AI-Perceptron-Simple
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docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN
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docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN
<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>
<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>
docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN
</dl>
<h2 id="get_attributes">get_attributes</h2>
<p>Obtains a hash of all the attributes of the perceptron</p>
<h2 id="learning_rate-value">learning_rate ( $value )</h2>
<h2 id="learning_rate">learning_rate</h2>
<p>If <code>$value</code> is given, sets the learning rate to <code>$value</code>. If not, then it returns the learning rate.</p>
<h2 id="threshold-value">threshold ( $value )</h2>
<h2 id="threshold">threshold</h2>
<p>If <code>$value</code> is given, sets the threshold / passing rate to <code>$value</code>. If not, then it returns the passing rate.</p>
<h1 id="TRAINING-RELATED-SUBROUTINES-METHODS">TRAINING RELATED SUBROUTINES/METHODS</h1>
<p>All the training methods here have the same parameters as the two actual <code>train</code> method and they all do the same stuff. They are also used in the same way.</p>
<h2 id="tame">tame ( ... )</h2>
<h2 id="exercise">exercise ( ... )</h2>
<h2 id="train-stimuli_train_csv-expected_output_header-save_nerve_to_file">train ( $stimuli_train_csv, $expected_output_header, $save_nerve_to_file )</h2>
<h2 id="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, $identifier )</h2>
<p>Trains the perceptron.</p>
<p><code>$stimuli_train_csv</code> is the set of data / input (in CSV format) to train the perceptron while <code>$save_nerve_to_file</code> is the filename that will be generate each time the perceptron finishes the training process. This data file ...
<p><code>$expected_output_header</code> is the header name of the columns in the csv file with the actual category or the exepcted values. This is used to determine to tune the nerve up or down. This value should only be 0 or 1 for the sake of simpli...
<p><code>$display_stats</code> is <b>optional</b> and the default is 0. It will display more output about the tuning process. It will show the followings:</p>
<dl>
<dt id="tuning-status">tuning status</dt>
<dd>
docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN
</dd>
<dt id="new-sum">new sum</dt>
<dd>
<p>The new sum of all <code>weightage * input</code> after fine-tuning the nerve</p>
</dd>
</dl>
<p>If <code>$display_stats</code> is specified ie. set to <code>1</code>, then you <b>MUST</b> specify the <code>$identifier</code>. <code>$identifier</code> is the column / header name that is used to identify a specific row of data in <code>$stimul...
<h2 id="calculate_output-self-stimuli_hash">&_calculate_output( $self, \%stimuli_hash )</h2>
<p>Calculates and returns the <code>sum(weightage*input)</code> for each individual row of data. Actually, it justs add up all the existing weight since the <code>input</code> is always 1 for now :)</p>
<p><code>%stimuli_hash</code> is the actual data to be used for training. It might contain useless columns.</p>
<p>This will get all the avaible dendrites using the <code>get_attributes</code> method and then use all the keys ie. headers to access the corresponding values.</p>
<p>This subroutine should be called in the procedural way for now.</p>
docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN
<h1 id="VALIDATION-RELATED-METHODS">VALIDATION RELATED METHODS</h1>
<p>All the validation methods here have the same parameters as the actual <code>validate</code> method and they all do the same stuff. They are also used in the same way.</p>
<h2 id="take_mock_exam">take_mock_exam (...)</h2>
<h2 id="take_lab_test">take_lab_test (...)</h2>
<h2 id="validate-options">validate ( \%options )</h2>
<p>This method validates the perceptron against another set of data after it has undergone the training process.</p>
<p>This method calculates the output of each row of data and write the result into the predicted column. The data begin written into the new file or the original file will maintain it's sequence.</p>
<p>Please take note that this method will load all the data of the validation stimuli, so please split your stimuli into multiple files if possible and call this method a few more times.</p>
<p>For <code>%options</code>, the followings are needed unless mentioned:</p>
<dl>
<dt id="stimuli_validate-csv_file">stimuli_validate => $csv_file</dt>
docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN
<h1 id="RESULTS-RELATED-SUBROUTINES-METHODS">RESULTS RELATED SUBROUTINES/METHODS</h1>
<p>This part is related to generating the confusion matrix.</p>
<h2 id="get_exam_results">get_exam_results ( ... )</h2>
<p>The parameters and usage are the same as <code>get_confusion_matrix</code>. See the next method.</p>
<h2 id="get_confusion_matrix-options">get_confusion_matrix ( \%options )</h2>
<p>Returns the confusion matrix in the form of a hash. The hash will contain these keys: <code>true_positive</code>, <code>true_negative</code>, <code>false_positive</code>, <code>false_negative</code>, <code>accuracy</code>, <code>sensitivity</code>...
<p>If you are trying to manipulate the confusion matrix hash or something, take note that all the stats are in percentage (%) in decimal (if any) except the total entries.</p>
<p>For <code>%options</code>, the followings are needed unless mentioned:</p>
<dl>
<dt id="full_data_file-filled_test_file">full_data_file => $filled_test_file</dt>
<dd>
lib/AI/Perceptron/Simple.pm view on Meta::CPAN
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>.
lib/AI/Perceptron/Simple.pm view on Meta::CPAN
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 };
}
lib/AI/Perceptron/Simple.pm view on Meta::CPAN
=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, $identifier )
Trains the perceptron.
C<$stimuli_train_csv> is the set of data / input (in CSV format) to train the perceptron while C<$save_nerve_to_file> is
the filename that will be generate each time the perceptron finishes the training process. This data file is the data of the C<AI::Perceptron::Simple>
object and it is used in the C<validate> method.
C<$expected_output_header> is the header name of the columns in the csv file with the actual category or the exepcted values. This is used to determine to tune the nerve up or down. This value should only be 0 or 1 for the sake of simplicity.
C<$display_stats> is B<optional> and the default is 0. It will display more output about the tuning process. It will show the followings:
=over 4
=item tuning status
lib/AI/Perceptron/Simple.pm view on Meta::CPAN
=item threshold
The threshold of the nerve
=item new sum
The new sum of all C<weightage * input> after fine-tuning the nerve
=back
If C<$display_stats> is specified ie. set to C<1>, then you B<MUST> specify the C<$identifier>. C<$identifier> is the column / header name that is used to identify a specific row of data in C<$stimuli_train_csv>.
=cut
sub tame {
train( @_ );
}
sub exercise {
train( @_ );
}
lib/AI/Perceptron/Simple.pm view on Meta::CPAN
=head1 VALIDATION RELATED METHODS
All the validation methods here have the same parameters as the actual C<validate> method and they all do the same stuff. They are also used in the same way.
=head2 take_mock_exam (...)
=head2 take_lab_test (...)
=head2 validate ( \%options )
This method validates the perceptron against another set of data after it has undergone the training process.
This method calculates the output of each row of data and write the result into the predicted column. The data begin written into the new file or the original file will maintain it's sequence.
Please take note that this method will load all the data of the validation stimuli, so please split your stimuli into multiple files if possible and call this method a few more times.
For C<%options>, the followings are needed unless mentioned:
=over 4
=item stimuli_validate => $csv_file
lib/AI/Perceptron/Simple.pm view on Meta::CPAN
=head1 RESULTS RELATED SUBROUTINES/METHODS
This part is related to generating the confusion matrix.
=head2 get_exam_results ( ... )
The parameters and usage are the same as C<get_confusion_matrix>. See the next method.
=head2 get_confusion_matrix ( \%options )
Returns the confusion matrix in the form of a hash. The hash will contain these keys: C<true_positive>, C<true_negative>, C<false_positive>, C<false_negative>, C<accuracy>, C<sensitivity>. More stats like C<precision>, C<specificity> and C<F1_Score> ...
If you are trying to manipulate the confusion matrix hash or something, take note that all the stats are in percentage (%) in decimal (if any) except the total entries.
For C<%options>, the followings are needed unless mentioned:
=over 4
=item full_data_file => $filled_test_file
This is the CSV file filled with the predicted values.
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";
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";
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";
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