From 451dd2a11f9f1815c7bacb11683f5d1ac2e21f9c Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Sun, 22 Jun 2025 19:59:17 +0300
Subject: [PATCH 01/22] SAM-7 added HyperbolicTangent
---
.../HyperbolicTangent/HyperbolicTangent.php | 29 +++--
.../HyperbolicTangentTest.php | 119 ++++++++++++++++++
2 files changed, 141 insertions(+), 7 deletions(-)
create mode 100644 tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
diff --git a/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php b/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
index 830d0becd..bb9a8c8a5 100644
--- a/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
+++ b/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
@@ -6,6 +6,8 @@
use NumPower;
use NDArray;
+use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\ActivationFunction;
+use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\IBufferDerivative;
/**
* Hyperbolic Tangent
@@ -16,11 +18,17 @@
* @category Machine Learning
* @package Rubix/ML
* @author Andrew DalPino
+ * @author Samuel Akopyan <leumas.a@gmail.com>
*/
-class HyperbolicTangent implements ActivationFunction
+class HyperbolicTangent implements ActivationFunction, IBufferDerivative
{
/**
- * @inheritdoc
+ * Apply the Hyperbolic Tangent activation function to the input.
+ *
+ * f(x) = tanh(x)
+ *
+ * @param NDArray $input The input values
+ * @return NDArray The activated values
*/
public function activate(NDArray $input) : NDArray
{
@@ -28,19 +36,26 @@ public function activate(NDArray $input) : NDArray
}
/**
- * @inheritdoc
+ * Calculate the derivative of the activation function.
+ *
+ * f'(x) = 1 - tanh^2(x)
+ *
+ * @param NDArray $output Output matrix
+ * @return NDArray Derivative matrix
*/
public function differentiate(NDArray $output) : NDArray
{
- return 1 - ($output ** 2);
+ // Calculate tanh^2(x)
+ $squared = NumPower::pow($output, 2);
+
+ // Calculate 1 - tanh^2(x)
+ return NumPower::subtract(1.0, $squared);
}
/**
* Return the string representation of the activation function.
*
- * @internal
- *
- * @return string
+ * @return string String representation
*/
public function __toString() : string
{
diff --git a/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php b/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
new file mode 100644
index 000000000..445b03110
--- /dev/null
+++ b/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
@@ -0,0 +1,119 @@
+<?php
+
+declare(strict_types = 1);
+
+namespace Rubix\ML\Tests\NeuralNet\ActivationFunctions\HyperbolicTangent;
+
+use PHPUnit\Framework\Attributes\CoversClass;
+use PHPUnit\Framework\Attributes\DataProvider;
+use PHPUnit\Framework\Attributes\Group;
+use PHPUnit\Framework\Attributes\Test;
+use PHPUnit\Framework\Attributes\TestDox;
+use NumPower;
+use NDArray;
+use Rubix\ML\NeuralNet\ActivationFunctions\HyperbolicTangent\HyperbolicTangent;
+use PHPUnit\Framework\TestCase;
+use Generator;
+
+#[Group('ActivationFunctions')]
+#[CoversClass(HyperbolicTangent::class)]
+class HyperbolicTangentTest extends TestCase
+{
+ /**
+ * @var HyperbolicTangent
+ */
+ protected HyperbolicTangent $activationFn;
+
+ /**
+ * @return Generator<array>
+ */
+ public static function computeProvider() : Generator
+ {
+ yield [
+ NumPower::array([
+ [9.0, 2.5, 2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
+ ]),
+ [
+ [0.9999999403953552, 0.9866142868995667, 0.9640275835990906, 0.7615941762924194, -0.46211716532707214, 0.0, 1.0, -1.0],
+ ],
+ ];
+
+ yield [
+ NumPower::array([
+ [-0.12, 0.31, -0.49],
+ [0.99, 0.08, -0.03],
+ [0.05, -0.52, 0.54],
+ ]),
+ [
+ [-0.11942730098962784, 0.3004370927810669, -0.45421645045280457],
+ [0.7573622465133667, 0.07982976734638214, -0.02999100275337696],
+ [0.049958378076553345, -0.47769999504089355, 0.4929879903793335],
+ ],
+ ];
+ }
+
+ /**
+ * @return Generator<array>
+ */
+ public static function differentiateProvider() : Generator
+ {
+ yield [
+ NumPower::array([
+ [0.9640275835990906, 0.7615941762924194, -0.46211716532707214, 0.0, 1.0, -1.0],
+ ]),
+ [
+ [0.07065081596374512, 0.41997432708740234, 0.7864477038383484, 1.0, 0.0, 0.0],
+ ],
+ ];
+
+ yield [
+ NumPower::array([
+ [-0.11942730098962784, 0.3004370927810669, -0.45421645045280457],
+ [0.7573623085022249, 0.07978830223560329, -0.029991223630861304],
+ [0.049958395721942955, -0.4778087574005698, 0.4930591567725708],
+ ]),
+ [
+ [0.985737144947052, 0.9097375273704529, 0.7936874032020569],
+ [0.42640233039855957, 0.9936338067054749, 0.9991005063056946],
+ [0.9975041747093201, 0.7716988325119019, 0.7568926811218262],
+ ],
+ ];
+ }
+
+ /**
+ * Set up the test case.
+ */
+ protected function setUp() : void
+ {
+ parent::setUp();
+
+ $this->activationFn = new HyperbolicTangent();
+ }
+
+ #[Test]
+ #[TestDox('Can be cast to a string')]
+ public function testToString() : void
+ {
+ static::assertEquals('Hyperbolic Tangent', (string) $this->activationFn);
+ }
+
+ #[Test]
+ #[TestDox('Correctly activates the input')]
+ #[DataProvider('computeProvider')]
+ public function testActivate(NDArray $input, array $expected) : void
+ {
+ $activations = $this->activationFn->activate($input)->toArray();
+
+ static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ }
+
+ #[Test]
+ #[TestDox('Correctly differentiates the output')]
+ #[DataProvider('differentiateProvider')]
+ public function testDifferentiate(NDArray $output, array $expected) : void
+ {
+ $derivatives = $this->activationFn->differentiate($output)->toArray();
+
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ }
+}
From 868830bb87565666f4bf17a478027b20969adab9 Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Sun, 22 Jun 2025 20:10:34 +0300
Subject: [PATCH 02/22] SAM-7 fix for function docblock
---
src/Datasets/Labeled.php | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/Datasets/Labeled.php b/src/Datasets/Labeled.php
index 99bc4f690..7392bc708 100644
--- a/src/Datasets/Labeled.php
+++ b/src/Datasets/Labeled.php
@@ -178,7 +178,7 @@ public function __construct(array $samples = [], array $labels = [], bool $verif
/**
* Return the labels.
*
- * @return mixed[]
+ * @return list<float|int|string>
*/
public function labels() : array
{
From 43fb507c67dafd0e057e8095ace9bf1722e23f3b Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Sun, 22 Jun 2025 22:46:57 +0300
Subject: [PATCH 03/22] Fix for merge issues
---
.github/workflows/ci.yml | 6 ------
docs/transformers/regex-filter.md | 15 ---------------
2 files changed, 21 deletions(-)
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 092650111..295cff531 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -8,14 +8,8 @@ jobs:
runs-on: ${{ matrix.operating-system }}
strategy:
matrix:
-<<<<<<< HEAD
operating-system: [windows-latest, ubuntu-latest, macos-latest]
php-versions: ['8.4']
-=======
- operating-system: [ubuntu-latest, macos-latest]
- php-versions: ['8.0', '8.1', '8.2']
->>>>>>> master
-
steps:
- name: Checkout
uses: actions/checkout@v3
diff --git a/docs/transformers/regex-filter.md b/docs/transformers/regex-filter.md
index 94540a0e2..8283b22a2 100644
--- a/docs/transformers/regex-filter.md
+++ b/docs/transformers/regex-filter.md
@@ -28,7 +28,6 @@ $transformer = new RegexFilter([
```
## Predefined Regex Patterns
-<<<<<<< HEAD
| Class Constant | Description |
|---|----------------------------------------------------------------------------------------------------------|
| EMAIL | A pattern to match any email address. |
@@ -41,20 +40,6 @@ $transformer = new RegexFilter([
| EXTRA_WHITESPACE | Matches consecutively repeated whitespace characters. |
| MENTION | A pattern that matches Twitter-style mentions (@example). |
| HASHTAG | Matches Twitter-style hashtags (#example). |
-=======
-| Class Constant | Description |
-|---|---|
-| EMAIL | A pattern to match any email address. |
-| URL | An alias for the default (Gruber 1) URL matching pattern. |
-| GRUBER_1 | The original Gruber URL matching pattern. |
-| GRUBER_2 | The improved Gruber URL matching pattern. |
-| EXTRA_CHARACTERS | Matches consecutively repeated non word or number characters such as punctuation and special characters. |
-| EXTRA_WORDS | Matches consecutively repeated words. |
-| EXTRA_WHITESPACE | Matches consecutively repeated whitespace characters. |
-| EMOJIS | A pattern to match unicode emojis. |
-| MENTION | A pattern that matches Twitter-style mentions (@example). |
-| HASHTAG | Matches Twitter-style hashtags (#example). |
->>>>>>> 2.4
## Additional Methods
This transformer does not have any additional methods.
From e16b4a4e891fc8786ae8bc1158e8db30cb9fbdbd Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Sun, 22 Jun 2025 23:50:09 +0300
Subject: [PATCH 04/22] SAM-8 implemented LeakyReLU
---
.../Exceptions/InvalidLeakageException.php | 14 ++
.../LeakyReLU/LeakyReLU.php | 87 ++++----
.../HyperbolicTangentTest.php | 8 +-
.../LeakyReLU/LeakyReLUTest.php | 186 ++++++++++++++++++
4 files changed, 251 insertions(+), 44 deletions(-)
create mode 100644 src/NeuralNet/ActivationFunctions/LeakyReLU/Exceptions/InvalidLeakageException.php
create mode 100644 tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
diff --git a/src/NeuralNet/ActivationFunctions/LeakyReLU/Exceptions/InvalidLeakageException.php b/src/NeuralNet/ActivationFunctions/LeakyReLU/Exceptions/InvalidLeakageException.php
new file mode 100644
index 000000000..5cc6b0fa9
--- /dev/null
+++ b/src/NeuralNet/ActivationFunctions/LeakyReLU/Exceptions/InvalidLeakageException.php
@@ -0,0 +1,14 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Rubix\ML\NeuralNet\ActivationFunctions\LeakyReLU\Exceptions;
+
+use Rubix\ML\Exceptions\InvalidArgumentException;
+
+/**
+ * Invalid `alpha` parameter for ELU Activation function
+ */
+class InvalidLeakageException extends InvalidArgumentException
+{
+}
diff --git a/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php b/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
index 54b2867df..29fab906d 100644
--- a/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
+++ b/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
@@ -1,9 +1,14 @@
<?php
+declare(strict_types=1);
+
namespace Rubix\ML\NeuralNet\ActivationFunctions\LeakyReLU;
-use Tensor\Matrix;
-use Rubix\ML\Exceptions\InvalidArgumentException;
+use NumPower;
+use NDArray;
+use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\ActivationFunction;
+use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\IBufferDerivative;
+use Rubix\ML\NeuralNet\ActivationFunctions\LeakyReLU\Exceptions\InvalidLeakageException;
/**
* Leaky ReLU
@@ -19,8 +24,9 @@
* @category Machine Learning
* @package Rubix/ML
* @author Andrew DalPino
+ * @author Samuel Akopyan <leumas.a@gmail.com>
*/
-class LeakyReLU implements ActivationFunction
+class LeakyReLU implements ActivationFunction, IBufferDerivative
{
/**
* The amount of leakage as a ratio of the input value to allow to pass through when inactivated.
@@ -30,73 +36,74 @@ class LeakyReLU implements ActivationFunction
protected float $leakage;
/**
- * @param float $leakage
- * @throws InvalidArgumentException
+ * Class constructor.
+ *
+ * @param float $leakage The amount of leakage as a ratio of the input value to allow to pass through when inactivated.
+ * @throws InvalidLeakageException
*/
public function __construct(float $leakage = 0.1)
{
- if ($leakage <= 0.0 or $leakage >= 1.0) {
- throw new InvalidArgumentException('Leakage must be between'
- . " 0 and 1, $leakage given.");
+ if ($leakage <= 0.0 || $leakage >= 1.0) {
+ throw new InvalidLeakageException(
+ message: "Leakage must be between 0 and 1, $leakage given."
+ );
}
$this->leakage = $leakage;
}
/**
- * Compute the activation.
+ * Apply the Leaky ReLU activation function to the input.
*
- * @internal
+ * f(x) = x if x > 0
+ * f(x) = leakage * x if x ≤ 0
*
- * @param Matrix $input
- * @return Matrix
+ * @param NDArray $input The input values
+ * @return NDArray The activated values
*/
public function activate(NDArray $input) : NDArray
{
+ // Calculate positive part: x for x > 0
$positive = NumPower::maximum($input, 0);
- $negative = NumPower::minimum($input, 0);
- $negativeLeakage = $negative * $this->leakage;
+ // Calculate negative part: leakage * x for x <= 0
+ $negative = NumPower::multiply(
+ NumPower::minimum($input, 0),
+ $this->leakage
+ );
- return $positive + $negativeLeakage;
+ // Combine both parts
+ return NumPower::add($positive, $negative);
}
/**
- * Calculate the derivative of the activation.
+ * Calculate the derivative of the activation function.
*
- * @internal
+ * f'(x) = 1 if x > 0
+ * f'(x) = leakage if x ≤ 0
*
- * @param Matrix $input
- * @param Matrix $output
- * @return Matrix
+ * @param NDArray $x Input matrix
+ * @return NDArray Derivative matrix
*/
- public function differentiate(Matrix $input, Matrix $output) : Matrix
+ public function differentiate(NDArray $x) : NDArray
{
- $positive = NumPower::greater($input, 0);
- $negative = NumPower::lessEqual($input, 0) * $this->leakage;
+ // For x > 0: 1
+ $positive = NumPower::greater($x, 0);
- return $positive + $negative;
- }
+ // For x <= 0: leakage
+ $negative = NumPower::multiply(
+ NumPower::lessEqual($x, 0),
+ $this->leakage
+ );
- /**
- * @internal
- *
- * @param float $input
- * @return float
- */
- public function _differentiate(float $input) : float
- {
- return $input > 0.0
- ? 1.0
- : $this->leakage;
+ // Combine both parts
+ return NumPower::add($positive, $negative);
}
/**
- * Return the string representation of the object.
- *
- * @internal
+ * Return the string representation of the activation function.
*
- * @return string
+ * @return string String representation
*/
public function __toString() : string
{
diff --git a/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php b/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
index 445b03110..79dc6b11c 100644
--- a/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
@@ -4,16 +4,16 @@
namespace Rubix\ML\Tests\NeuralNet\ActivationFunctions\HyperbolicTangent;
+use Generator;
+use NumPower;
+use NDArray;
use PHPUnit\Framework\Attributes\CoversClass;
use PHPUnit\Framework\Attributes\DataProvider;
use PHPUnit\Framework\Attributes\Group;
use PHPUnit\Framework\Attributes\Test;
use PHPUnit\Framework\Attributes\TestDox;
-use NumPower;
-use NDArray;
-use Rubix\ML\NeuralNet\ActivationFunctions\HyperbolicTangent\HyperbolicTangent;
use PHPUnit\Framework\TestCase;
-use Generator;
+use Rubix\ML\NeuralNet\ActivationFunctions\HyperbolicTangent\HyperbolicTangent;
#[Group('ActivationFunctions')]
#[CoversClass(HyperbolicTangent::class)]
diff --git a/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php b/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
new file mode 100644
index 000000000..38ab6952d
--- /dev/null
+++ b/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
@@ -0,0 +1,186 @@
+<?php
+
+declare(strict_types = 1);
+
+namespace Rubix\ML\Tests\NeuralNet\ActivationFunctions\LeakyReLU;
+
+use Generator;
+use NDArray;
+use NumPower;
+use PHPUnit\Framework\Attributes\CoversClass;
+use PHPUnit\Framework\Attributes\DataProvider;
+use PHPUnit\Framework\Attributes\Group;
+use PHPUnit\Framework\Attributes\Test;
+use PHPUnit\Framework\Attributes\TestDox;
+use PHPUnit\Framework\TestCase;
+use Rubix\ML\NeuralNet\ActivationFunctions\LeakyReLU\LeakyReLU;
+use Rubix\ML\NeuralNet\ActivationFunctions\LeakyReLU\Exceptions\InvalidLeakageException;
+
+#[Group('ActivationFunctions')]
+#[CoversClass(LeakyReLU::class)]
+class LeakyReLUTest extends TestCase
+{
+ /**
+ * @var LeakyReLU
+ */
+ protected LeakyReLU $activationFn;
+
+ /**
+ * @return Generator<array>
+ */
+ public static function computeProvider() : Generator
+ {
+ yield [
+ NumPower::array([
+ [2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
+ ]),
+ [
+ [2.0, 1.0, -0.05000000074505806, 0.0, 20.0, -1.0],
+ ],
+ ];
+
+ yield [
+ NumPower::array([
+ [-0.12, 0.31, -0.49],
+ [0.99, 0.08, -0.03],
+ [0.05, -0.52, 0.54],
+ ]),
+ [
+ [-0.012000000104308128, 0.3100000023841858, -0.049000002443790436],
+ [0.9900000095367432, 0.07999999821186066, -0.003000000026077032],
+ [0.05000000074505806, -0.05199999734759331, 0.5400000214576721],
+ ],
+ ];
+ }
+
+ /**
+ * @return Generator<array>
+ */
+ public static function differentiateProvider() : Generator
+ {
+ yield [
+ NumPower::array([
+ [2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
+ ]),
+ [
+ [1.0, 1.0, 0.10000000149011612, 0.10000000149011612, 1.0, 0.10000000149011612],
+ ],
+ ];
+
+ yield [
+ NumPower::array([
+ [-0.12, 0.31, -0.49],
+ [0.99, 0.08, -0.03],
+ [0.05, -0.52, 0.54],
+ ]),
+ [
+ [0.10000000149011612, 1.0, 0.10000000149011612],
+ [1.0, 1.0, 0.10000000149011612],
+ [1.0, 0.10000000149011612, 1.0],
+ ],
+ ];
+ }
+
+ /**
+ * @return Generator<array>
+ */
+ public static function boundaryProvider() : Generator
+ {
+ // Test very large positive values (should be equal to input)
+ yield [
+ NumPower::array([
+ [100.0, 500.0, 1000.0],
+ ]),
+ [
+ [100.0, 500.0, 1000.0],
+ ],
+ ];
+
+ // Test very large negative values (should be input * leakage)
+ yield [
+ NumPower::array([
+ [-100.0, -500.0, -1000.0],
+ ]),
+ [
+ [-10.0, -50.0, -100.0],
+ ],
+ ];
+
+ // Test values close to zero
+ yield [
+ NumPower::array([
+ [0.001, -0.001, 0.0001, -0.0001],
+ ]),
+ [
+
+ [0.0010000000474974513, -0.00010000000474974513, 0.00009999999747378752, -0.000009999999747378752],
+ ],
+ ];
+ }
+
+ /**
+ * Set up the test case.
+ */
+ protected function setUp() : void
+ {
+ parent::setUp();
+
+ $this->activationFn = new LeakyReLU(0.1);
+ }
+
+ #[Test]
+ #[TestDox('Can be constructed with valid leakage parameter')]
+ public function testConstructorWithValidLeakage() : void
+ {
+ $activationFn = new LeakyReLU(0.2);
+
+ static::assertInstanceOf(LeakyReLU::class, $activationFn);
+ static::assertEquals('Leaky ReLU (leakage: 0.2)', (string) $activationFn);
+ }
+
+ #[Test]
+ #[TestDox('Throws exception when constructed with invalid leakage parameter')]
+ public function testConstructorWithInvalidLeakage() : void
+ {
+ $this->expectException(InvalidLeakageException::class);
+
+ new LeakyReLU(1.5);
+ }
+
+ #[Test]
+ #[TestDox('Can be cast to a string')]
+ public function testToString() : void
+ {
+ static::assertEquals('Leaky ReLU (leakage: 0.1)', (string) $this->activationFn);
+ }
+
+ #[Test]
+ #[TestDox('Correctly activates the input')]
+ #[DataProvider('computeProvider')]
+ public function testActivate(NDArray $input, array $expected) : void
+ {
+ $activations = $this->activationFn->activate($input)->toArray();
+
+ static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ }
+
+ #[Test]
+ #[TestDox('Correctly differentiates the input')]
+ #[DataProvider('differentiateProvider')]
+ public function testDifferentiate(NDArray $input, array $expected) : void
+ {
+ $derivatives = $this->activationFn->differentiate($input)->toArray();
+
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ }
+
+ #[Test]
+ #[TestDox('Correctly handles boundary values during activation')]
+ #[DataProvider('boundaryProvider')]
+ public function testBoundaryActivate(NDArray $input, array $expected) : void
+ {
+ $activations = $this->activationFn->activate($input)->toArray();
+
+ static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ }
+}
From c3f6530c1719a039bade251d933eeb9ee3bcb1cf Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Mon, 23 Jun 2025 00:04:29 +0300
Subject: [PATCH 05/22] SAM-8 style fixes for import classes
---
tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php | 8 ++++----
tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php | 8 ++++----
.../ActivationFunctions/HardSiLU/HardSiLUTest.php | 8 ++++----
.../ActivationFunctions/HardSigmoid/HardSigmoidTest.php | 8 ++++----
4 files changed, 16 insertions(+), 16 deletions(-)
diff --git a/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php b/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
index 657bab1be..14e2dff9a 100644
--- a/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
@@ -4,16 +4,16 @@
namespace Rubix\ML\Tests\NeuralNet\ActivationFunctions\ELU;
+use Generator;
+use NDArray;
+use NumPower;
use PHPUnit\Framework\Attributes\CoversClass;
use PHPUnit\Framework\Attributes\DataProvider;
use PHPUnit\Framework\Attributes\Group;
use PHPUnit\Framework\Attributes\Test;
use PHPUnit\Framework\Attributes\TestDox;
-use NumPower;
-use NDArray;
-use Rubix\ML\NeuralNet\ActivationFunctions\ELU\ELU;
use PHPUnit\Framework\TestCase;
-use Generator;
+use Rubix\ML\NeuralNet\ActivationFunctions\ELU\ELU;
use Rubix\ML\NeuralNet\ActivationFunctions\ELU\Exceptions\InvalidAlphaException;
#[Group('ActivationFunctions')]
diff --git a/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php b/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
index 6d3177015..b9cc02293 100644
--- a/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
@@ -4,16 +4,16 @@
namespace Rubix\ML\Tests\NeuralNet\ActivationFunctions\GELU;
+use Generator;
+use NDArray;
+use NumPower;
use PHPUnit\Framework\Attributes\CoversClass;
use PHPUnit\Framework\Attributes\DataProvider;
use PHPUnit\Framework\Attributes\Group;
use PHPUnit\Framework\Attributes\Test;
use PHPUnit\Framework\Attributes\TestDox;
-use NumPower;
-use NDArray;
-use Rubix\ML\NeuralNet\ActivationFunctions\GELU\GELU;
use PHPUnit\Framework\TestCase;
-use Generator;
+use Rubix\ML\NeuralNet\ActivationFunctions\GELU\GELU;
#[Group('ActivationFunctions')]
#[CoversClass(GELU::class)]
diff --git a/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php b/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
index 22ef0bdea..812f1bf1d 100644
--- a/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
@@ -4,16 +4,16 @@
namespace Rubix\ML\Tests\NeuralNet\ActivationFunctions\HardSiLU;
+use Generator;
+use NDArray;
+use NumPower;
use PHPUnit\Framework\Attributes\CoversClass;
use PHPUnit\Framework\Attributes\DataProvider;
use PHPUnit\Framework\Attributes\Group;
use PHPUnit\Framework\Attributes\Test;
use PHPUnit\Framework\Attributes\TestDox;
-use NumPower;
-use NDArray;
-use Rubix\ML\NeuralNet\ActivationFunctions\HardSiLU\HardSiLU;
use PHPUnit\Framework\TestCase;
-use Generator;
+use Rubix\ML\NeuralNet\ActivationFunctions\HardSiLU\HardSiLU;
#[Group('ActivationFunctions')]
#[CoversClass(HardSiLU::class)]
diff --git a/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php b/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
index 5320e796b..12b6cdcd3 100644
--- a/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
@@ -4,16 +4,16 @@
namespace Rubix\ML\Tests\NeuralNet\ActivationFunctions\HardSigmoid;
+use Generator;
+use NDArray;
+use NumPower;
use PHPUnit\Framework\Attributes\CoversClass;
use PHPUnit\Framework\Attributes\DataProvider;
use PHPUnit\Framework\Attributes\Group;
use PHPUnit\Framework\Attributes\Test;
use PHPUnit\Framework\Attributes\TestDox;
-use NumPower;
-use NDArray;
-use Rubix\ML\NeuralNet\ActivationFunctions\HardSigmoid\HardSigmoid;
use PHPUnit\Framework\TestCase;
-use Generator;
+use Rubix\ML\NeuralNet\ActivationFunctions\HardSigmoid\HardSigmoid;
#[Group('ActivationFunctions')]
#[CoversClass(HardSigmoid::class)]
From 287ec5f31bf2714a586257ac4e422a4a74e1c1f5 Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Mon, 23 Jun 2025 00:04:53 +0300
Subject: [PATCH 06/22] SAM-8 changed LeakyReLUTest initial values
---
.../LeakyReLU/LeakyReLUTest.php | 24 +++++++++----------
1 file changed, 12 insertions(+), 12 deletions(-)
diff --git a/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php b/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
index 38ab6952d..2e00f5f5a 100644
--- a/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
@@ -35,7 +35,7 @@ public static function computeProvider() : Generator
[2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [2.0, 1.0, -0.05000000074505806, 0.0, 20.0, -1.0],
+ [2.0, 1.0, -0.004999999888241291, 0.0, 20.0, -0.09999999403953552],
],
];
@@ -46,9 +46,9 @@ public static function computeProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [-0.012000000104308128, 0.3100000023841858, -0.049000002443790436],
- [0.9900000095367432, 0.07999999821186066, -0.003000000026077032],
- [0.05000000074505806, -0.05199999734759331, 0.5400000214576721],
+ [-0.0011999999405816197, 0.3100000023841858, -0.004900000058114529],
+ [0.9900000095367432, 0.07999999821186066, -0.00029999998514540493],
+ [0.05000000074505806, -0.005199999548494816, 0.5400000214576721],
],
];
}
@@ -63,7 +63,7 @@ public static function differentiateProvider() : Generator
[2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [1.0, 1.0, 0.10000000149011612, 0.10000000149011612, 1.0, 0.10000000149011612],
+ [1.0, 1.0, 0.009999999776482582, 0.009999999776482582, 1.0, 0.009999999776482582],
],
];
@@ -74,9 +74,9 @@ public static function differentiateProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [0.10000000149011612, 1.0, 0.10000000149011612],
- [1.0, 1.0, 0.10000000149011612],
- [1.0, 0.10000000149011612, 1.0],
+ [0.009999999776482582, 1.0, 0.009999999776482582],
+ [1.0, 1.0, 0.009999999776482582],
+ [1.0, 0.009999999776482582, 1.0],
],
];
}
@@ -102,7 +102,7 @@ public static function boundaryProvider() : Generator
[-100.0, -500.0, -1000.0],
]),
[
- [-10.0, -50.0, -100.0],
+ [-1.0, -5.0, -10.0],
],
];
@@ -113,7 +113,7 @@ public static function boundaryProvider() : Generator
]),
[
- [0.0010000000474974513, -0.00010000000474974513, 0.00009999999747378752, -0.000009999999747378752],
+ [0.0010000000474974513, -0.000010000000656873453, 0.00009999999747378752, -0.0000009999999974752427],
],
];
}
@@ -125,7 +125,7 @@ protected function setUp() : void
{
parent::setUp();
- $this->activationFn = new LeakyReLU(0.1);
+ $this->activationFn = new LeakyReLU(0.01);
}
#[Test]
@@ -151,7 +151,7 @@ public function testConstructorWithInvalidLeakage() : void
#[TestDox('Can be cast to a string')]
public function testToString() : void
{
- static::assertEquals('Leaky ReLU (leakage: 0.1)', (string) $this->activationFn);
+ static::assertEquals('Leaky ReLU (leakage: 0.01)', (string) $this->activationFn);
}
#[Test]
From eef5c57735a06d963901f5cb7b8b15a1912c7de6 Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Mon, 23 Jun 2025 00:20:55 +0300
Subject: [PATCH 07/22] SAM-8 typo fixes
---
.../LeakyReLU/Exceptions/InvalidLeakageException.php | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/NeuralNet/ActivationFunctions/LeakyReLU/Exceptions/InvalidLeakageException.php b/src/NeuralNet/ActivationFunctions/LeakyReLU/Exceptions/InvalidLeakageException.php
index 5cc6b0fa9..c8f081b85 100644
--- a/src/NeuralNet/ActivationFunctions/LeakyReLU/Exceptions/InvalidLeakageException.php
+++ b/src/NeuralNet/ActivationFunctions/LeakyReLU/Exceptions/InvalidLeakageException.php
@@ -7,7 +7,7 @@
use Rubix\ML\Exceptions\InvalidArgumentException;
/**
- * Invalid `alpha` parameter for ELU Activation function
+ * Invalid `Leakage` parameter for LeakyReLU Activation function
*/
class InvalidLeakageException extends InvalidArgumentException
{
From cd980eaebb16a769b1d6b14fa41de86681a0285d Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Mon, 23 Jun 2025 00:36:26 +0300
Subject: [PATCH 08/22] SAM-8 typo fixes
---
.../ActivationFunctions/LeakyReLU/LeakyReLU.php | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php b/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
index 29fab906d..c68237a9a 100644
--- a/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
+++ b/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
@@ -64,16 +64,16 @@ public function __construct(float $leakage = 0.1)
public function activate(NDArray $input) : NDArray
{
// Calculate positive part: x for x > 0
- $positive = NumPower::maximum($input, 0);
+ $positiveActivation = NumPower::maximum($input, 0);
// Calculate negative part: leakage * x for x <= 0
- $negative = NumPower::multiply(
+ $negativeActivation = NumPower::multiply(
NumPower::minimum($input, 0),
$this->leakage
);
// Combine both parts
- return NumPower::add($positive, $negative);
+ return NumPower::add($positiveActivation, $negativeActivation);
}
/**
@@ -88,16 +88,16 @@ public function activate(NDArray $input) : NDArray
public function differentiate(NDArray $x) : NDArray
{
// For x > 0: 1
- $positive = NumPower::greater($x, 0);
+ $positivePart = NumPower::greater($x, 0);
// For x <= 0: leakage
- $negative = NumPower::multiply(
+ $negativePart = NumPower::multiply(
NumPower::lessEqual($x, 0),
$this->leakage
);
// Combine both parts
- return NumPower::add($positive, $negative);
+ return NumPower::add($positivePart, $negativePart);
}
/**
From 0025293a89263b7011935c08e1ad7b91f69dea31 Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Mon, 23 Jun 2025 19:20:57 +0300
Subject: [PATCH 09/22] SAM-8 turned back function signature in docblock
---
src/Datasets/Labeled.php | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/Datasets/Labeled.php b/src/Datasets/Labeled.php
index 7392bc708..99bc4f690 100644
--- a/src/Datasets/Labeled.php
+++ b/src/Datasets/Labeled.php
@@ -178,7 +178,7 @@ public function __construct(array $samples = [], array $labels = [], bool $verif
/**
* Return the labels.
*
- * @return list<float|int|string>
+ * @return mixed[]
*/
public function labels() : array
{
From 45361aa78bce5adcc9ef57c1cac74518dc3338bc Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Mon, 23 Jun 2025 19:29:02 +0300
Subject: [PATCH 10/22] SAM-8 fixed parameter name like in interface
---
.../HyperbolicTangent/HyperbolicTangent.php | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php b/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
index bb9a8c8a5..305c621b0 100644
--- a/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
+++ b/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
@@ -40,13 +40,13 @@ public function activate(NDArray $input) : NDArray
*
* f'(x) = 1 - tanh^2(x)
*
- * @param NDArray $output Output matrix
+ * @param NDArray $x Output matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $output) : NDArray
+ public function differentiate(NDArray $x) : NDArray
{
// Calculate tanh^2(x)
- $squared = NumPower::pow($output, 2);
+ $squared = NumPower::pow($x, 2);
// Calculate 1 - tanh^2(x)
return NumPower::subtract(1.0, $squared);
From eb864c297906761750d35a80b41097600920f67f Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Mon, 23 Jun 2025 19:48:08 +0300
Subject: [PATCH 11/22] Renamed parameter name like in interface
---
.../Base/Contracts/IBufferDerivative.php | 5 +++--
src/NeuralNet/ActivationFunctions/ELU/ELU.php | 10 +++++-----
src/NeuralNet/ActivationFunctions/GELU/GELU.php | 12 ++++++------
.../ActivationFunctions/HardSiLU/HardSiLU.php | 8 ++++----
.../ActivationFunctions/HardSigmoid/HardSigmoid.php | 8 ++++----
.../HyperbolicTangent/HyperbolicTangent.php | 6 +++---
6 files changed, 25 insertions(+), 24 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/Base/Contracts/IBufferDerivative.php b/src/NeuralNet/ActivationFunctions/Base/Contracts/IBufferDerivative.php
index 239cfc0f6..9e9b071f5 100644
--- a/src/NeuralNet/ActivationFunctions/Base/Contracts/IBufferDerivative.php
+++ b/src/NeuralNet/ActivationFunctions/Base/Contracts/IBufferDerivative.php
@@ -13,14 +13,15 @@
* @package Rubix/ML
* @author Andrew DalPino
* @author Aleksei Nechaev <omfg.rus@gmail.com>
+ * @author Samuel Akopyan <leumas.a@gmail.com>
*/
interface IBufferDerivative extends Derivative
{
/**
* Calculate the derivative of the single parameter.
*
- * @param NDArray $x Input matrix
+ * @param NDArray $output Output matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $x) : NDArray;
+ public function differentiate(NDArray $output) : NDArray;
}
diff --git a/src/NeuralNet/ActivationFunctions/ELU/ELU.php b/src/NeuralNet/ActivationFunctions/ELU/ELU.php
index ff165dbc7..5f52d2293 100644
--- a/src/NeuralNet/ActivationFunctions/ELU/ELU.php
+++ b/src/NeuralNet/ActivationFunctions/ELU/ELU.php
@@ -76,18 +76,18 @@ public function activate(NDArray $input) : NDArray
* f'(x) = 1 if x > 0
* f'(x) = α * e^x if x ≤ 0
*
- * @param NDArray $x Output matrix
+ * @param NDArray $output Output matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $x) : NDArray
+ public function differentiate(NDArray $output) : NDArray
{
// For x > 0: 1
- $positivePart = NumPower::greater($x, 0);
+ $positivePart = NumPower::greater($output, 0);
// For x <= 0: α * e^x
- $negativeMask = NumPower::lessEqual($x, 0);
+ $negativeMask = NumPower::lessEqual($output, 0);
$negativePart = NumPower::multiply(
- NumPower::multiply($negativeMask, NumPower::exp($x)),
+ NumPower::multiply($negativeMask, NumPower::exp($output)),
$this->alpha
);
diff --git a/src/NeuralNet/ActivationFunctions/GELU/GELU.php b/src/NeuralNet/ActivationFunctions/GELU/GELU.php
index 1667b1131..195868b29 100644
--- a/src/NeuralNet/ActivationFunctions/GELU/GELU.php
+++ b/src/NeuralNet/ActivationFunctions/GELU/GELU.php
@@ -87,19 +87,19 @@ public function activate(NDArray $input) : NDArray
* - β = 0.044715
* - sech^2(z) = (1/cosh(z))^2
*
- * @param NDArray $x Output matrix
+ * @param NDArray $output Output matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $x) : NDArray
+ public function differentiate(NDArray $output) : NDArray
{
// Calculate x^3
- $cubed = NumPower::pow($x, 3);
+ $cubed = NumPower::pow($output, 3);
// Calculate inner term: ALPHA * (x + BETA * x^3)
$innerTerm = NumPower::multiply(
self::ALPHA,
NumPower::add(
- $x,
+ $output,
NumPower::multiply(self::BETA, $cubed)
)
);
@@ -122,7 +122,7 @@ public function differentiate(NDArray $x) : NDArray
NumPower::multiply(
NumPower::multiply(
0.5 * self::ALPHA,
- $x
+ $output
),
$sech2
),
@@ -130,7 +130,7 @@ public function differentiate(NDArray $x) : NDArray
1.0,
NumPower::multiply(
3.0 * self::BETA,
- NumPower::pow($x, 2)
+ NumPower::pow($output, 2)
)
)
);
diff --git a/src/NeuralNet/ActivationFunctions/HardSiLU/HardSiLU.php b/src/NeuralNet/ActivationFunctions/HardSiLU/HardSiLU.php
index 8fbc39e83..0f9bd86fa 100644
--- a/src/NeuralNet/ActivationFunctions/HardSiLU/HardSiLU.php
+++ b/src/NeuralNet/ActivationFunctions/HardSiLU/HardSiLU.php
@@ -63,16 +63,16 @@ public function activate(NDArray $input) : NDArray
*
* f'(x) = HardSigmoid(x) + x * HardSigmoid'(x)
*
- * @param NDArray $x Input matrix
+ * @param NDArray $output Output matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $x) : NDArray
+ public function differentiate(NDArray $output) : NDArray
{
// Calculate HardSigmoid(x)
- $hardSigmoid = $this->hardSigmoid->activate($x);
+ $hardSigmoid = $this->hardSigmoid->activate($output);
// Calculate HardSigmoid'(x)
- $hardSigmoidDerivative = $this->hardSigmoid->differentiate($x);
+ $hardSigmoidDerivative = $this->hardSigmoid->differentiate($output);
// Calculate x * HardSigmoid'(x)
$xTimesDerivative = NumPower::multiply($x, $hardSigmoidDerivative);
diff --git a/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php b/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
index 1c82ade35..801e92f9a 100644
--- a/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
+++ b/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
@@ -79,14 +79,14 @@ public function activate(NDArray $input) : NDArray
* f'(x) = 0.2 if -2.5 < x < 2.5
* f'(x) = 0 otherwise
*
- * @param NDArray $x Input matrix
+ * @param NDArray $output Output matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $x) : NDArray
+ public function differentiate(NDArray $output) : NDArray
{
// For values in the linear region (-2.5 < x < 2.5): SLOPE
- $inLinearRegion = NumPower::greater($x, self::LOWER_BOUND);
- $inLinearRegion = NumPower::multiply($inLinearRegion, NumPower::less($x, self::UPPER_BOUND));
+ $inLinearRegion = NumPower::greater($output, self::LOWER_BOUND);
+ $inLinearRegion = NumPower::multiply($inLinearRegion, NumPower::less($output, self::UPPER_BOUND));
$linearPart = NumPower::multiply($inLinearRegion, self::SLOPE);
// For values outside the linear region: 0
diff --git a/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php b/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
index 305c621b0..bb9a8c8a5 100644
--- a/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
+++ b/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
@@ -40,13 +40,13 @@ public function activate(NDArray $input) : NDArray
*
* f'(x) = 1 - tanh^2(x)
*
- * @param NDArray $x Output matrix
+ * @param NDArray $output Output matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $x) : NDArray
+ public function differentiate(NDArray $output) : NDArray
{
// Calculate tanh^2(x)
- $squared = NumPower::pow($x, 2);
+ $squared = NumPower::pow($output, 2);
// Calculate 1 - tanh^2(x)
return NumPower::subtract(1.0, $squared);
From 5203c0a61a3d45d6927577bae9a54d4369e11f9f Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Mon, 23 Jun 2025 19:56:23 +0300
Subject: [PATCH 12/22] SAM-7 differentiate parameter renamed
---
.../LeakyReLU/LeakyReLU.php | 8 ++++----
.../ActivationFunctions/ELU/ELUTest.php | 6 +++---
.../ActivationFunctions/GELU/GELUTest.php | 6 +++---
.../HardSiLU/HardSiLUTest.php | 6 +++---
.../HardSigmoid/HardSigmoidTest.php | 6 +++---
.../LeakyReLU/LeakyReLUTest.php | 20 +++++++++----------
6 files changed, 26 insertions(+), 26 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php b/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
index c68237a9a..92d6e3792 100644
--- a/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
+++ b/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
@@ -82,17 +82,17 @@ public function activate(NDArray $input) : NDArray
* f'(x) = 1 if x > 0
* f'(x) = leakage if x ≤ 0
*
- * @param NDArray $x Input matrix
+ * @param NDArray $output Output matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $x) : NDArray
+ public function differentiate(NDArray $output) : NDArray
{
// For x > 0: 1
- $positivePart = NumPower::greater($x, 0);
+ $positivePart = NumPower::greater($output, 0);
// For x <= 0: leakage
$negativePart = NumPower::multiply(
- NumPower::lessEqual($x, 0),
+ NumPower::lessEqual($output, 0),
$this->leakage
);
diff --git a/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php b/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
index 14e2dff9a..a872fdcbd 100644
--- a/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
@@ -128,11 +128,11 @@ public function testActivate(NDArray $input, array $expected) : void
}
#[Test]
- #[TestDox('Correctly differentiates the input')]
+ #[TestDox('Correctly differentiates the output')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $input, array $expected) : void
+ public function testDifferentiate(NDArray $output, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($input)->toArray();
+ $derivatives = $this->activationFn->differentiate($output)->toArray();
static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
}
diff --git a/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php b/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
index b9cc02293..0a7295134 100644
--- a/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
@@ -108,11 +108,11 @@ public function testActivate(NDArray $input, array $expected) : void
}
#[Test]
- #[TestDox('Correctly differentiates the input')]
+ #[TestDox('Correctly differentiates the output')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $input, array $expected) : void
+ public function testDifferentiate(NDArray $output, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($input)->toArray();
+ $derivatives = $this->activationFn->differentiate($output)->toArray();
static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
}
diff --git a/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php b/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
index 812f1bf1d..749a1f322 100644
--- a/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
@@ -175,11 +175,11 @@ public function testActivate(NDArray $input, array $expected) : void
}
#[Test]
- #[TestDox('Correctly differentiates the input')]
+ #[TestDox('Correctly differentiates the output')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $input, array $expected) : void
+ public function testDifferentiate(NDArray $output, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($input)->toArray();
+ $derivatives = $this->activationFn->differentiate($output)->toArray();
static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
diff --git a/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php b/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
index 12b6cdcd3..bd46e342d 100644
--- a/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
@@ -108,11 +108,11 @@ public function testActivate(NDArray $input, array $expected) : void
}
#[Test]
- #[TestDox('Correctly differentiates the input')]
+ #[TestDox('Correctly differentiates the output')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $input, array $expected) : void
+ public function testDifferentiate(NDArray $output, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($input)->toArray();
+ $derivatives = $this->activationFn->differentiate($output)->toArray();
static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
}
diff --git a/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php b/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
index 2e00f5f5a..982e94de3 100644
--- a/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
@@ -164,16 +164,6 @@ public function testActivate(NDArray $input, array $expected) : void
static::assertEqualsWithDelta($expected, $activations, 1e-16);
}
- #[Test]
- #[TestDox('Correctly differentiates the input')]
- #[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $input, array $expected) : void
- {
- $derivatives = $this->activationFn->differentiate($input)->toArray();
-
- static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
- }
-
#[Test]
#[TestDox('Correctly handles boundary values during activation')]
#[DataProvider('boundaryProvider')]
@@ -183,4 +173,14 @@ public function testBoundaryActivate(NDArray $input, array $expected) : void
static::assertEqualsWithDelta($expected, $activations, 1e-16);
}
+
+ #[Test]
+ #[TestDox('Correctly differentiates the output')]
+ #[DataProvider('differentiateProvider')]
+ public function testDifferentiate(NDArray $output, array $expected) : void
+ {
+ $derivatives = $this->activationFn->differentiate($output)->toArray();
+
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ }
}
From ed71ea5663b7a26df9f9f3dc7acad2f955ad121f Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Tue, 24 Jun 2025 06:53:39 +0300
Subject: [PATCH 13/22] Refactoring Derivative sub-interfaces
---
.../Base/Contracts/IBufferDerivative.php | 4 ++--
.../Base/Contracts/IOBufferDerivative.php | 19 +++++++++++++++++++
.../Base/Contracts/OBufferDerivative.php | 18 ++++++++++++++++++
3 files changed, 39 insertions(+), 2 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/Base/Contracts/IBufferDerivative.php b/src/NeuralNet/ActivationFunctions/Base/Contracts/IBufferDerivative.php
index 9e9b071f5..b29011bc8 100644
--- a/src/NeuralNet/ActivationFunctions/Base/Contracts/IBufferDerivative.php
+++ b/src/NeuralNet/ActivationFunctions/Base/Contracts/IBufferDerivative.php
@@ -20,8 +20,8 @@ interface IBufferDerivative extends Derivative
/**
* Calculate the derivative of the single parameter.
*
- * @param NDArray $output Output matrix
+ * @param NDArray $input Input matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $output) : NDArray;
+ public function differentiate(NDArray $input) : NDArray;
}
diff --git a/src/NeuralNet/ActivationFunctions/Base/Contracts/IOBufferDerivative.php b/src/NeuralNet/ActivationFunctions/Base/Contracts/IOBufferDerivative.php
index c77ff3e04..4263fcea9 100644
--- a/src/NeuralNet/ActivationFunctions/Base/Contracts/IOBufferDerivative.php
+++ b/src/NeuralNet/ActivationFunctions/Base/Contracts/IOBufferDerivative.php
@@ -2,6 +2,25 @@
namespace Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts;
+use NDArray;
+
+/**
+ * Derivative based on input / output buffer
+ *
+ * @category Machine Learning
+ * @package Rubix/ML
+ * @author Andrew DalPino
+ * @author Aleksei Nechaev <omfg.rus@gmail.com>
+ * @author Samuel Akopyan <leumas.a@gmail.com>
+ */
interface IOBufferDerivative
{
+ /**
+ * Calculate the derivative of the activation.
+ *
+ * @param NDArray $input Input matrix
+ * @param NDArray $output Output matrix
+ * @return NDArray Derivative matrix
+ */
+ public function differentiate(NDArray $input, NDArray $output) : NDArray;
}
diff --git a/src/NeuralNet/ActivationFunctions/Base/Contracts/OBufferDerivative.php b/src/NeuralNet/ActivationFunctions/Base/Contracts/OBufferDerivative.php
index 0ae53eeb8..760dc4593 100644
--- a/src/NeuralNet/ActivationFunctions/Base/Contracts/OBufferDerivative.php
+++ b/src/NeuralNet/ActivationFunctions/Base/Contracts/OBufferDerivative.php
@@ -2,6 +2,24 @@
namespace Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts;
+use NDArray;
+
+/**
+ * Derivative based on output buffer
+ *
+ * @category Machine Learning
+ * @package Rubix/ML
+ * @author Andrew DalPino
+ * @author Aleksei Nechaev <omfg.rus@gmail.com>
+ * @author Samuel Akopyan <leumas.a@gmail.com>
+ */
interface OBufferDerivative
{
+ /**
+ * Calculate the derivative of the activation.
+ *
+ * @param NDArray $output Output matrix
+ * @return NDArray Derivative matrix
+ */
+ public function differentiate(NDArray $output) : NDArray;
}
From b0ce0ff049f335ee26933b16cd0b3a6783825380 Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Tue, 24 Jun 2025 19:08:56 +0300
Subject: [PATCH 14/22] Refactoring ELU with IOBufferDerivative
---
src/NeuralNet/ActivationFunctions/ELU/ELU.php | 29 ++++++++++---------
.../ActivationFunctions/ELU/ELUTest.php | 27 ++++++++---------
2 files changed, 29 insertions(+), 27 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/ELU/ELU.php b/src/NeuralNet/ActivationFunctions/ELU/ELU.php
index 5f52d2293..654f8cb06 100644
--- a/src/NeuralNet/ActivationFunctions/ELU/ELU.php
+++ b/src/NeuralNet/ActivationFunctions/ELU/ELU.php
@@ -7,7 +7,7 @@
use NumPower;
use NDArray;
use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\ActivationFunction;
-use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\IBufferDerivative;
+use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\IOBufferDerivative;
use Rubix\ML\NeuralNet\ActivationFunctions\ELU\Exceptions\InvalidAlphaException;
/**
@@ -26,7 +26,7 @@
* @author Aleksei Nechaev <omfg.rus@gmail.com>
* @author Samuel Akopyan <leumas.a@gmail.com>
*/
-class ELU implements ActivationFunction, IBufferDerivative
+class ELU implements ActivationFunction, IOBufferDerivative
{
/**
* Class constructor.
@@ -71,28 +71,29 @@ public function activate(NDArray $input) : NDArray
}
/**
- * Calculate the derivative of the activation function.
+ * Calculate the derivative of the ELU activation function using input and output.
*
- * f'(x) = 1 if x > 0
- * f'(x) = α * e^x if x ≤ 0
+ * f'(x) = 1 if x > 0
+ * f'(x) = f(x) + α if x ≤ 0, where f(x) is the ELU output
*
- * @param NDArray $output Output matrix
- * @return NDArray Derivative matrix
+ * @param NDArray $input Input matrix (used to determine x > 0 mask)
+ * @param NDArray $output Output from the ELU activation function
+ * @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $output) : NDArray
+ public function differentiate(NDArray $input, NDArray $output): NDArray
{
// For x > 0: 1
- $positivePart = NumPower::greater($output, 0);
+ $positiveMask = NumPower::greater($input, 0);
- // For x <= 0: α * e^x
- $negativeMask = NumPower::lessEqual($output, 0);
+ // For x <= 0: output + α
+ $negativeMask = NumPower::lessEqual($input, 0);
$negativePart = NumPower::multiply(
- NumPower::multiply($negativeMask, NumPower::exp($output)),
- $this->alpha
+ NumPower::add($output, $this->alpha),
+ $negativeMask
);
// Combine both parts
- return NumPower::add($positivePart, $negativePart);
+ return NumPower::add($positiveMask, $negativePart);
}
/**
diff --git a/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php b/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
index a872fdcbd..a375347d2 100644
--- a/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/ELU/ELUTest.php
@@ -35,7 +35,7 @@ public static function computeProvider() : Generator
[1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [1.0, -0.39346933364868164, 0.0, 20.0, -0.9999545812606812],
+ [1.0, -0.3934693, 0.0, 20.0, -0.9999545],
],
];
@@ -46,9 +46,9 @@ public static function computeProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [-0.11307956278324127, 0.3100000023841858, -0.3873736262321472],
- [0.9900000095367432, 0.07999999821186066, -0.029554465785622597],
- [0.05000000074505806, -0.40547943115234375, 0.5400000214576721],
+ [-0.1130795, 0.3100000, -0.3873736],
+ [0.9900000, 0.0799999, -0.0295544],
+ [0.0500000, -0.4054794, 0.5400000],
],
];
}
@@ -63,7 +63,7 @@ public static function differentiateProvider() : Generator
[1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [1.0, 0.6065306663513184, 1.0, 1.0, 4.539993096841499E-5],
+ [1.0, 0.6065306, 1.0, 1.0, 0.0000454],
],
];
@@ -74,9 +74,9 @@ public static function differentiateProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [0.8869204521179199, 1.0, 0.6126263737678528],
- [1.0, 1.0, 0.9704455137252808],
- [1.0, 0.5945205688476562, 1.0],
+ [0.8869204, 1.0, 0.6126263],
+ [1.0, 1.0, 0.9704455],
+ [1.0, 0.5945205, 1.0],
],
];
}
@@ -124,16 +124,17 @@ public function testActivate(NDArray $input, array $expected) : void
{
$activations = $this->activationFn->activate($input)->toArray();
- static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ static::assertEqualsWithDelta($expected, $activations, 1e-7);
}
#[Test]
- #[TestDox('Correctly differentiates the output')]
+ #[TestDox('Correctly differentiates the input using buffered output')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $output, array $expected) : void
+ public function testDifferentiate(NDArray $input, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($output)->toArray();
+ $output = $this->activationFn->activate($input);
+ $derivatives = $this->activationFn->differentiate($input, $output)->toArray();
- static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-7);
}
}
From c84319e5f2b06252c2295f897c2bbcfe4d871c2c Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Tue, 24 Jun 2025 19:28:41 +0300
Subject: [PATCH 15/22] Refactoring GELU with IBufferDerivative
---
.../ActivationFunctions/GELU/GELU.php | 12 ++++-----
.../ActivationFunctions/GELU/GELUTest.php | 26 +++++++++----------
2 files changed, 19 insertions(+), 19 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/GELU/GELU.php b/src/NeuralNet/ActivationFunctions/GELU/GELU.php
index 195868b29..008219469 100644
--- a/src/NeuralNet/ActivationFunctions/GELU/GELU.php
+++ b/src/NeuralNet/ActivationFunctions/GELU/GELU.php
@@ -87,19 +87,19 @@ public function activate(NDArray $input) : NDArray
* - β = 0.044715
* - sech^2(z) = (1/cosh(z))^2
*
- * @param NDArray $output Output matrix
+ * @param NDArray $input Input matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $output) : NDArray
+ public function differentiate(NDArray $input) : NDArray
{
// Calculate x^3
- $cubed = NumPower::pow($output, 3);
+ $cubed = NumPower::pow($input, 3);
// Calculate inner term: ALPHA * (x + BETA * x^3)
$innerTerm = NumPower::multiply(
self::ALPHA,
NumPower::add(
- $output,
+ $input,
NumPower::multiply(self::BETA, $cubed)
)
);
@@ -122,7 +122,7 @@ public function differentiate(NDArray $output) : NDArray
NumPower::multiply(
NumPower::multiply(
0.5 * self::ALPHA,
- $output
+ $input
),
$sech2
),
@@ -130,7 +130,7 @@ public function differentiate(NDArray $output) : NDArray
1.0,
NumPower::multiply(
3.0 * self::BETA,
- NumPower::pow($output, 2)
+ NumPower::pow($input, 2)
)
)
);
diff --git a/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php b/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
index 0a7295134..2a4e35b14 100644
--- a/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/GELU/GELUTest.php
@@ -34,7 +34,7 @@ public static function computeProvider() : Generator
[2, 1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [1.9545977115631104, 0.8411920070648193, -0.1542859971523285, 0.0, 20.0, 0.0],
+ [1.9545977, 0.8411920, -0.1542859, 0.0, 20.0, 0.0],
],
];
@@ -45,9 +45,9 @@ public static function computeProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [-0.054269056767225266, 0.19273021817207336, -0.15292881429195404],
- [0.830374538898468, 0.04255049675703049, -0.014641005545854568],
- [0.025996938347816467, -0.15681639313697815, 0.38089409470558167],
+ [-0.0542690, 0.1927302, -0.1529288],
+ [0.8303745, 0.0425504, -0.0146410],
+ [0.0259969, -0.1568163, 0.3808940],
],
];
}
@@ -62,7 +62,7 @@ public static function differentiateProvider() : Generator
[1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [1.0829640626907349, 0.1326301246881485, 0.5, 1.0, -0.0],
+ [1.0829640, 0.1326301, 0.5, 1.0, -0.0],
],
];
@@ -73,9 +73,9 @@ public static function differentiateProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [0.4047141969203949, 0.7395542860031128, 0.13881805539131165],
- [1.080506443977356, 0.5636941194534302, 0.47607067227363586],
- [0.5398608446121216, 0.12045331299304962, 0.8914529085159302],
+ [0.4047141, 0.7395542, 0.1388180],
+ [1.0805064, 0.5636941, 0.4760706],
+ [0.5398608, 0.1204533, 0.8914529],
],
];
}
@@ -104,16 +104,16 @@ public function testActivate(NDArray $input, array $expected) : void
{
$activations = $this->activationFn->activate($input)->toArray();
- static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ static::assertEqualsWithDelta($expected, $activations, 1e-7);
}
#[Test]
- #[TestDox('Correctly differentiates the output')]
+ #[TestDox('Correctly differentiates the input')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $output, array $expected) : void
+ public function testDifferentiate(NDArray $input, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($output)->toArray();
+ $derivatives = $this->activationFn->differentiate($input)->toArray();
- static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-7);
}
}
From de4ef87bb05a5fe2d5db572c1ec00ed23884b8fb Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Tue, 24 Jun 2025 23:12:09 +0300
Subject: [PATCH 16/22] Refactoring HardSigmoid with IBufferDerivative
---
.../ActivationFunctions/HardSigmoid/HardSigmoid.php | 8 ++++----
.../HardSigmoid/HardSigmoidTest.php | 12 ++++++------
2 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php b/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
index 801e92f9a..78df22dcb 100644
--- a/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
+++ b/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
@@ -79,14 +79,14 @@ public function activate(NDArray $input) : NDArray
* f'(x) = 0.2 if -2.5 < x < 2.5
* f'(x) = 0 otherwise
*
- * @param NDArray $output Output matrix
+ * @param NDArray $input Input matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $output) : NDArray
+ public function differentiate(NDArray $input) : NDArray
{
// For values in the linear region (-2.5 < x < 2.5): SLOPE
- $inLinearRegion = NumPower::greater($output, self::LOWER_BOUND);
- $inLinearRegion = NumPower::multiply($inLinearRegion, NumPower::less($output, self::UPPER_BOUND));
+ $inLinearRegion = NumPower::greaterEqual($input, self::LOWER_BOUND);
+ $inLinearRegion = NumPower::multiply($inLinearRegion, NumPower::lessEqual($input, self::UPPER_BOUND));
$linearPart = NumPower::multiply($inLinearRegion, self::SLOPE);
// For values outside the linear region: 0
diff --git a/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php b/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
index bd46e342d..6fd535c11 100644
--- a/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
@@ -62,7 +62,7 @@ public static function differentiateProvider() : Generator
[2.5, 1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [0.0, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.0, 0.0],
+ [0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.0, 0.0],
],
];
@@ -104,16 +104,16 @@ public function testActivate(NDArray $input, array $expected) : void
{
$activations = $this->activationFn->activate($input)->toArray();
- static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ static::assertEqualsWithDelta($expected, $activations, 1e-7);
}
#[Test]
- #[TestDox('Correctly differentiates the output')]
+ #[TestDox('Correctly differentiates the input')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $output, array $expected) : void
+ public function testDifferentiate(NDArray $input, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($output)->toArray();
+ $derivatives = $this->activationFn->differentiate($input)->toArray();
- static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-7);
}
}
From 6dd013f2351d0af10090d8ecdbfb239566efc42b Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Tue, 24 Jun 2025 23:18:43 +0300
Subject: [PATCH 17/22] Refactoring HardSigmoid with IBufferDerivative
---
.../HardSigmoid/HardSigmoidTest.php | 18 +++++++++---------
1 file changed, 9 insertions(+), 9 deletions(-)
diff --git a/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php b/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
index 6fd535c11..a39bf1d86 100644
--- a/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoidTest.php
@@ -34,7 +34,7 @@ public static function computeProvider() : Generator
[2.5, 2.4, 2.0, 1.0, -0.5, 0.0, 20.0, -2.5, -2.4, -10.0],
]),
[
- [1.0, 0.9800000190734863, 0.8999999761581421, 0.699999988079071, 0.4000000059604645, 0.5, 1.0, 0.0, 0.019999980926513672, 0.0],
+ [1.0, 0.9800000, 0.8999999, 0.6999999, 0.4000000, 0.5, 1.0, 0.0, 0.0199999, 0.0],
],
];
@@ -45,9 +45,9 @@ public static function computeProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [0.47600001096725464, 0.5619999766349792, 0.4020000100135803],
- [0.6980000138282776, 0.515999972820282, 0.49399998784065247],
- [0.5099999904632568, 0.3959999978542328, 0.6079999804496765],
+ [0.4760000, 0.5619999, 0.4020000],
+ [0.6980000, 0.5159999, 0.4939999],
+ [0.5099999, 0.3959999, 0.6079999],
],
];
}
@@ -59,10 +59,10 @@ public static function differentiateProvider() : Generator
{
yield [
NumPower::array([
- [2.5, 1.0, -0.5, 0.0, 20.0, -10.0],
+ [2.5, 1.0, -0.5, 0.0, 20.0, -2.5, -10.0],
]),
[
- [0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.0, 0.0],
+ [0.2000000, 0.2000000, 0.2000000, 0.2000000, 0.0, 0.2000000, 0.0],
],
];
@@ -73,9 +73,9 @@ public static function differentiateProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [0.20000000298023224, 0.20000000298023224, 0.20000000298023224],
- [0.0, 0.20000000298023224, 0.20000000298023224],
- [0.20000000298023224, 0.20000000298023224, 0.20000000298023224],
+ [0.2000000, 0.2000000, 0.2000000],
+ [0.0, 0.2000000, 0.2000000],
+ [0.2000000, 0.2000000, 0.2000000],
],
];
}
From 6020364cc628fbbdf65e6fa19a540f8ad1cf87b9 Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Tue, 24 Jun 2025 23:22:30 +0300
Subject: [PATCH 18/22] Refactoring HardSigmoid with IBufferDerivative
---
src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php b/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
index 78df22dcb..40039d4cc 100644
--- a/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
+++ b/src/NeuralNet/ActivationFunctions/HardSigmoid/HardSigmoid.php
@@ -76,7 +76,7 @@ public function activate(NDArray $input) : NDArray
/**
* Calculate the derivative of the activation function.
*
- * f'(x) = 0.2 if -2.5 < x < 2.5
+ * f'(x) = 0.2 if -2.5 <= x <= 2.5
* f'(x) = 0 otherwise
*
* @param NDArray $input Input matrix
@@ -84,7 +84,7 @@ public function activate(NDArray $input) : NDArray
*/
public function differentiate(NDArray $input) : NDArray
{
- // For values in the linear region (-2.5 < x < 2.5): SLOPE
+ // For values in the linear region (-2.5 <= x <= 2.5): SLOPE
$inLinearRegion = NumPower::greaterEqual($input, self::LOWER_BOUND);
$inLinearRegion = NumPower::multiply($inLinearRegion, NumPower::lessEqual($input, self::UPPER_BOUND));
$linearPart = NumPower::multiply($inLinearRegion, self::SLOPE);
From e278894d1ac5a399ab21bea9ee9ca95e8ca0b0d2 Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Wed, 25 Jun 2025 16:55:36 +0300
Subject: [PATCH 19/22] Refactoring HardSiLU with IBufferDerivative
---
.../hard-silu-derivative.png | Bin 48315 -> 74612 bytes
.../images/activation-functions/hard-silu.png | Bin 48031 -> 62872 bytes
.../ActivationFunctions/HardSiLU/HardSiLU.php | 10 ++--
.../HardSiLU/HardSiLUTest.php | 43 +++++++++---------
4 files changed, 26 insertions(+), 27 deletions(-)
diff --git a/docs/images/activation-functions/hard-silu-derivative.png b/docs/images/activation-functions/hard-silu-derivative.png
index 089d0eac59569a612c7837c9179d9286f5b2848f..4cb2a03206ecf352adc4c640eb1ba00b73aecdf1 100644
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diff --git a/docs/images/activation-functions/hard-silu.png b/docs/images/activation-functions/hard-silu.png
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diff --git a/src/NeuralNet/ActivationFunctions/HardSiLU/HardSiLU.php b/src/NeuralNet/ActivationFunctions/HardSiLU/HardSiLU.php
index 0f9bd86fa..6ed54631a 100644
--- a/src/NeuralNet/ActivationFunctions/HardSiLU/HardSiLU.php
+++ b/src/NeuralNet/ActivationFunctions/HardSiLU/HardSiLU.php
@@ -63,19 +63,19 @@ public function activate(NDArray $input) : NDArray
*
* f'(x) = HardSigmoid(x) + x * HardSigmoid'(x)
*
- * @param NDArray $output Output matrix
+ * @param NDArray $input Input matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $output) : NDArray
+ public function differentiate(NDArray $input) : NDArray
{
// Calculate HardSigmoid(x)
- $hardSigmoid = $this->hardSigmoid->activate($output);
+ $hardSigmoid = $this->hardSigmoid->activate($input);
// Calculate HardSigmoid'(x)
- $hardSigmoidDerivative = $this->hardSigmoid->differentiate($output);
+ $hardSigmoidDerivative = $this->hardSigmoid->differentiate($input);
// Calculate x * HardSigmoid'(x)
- $xTimesDerivative = NumPower::multiply($x, $hardSigmoidDerivative);
+ $xTimesDerivative = NumPower::multiply($input, $hardSigmoidDerivative);
// Calculate HardSigmoid(x) + x * HardSigmoid'(x)
return NumPower::add($hardSigmoid, $xTimesDerivative);
diff --git a/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php b/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
index 749a1f322..5eee0a332 100644
--- a/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HardSiLU/HardSiLUTest.php
@@ -34,7 +34,7 @@ public static function computeProvider() : Generator
[2.5, 2.0, 1.0, -0.5, 0.0, 20.0, -2.5, -10.0],
]),
[
- [2.5, 1.7999999523162842, 0.699999988079071, -0.20000000298023224, 0.0, 20.0, 0.0, 0.0],
+ [2.5, 1.7999999, 0.6999999, -0.2000000, 0.0, 20.0, 0.0, 0.0],
],
];
@@ -45,9 +45,9 @@ public static function computeProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [-0.05711999908089638, 0.1742199957370758, -0.19698001444339752],
- [0.6910200119018555, 0.04127999767661095, -0.014819999225437641],
- [0.025499999523162842, -0.2059199959039688, 0.3283199965953827],
+ [-0.0571199, 0.1742199, -0.1969800],
+ [0.6910200, 0.0412799, -0.0148199],
+ [0.0254999, -0.2059199, 0.3283199],
],
];
@@ -66,9 +66,9 @@ public static function computeProvider() : Generator
// At x = 2.5, HardSigmoid(x) = 1, so HardSiLU(2.5) = 2.5 * 1 = 2.5
[0.0, 2.5],
// Just inside boundaries
- [-0.0004997340147383511, 2.498500347137451],
+ [-0.0004997, 2.4985003],
// Just outside boundaries
- [0.0, 2.500999927520752],
+ [0.0, 2.5009999],
],
];
@@ -76,12 +76,12 @@ public static function computeProvider() : Generator
yield [
NumPower::array([
// Zero and very small values around zero
- [0.0, 0.0000001, -0.0000001, 0.0000000001, -0.0000000001],
+ [0.0, 0.000001, -0.0000001, 0.0000000001, -0.0000000001],
]),
[
// HardSiLU(0) = 0 * 0.5 = 0
// For very small values, HardSigmoid(x) ≈ 0.5, so HardSiLU(x) ≈ x * 0.5
- [0.0, 0.00000005000000058430487, -0.00000004999999703159119, 0.0000000000500000006675716, -0.0000000000500000006675716],
+ [0.0, 0.0000005, -0.0000000, 0.0000000, -0.0000000],
],
];
}
@@ -96,7 +96,7 @@ public static function differentiateProvider() : Generator
[2.5, 1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [1.0, 0.8999999761581421, 0.30000001192092896, 0.5, 1.0, 0.0],
+ [1.5, 0.8999999, 0.30000001192092896, 0.5, 1.0, 0.0],
],
];
@@ -107,9 +107,9 @@ public static function differentiateProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [0.45200002193450928, 0.6239999532699585, 0.30400002002716064],
- [0.8960000276565552, 0.531999945640564, 0.48799997568130493],
- [0.5199999809265137, 0.2919999957084656, 0.715999960899353],
+ [0.4520000, 0.6239999, 0.3040000],
+ [0.8960000, 0.5319999, 0.4879999],
+ [0.5199999, 0.2919999, 0.7159999],
],
];
@@ -125,9 +125,9 @@ public static function differentiateProvider() : Generator
]),
[
// At boundaries: derivative is 0 at x = -2.5 and 1 at x = 2.5
- [0.0, 1.0],
+ [-0.5, 1.5],
// Just inside boundaries
- [-0.49960005283355713, 1.4996000528335571],
+ [-0.4996000, 1.4996000],
// Just outside boundaries
[0.0, 1.0],
],
@@ -137,12 +137,12 @@ public static function differentiateProvider() : Generator
yield [
NumPower::array([
// Zero and very small values around zero
- [0.0, 0.0000001, -0.0000001, 0.0000000001, -0.0000000001],
+ [0.0, -0.00001, 0.000001, -0.0000001, 0.00000001, -0.000000001],
]),
[
// At x = 0, derivative is 0.5
// For very small values, derivative is close to 0.5
- [0.5, 0.5, 0.4999999403953552, 0.5, 0.5],
+ [0.5, 0.4999960, 0.5000003, 0.4999999, 0.5, 0.5],
],
];
}
@@ -171,17 +171,16 @@ public function testActivate(NDArray $input, array $expected) : void
{
$activations = $this->activationFn->activate($input)->toArray();
- static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ static::assertEqualsWithDelta($expected, $activations, 1e-7);
}
#[Test]
- #[TestDox('Correctly differentiates the output')]
+ #[TestDox('Correctly differentiates the input')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $output, array $expected) : void
+ public function testDifferentiate(NDArray $input, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($output)->toArray();
-
- static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ $derivatives = $this->activationFn->differentiate($input)->toArray();
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-7);
}
}
From 87cb3b9aa3b69e78f5ede2074db4f3e72f44092e Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Wed, 25 Jun 2025 19:44:40 +0300
Subject: [PATCH 20/22] Refactoring HyperbolicTangent with IBufferDerivative
---
.../HyperbolicTangent/HyperbolicTangent.php | 4 +--
.../HyperbolicTangentTest.php | 28 +++++++++----------
2 files changed, 16 insertions(+), 16 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php b/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
index bb9a8c8a5..629422c86 100644
--- a/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
+++ b/src/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangent.php
@@ -7,7 +7,7 @@
use NumPower;
use NDArray;
use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\ActivationFunction;
-use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\IBufferDerivative;
+use Rubix\ML\NeuralNet\ActivationFunctions\Base\Contracts\OBufferDerivative;
/**
* Hyperbolic Tangent
@@ -20,7 +20,7 @@
* @author Andrew DalPino
* @author Samuel Akopyan <leumas.a@gmail.com>
*/
-class HyperbolicTangent implements ActivationFunction, IBufferDerivative
+class HyperbolicTangent implements ActivationFunction, OBufferDerivative
{
/**
* Apply the Hyperbolic Tangent activation function to the input.
diff --git a/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php b/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
index 79dc6b11c..948d1c297 100644
--- a/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
+++ b/tests/NeuralNet/ActivationFunctions/HyperbolicTangent/HyperbolicTangentTest.php
@@ -34,7 +34,7 @@ public static function computeProvider() : Generator
[9.0, 2.5, 2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [0.9999999403953552, 0.9866142868995667, 0.9640275835990906, 0.7615941762924194, -0.46211716532707214, 0.0, 1.0, -1.0],
+ [0.9999999, 0.9866142, 0.9640275, 0.7615941, -0.4621171, 0.0, 1.0, -1.0],
],
];
@@ -45,9 +45,9 @@ public static function computeProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [-0.11942730098962784, 0.3004370927810669, -0.45421645045280457],
- [0.7573622465133667, 0.07982976734638214, -0.02999100275337696],
- [0.049958378076553345, -0.47769999504089355, 0.4929879903793335],
+ [-0.1194273, 0.3004370, -0.4542164],
+ [0.7573622, 0.0798297, -0.0299910],
+ [0.0499583, -0.4776999, 0.4929879],
],
];
}
@@ -59,23 +59,23 @@ public static function differentiateProvider() : Generator
{
yield [
NumPower::array([
- [0.9640275835990906, 0.7615941762924194, -0.46211716532707214, 0.0, 1.0, -1.0],
+ [0.9640275, 0.7615941, -0.4621171, 0.0, 1.0, -1.0],
]),
[
- [0.07065081596374512, 0.41997432708740234, 0.7864477038383484, 1.0, 0.0, 0.0],
+ [0.0706509, 0.4199743, 0.7864477, 1.0, 0.0, 0.0],
],
];
yield [
NumPower::array([
- [-0.11942730098962784, 0.3004370927810669, -0.45421645045280457],
- [0.7573623085022249, 0.07978830223560329, -0.029991223630861304],
- [0.049958395721942955, -0.4778087574005698, 0.4930591567725708],
+ [-0.1194273, 0.3004370, -0.4542164],
+ [0.7573623, 0.0797883, -0.0299912],
+ [0.0499583, -0.4778087, 0.4930591],
]),
[
- [0.985737144947052, 0.9097375273704529, 0.7936874032020569],
- [0.42640233039855957, 0.9936338067054749, 0.9991005063056946],
- [0.9975041747093201, 0.7716988325119019, 0.7568926811218262],
+ [0.9857371, 0.9097375, 0.7936874],
+ [0.4264023, 0.9936338, 0.9991005],
+ [0.9975042, 0.7716988, 0.7568927],
],
];
}
@@ -104,7 +104,7 @@ public function testActivate(NDArray $input, array $expected) : void
{
$activations = $this->activationFn->activate($input)->toArray();
- static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ static::assertEqualsWithDelta($expected, $activations, 1e-7);
}
#[Test]
@@ -114,6 +114,6 @@ public function testDifferentiate(NDArray $output, array $expected) : void
{
$derivatives = $this->activationFn->differentiate($output)->toArray();
- static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-7);
}
}
From edb02d02423ed704708905aa6c38c6079d7adf8b Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Wed, 25 Jun 2025 20:03:34 +0300
Subject: [PATCH 21/22] Refactoring LeakyReLU with IBufferDerivative
---
.../LeakyReLU/LeakyReLU.php | 8 ++---
.../LeakyReLU/LeakyReLUTest.php | 32 +++++++++----------
2 files changed, 20 insertions(+), 20 deletions(-)
diff --git a/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php b/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
index 92d6e3792..bace16275 100644
--- a/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
+++ b/src/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLU.php
@@ -82,17 +82,17 @@ public function activate(NDArray $input) : NDArray
* f'(x) = 1 if x > 0
* f'(x) = leakage if x ≤ 0
*
- * @param NDArray $output Output matrix
+ * @param NDArray $input Input matrix
* @return NDArray Derivative matrix
*/
- public function differentiate(NDArray $output) : NDArray
+ public function differentiate(NDArray $input) : NDArray
{
// For x > 0: 1
- $positivePart = NumPower::greater($output, 0);
+ $positivePart = NumPower::greater($input, 0);
// For x <= 0: leakage
$negativePart = NumPower::multiply(
- NumPower::lessEqual($output, 0),
+ NumPower::lessEqual($input, 0),
$this->leakage
);
diff --git a/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php b/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
index 982e94de3..179ea5bd9 100644
--- a/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
+++ b/tests/NeuralNet/ActivationFunctions/LeakyReLU/LeakyReLUTest.php
@@ -35,7 +35,7 @@ public static function computeProvider() : Generator
[2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [2.0, 1.0, -0.004999999888241291, 0.0, 20.0, -0.09999999403953552],
+ [2.0, 1.0, -0.0049999, 0.0, 20.0, -0.0999999],
],
];
@@ -46,9 +46,9 @@ public static function computeProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [-0.0011999999405816197, 0.3100000023841858, -0.004900000058114529],
- [0.9900000095367432, 0.07999999821186066, -0.00029999998514540493],
- [0.05000000074505806, -0.005199999548494816, 0.5400000214576721],
+ [-0.0011999, 0.3100000, -0.0049000],
+ [0.9900000, 0.0799999, -0.0002999],
+ [0.0500000, -0.0051999, 0.5400000],
],
];
}
@@ -60,10 +60,10 @@ public static function differentiateProvider() : Generator
{
yield [
NumPower::array([
- [2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
+ [4.0, 2.0, 1.0, -0.5, 0.0, 20.0, -10.0],
]),
[
- [1.0, 1.0, 0.009999999776482582, 0.009999999776482582, 1.0, 0.009999999776482582],
+ [1.0, 1.0, 1.0, 0.0099999, 0.0099999, 1.0, 0.0099999],
],
];
@@ -74,9 +74,9 @@ public static function differentiateProvider() : Generator
[0.05, -0.52, 0.54],
]),
[
- [0.009999999776482582, 1.0, 0.009999999776482582],
- [1.0, 1.0, 0.009999999776482582],
- [1.0, 0.009999999776482582, 1.0],
+ [0.0099999, 1.0, 0.0099999],
+ [1.0, 1.0, 0.0099999],
+ [1.0, 0.0099999, 1.0],
],
];
}
@@ -113,7 +113,7 @@ public static function boundaryProvider() : Generator
]),
[
- [0.0010000000474974513, -0.000010000000656873453, 0.00009999999747378752, -0.0000009999999974752427],
+ [0.0010000, -0.0000100, 0.0000999, -0.0000009],
],
];
}
@@ -161,7 +161,7 @@ public function testActivate(NDArray $input, array $expected) : void
{
$activations = $this->activationFn->activate($input)->toArray();
- static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ static::assertEqualsWithDelta($expected, $activations, 1e-7);
}
#[Test]
@@ -171,16 +171,16 @@ public function testBoundaryActivate(NDArray $input, array $expected) : void
{
$activations = $this->activationFn->activate($input)->toArray();
- static::assertEqualsWithDelta($expected, $activations, 1e-16);
+ static::assertEqualsWithDelta($expected, $activations, 1e-7);
}
#[Test]
- #[TestDox('Correctly differentiates the output')]
+ #[TestDox('Correctly differentiates the input')]
#[DataProvider('differentiateProvider')]
- public function testDifferentiate(NDArray $output, array $expected) : void
+ public function testDifferentiate(NDArray $input, array $expected) : void
{
- $derivatives = $this->activationFn->differentiate($output)->toArray();
+ $derivatives = $this->activationFn->differentiate($input)->toArray();
- static::assertEqualsWithDelta($expected, $derivatives, 1e-16);
+ static::assertEqualsWithDelta($expected, $derivatives, 1e-7);
}
}
From 6c4d9f4c3c3c4fca59dd60492eb44ab7c3979a1c Mon Sep 17 00:00:00 2001
From: Samuel Akopyan <samuel.akopyan@thrivedx.com>
Date: Wed, 25 Jun 2025 20:14:19 +0300
Subject: [PATCH 22/22] Typo fix in exception text
---
src/NeuralNet/ActivationFunctions/ELU/ELU.php | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/NeuralNet/ActivationFunctions/ELU/ELU.php b/src/NeuralNet/ActivationFunctions/ELU/ELU.php
index 654f8cb06..a0ca1fe36 100644
--- a/src/NeuralNet/ActivationFunctions/ELU/ELU.php
+++ b/src/NeuralNet/ActivationFunctions/ELU/ELU.php
@@ -40,7 +40,7 @@ public function __construct(protected float $alpha = 1.0)
{
if ($this->alpha < 0.0) {
throw new InvalidAlphaException(
- message: "Alpha must be greater than 0, $alpha given."
+ message: "Alpha must be greater than 0, {$this->alpha} given."
);
}
}