
#
my $DEBUG=0;

# http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm
# http://www.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf


=head1 NAME

AI::Nerl::Network - 3-layer neural network with backpropagation

=head1 SYNOPSIS

 use AI::Nerl::Network;
 use PDL;
   
 my $x = pdl([0,0,1,1],
             [0,1,0,1],
             [1,0,0,1]);
 my $y = pdl([1,1,0,1]);
 my $nn = AI::Nerl::Network->new(
            l1 => 3,       # 3 inputs
            l2 => 18,      # 18 hidden neurons
            l3 => 1,       # 1 output
            alpha => .3,   # learning rate
            lambda => .01, # 'squashing' parameter
 );
 $nn->train($x,$y, passes=>45);
 my ($cost,$num_correct) = $nn->cost($x,$y);
 #$nn wasn't programmed with this input. could be anything:
 print $nn->run(pdl([0,0,0])); 

=head1 DESCRIPTION

=head1 METHODS

=head2 train($x,$y, %params)

Train with backpropagation using $x as input & $y as target.
$x and $y are both pdls. If there are multiple cases, each one will
occupy a column (dimension 2) of the pdl. If your dimensions are off,
you will experience an pdl error of some sort.

=head3 %params

=head4 passes

number of passes.

=head2 run($x)

 $output = $nn->run($x);

=head2 cost($x,$y)

 ($cost,$num_correct) = $nn->cost($x,$y);

Calculate the 'cost' of the network. This is basically the difference between the
actual output ($nn->run($x)) and the the target output($y), added to the sum of
the neural weights if you're penalizing weights with lambda. The cost should 
B<Always> decrease after being trained with ($x,$y).

This function returns both the cost, and the number of "correct" responses
if using output neurons for classification.

=head1 SEE ALSO

L<http://en.wikipedia.org/wiki/Feedforward_neural_network#Multi-layer_perceptron>

L<http://en.wikipedia.org/wiki/Backpropagation>

=head1 AUTHOR

Zach Morgan C<< <zpmorgan@gmail.com> >>


=head1 COPYRIGHT

Copyright 2012 by Zach Morgan

This package is free software; you can redistribute it and/or modify it under the 
same terms as Perl itself.

=cut

# Simple nn with 1 hidden layer
# train with $nn->train(data,labels);

has scale_input => (
   is => 'ro',
   required => 0,
   isa => 'Num',
   default => 0,
);

# number of input,hidden,output neurons
has [qw/ l1 l2 l3 /] => (
   is => 'ro',
   isa => 'Int',
);

has theta1 => (