388 convert parameter class to numpower

docs/neural-network/optimizers/stochastic.md

@@ -1,7 +1,7 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/NeuralNet/Optimizers/Stochastic.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/NeuralNet/Optimizers/Stochastic/Stochastic.php">[source]</a></span>
 
 # Stochastic
-A constant learning rate optimizer based on vanilla Stochastic Gradient Descent.
+A constant learning rate optimizer based on vanilla Stochastic Gradient Descent (SGD).
 
 ## Parameters
 | # | Name | Default | Type | Description |
@@ -10,7 +10,7 @@ A constant learning rate optimizer based on vanilla Stochastic Gradient Descent.
 
 ## Example
 ```php
-use Rubix\ML\NeuralNet\Optimizers\Stochastic;
+use Rubix\ML\NeuralNet\Optimizers\Stochastic\Stochastic;
 
 $optimizer = new Stochastic(0.01);
-```
+```

src/NeuralNet/Optimizers/Base/Optimizer.php

@@ -0,0 +1,29 @@
+<?php
+
+namespace Rubix\ML\NeuralNet\Optimizers\Base;
+
+use NDArray;
+use Rubix\ML\NeuralNet\Parameters\Parameter;
+use Stringable;
+
+/**
+ * Optimizer
+ *
+ * @category    Machine Learning
+ * @package     Rubix/ML
+ * @author      Andrew DalPino
+ * @author      Samuel Akopyan <leumas.a@gmail.com>
+ */
+interface Optimizer extends Stringable
+{
+    /**
+     * Take a step of gradient descent for a given parameter.
+     *
+     * @internal
+     *
+     * @param Parameter $param
+     * @param NDArray $gradient
+     * @return NDArray
+     */
+    public function step(Parameter $param, NDArray $gradient) : NDArray;
+}

src/NeuralNet/Optimizers/Stochastic/Stochastic.php

@@ -0,0 +1,69 @@
+<?php
+
+namespace Rubix\ML\NeuralNet\Optimizers\Stochastic;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\NeuralNet\Optimizers\Base\Optimizer;
+use Rubix\ML\NeuralNet\Parameters\Parameter;
+use Rubix\ML\Exceptions\InvalidArgumentException;
+
+/**
+ * Stochastic
+ *
+ * SGD (Stochastic Gradient Descent) optimizer -
+ * a constant learning rate gradient descent optimizer.
+ *
+ * @category    Machine Learning
+ * @package     Rubix/ML
+ * @author      Andrew DalPino
+ * @author      Samuel Akopyan <leumas.a@gmail.com>
+ */
+class Stochastic implements Optimizer
+{
+    /**
+     * The learning rate that controls the global step size.
+     *
+     * @var float
+     */
+    protected float $rate;
+
+    /**
+     * @param float $rate
+     * @throws InvalidArgumentException
+     */
+    public function __construct(float $rate = 0.01)
+    {
+        if ($rate <= 0.0) {
+            throw new InvalidArgumentException("Learning rate must be greater than 0, $rate given.");
+        }
+
+        $this->rate = $rate;
+    }
+
+    /**
+     * Take a step of gradient descent for a given parameter.
+     *
+     * @internal
+     *
+     * @param Parameter $param
+     * @param NDArray $gradient
+     * @return NDArray
+     */
+    public function step(Parameter $param, NDArray $gradient) : NDArray
+    {
+        return NumPower::multiply($gradient, $this->rate);
+    }
+
+    /**
+     * Return the string representation of the object.
+     *
+     * @internal
+     *
+     * @return string
+     */
+    public function __toString() : string
+    {
+        return "Stochastic (rate: {$this->rate})";
+    }
+}

src/NeuralNet/Parameters/Parameter.php

@@ -0,0 +1,99 @@
+<?php
+
+namespace Rubix\ML\NeuralNet\Parameters;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\NeuralNet\Optimizers\Base\Optimizer;
+
+/**
+ * Parameter
+ *
+ * A wrapper over an NDArray from NumPower that marks the parameter as trainable
+ * and provides updates via the optimizer.
+ *
+ * @internal
+ *
+ * @category    Machine Learning
+ * @package     Rubix/ML
+ * @author      Andrew DalPino
+ * @author      Samuel Akopyan <leumas.a@gmail.com>
+ */
+
+/**
+ * Parameter
+ *
+ */
+class Parameter
+{
+    /**
+     * The auto incrementing id.
+     *
+     * @var int
+     */
+    protected static int $counter = 0;
+
+    /**
+     * The unique identifier of the parameter.
+     *
+     * @var int
+     */
+    protected int $id;
+
+    /**
+     * The parameter.
+     *
+     * @var NDArray
+     */
+    protected NDArray $param;
+
+    /**
+     * @param NDArray $param
+     */
+    public function __construct(NDArray $param)
+    {
+        $this->id = self::$counter++;
+        $this->param = $param;
+    }
+
+    /**
+     * Return the unique identifier of the parameter.
+     *
+     * @return int
+     */
+    public function id(): int
+    {
+        return $this->id;
+    }
+
+    /**
+     * Return the wrapped parameter.
+     *
+     * @return NDArray
+     */
+    public function param(): NDArray
+    {
+        return $this->param;
+    }
+
+    /**
+     * Update the parameter with the gradient and optimizer.
+     *
+     * @param NDArray    $gradient
+     * @param Optimizer  $optimizer
+     */
+    public function update(NDArray $gradient, Optimizer $optimizer): void
+    {
+        $step = $optimizer->step($this, $gradient);
+
+        $this->param = NumPower::subtract($this->param, $step);
+    }
+
+    /**
+     * Perform a deep copy of the object upon cloning.
+     */
+    public function __clone(): void
+    {
+        $this->param = clone $this->param;
+    }
+}

tests/NeuralNet/Optimizers/Stochastic/StochasticTest.php

@@ -0,0 +1,80 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Rubix\ML\Tests\NeuralNet\Optimizers\Stochastic;
+
+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\Exceptions\InvalidArgumentException;
+use Rubix\ML\NeuralNet\Parameters\Parameter;
+use Rubix\ML\NeuralNet\Optimizers\Stochastic\Stochastic;
+
+#[Group('Optimizers')]
+#[CoversClass(Stochastic::class)]
+class StochasticTest extends TestCase
+{
+    protected Stochastic $optimizer;
+
+    public static function stepProvider() : Generator
+    {
+        yield [
+            new Parameter(NumPower::array([
+                [0.1, 0.6, -0.4],
+                [0.5, 0.6, -0.4],
+                [0.1, 0.1, -0.7],
+            ])),
+            NumPower::array([
+                [0.01, 0.05, -0.02],
+                [-0.01, 0.02, 0.03],
+                [0.04, -0.01, -0.5],
+            ]),
+            [
+                [0.00001, 0.00005, -0.00002],
+                [-0.00001, 0.00002, 0.00003],
+                [0.00004, -0.00001, -0.0005],
+            ],
+        ];
+    }
+
+    protected function setUp() : void
+    {
+        $this->optimizer = new Stochastic(0.001);
+    }
+
+    #[Test]
+    #[TestDox('Throws exception when constructed with invalid learning rate')]
+    public function testConstructorWithInvalidRate() : void
+    {
+        $this->expectException(InvalidArgumentException::class);
+
+        new Stochastic(0.0);
+    }
+
+    #[Test]
+    #[TestDox('Can be cast to a string')]
+    public function testToString() : void
+    {
+        self::assertEquals('Stochastic (rate: 0.001)', (string) $this->optimizer);
+    }
+
+    /**
+     * @param Parameter $param
+     * @param NDArray $gradient
+     * @param list<list<float>> $expected
+     */
+    #[DataProvider('stepProvider')]
+    public function testStep(Parameter $param, NDArray $gradient, array $expected) : void
+    {
+        $step = $this->optimizer->step(param: $param, gradient: $gradient);
+
+        self::assertEqualsWithDelta($expected, $step->toArray(), 1e-7);
+    }
+}

tests/NeuralNet/Parameters/ParameterTest.php

@@ -0,0 +1,49 @@
+<?php
+
+declare(strict_types = 1);
+
+namespace Rubix\ML\Tests\NeuralNet\Parameters;
+
+use NumPower;
+use PHPUnit\Framework\Attributes\CoversClass;
+use PHPUnit\Framework\Attributes\Group;
+use Rubix\ML\NeuralNet\Optimizers\Base\Optimizer;
+use Rubix\ML\NeuralNet\Parameters\Parameter;
+use Rubix\ML\NeuralNet\Optimizers\Stochastic\Stochastic;
+use PHPUnit\Framework\TestCase;
+
+#[Group('Parameters')]
+#[CoversClass(Parameter::class)]
+class ParameterTest extends TestCase
+{
+    protected Parameter $param;
+
+    protected Optimizer $optimizer;
+
+    protected function setUp() : void
+    {
+        $this->param = new Parameter(NumPower::array([
+            [5, 4],
+            [-2, 6],
+        ]));
+
+        $this->optimizer = new Stochastic();
+    }
+
+    public function testUpdate() : void
+    {
+        $gradient = NumPower::array([
+            [2, 1],
+            [1, -2],
+        ]);
+
+        $expected = [
+            [4.98, 3.99],
+            [-2.01, 6.02],
+        ];
+
+        $this->param->update(gradient: $gradient, optimizer: $this->optimizer);
+
+        self::assertEqualsWithDelta($expected, $this->param->param()->toArray(), 1e-7);
+    }
+}