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Generally speaking, this value is usually between 0 and 1. However, it all depend on your combination of numbers for the other options.

=item attribs => $array_ref

An array reference containing all the attributes / dendrites names. Yes, give them some names :)

=item learning_rate => $decimal

Optional. The default is C<0.05>.

The learning rate of the perceptron for the fine-tuning process.

This value is usually between 0 and 1. However, it all depends on your combination of numbers for the other options.

=item threshold => $decimal

Optional. The default is C<0.5>

This is the passing rate to determine the neuron output (C<0> or C<1>).

Generally speaking, this value is usually between C<0> and C<1>. However, it all depend on your combination of numbers for the other options.

=back

=cut

sub new {
    my $class = shift;
    
    my $data_ref = shift;
    my %data = %{ $data_ref };
    
    # check keys
    $data{ learning_rate } = LEARNING_RATE if not exists $data{ learning_rate };
    $data{ threshold } = THRESHOLD if not exists $data{ threshold };
    
    #####
    # don't pack this key checking process into a subroutine for now
    # this is also used in &_real_validate_or_test
    my @missing_keys;
    for ( qw( initial_value attribs ) ) {
        push @missing_keys, $_ unless exists $data{ $_ };
    }
    
    croak "Missing keys: @missing_keys" if @missing_keys;
    #####
    
    # continue to process the rest of the data
    my %attributes;
    for ( @{ $data{ attribs } } ) {
        $attributes{ $_ } = $data{ initial_value };
    }
    
    my %processed_data = (
        learning_rate => $data{ learning_rate },
        threshold => $data{ threshold },
        attributes_hash_ref => \%attributes,
    );
    
    bless \%processed_data, $class;
}

=head2 get_attributes

Obtains a hash of all the attributes of the perceptron

=cut

sub get_attributes {
    my $self = shift;
    %{ $self->{attributes_hash_ref} };
}

=head2 learning_rate ( $value )

=head2 learning_rate

If C<$value> is given, sets the learning rate to C<$value>. If not, then it returns the learning rate.

=cut

sub learning_rate {
    my $self = shift;
    if ( @_ ) {
        $self->{learning_rate} = shift;
    } else {
        $self->{learning_rate}
    }
}

=head2 threshold ( $value )

=head2 threshold

If C<$value> is given, sets the threshold / passing rate to C<$value>. If not, then it returns the passing rate.

=cut

sub threshold {
    my $self = shift;
    if ( @_ ) {
        $self->{ threshold } = shift;
    } else {
        $self->{ threshold };
    }
}

=head1 TRAINING RELATED SUBROUTINES/METHODS

All the training methods here have the same parameters as the two actual C<train> method and they all do the same stuff. They are also used in the same way.

=head2 tame ( ... )

=head2 exercise ( ... )

=head2 train ( $stimuli_train_csv, $expected_output_header, $save_nerve_to_file )

=head2 train ( $stimuli_train_csv, $expected_output_header, $save_nerve_to_file, $display_stats, $identifier )

Trains the perceptron.