AI-Perceptron-Simple
view release on metacpan or search on metacpan
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"YAML" : "0",
"local::lib" : "0",
"perl" : "5.008001",
"utf8" : "0"
}
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"requires" : {
"FindBin" : "0",
"Test::More" : "0",
"Test::Output" : "1.033"
}
}
},
"release_status" : "stable",
"version" : "1.04",
"x_serialization_backend" : "JSON::PP version 4.02"
}
---
abstract: unknown
author:
- 'Raphael Jong Jun Jie <ellednera@cpan.org>'
build_requires:
ExtUtils::MakeMaker: '0'
FindBin: '0'
Test::More: '0'
Test::Output: '1.033'
configure_requires:
ExtUtils::MakeMaker: '0'
dynamic_config: 1
generated_by: 'ExtUtils::MakeMaker version 7.62, CPAN::Meta::Converter version 2.150010'
license: artistic_2
meta-spec:
url: http://module-build.sourceforge.net/META-spec-v1.4.html
version: '1.4'
name: AI-Perceptron-Simple
no_index:
Makefile.PL view on Meta::CPAN
NAME => 'AI::Perceptron::Simple',
AUTHOR => q{Raphael Jong Jun Jie <ellednera@cpan.org>},
VERSION_FROM => 'lib/AI/Perceptron/Simple.pm',
#ABSTRACT_FROM => 'lib/AI/Perceptron/Simple.pm',
LICENSE => 'artistic_2',
MIN_PERL_VERSION => '5.008001',
CONFIGURE_REQUIRES => {
'ExtUtils::MakeMaker' => '0',
},
TEST_REQUIRES => {
'Test::More' => '0',
'Test::Output' => '1.033',
'FindBin' => '0',
},
PREREQ_PM => {
'utf8' => '0',
'local::lib' => '0',
'Carp' => '0',
'Storable' => '0',
'Text::CSV' => '2.01',
'Text::Matrix' => '1.00',
'YAML' => '0',
AI-Perceptron-Simple (v1.04)
A Newbie Friendly Module to Create, Train, Validate and Test Perceptrons / Neurons
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.
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 script...
INSTALLATION
To install this module, run the following commands:
perl Makefile.PL
docs/AI-Perceptron-Simple-1.04.html view on Meta::CPAN
<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(...);
docs/specifications.t view on Meta::CPAN
#!/usr/bin/perl
use AI::Perceptron::Simple;
use Test::More;
plan( skip_all => "This is just the specification" );
done_testing;
######### specifications ##############
#
# This specification is based on My::Perceptron (see my github repo) and packed into AI::Perceptron::Simple v1.00
#
# Version 0.01 - completed on 8 August 2021
# [v] able to create perceptron
docs/specifications.t view on Meta::CPAN
# generate_confusion_matrix: [v] get_exam_results
# display_confusion_matrix: [v] display_exam_results
# save_perceptron: [v] preserve
# load_perceptron: [v] revive
#
# Version 1.01
# [v] fixed currently known issues as much as possible (see 'Changes')
# - "long size integer" === "byte order not compatible"
#
# Version 1.02
# [v] minimum perl version changed to 5.8 due to Test::Output
# [v] YAML (nerve file) for portability
# [v] make subroutines exportable, the names are too long
# [v] :local_data
# [v] :portable_data
# [v] fix test for display_confusion_matrix
# [v] modifier "n" (perl 5.22 and above) changed to primitive '?:', 5.22 is too high
# [v] fixed inaccurate test for output part
# [v] clean & refactor codes
# [v] refactored &display_confusion_matrix
# [v] improve the documentation
lib/AI/Perceptron/Simple.pm view on Meta::CPAN
require local::lib; # no local::lib in tests, this is also to avoid loading local::lib multiple times
use Text::CSV qw( csv );
use Text::Matrix;
use File::Basename qw( basename );
use List::Util qw( shuffle );
=head1 NAME
AI::Perceptron::Simple
A Newbie Friendly Module to Create, Train, Validate and Test Perceptrons / Neurons
=head1 VERSION
Version 1.04
=cut
our $VERSION = '1.04';
# default values
t/00-load.t view on Meta::CPAN
#!perl
use 5.006;
use strict;
use warnings;
use Test::More;
plan tests => 1;
BEGIN {
use_ok( 'AI::Perceptron::Simple' ) || print "Bail out!\n";
}
diag( "Testing AI::Perceptron::Simple $AI::Perceptron::Simple::VERSION, Perl $], $^X" );
t/00-manifest.t view on Meta::CPAN
#!perl
use 5.006;
use strict;
use warnings;
use Test::More;
unless ( $ENV{RELEASE_TESTING} ) {
plan( skip_all => "Author tests not required for installation" );
}
my $min_tcm = 0.9;
eval "use Test::CheckManifest $min_tcm";
plan skip_all => "Test::CheckManifest $min_tcm required" if $@;
ok_manifest();
t/00-pod-coverage.t view on Meta::CPAN
#!perl
use 5.006;
use strict;
use warnings;
use Test::More;
unless ( $ENV{RELEASE_TESTING} ) {
plan( skip_all => "Author tests not required for installation" );
}
# Ensure a recent version of Test::Pod::Coverage
my $min_tpc = 1.08;
eval "use Test::Pod::Coverage $min_tpc";
plan skip_all => "Test::Pod::Coverage $min_tpc required for testing POD coverage"
if $@;
# Test::Pod::Coverage doesn't require a minimum Pod::Coverage version,
# but older versions don't recognize some common documentation styles
my $min_pc = 0.18;
eval "use Pod::Coverage $min_pc";
plan skip_all => "Pod::Coverage $min_pc required for testing POD coverage"
if $@;
all_pod_coverage_ok();
#!perl
use 5.006;
use strict;
use warnings;
use Test::More;
unless ( $ENV{RELEASE_TESTING} ) {
plan( skip_all => "Author tests not required for installation" );
}
# Ensure a recent version of Test::Pod
my $min_tp = 1.22;
eval "use Test::Pod $min_tp";
plan skip_all => "Test::Pod $min_tp required for testing POD" if $@;
all_pod_files_ok();
t/02-creation.t view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use Test::More;
use AI::Perceptron::Simple;
my $module_name = "AI::Perceptron::Simple";
my $initial_value = 1.5;
my @attributes = qw( glossy_look has_flowers );
# print AI::Perceptron::Simple::LEARNING_RATE;
# all important parameter test
t/02-creation.t view on Meta::CPAN
is( $perceptron->learning_rate, 0.3, "Correct custom learning_rate -> ".$perceptron->learning_rate );
is( $perceptron->threshold, 0.85, "Correct custom passing rate -> ".$perceptron->threshold );
# get_attributes()
my %attributes = $perceptron->get_attributes;
for ( @attributes ) {
ok( $attributes{ $_ }, "Attribute \'$_\' present" );
is( $attributes{ $_ }, $initial_value, "Correct initial value (".$attributes{$_}.") for \'$_\'" );
}
# don't try to use Test::Carp, it won't work, it only tests for direct calling of carp and croak etc
subtest "Caught missing mandatory parameters" => sub {
eval {
my $no_attribs = AI::Perceptron::Simple->new( { initial_value => $initial_value} );
};
like( $@, qr/attribs/, "Caught missing attribs" );
eval {
my $perceptron = AI::Perceptron::Simple->new( { attribs => \@attributes} );
};
like($@, qr/initial_value/, "Caught missing initial_value");
t/02-state_portable.t view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use Test::More;
use AI::Perceptron::Simple qw( :portable_data );
# for :local_data test, see 04-train.t 02-state_synonyms.t utilizes the full invocation
use FindBin;
use constant MODULE_NAME => "AI::Perceptron::Simple";
my @attributes = qw ( has_trees trees_coverage_more_than_half has_other_living_things );
my $total_headers = scalar @attributes;
t/02-state_synonyms.t view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use Test::More;
use AI::Perceptron::Simple;
use FindBin;
use constant MODULE_NAME => "AI::Perceptron::Simple";
# 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
t/04-train.t view on Meta::CPAN
#!/usr/bin/perl
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";
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
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
t/06-validate.t view on Meta::CPAN
#!/usr/bin/perl
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";
t/06-validate_synonyms_lab.t view on Meta::CPAN
#!/usr/bin/perl
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";
t/06-validate_synonyms_mock.t view on Meta::CPAN
#!/usr/bin/perl
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";
t/08-confusion_matrix.t view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use Test::More;
use Test::Output;
use AI::Perceptron::Simple;
#use local::lib;
use Text::Matrix;
use FindBin;
use constant TEST_FILE => $FindBin::Bin . "/book_list_test-filled.csv";
use constant NON_BINARY_FILE => $FindBin::Bin . "/book_list_test-filled-non-binary.csv";
t/08-confusion_matrix.t view on Meta::CPAN
is ( ref \%c_matrix, ref {}, "Confusion matrix in correct data structure" );
is ( $c_matrix{ true_positive }, 2, "Correct true_positive" );
is ( $c_matrix{ true_negative }, 4, "Correct true_negative" );
is ( $c_matrix{ false_positive }, 1, "Correct false_positive" );
is ( $c_matrix{ false_negative }, 3, "Correct false_negative" );
is ( $c_matrix{ total_entries }, 10, "Total entries is correct" );
ok ( AI::Perceptron::Simple::_calculate_total_entries( \%c_matrix ),
"Testing the 'untestable' &_calculate_total_entries" );
is ( $c_matrix{ total_entries }, 10, "'illegal' calculation of total entries is correct" );
like ( $c_matrix{ accuracy }, qr/60/, "Accuracy seems correct to me" );
ok ( AI::Perceptron::Simple::_calculate_accuracy( \%c_matrix ),
"Testing the 'untestable' &_calculate_accuracy" );
like ( $c_matrix{ accuracy }, qr/60/, "'illegal' calculation of accuracy seems correct to me" );
like ( $c_matrix{ sensitivity }, qr/40/, "Accuracy seems correct to me" );
ok ( AI::Perceptron::Simple::_calculate_sensitivity( \%c_matrix ),
"Testing the 'untestable' &_calculate_sensitivity" );
like ( $c_matrix{ accuracy }, qr/60/, "'illegal' calculation of sensitivity seems correct to me" );
{
local $@;
eval {
$perceptron->get_confusion_matrix( {
full_data_file => NON_BINARY_FILE,
actual_output_header => "brand",
predicted_output_header => "predicted",
} );
t/08-confusion_matrix_synonyms.t view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use Test::More;
use Test::Output;
use AI::Perceptron::Simple;
#use local::lib;
use Text::Matrix;
use FindBin;
use constant TEST_FILE => $FindBin::Bin . "/book_list_test-filled.csv";
use constant NON_BINARY_FILE => $FindBin::Bin . "/book_list_test-filled-non-binary.csv";
t/08-confusion_matrix_synonyms.t view on Meta::CPAN
is ( ref \%c_matrix, ref {}, "Confusion matrix in correct data structure" );
is ( $c_matrix{ true_positive }, 2, "Correct true_positive" );
is ( $c_matrix{ true_negative }, 4, "Correct true_negative" );
is ( $c_matrix{ false_positive }, 1, "Correct false_positive" );
is ( $c_matrix{ false_negative }, 3, "Correct false_negative" );
is ( $c_matrix{ total_entries }, 10, "Total entries is correct" );
ok ( AI::Perceptron::Simple::_calculate_total_entries( \%c_matrix ),
"Testing the 'untestable' &_calculate_total_entries" );
is ( $c_matrix{ total_entries }, 10, "'illegal' calculation of total entries is correct" );
like ( $c_matrix{ accuracy }, qr/60/, "Accuracy seems correct to me" );
ok ( AI::Perceptron::Simple::_calculate_accuracy( \%c_matrix ),
"Testing the 'untestable' &_calculate_accuracy" );
like ( $c_matrix{ accuracy }, qr/60/, "'illegal' calculation of accuracy seems correct to me" );
like ( $c_matrix{ sensitivity }, qr/40/, "Accuracy seems correct to me" );
ok ( AI::Perceptron::Simple::_calculate_sensitivity( \%c_matrix ),
"Testing the 'untestable' &_calculate_sensitivity" );
like ( $c_matrix{ accuracy }, qr/60/, "'illegal' calculation of sensitivity seems correct to me" );
{
local $@;
eval {
$perceptron->get_exam_results( {
full_data_file => NON_BINARY_FILE,
actual_output_header => "brand",
predicted_output_header => "predicted",
} );
t/10-test.t view on Meta::CPAN
#!/usr/bin/perl
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";
t/10-test.t view on Meta::CPAN
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
#!/usr/bin/perl
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";
t/10-test_synonyms_exam.t view on Meta::CPAN
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
#!/usr/bin/perl
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";
t/10-test_synonyms_work.t view on Meta::CPAN
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
t/12-shuffle_data.t view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use Test::More;
use Test::Output;
# use AI::Perceptron::Simple "shuffle_data";
use AI::Perceptron::Simple ":process_data";
use FindBin;
# this one will directly use "ORIGINAL_STIMULI" as the filename if use with "=>", strange
use constant ORIGINAL_STIMULI => $FindBin::Bin . "/book_list_to_shuffle.csv";
my $original_stimuli = $FindBin::Bin . "/book_list_to_shuffle.csv";
my $shuffled_data_1 = $FindBin::Bin . "/shuffled_1.csv";
t/12-shuffle_data_synonym.t view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use Test::More;
use Test::Output;
use AI::Perceptron::Simple "shuffle_stimuli";
#use AI::Perceptron::Simple ":process_data";
use FindBin;
# this one will directly use "ORIGINAL_STIMULI" as the filename if use with "=>", strange
use constant ORIGINAL_STIMULI => $FindBin::Bin . "/book_list_to_shuffle.csv";
my $original_stimuli = $FindBin::Bin . "/book_list_to_shuffle.csv";
my $shuffled_data_1 = $FindBin::Bin . "/shuffled_1.csv";
xt/boilerplate.t view on Meta::CPAN
#!perl
use 5.006;
use strict;
use warnings;
use Test::More;
plan tests => 3;
sub not_in_file_ok {
my ($filename, %regex) = @_;
open( my $fh, '<', $filename )
or die "couldn't open $filename for reading: $!";
my %violated;
( run in 0.527 second using v1.01-cache-2.11-cpan-ec4f86ec37b )