AI-ActivationFunctions
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AI-ActivationFunctions-0.01/AI-ActivationFunctions-0.01/Makefile.PL view on Meta::CPAN
AUTHOR => 'Ulisses Manzo Castello <umcastello@gmail.com>',
LICENSE => 'perl_5',
PREREQ_PM => {
'Test::More' => 0,
'Exporter' => 0,
# PDL é recomendado, mas não obrigatório
'PDL' => 0, # em Build.PL seria 'recommends'
},
META_MERGE => {
'meta-spec' => { version => 2 },
resources => {
repository => {
type => 'git',
url => 'https://github.com/seuusuario/ai-activationfunctions.git',
web => 'https://github.com/seuusuario/ai-activationfunctions',
},
},
},
);
AI-ActivationFunctions-0.01/AI-ActivationFunctions-0.01/examples/simple.pl view on Meta::CPAN
printf(" elu(-1, 1) = %.4f\n", elu(-1, 1));
printf(" swish(1) = %.4f\n", swish(1));
printf(" gelu(1) = %.4f\n", gelu(1));
print "\n3. Derivadas (para backpropagation):\n";
printf(" relu_derivative(5) = %d\n", relu_derivative(5));
printf(" relu_derivative(-5) = %d\n", relu_derivative(-5));
printf(" sigmoid_derivative(0) = %.4f\n", sigmoid_derivative(0));
print "\n4. Softmax (distribuição):\n";
my $scores = [1.0, 2.0, 3.0];
my $probs = softmax($scores);
printf(" Entrada: [%.1f, %.1f, %.1f]\n", @$scores);
printf(" SaÃda: [%.4f, %.4f, %.4f]\n", @$probs);
printf(" Soma: %.4f\n", $probs->[0] + $probs->[1] + $probs->[2]);
print "\n=== Fim da Demonstração ===\n";
AI-ActivationFunctions-0.01/AI-ActivationFunctions-0.01/lib/AI/ActivationFunctions.pm view on Meta::CPAN
Version 0.01
=head1 ABSTRACT
Activation functions for neural networks in Perl
=head1 SYNOPSIS
use AI::ActivationFunctions qw(relu prelu sigmoid);
my $result = relu(-5); # returns 0
my $prelu_result = prelu(-2, 0.1); # returns -0.2
# Array version works too
my $array_result = relu([-2, -1, 0, 1, 2]); # returns [0, 0, 0, 1, 2]
=head1 DESCRIPTION
This module provides various activation functions commonly used in neural networks
and machine learning. It includes basic functions like ReLU and sigmoid, as well
as advanced functions like GELU and Swish.
=head1 FUNCTIONS
=head2 Basic Functions
AI-ActivationFunctions-0.01/AI-ActivationFunctions-0.01/t/basic.t view on Meta::CPAN
ok($sigmoid1 > 0.73 && $sigmoid1 < 0.74, "sigmoid(1) ≈ 0.731 ($sigmoid1)");
# Teste 5: Tanh
my $tanh0 = tanh(0);
ok(abs($tanh0) < 0.001, "tanh(0) ≈ 0 ($tanh0)");
my $tanh1 = tanh(1);
ok($tanh1 > 0.76 && $tanh1 < 0.77, "tanh(1) ≈ 0.761 ($tanh1)");
# Teste 6: Softmax
my $scores = [1, 2, 3];
my $probs = softmax($scores);
is(ref($probs), 'ARRAY', 'softmax retorna arrayref');
is(scalar @$probs, 3, 'softmax retorna 3 elementos');
my $sum = 0;
$sum += $_ for @$probs;
ok(abs($sum - 1) < 0.0001, "softmax soma ≈ 1 ($sum)");
# Verifica ordem (maior score = maior probabilidade)
ok($probs->[2] > $probs->[1], "Prob[2] > Prob[1]");
},
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"t",
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"type" : "git",
"url" : "https://github.com/seuusuario/ai-activationfunctions.git",
"web" : "https://github.com/seuusuario/ai-activationfunctions"
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"version" : "0.01",
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}
---
abstract: 'Activation functions for neural networks in Perl'
author:
- 'Ulisses Manzo Castello <umcastello@gmail.com>'
build_requires:
ExtUtils::MakeMaker: '0'
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ExtUtils::MakeMaker: '0'
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generated_by: 'ExtUtils::MakeMaker version 7.70, CPAN::Meta::Converter version 2.150010'
license: perl
meta-spec:
url: http://module-build.sourceforge.net/META-spec-v1.4.html
version: '1.4'
name: AI-ActivationFunctions
no_index:
directory:
- t
- inc
requires:
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repository: https://github.com/seuusuario/ai-activationfunctions.git
version: '0.01'
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Makefile.PL view on Meta::CPAN
AUTHOR => 'Ulisses Manzo Castello <umcastello@gmail.com>',
LICENSE => 'perl_5',
PREREQ_PM => {
'Test::More' => 0,
'Exporter' => 0,
# PDL é recomendado, mas não obrigatório
'PDL' => 0, # em Build.PL seria 'recommends'
},
META_MERGE => {
'meta-spec' => { version => 2 },
resources => {
repository => {
type => 'git',
url => 'https://github.com/seuusuario/ai-activationfunctions.git',
web => 'https://github.com/seuusuario/ai-activationfunctions',
},
},
},
);
```bash
cpanm AI::ActivationFunctions
## Usage
use AI::ActivationFunctions qw(relu prelu sigmoid softmax);
# Basic usage
my $result = relu(-5); # returns 0
# With custom parameter
my $prelu = prelu(-2, 0.1); # returns -0.2
# Probability distribution
my $probs = softmax([1, 2, 3]);
# Functions
relu($x) - Rectified Linear Unit
create_structure.sh view on Meta::CPAN
PREREQ_PM => {
'Test::More' => 0,
'Exporter' => 0,
'Carp' => 0,
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TEST_REQUIRES => {
'Test::More' => 0,
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resources => {
repository => {
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url => 'https://github.com/test/ai-activationfunctions.git',
},
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},
);
EOF
echo "Estrutura criada! Agora copie os arquivos .pm e .t para os diretórios."
examples/simple.pl view on Meta::CPAN
printf(" elu(-1, 1) = %.4f\n", elu(-1, 1));
printf(" swish(1) = %.4f\n", swish(1));
printf(" gelu(1) = %.4f\n", gelu(1));
print "\n3. Derivadas (para backpropagation):\n";
printf(" relu_derivative(5) = %d\n", relu_derivative(5));
printf(" relu_derivative(-5) = %d\n", relu_derivative(-5));
printf(" sigmoid_derivative(0) = %.4f\n", sigmoid_derivative(0));
print "\n4. Softmax (distribuição):\n";
my $scores = [1.0, 2.0, 3.0];
my $probs = softmax($scores);
printf(" Entrada: [%.1f, %.1f, %.1f]\n", @$scores);
printf(" SaÃda: [%.4f, %.4f, %.4f]\n", @$probs);
printf(" Soma: %.4f\n", $probs->[0] + $probs->[1] + $probs->[2]);
print "\n=== Fim da Demonstração ===\n";
lib/AI/ActivationFunctions.pm view on Meta::CPAN
Version 0.01
=head1 ABSTRACT
Activation functions for neural networks in Perl
=head1 SYNOPSIS
use AI::ActivationFunctions qw(relu prelu sigmoid);
my $result = relu(-5); # returns 0
my $prelu_result = prelu(-2, 0.1); # returns -0.2
# Array version works too
my $array_result = relu([-2, -1, 0, 1, 2]); # returns [0, 0, 0, 1, 2]
=head1 DESCRIPTION
This module provides various activation functions commonly used in neural networks
and machine learning. It includes basic functions like ReLU and sigmoid, as well
as advanced functions like GELU and Swish.
=head1 FUNCTIONS
=head2 Basic Functions
ok($sigmoid1 > 0.73 && $sigmoid1 < 0.74, "sigmoid(1) ≈ 0.731 ($sigmoid1)");
# Teste 5: Tanh
my $tanh0 = tanh(0);
ok(abs($tanh0) < 0.001, "tanh(0) ≈ 0 ($tanh0)");
my $tanh1 = tanh(1);
ok($tanh1 > 0.76 && $tanh1 < 0.77, "tanh(1) ≈ 0.761 ($tanh1)");
# Teste 6: Softmax
my $scores = [1, 2, 3];
my $probs = softmax($scores);
is(ref($probs), 'ARRAY', 'softmax retorna arrayref');
is(scalar @$probs, 3, 'softmax retorna 3 elementos');
my $sum = 0;
$sum += $_ for @$probs;
ok(abs($sum - 1) < 0.0001, "softmax soma ≈ 1 ($sum)");
# Verifica ordem (maior score = maior probabilidade)
ok($probs->[2] > $probs->[1], "Prob[2] > Prob[1]");
test_all.sh view on Meta::CPAN
# 1. Crie a estrutura
#mkdir -p AI-ActivationFunctions-simple/lib/AI
#mkdir AI-ActivationFunctions-simple/t
#mkdir AI-ActivationFunctions-simple/examples
#cd AI-ActivationFunctions-simple
# 2. Copie os arquivos acima para seus respectivos diretórios
# 3. Teste
perl -Ilib -e "use AI::ActivationFunctions; print 'Carregou!\\n';"
# 4. Execute o teste rápido
perl test_quick.pl
# 5. Execute os testes formais
prove -Ilib t/
test_quick.pl view on Meta::CPAN
} or die "Erro ao carregar módulo: $@";
print "=== Teste Rápido ===\n\n";
print "relu(5) = " . AI::ActivationFunctions::relu(5) . "\n";
print "relu(-3) = " . AI::ActivationFunctions::relu(-3) . "\n";
print "prelu(-2, 0.1) = " . AI::ActivationFunctions::prelu(-2, 0.1) . "\n";
print "sigmoid(0) = " . AI::ActivationFunctions::sigmoid(0) . "\n";
my $arr = [-2, -1, 0, 1, 2];
my $result = AI::ActivationFunctions::relu($arr);
print "relu([-2,-1,0,1,2]) = [" . join(", ", @$result) . "]\n";
print "\nOK!\n";
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