DATA results from the CPAN

AI-Perceptron-Simple




<ul id="index">
  <li><a href="#NAME">NAME</a></li>
  <li><a href="#VERSION">VERSION</a></li>
  <li><a href="#SYNOPSIS">SYNOPSIS</a></li>
  <li><a href="#EXPORT">EXPORT</a></li>
  <li><a href="#DESCRIPTION">DESCRIPTION</a></li>
  <li><a href="#CONVENTIONS-USED">CONVENTIONS USED</a></li>
  <li><a href="#DATASET-STRUCTURE">DATASET STRUCTURE</a></li>
  <li><a href="#PERCEPTRON-DATA">PERCEPTRON DATA</a></li>
  <li><a href="#DATA-PROCESSING-RELATED-SUBROUTINES">DATA PROCESSING RELATED SUBROUTINES</a>
    <ul>
      <li><a href="#shuffle_stimuli">shuffle_stimuli ( ... )</a></li>
      <li><a href="#shuffle_data-original_data-shuffled_1-shuffled_2">shuffle_data ( $original_data =&gt; $shuffled_1, $shuffled_2, ... )</a></li>
      <li><a href="#shuffle_data-ORIGINAL_DATA-shuffled_1-shuffled_2">shuffle_data ( ORIGINAL_DATA, $shuffled_1, $shuffled_2, ... )</a></li>
    </ul>
  </li>
  <li><a href="#CREATION-RELATED-SUBROUTINES-METHODS">CREATION RELATED SUBROUTINES/METHODS</a>
    <ul>
      <li><a href="#new-options">new ( \%options )</a></li>
      <li><a href="#get_attributes">get_attributes</a></li>
      <li><a href="#learning_rate-value">learning_rate ( $value )</a></li>
      <li><a href="#learning_rate">learning_rate</a></li>
      <li><a href="#threshold-value">threshold ( $value )</a></li>
      <li><a href="#threshold">threshold</a></li>

docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN

      <li><a href="#calculate_false_positive_rate-c_matrix_ref">&amp;_calculate_false_positive_rate( $c_matrix_ref )</a></li>
      <li><a href="#calculate_false_discovery_rate-c_matrix_ref">&amp;_calculate_false_discovery_rate( $c_matrix_ref )</a></li>
      <li><a href="#calculate_false_omission_rate-c_matrix_ref">&amp;_calculate_false_omission_rate( $c_matrix_ref )</a></li>
      <li><a href="#calculate_balanced_accuracy-c_matrix_ref">&amp;_calculate_balanced_accuracy( $c_matrix_ref )</a></li>
      <li><a href="#display_exam_results">display_exam_results ( ... )</a></li>
      <li><a href="#display_confusion_matrix-confusion_matrix-labels">display_confusion_matrix ( \%confusion_matrix, \%labels )</a></li>
      <li><a href="#build_matrix-c_matrix-labels">&amp;_build_matrix ( $c_matrix, $labels )</a></li>
      <li><a href="#print_extended_matrix-matrix-c_matrix">&amp;_print_extended_matrix ( $matrix, $c_matrix )</a></li>
    </ul>
  </li>
  <li><a href="#NERVE-DATA-RELATED-SUBROUTINES">NERVE DATA RELATED SUBROUTINES</a>
    <ul>
      <li><a href="#preserve">preserve ( ... )</a></li>
      <li><a href="#save_perceptron-nerve-nerve_file">save_perceptron ( $nerve, $nerve_file )</a></li>
      <li><a href="#revive">revive (...)</a></li>
      <li><a href="#load_perceptron-nerve_file_to_load">load_perceptron ( $nerve_file_to_load )</a></li>
    </ul>
  </li>
  <li><a href="#NERVE-PORTABILITY-RELATED-SUBROUTINES">NERVE PORTABILITY RELATED SUBROUTINES</a>
    <ul>
      <li><a href="#preserve_as_yaml">preserve_as_yaml ( ... )</a></li>

docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN

    # processing data
    use AI::Perceptron::Simple &quot;:process_data&quot;;
    shuffle_stimuli ( ... )
    shuffle_data ( ORIGINAL_STIMULI, $new_file_1, $new_file_2, ... );
    shuffle_data ( $original_stimuli =&gt; $new_file_1, $new_file_2, ... );</code></pre>

<h1 id="EXPORT">EXPORT</h1>

<p>None by default.</p>

<p>All the subroutines from <code>DATA PROCESSING RELATED SUBROUTINES</code>, <code>NERVE DATA RELATED SUBROUTINES</code> and <code>NERVE PORTABILITY RELATED SUBROUTINES</code> sections are importable through tags or manually specifying them.</p>

<p>The tags available include the following:</p>

<dl>

<dt id="process_data---subroutines-under-DATA-PROCESSING-RELATED-SUBROUTINES-section"><code>:process_data</code> - subroutines under <code>DATA PROCESSING RELATED SUBROUTINES</code> section.</dt>
<dd>

</dd>
<dt id="local_data---subroutines-under-NERVE-DATA-RELATED-SUBROUTINES-section"><code>:local_data</code> - subroutines under <code>NERVE DATA RELATED SUBROUTINES</code> section.</dt>
<dd>

</dd>
<dt id="portable_data---subroutines-under-NERVE-PORTABILITY-RELATED-SUBROUTINES-section"><code>:portable_data</code> - subroutines under <code>NERVE PORTABILITY RELATED SUBROUTINES</code> section.</dt>
<dd>

</dd>
</dl>

<p>Most of the stuff are OO.</p>

docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN

<p>Whenever the perceptron fine tunes itself, it will increase/decrease all the dendrites that is significant (attributes labelled 1) for each input. This means that even when the perceptron successfully fine tunes itself to suite all the data in you...

<h1 id="CONVENTIONS-USED">CONVENTIONS USED</h1>

<p>Please take note that not all subroutines/method must be used to make things work. All the subroutines and methods are listed out for the sake of writing the documentation.</p>

<p>Private methods/subroutines are prefixed with <code>_</code> or <code>&amp;_</code> and they aren&#39;t meant to be called directly. You can if you want to. There are quite a number of them to be honest, just ignore them if you happen to see them ...

<p>Synonyms are placed before the actual ie. technical subroutines/methods. You will see <code>...</code> as the parameters if they are synonyms. Move to the next subroutine/method until you find something like <code>\%options</code> as the parameter...

<h1 id="DATASET-STRUCTURE">DATASET STRUCTURE</h1>

<p><i>This module can only process CSV files.</i></p>

<p>Any field ie columns that will be used for processing must be binary ie. <code>0</code> or <code>1</code> only. Your dataset can contain other columns with non-binary data as long as they are not one of the dendrites.</p>

<p>There are soem sample dataset which can be found in the <code>t</code> directory. The original dataset can also be found in <code>docs/book_list.csv</code>. The files can also be found <a href="https://github.com/Ellednera/AI-Perceptron-Simple">he...

<h1 id="PERCEPTRON-DATA">PERCEPTRON DATA</h1>

<p>The perceptron/neuron data is stored using the <code>Storable</code> module.</p>

<p>See <code>Portability of Nerve Data</code> section below for more info on some known issues.</p>

<h1 id="DATA-PROCESSING-RELATED-SUBROUTINES">DATA PROCESSING RELATED SUBROUTINES</h1>

<p>These subroutines can be imported using the tag <code>:process_data</code>.</p>

<p>These subroutines should be called in the procedural way.</p>

<h2 id="shuffle_stimuli">shuffle_stimuli ( ... )</h2>

<p>The parameters and usage are the same as <code>shuffled_data</code>. See the next two subroutines.</p>

<h2 id="shuffle_data-original_data-shuffled_1-shuffled_2">shuffle_data ( $original_data =&gt; $shuffled_1, $shuffled_2, ... )</h2>

<h2 id="shuffle_data-ORIGINAL_DATA-shuffled_1-shuffled_2">shuffle_data ( ORIGINAL_DATA, $shuffled_1, $shuffled_2, ... )</h2>

<p>Shuffles <code>$original_data</code> or <code>ORIGINAL_DATA</code> and saves them to other files.</p>

<h1 id="CREATION-RELATED-SUBROUTINES-METHODS">CREATION RELATED SUBROUTINES/METHODS</h1>

<h2 id="new-options">new ( \%options )</h2>

<p>Creates a brand new perceptron and initializes the value of each attribute / dendrite aka. weight. Think of it as the thickness or plasticity of the dendrites.</p>

<p>For <code>%options</code>, the followings are needed unless mentioned:</p>

<dl>

docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN

<p><code>$c_matrix</code> and <code>$labels</code> are the same as the ones passed to <code>display_exam_results</code> and <code></code>display_confusion_matrix.</p>

<p>Returns a list <code>( $matrix, $c_matrix )</code> which can directly be passed to <code>_print_extended_matrix</code>.</p>

<h2 id="print_extended_matrix-matrix-c_matrix">&amp;_print_extended_matrix ( $matrix, $c_matrix )</h2>

<p>Extends and outputs the matrix on the screen.</p>

<p><code>$matrix</code> and <code>$c_matrix</code> are the same as returned by <code>&amp;_build_matrix</code>.</p>

<h1 id="NERVE-DATA-RELATED-SUBROUTINES">NERVE DATA RELATED SUBROUTINES</h1>

<p>This part is about saving the data of the nerve. These subroutines can be imported using the <code>:local_data</code> tag.</p>

<p><b>The subroutines are to be called in the procedural way</b>. No checking is done currently.</p>

<p>See <code>PERCEPTRON DATA</code> and <code>KNOWN ISSUES</code> sections for more details on the subroutines in this section.</p>

<h2 id="preserve">preserve ( ... )</h2>

<p>The parameters and usage are the same as <code>save_perceptron</code>. See the next subroutine.</p>

<h2 id="save_perceptron-nerve-nerve_file">save_perceptron ( $nerve, $nerve_file )</h2>

<p>Saves the <code>AI::Perceptron::Simple</code> object into a <code>Storable</code> file. There shouldn&#39;t be a need to call this method manually since after every training process this will be called automatically.</p>

<h2 id="revive">revive (...)</h2>

lib/AI/Perceptron/Simple.pm view on Meta::CPAN

    # processing data
    use AI::Perceptron::Simple ":process_data";
    shuffle_stimuli ( ... )
    shuffle_data ( ORIGINAL_STIMULI, $new_file_1, $new_file_2, ... );
    shuffle_data ( $original_stimuli => $new_file_1, $new_file_2, ... );

=head1 EXPORT

None by default.

All the subroutines from C<DATA PROCESSING RELATED SUBROUTINES>, C<NERVE DATA RELATED SUBROUTINES> and C<NERVE PORTABILITY RELATED SUBROUTINES> sections are importable through tags or manually specifying them.

The tags available include the following:

=over 4

=item C<:process_data> - subroutines under C<DATA PROCESSING RELATED SUBROUTINES> section.

=item C<:local_data> - subroutines under C<NERVE DATA RELATED SUBROUTINES> section.

=item C<:portable_data> - subroutines under C<NERVE PORTABILITY RELATED SUBROUTINES> section.

=back

Most of the stuff are OO.

=cut

use Exporter qw( import );

lib/AI/Perceptron/Simple.pm view on Meta::CPAN

No one defines how "mature" it is except the programmer himself/herself :)

=head1 CONVENTIONS USED

Please take note that not all subroutines/method must be used to make things work. All the subroutines and methods are listed out for the sake of writing the documentation. 

Private methods/subroutines are prefixed with C<_> or C<&_> and they aren't meant to be called directly. You can if you want to. There are quite a number of them to be honest, just ignore them if you happen to see them :)

Synonyms are placed before the actual ie. technical subroutines/methods. You will see C<...> as the parameters if they are synonyms. Move to the next subroutine/method until you find something like C<\%options> as the parameter or anything that isn't...

=head1 DATASET STRUCTURE

I<This module can only process CSV files.>

Any field ie columns that will be used for processing must be binary ie. C<0> or C<1> only. Your dataset can contain other columns with non-binary data as long as they are not one of the dendrites.

There are soem sample dataset which can be found in the C<t> directory. The original dataset can also be found in C<docs/book_list.csv>. The files can also be found L<here|https://github.com/Ellednera/AI-Perceptron-Simple>.

=head1 PERCEPTRON DATA

The perceptron/neuron data is stored using the C<Storable> module. 

See C<Portability of Nerve Data> section below for more info on some known issues.

=head1 DATA PROCESSING RELATED SUBROUTINES

These subroutines can be imported using the tag C<:process_data>.

These subroutines should be called in the procedural way.

=head2 shuffle_stimuli ( ... )

The parameters and usage are the same as C<shuffled_data>. See the next two subroutines.

=head2 shuffle_data ( $original_data => $shuffled_1, $shuffled_2, ... )

=head2 shuffle_data ( ORIGINAL_DATA, $shuffled_1, $shuffled_2, ... )

Shuffles C<$original_data> or C<ORIGINAL_DATA> and saves them to other files.

=cut

sub shuffle_stimuli {
    shuffle_data( @_ );
}

sub shuffle_data {
    my $stimuli = shift or croak "Please specify the original file name";
    my @shuffled_stimuli_names = @_

lib/AI/Perceptron/Simple.pm view on Meta::CPAN

    print "  F1 Score: $c_matrix->{ F1_Score } %\n" if exists $c_matrix->{ F1_Score };
    print "  Negative Predicted Value: $c_matrix->{ negative_predicted_value } %\n" if exists $c_matrix->{ negative_predicted_value };
    print "  False Negative Rate: $c_matrix->{ false_negative_rate } %\n" if exists $c_matrix->{ false_negative_rate };
    print "  False Positive Rate: $c_matrix->{ false_positive_rate } %\n" if exists $c_matrix->{ false_positive_rate };
    print "  False Discovery Rate: $c_matrix->{ false_discovery_rate } %\n" if exists $c_matrix->{ false_discovery_rate };
    print "  False Omission Rate: $c_matrix->{ false_omission_rate } %\n" if exists $c_matrix->{ false_omission_rate };
    print "  Balanced Accuracy: $c_matrix->{ balanced_accuracy } %\n" if exists $c_matrix->{ balanced_accuracy };
    print "~~" x24, "\n";
}

=head1 NERVE DATA RELATED SUBROUTINES

This part is about saving the data of the nerve. These subroutines can be imported using the C<:local_data> tag.

B<The subroutines are to be called in the procedural way>. No checking is done currently.

See C<PERCEPTRON DATA> and C<KNOWN ISSUES> sections for more details on the subroutines in this section.

=head2 preserve ( ... )

The parameters and usage are the same as C<save_perceptron>. See the next subroutine.

=head2 save_perceptron ( $nerve, $nerve_file )

Saves the C<AI::Perceptron::Simple> object into a C<Storable> file. There shouldn't be a need to call this method manually since after every training 
process this will be called automatically.

t/04-train.t view on Meta::CPAN


use strict;
use warnings;
use Test::More;

use AI::Perceptron::Simple qw( :local_data );

# pwd is the actual .pm module in blib
# ie. My-Perceptron/blib/lib/My/Perceptron.pm
use FindBin;
use constant TRAINING_DATA => $FindBin::Bin . "/book_list_train.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

# 36 headers
my @attributes = qw ( 
    glossy_cover	has_plastic_layer_on_cover	male_present	female_present	total_people_1	total_people_2	total_people_3
	total_people_4	total_people_5_n_above	has_flowers	flower_coverage_more_than_half	has_leaves	leaves_coverage_more_than_half	has_trees
	trees_coverage_more_than_half	has_other_living_things	has_fancy_stuff	has_obvious_inanimate_objects	red_shades	blue_shades	yellow_shades
	orange_shades	green_shades	purple_shades	brown_shades	black_shades	overall_red_dominant	overall_green_dominant

t/04-train.t view on Meta::CPAN

my $perceptron = AI::Perceptron::Simple->new( {
    initial_value => 0.01,
    attribs => \@attributes
} );

my %attribs = $perceptron->get_attributes; # merging will cause problems
my $perceptron_headers = keys %attribs; # scalar context directly return number of keys

is( $perceptron_headers, $total_headers, "Correct headers" );
# print $FindBin::Bin, "\n";
# print TRAINING_DATA, "\n";
ok ( -e TRAINING_DATA, "Found the training file" );

# saving and loading - this should go into a new test script
my $nerve_file = $FindBin::Bin . "/perceptron_1.nerve";
#ok( $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER ), "No problem with \'train\' method so far" );

{
local $@ = "";
eval { $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER) };
is ( $@, "", "No problem with \'train\' method (verbose) so far" );
}

ok ( $perceptron->train( TRAINING_DATA, "brand", $nerve_file), "No problem with \'train\' method (non-verbose) so far" );

# no longer returns the file anymore since v0.03
# is ( $perceptron->train( TRAINING_DATA, "brand", $nerve_file), $nerve_file, "\'train\' method returns the correct value" );

subtest "Data related subroutine found" => sub {
    ok( AI::Perceptron::Simple->can("save_perceptron"), "&save_perceptron is persent" );
    ok( AI::Perceptron::Simple->can("load_perceptron"), "&loaded_perceptron is present" );
};


ok( save_perceptron( $perceptron, $nerve_file ), "save_perceptron is working good so far" );
ok( -e $nerve_file, "Found the perceptron file" );

t/04-train_synonyms_exercise.t view on Meta::CPAN

#!/usr/bin/perl

use strict;
use warnings;
use Test::More;

use AI::Perceptron::Simple;

use FindBin;
use constant TRAINING_DATA => $FindBin::Bin . "/book_list_train.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

# 36 headers
my @attributes = qw ( 
    glossy_cover	has_plastic_layer_on_cover	male_present	female_present	total_people_1	total_people_2	total_people_3
	total_people_4	total_people_5_n_above	has_flowers	flower_coverage_more_than_half	has_leaves	leaves_coverage_more_than_half	has_trees
	trees_coverage_more_than_half	has_other_living_things	has_fancy_stuff	has_obvious_inanimate_objects	red_shades	blue_shades	yellow_shades
	orange_shades	green_shades	purple_shades	brown_shades	black_shades	overall_red_dominant	overall_green_dominant

t/04-train_synonyms_exercise.t view on Meta::CPAN

my $perceptron = AI::Perceptron::Simple->new( {
    initial_value => 0.01,
    attribs => \@attributes
} );

my %attribs = $perceptron->get_attributes; # merging will cause problems
my $perceptron_headers = keys %attribs; # scalar context directly return number of keys

is( $perceptron_headers, $total_headers, "Correct headers" );
# print $FindBin::Bin, "\n";
# print TRAINING_DATA, "\n";
ok ( -e TRAINING_DATA, "Found the training file" );

# saving and loading - this should go into a new test script
my $nerve_file = $FindBin::Bin . "/perceptron_exercise.nerve";
#ok( $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER ), "No problem with \'train\' method so far" );

{
local $@ = "";
eval { $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER) };
is ( $@, "", "No problem with \'train\' method (verbose) so far" );
}

ok ( $perceptron->train( TRAINING_DATA, "brand", $nerve_file), "No problem with \'train\' method (non-verbose) so far" );

# no longer returns the file anymore since v0.03
# is ( $perceptron->train( TRAINING_DATA, "brand", $nerve_file), $nerve_file, "\'train\' method returns the correct value" );

ok( AI::Perceptron::Simple->can("save_perceptron"), "&save_perceptron is persent" );
ok( AI::Perceptron::Simple->can("load_perceptron"), "&loaded_perceptron is present" );

ok( AI::Perceptron::Simple::save_perceptron( $perceptron, $nerve_file ), "save_perceptron is working good so far" );
ok( -e $nerve_file, "Found the perceptron file" );

ok( AI::Perceptron::Simple::load_perceptron( $nerve_file ), "Perceptron loaded" );
my $loaded_perceptron = AI::Perceptron::Simple::load_perceptron( $nerve_file );
is( ref $loaded_perceptron, MODULE_NAME, "Correct class after loading" );

t/04-train_synonyms_tame.t view on Meta::CPAN

#!/usr/bin/perl

use strict;
use warnings;
use Test::More;

use AI::Perceptron::Simple;

use FindBin;
use constant TRAINING_DATA => $FindBin::Bin . "/book_list_train.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

# 36 headers
my @attributes = qw ( 
    glossy_cover	has_plastic_layer_on_cover	male_present	female_present	total_people_1	total_people_2	total_people_3
	total_people_4	total_people_5_n_above	has_flowers	flower_coverage_more_than_half	has_leaves	leaves_coverage_more_than_half	has_trees
	trees_coverage_more_than_half	has_other_living_things	has_fancy_stuff	has_obvious_inanimate_objects	red_shades	blue_shades	yellow_shades
	orange_shades	green_shades	purple_shades	brown_shades	black_shades	overall_red_dominant	overall_green_dominant

t/04-train_synonyms_tame.t view on Meta::CPAN

my $perceptron = AI::Perceptron::Simple->new( {
    initial_value => 0.01,
    attribs => \@attributes
} );

my %attribs = $perceptron->get_attributes; # merging will cause problems
my $perceptron_headers = keys %attribs; # scalar context directly return number of keys

is( $perceptron_headers, $total_headers, "Correct headers" );
# print $FindBin::Bin, "\n";
# print TRAINING_DATA, "\n";
ok ( -e TRAINING_DATA, "Found the training file" );

# saving and loading - this should go into a new test script
my $nerve_file = $FindBin::Bin . "/perceptron_tame.nerve";
#ok( $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER ), "No problem with \'train\' method so far" );

{
local $@ = "";
eval { $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER) };
is ( $@, "", "No problem with \'train\' method (verbose) so far" );
}

ok ( $perceptron->train( TRAINING_DATA, "brand", $nerve_file), "No problem with \'train\' method (non-verbose) so far" );

# no longer returns the file anymore since v0.03
# is ( $perceptron->train( TRAINING_DATA, "brand", $nerve_file), $nerve_file, "\'train\' method returns the correct value" );

ok( AI::Perceptron::Simple->can("save_perceptron"), "&save_perceptron is persent" );
ok( AI::Perceptron::Simple->can("load_perceptron"), "&loaded_perceptron is present" );

ok( AI::Perceptron::Simple::save_perceptron( $perceptron, $nerve_file ), "save_perceptron is working good so far" );
ok( -e $nerve_file, "Found the perceptron file" );

ok( AI::Perceptron::Simple::load_perceptron( $nerve_file ), "Perceptron loaded" );
my $loaded_perceptron = AI::Perceptron::Simple::load_perceptron( $nerve_file );
is( ref $loaded_perceptron, MODULE_NAME, "Correct class after loading" );

t/06-validate.t view on Meta::CPAN


use strict;
use warnings;
use Test::More;
use Test::Output;

use AI::Perceptron::Simple;

use FindBin;

# TRAINING_DATA & VALIDATION_DATA have the same contents, in real world, don't do this
    # use different sets of data for training and validating the nerve. Same goes to testing data.
    # I'm doing this only to make sure the nerve is working correctly

use constant TRAINING_DATA => $FindBin::Bin . "/book_list_train.csv";
use constant VALIDATION_DATA => $FindBin::Bin . "/book_list_validate.csv";


use constant VALIDATION_DATA_OUTPUT_FILE => $FindBin::Bin . "/book_list_validate-filled.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

# 36 headers
my @attributes = qw ( 
    glossy_cover	has_plastic_layer_on_cover	male_present	female_present	total_people_1	total_people_2	total_people_3
	total_people_4	total_people_5_n_above	has_flowers	flower_coverage_more_than_half	has_leaves	leaves_coverage_more_than_half	has_trees
	trees_coverage_more_than_half	has_other_living_things	has_fancy_stuff	has_obvious_inanimate_objects	red_shades	blue_shades	yellow_shades
	orange_shades	green_shades	purple_shades	brown_shades	black_shades	overall_red_dominant	overall_green_dominant

t/06-validate.t view on Meta::CPAN

    initial_value => 0.01,
    learning_rate => 0.001,
    threshold => 0.8,
    attribs => \@attributes
} );

my $nerve_file = $FindBin::Bin . "/perceptron_1.nerve";

for ( 0..5 ) {
    print "Round $_\n";
    $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER );
    print "\n";
}
#print Dumper($perceptron), "\n";

# write ack to original file
my $ori_file_size = -s VALIDATION_DATA;
stdout_like {
    ok ( $perceptron->validate( {
                stimuli_validate => VALIDATION_DATA,
                predicted_column_index => 4,
            } ), 
            "Validate succedded!" );
} qr/book_list_validate\.csv/, "Correct output for validate when saving file";

# with new output file
stdout_like {
    ok ( $perceptron->validate( {
            stimuli_validate => VALIDATION_DATA,
            predicted_column_index => 4,
            results_write_to => VALIDATION_DATA_OUTPUT_FILE
        } ), 
        "Validate succedded!" );

} qr/book_list_validate\-filled\.csv/, "Correct output for validate when saving to NEW file";

ok( -e VALIDATION_DATA_OUTPUT_FILE, "New validation file found" );
isnt( -s VALIDATION_DATA_OUTPUT_FILE, 0, "New output file is not empty" );

done_testing;
# besiyata d'shmaya

t/06-validate_synonyms_lab.t view on Meta::CPAN


use strict;
use warnings;
use Test::More;
use Test::Output;

use AI::Perceptron::Simple;

use FindBin;

# TRAINING_DATA & VALIDATION_DATA have the same contents, in real world, don't do this
    # use different sets of data for training and validating the nerve. Same goes to testing data.
    # I'm doing this only to make sure the nerve is working correctly

use constant TRAINING_DATA => $FindBin::Bin . "/book_list_train.csv";
use constant VALIDATION_DATA => $FindBin::Bin . "/book_list_validate.csv";


use constant VALIDATION_DATA_OUTPUT_FILE => $FindBin::Bin . "/book_list_validate_lab-filled.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

# 36 headers
my @attributes = qw ( 
    glossy_cover	has_plastic_layer_on_cover	male_present	female_present	total_people_1	total_people_2	total_people_3
	total_people_4	total_people_5_n_above	has_flowers	flower_coverage_more_than_half	has_leaves	leaves_coverage_more_than_half	has_trees
	trees_coverage_more_than_half	has_other_living_things	has_fancy_stuff	has_obvious_inanimate_objects	red_shades	blue_shades	yellow_shades
	orange_shades	green_shades	purple_shades	brown_shades	black_shades	overall_red_dominant	overall_green_dominant

t/06-validate_synonyms_lab.t view on Meta::CPAN

    initial_value => 0.01,
    learning_rate => 0.001,
    threshold => 0.8,
    attribs => \@attributes
} );

my $nerve_file = $FindBin::Bin . "/perceptron_1.nerve";

for ( 0..5 ) {
    print "Round $_\n";
    $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER );
    print "\n";
}
#print Dumper($perceptron), "\n";

# write ack to original file
my $ori_file_size = -s VALIDATION_DATA;
stdout_like {
    ok ( $perceptron->take_lab_test( {
                stimuli_validate => VALIDATION_DATA,
                predicted_column_index => 4,
            } ), 
            "Validate succedded!" );
} qr/book_list_validate\.csv/, "Correct output for take_lab_test when saving file";

# with new output file
stdout_like {
    ok ( $perceptron->take_lab_test( {
            stimuli_validate => VALIDATION_DATA,
            predicted_column_index => 4,
            results_write_to => VALIDATION_DATA_OUTPUT_FILE
        } ), 
        "Validate succedded!" );

} qr/book_list_validate_lab\-filled\.csv/, "Correct output for take_lab_test when saving to NEW file";

ok( -e VALIDATION_DATA_OUTPUT_FILE, "New validation file found" );
isnt( -s VALIDATION_DATA_OUTPUT_FILE, 0, "New output file is not empty" );

done_testing;
# besiyata d'shmaya

t/06-validate_synonyms_mock.t view on Meta::CPAN


use strict;
use warnings;
use Test::More;
use Test::Output;

use AI::Perceptron::Simple;

use FindBin;

# TRAINING_DATA & VALIDATION_DATA have the same contents, in real world, don't do this
    # use different sets of data for training and validating the nerve. Same goes to testing data.
    # I'm doing this only to make sure the nerve is working correctly

use constant TRAINING_DATA => $FindBin::Bin . "/book_list_train.csv";
use constant VALIDATION_DATA => $FindBin::Bin . "/book_list_validate.csv";


use constant VALIDATION_DATA_OUTPUT_FILE => $FindBin::Bin . "/book_list_validate_mock-filled.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

# 36 headers
my @attributes = qw ( 
    glossy_cover	has_plastic_layer_on_cover	male_present	female_present	total_people_1	total_people_2	total_people_3
	total_people_4	total_people_5_n_above	has_flowers	flower_coverage_more_than_half	has_leaves	leaves_coverage_more_than_half	has_trees
	trees_coverage_more_than_half	has_other_living_things	has_fancy_stuff	has_obvious_inanimate_objects	red_shades	blue_shades	yellow_shades
	orange_shades	green_shades	purple_shades	brown_shades	black_shades	overall_red_dominant	overall_green_dominant

t/06-validate_synonyms_mock.t view on Meta::CPAN

    initial_value => 0.01,
    learning_rate => 0.001,
    threshold => 0.8,
    attribs => \@attributes
} );

my $nerve_file = $FindBin::Bin . "/perceptron_1.nerve";

for ( 0..5 ) {
    print "Round $_\n";
    $perceptron->train( TRAINING_DATA, "brand", $nerve_file, WANT_STATS, IDENTIFIER );
    print "\n";
}
#print Dumper($perceptron), "\n";

# write ack to original file
my $ori_file_size = -s VALIDATION_DATA;
stdout_like {
    ok ( $perceptron->take_mock_exam( {
                stimuli_validate => VALIDATION_DATA,
                predicted_column_index => 4,
            } ), 
            "Validate succedded!" );
} qr/book_list_validate\.csv/, "Correct output for take_mock_exam when saving file";

# with new output file
stdout_like {
    ok ( $perceptron->take_mock_exam( {
            stimuli_validate => VALIDATION_DATA,
            predicted_column_index => 4,
            results_write_to => VALIDATION_DATA_OUTPUT_FILE
        } ), 
        "Validate succedded!" );

} qr/book_list_validate_mock\-filled\.csv/, "Correct output for take_mock_exam when saving to NEW file";

ok( -e VALIDATION_DATA_OUTPUT_FILE, "New validation file found" );
isnt( -s VALIDATION_DATA_OUTPUT_FILE, 0, "New output file is not empty" );

done_testing;
# besiyata d'shmaya

t/10-test.t view on Meta::CPAN


use strict;
use warnings;
use Test::More;
use Test::Output;

use AI::Perceptron::Simple;

use FindBin;

use constant TEST_DATA => $FindBin::Bin . "/book_list_test.csv";
use constant TEST_DATA_NEW_FILE => $FindBin::Bin . "/book_list_test-filled.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

my $nerve_file = $FindBin::Bin . "/perceptron_1.nerve";
ok( -s $nerve_file, "Found nerve file to load" );

my $mature_nerve = AI::Perceptron::Simple::load_perceptron( $nerve_file );

# write to original file
stdout_like {
    ok ( $mature_nerve->test( {
            stimuli_validate => TEST_DATA,
            predicted_column_index => 4,
        } ), 
        "Testing stage succedded!" );

} qr/book_list_test\.csv/, "Correct output for testing when saving back to original file";


# with new output file
stdout_like {
    ok ( $mature_nerve->test( {
            stimuli_validate => TEST_DATA,
            predicted_column_index => 4,
            results_write_to => TEST_DATA_NEW_FILE
        } ), 
        "Testing stage succedded!" );

} qr/book_list_test\-filled\.csv/, "Correct output for testing when saving to NEW file";

ok( -e TEST_DATA_NEW_FILE, "New testing file found" );
isnt( -s TEST_DATA_NEW_FILE, 0, "New output file is not empty" );

done_testing;
# besiyata d'shmaya

t/10-test_synonyms_exam.t view on Meta::CPAN


use strict;
use warnings;
use Test::More;
use Test::Output;

use AI::Perceptron::Simple;

use FindBin;

use constant TEST_DATA => $FindBin::Bin . "/book_list_test.csv";
use constant TEST_DATA_NEW_FILE => $FindBin::Bin . "/book_list_test_exam-filled.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

my $nerve_file = $FindBin::Bin . "/perceptron_1.nerve";
ok( -s $nerve_file, "Found nerve file to load" );

my $mature_nerve = AI::Perceptron::Simple::load_perceptron( $nerve_file );

# write to original file
stdout_like {
    ok ( $mature_nerve->take_real_exam( {
            stimuli_validate => TEST_DATA,
            predicted_column_index => 4,
        } ), 
        "Testing stage succedded!" );

} qr/book_list_test\.csv/, "Correct output for testing when saving back to original file";


# with new output file
stdout_like {
    ok ( $mature_nerve->take_real_exam( {
            stimuli_validate => TEST_DATA,
            predicted_column_index => 4,
            results_write_to => TEST_DATA_NEW_FILE
        } ), 
        "Testing stage succedded!" );

} qr/book_list_test_exam\-filled\.csv/, "Correct output for testing when saving to NEW file";

ok( -e TEST_DATA_NEW_FILE, "New testing file found" );
isnt( -s TEST_DATA_NEW_FILE, 0, "New output file is not empty" );

done_testing;
# besiyata d'shmaya

t/10-test_synonyms_work.t view on Meta::CPAN


use strict;
use warnings;
use Test::More;
use Test::Output;

use AI::Perceptron::Simple;

use FindBin;

use constant TEST_DATA => $FindBin::Bin . "/book_list_test.csv";
use constant TEST_DATA_NEW_FILE => $FindBin::Bin . "/book_list_test_work-filled.csv";
use constant MODULE_NAME => "AI::Perceptron::Simple";
use constant WANT_STATS => 1;
use constant IDENTIFIER => "book_name";

my $nerve_file = $FindBin::Bin . "/perceptron_1.nerve";
ok( -s $nerve_file, "Found nerve file to load" );

my $mature_nerve = AI::Perceptron::Simple::load_perceptron( $nerve_file );

# write to original file
stdout_like {
    ok ( $mature_nerve->work_in_real_world( {
            stimuli_validate => TEST_DATA,
            predicted_column_index => 4,
        } ), 
        "Testing stage succedded!" );

} qr/book_list_test\.csv/, "Correct output for testing when saving back to original file";


# with new output file
stdout_like {
    ok ( $mature_nerve->work_in_real_world( {
            stimuli_validate => TEST_DATA,
            predicted_column_index => 4,
            results_write_to => TEST_DATA_NEW_FILE
        } ), 
        "Testing stage succedded!" );

} qr/book_list_test_work\-filled\.csv/, "Correct output for testing when saving to NEW file";

ok( -e TEST_DATA_NEW_FILE, "New testing file found" );
isnt( -s TEST_DATA_NEW_FILE, 0, "New output file is not empty" );

done_testing;
# besiyata d'shmaya

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