AI-Perceptron-Simple
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<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 => $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>
</ul>
</li>
<li><a href="#TRAINING-RELATED-SUBROUTINES-METHODS">TRAINING RELATED SUBROUTINES/METHODS</a>
<ul>
<li><a href="#tame">tame ( ... )</a></li>
<li><a href="#exercise">exercise ( ... )</a></li>
<li><a href="#train-stimuli_train_csv-expected_output_header-save_nerve_to_file">train ( $stimuli_train_csv, $expected_output_header, $save_nerve_to_file )</a></li>
<li><a href="#train-stimuli_train_csv-expected_output_header-save_nerve_to_file-display_stats-identifier">train ( $stimuli_train_csv, $expected_output_header, $save_nerve_to_file, $display_stats, $identifier )</a></li>
<li><a href="#calculate_output-self-stimuli_hash">&_calculate_output( $self, \%stimuli_hash )</a></li>
<li><a href="#tune-self-stimuli_hash-tune_up_or_down">&_tune( $self, \%stimuli_hash, $tune_up_or_down )</a></li>
</ul>
</li>
<li><a href="#VALIDATION-RELATED-METHODS">VALIDATION RELATED METHODS</a>
<ul>
<li><a href="#take_mock_exam">take_mock_exam (...)</a></li>
<li><a href="#take_lab_test">take_lab_test (...)</a></li>
<li><a href="#validate-options">validate ( \%options )</a></li>
</ul>
</li>
<li><a href="#TESTING-RELATED-SUBROUTINES-METHODS">TESTING RELATED SUBROUTINES/METHODS</a>
<ul>
<li><a href="#take_real_exam">take_real_exam (...)</a></li>
<li><a href="#work_in_real_world">work_in_real_world (...)</a></li>
<li><a href="#test-options">test ( \%options )</a></li>
<li><a href="#real_validate_or_test-data_hash_ref">_real_validate_or_test ( $data_hash_ref )</a></li>
<li><a href="#fill_predicted_values-self-stimuli_validate-predicted_index-aoa">&_fill_predicted_values ( $self, $stimuli_validate, $predicted_index, $aoa )</a></li>
</ul>
</li>
<li><a href="#RESULTS-RELATED-SUBROUTINES-METHODS">RESULTS RELATED SUBROUTINES/METHODS</a>
<ul>
<li><a href="#get_exam_results">get_exam_results ( ... )</a></li>
<li><a href="#get_confusion_matrix-options">get_confusion_matrix ( \%options )</a></li>
<li><a href="#collect_stats-options">&_collect_stats ( \%options )</a></li>
<li><a href="#calculate_total_entries-c_matrix_ref">&_calculate_total_entries ( $c_matrix_ref )</a></li>
<li><a href="#calculate_accuracy-c_matrix_ref">&_calculate_accuracy ( $c_matrix_ref )</a></li>
<li><a href="#calculate_sensitivity-c_matrix_ref">&_calculate_sensitivity ( $c_matrix_ref )</a></li>
<li><a href="#calculate_precision-c_matrix_ref">&_calculate_precision ( $c_matrix_ref )</a></li>
<li><a href="#calculate_specificity-c_matrix_ref">&_calculate_specificity ( $c_matrix_ref )</a></li>
<li><a href="#calculate_f1_score-c_matrix_ref">&_calculate_f1_score ( $c_matrix_ref )</a></li>
<li><a href="#calculate_negative_predicted_value-c_matrix_ref">&_calculate_negative_predicted_value( $c_matrix_ref )</a></li>
<li><a href="#calculate_false_negative_rate-c_matrix_ref">&_calculate_false_negative_rate( $c_matrix_ref )</a></li>
<li><a href="#calculate_false_positive_rate-c_matrix_ref">&_calculate_false_positive_rate( $c_matrix_ref )</a></li>
<li><a href="#calculate_false_discovery_rate-c_matrix_ref">&_calculate_false_discovery_rate( $c_matrix_ref )</a></li>
<li><a href="#calculate_false_omission_rate-c_matrix_ref">&_calculate_false_omission_rate( $c_matrix_ref )</a></li>
<li><a href="#calculate_balanced_accuracy-c_matrix_ref">&_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">&_build_matrix ( $c_matrix, $labels )</a></li>
<li><a href="#print_extended_matrix-matrix-c_matrix">&_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>
<li><a href="#save_perceptron_yaml-nerve-yaml_nerve_file">save_perceptron_yaml ( $nerve, $yaml_nerve_file )</a></li>
<li><a href="#revive_from_yaml">revive_from_yaml (...)</a></li>
<li><a href="#load_perceptron_yaml-yaml_nerve_file">load_perceptron_yaml ( $yaml_nerve_file )</a></li>
</ul>
</li>
<li><a href="#TO-DO">TO DO</a></li>
<li><a href="#KNOWN-ISSUES">KNOWN ISSUES</a>
<ul>
<li><a href="#Portability-of-Nerve-Data">Portability of Nerve Data</a></li>
</ul>
</li>
<li><a href="#AUTHOR">AUTHOR</a></li>
<li><a href="#BUGS">BUGS</a></li>
<li><a href="#SUPPORT">SUPPORT</a></li>
<li><a href="#ACKNOWLEDGEMENTS">ACKNOWLEDGEMENTS</a></li>
<li><a href="#SEE-ALSO">SEE ALSO</a></li>
<li><a href="#LICENSE-AND-COPYRIGHT">LICENSE AND COPYRIGHT</a></li>
</ul>
<h1 id="NAME">NAME</h1>
<p>AI::Perceptron::Simple</p>
<p>A Newbie Friendly Module to Create, Train, Validate and Test Perceptrons / Neurons</p>
<h1 id="VERSION">VERSION</h1>
<p>Version 1.04</p>
<h1 id="SYNOPSIS">SYNOPSIS</h1>
<pre><code> #!/usr/bin/perl
use AI::Perceptron::Simple qw(...);
# create a new nerve / neuron / perceptron
$nerve = AI::Perceptron::Simple->new( {
initial_value => $size_of_each_dendrite,
learning_rate => 0.3, # optional
threshold => 0.85, # optional
attribs => \@dendrites,
} );
# train
$nerve->tame( ... );
$nerve->exercise( ... );
$nerve->train( $training_data_csv, $expected_column_name, $save_nerve_to );
# or
$nerve->train(
$training_data_csv, $expected_column_name, $save_nerve_to,
$show_progress, $identifier); # these two parameters must go together
# validate
$nerve->take_lab_test( ... );
$nerve->take_mock_exam( ... );
# fill results to original file
$nerve->validate( {
stimuli_validate => $validation_data_csv,
predicted_column_index => 4,
} );
# or
# fill results to a new file
$nerve->validate( {
stimuli_validate => $validation_data_csv,
predicted_column_index => 4,
results_write_to => $new_csv
} );
# test - see "validate" method, same usage
$nerve->take_real_exam( ... );
$nerve->work_in_real_world( ... );
$nerve->test( ... );
# confusion matrix
my %c_matrix = $nerve->get_confusion_matrix( {
full_data_file => $file_csv,
actual_output_header => $header_name,
predicted_output_header => $predicted_header_name,
more_stats => 1, # optional
} );
# accessing the confusion matrix
my @keys = qw( true_positive true_negative false_positive false_negative
total_entries accuracy sensitivity );
for ( @keys ) {
print $_, " => ", $c_matrix{ $_ }, "\n";
}
# output to console
$nerve->display_confusion_matrix( \%c_matrix, {
zero_as => "bad apples", # cat milk green etc.
one_as => "good apples", # dog honey pink etc.
} );
# saving and loading data of perceptron locally
# NOTE: nerve data is automatically saved after each trainning process
use AI::Perceptron::Simple ":local_data";
my $nerve_file = "apples.nerve";
preserve( ... );
save_perceptron( $nerve, $nerve_file );
# load data of percpetron for use in actual program
my $apple_nerve = revive( ... );
my $apple_nerve = load_perceptron( $nerve_file );
# for portability of nerve data
use AI::Perceptron::Simple ":portable_data";
my $yaml_nerve_file = "pearls.yaml";
preserve_as_yaml ( ... );
save_perceptron_yaml ( $nerve, $yaml_nerve_file );
# load nerve data on the other computer
my $pearl_nerve = revive_from_yaml ( ... );
my $pearl_nerve = load_perceptron_yaml ( $yaml_nerve_file );
# 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, ... );</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>
<h1 id="DESCRIPTION">DESCRIPTION</h1>
<p>This module provides methods to build, train, validate and test a perceptron. It can also save the data of the perceptron for future use for any actual AI programs.</p>
<p>This module is also aimed to help newbies grasp hold of the concept of perceptron, training, validation and testing as much as possible. Hence, all the methods and subroutines in this module are decoupled as much as possible so that the actual scr...
<p>The implementation here is super basic as it only takes in input of the dendrites and calculate the output. If the output is higher than the threshold, the final result (category) will be 1 aka perceptron is activated. If not, then the result will...
<p>Depending on how you view or categorize the final result, the perceptron will fine tune itself (aka train) based on the learning rate until the desired result is met. Everything from here on is all mathematics and numbers which only makes sense to...
<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>&_</code> 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 ...
<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>
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