400 refactor network class to interface

CHANGELOG.md

@@ -8,6 +8,8 @@
     - Removed output layer L2 Penalty parameter from MLP Learners
     - Remove Network interface
     - RBX Serializer only tracks major library version number
+    - Convert NeuralNet classes to use NDArray instead of Matrix
+    - Turn back Network interface
 
 - 2.5.0
     - Added Vantage Point Spatial tree

src/Classifiers/LogisticRegression.php

@@ -2,6 +2,7 @@
 
 namespace Rubix\ML\Classifiers;
 
+use Rubix\ML\NeuralNet\FeedForward;
 use Rubix\ML\Online;
 use Rubix\ML\Learner;
 use Rubix\ML\Verbose;
@@ -289,7 +290,7 @@
 
         $classes = $dataset->possibleOutcomes();
 
-        $this->network = new Network(
+        $this->network = new FeedForward(
             new Placeholder1D($dataset->numFeatures()),
             [new Dense(1, $this->l2Penalty, true, new Xavier1())],
             new Binary($classes, $this->costFn),
@@ -321,7 +322,7 @@
             new DatasetIsNotEmpty($dataset),
             new SamplesAreCompatibleWithEstimator($dataset, $this),
             new LabelsAreCompatibleWithLearner($dataset, $this),
             new DatasetHasDimensionality($dataset, $this->network->input()->width()),
         ])->check();
 
         if ($this->logger) {

src/Classifiers/MultilayerPerceptron.php

@@ -2,6 +2,7 @@
 
 namespace Rubix\ML\Classifiers;
 
+use Rubix\ML\NeuralNet\FeedForward;
 use Rubix\ML\Online;
 use Rubix\ML\Learner;
 use Rubix\ML\Verbose;
@@ -370,7 +371,7 @@ public function train(Dataset $dataset) : void
 
         $hiddenLayers[] = new Dense(count($classes), 0.0, true, new Xavier1());
 
-        $this->network = new Network(
+        $this->network = new FeedForward(
             new Placeholder1D($dataset->numFeatures()),
             $hiddenLayers,
             new Multiclass($classes, $this->costFn),

src/Classifiers/SoftmaxClassifier.php

@@ -2,6 +2,7 @@
 
 namespace Rubix\ML\Classifiers;
 
+use Rubix\ML\NeuralNet\FeedForward;
 use Rubix\ML\Online;
 use Rubix\ML\Learner;
 use Rubix\ML\Verbose;
@@ -285,7 +286,7 @@ public function train(Dataset $dataset) : void
 
         $classes = $dataset->possibleOutcomes();
 
-        $this->network = new Network(
+        $this->network = new FeedForward(
             new Placeholder1D($dataset->numFeatures()),
             [new Dense(count($classes), $this->l2Penalty, true, new Xavier1())],
             new Multiclass($classes, $this->costFn),

src/Datasets/Generators/SwissRoll/SwissRoll.php

@@ -0,0 +1,188 @@
+<?php
+
+namespace Rubix\ML\Datasets\Generators\SwissRoll;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\Datasets\Generators\Generator;
+use Rubix\ML\Datasets\Labeled;
+use Rubix\ML\Exceptions\InvalidArgumentException;
+
+use function cos;
+use function sin;
+use function log;
+use function sqrt;
+use function mt_rand;
+
+use const Rubix\ML\HALF_PI;
+
+/**
+ * Swiss Roll
+ *
+ * Generate a 3-dimensional swiss roll dataset with continuous valued labels.
+ * The labels are the inputs to the swiss roll transformation and are suitable
+ * for non-linear regression problems.
+ *
+ * References:
+ * [1] S. Marsland. (2009). Machine Learning: An Algorithmic Perspective,
+ * Chapter 10.
+ *
+ * @category    Machine Learning
+ * @package     Rubix/ML
+ * @author      Andrew DalPino
+ * @author      Samuel Akopyan <leumas.a@gmail.com>
+ */
+class SwissRoll implements Generator
+{
+    /**
+     * The center vector of the swiss roll.
+     *
+     * @var list<float>
+     */
+    protected array $center;
+
+    /**
+     * The scaling factor of the swiss roll.
+     *
+     * @var float
+     */
+    protected float $scale;
+
+    /**
+     * The depth of the swiss roll i.e the scale of the y dimension.
+     *
+     * @var float
+     */
+    protected float $depth;
+
+    /**
+     * The standard deviation of the gaussian noise.
+     *
+     * @var float
+     */
+    protected float $noise;
+
+    /**
+     * @param float $x
+     * @param float $y
+     * @param float $z
+     * @param float $scale
+     * @param float $depth
+     * @param float $noise
+     * @throws InvalidArgumentException
+     */
+    public function __construct(
+        float $x = 0.0,
+        float $y = 0.0,
+        float $z = 0.0,
+        float $scale = 1.0,
+        float $depth = 21.0,
+        float $noise = 0.1
+    ) {
+        if ($scale < 0.0) {
+            throw new InvalidArgumentException('Scale must be'
+                . " greater than 0, $scale given.");
+        }
+
+        if ($depth < 0) {
+            throw new InvalidArgumentException('Depth must be'
+                . " greater than 0, $depth given.");
+        }
+
+        if ($noise < 0.0) {
+            throw new InvalidArgumentException('Noise factor cannot be less'
+                . " than 0, $noise given.");
+        }
+
+        $this->center = [$x, $y, $z];
+        $this->scale = $scale;
+        $this->depth = $depth;
+        $this->noise = $noise;
+    }
+
+    /**
+     * Return the dimensionality of the data this generates.
+     *
+     * @internal
+     *
+     * @return int<0,max>
+     */
+    public function dimensions() : int
+    {
+        return 3;
+    }
+
+    /**
+     * Generate n data points.
+     *
+     * @param int<0,max> $n
+     * @return Labeled
+     */
+    public function generate(int $n) : Labeled
+    {
+        $range = M_PI + HALF_PI;
+
+        $t = [];
+        $y = [];
+        $coords = [];
+
+        for ($i = 0; $i < $n; ++$i) {
+            $u = mt_rand() / mt_getrandmax();
+            $ti = (($u * 2.0) + 1.0) * $range;
+            $t[] = $ti;
+
+            $uy = mt_rand() / mt_getrandmax();
+            $y[] = $uy * $this->depth;
+
+            $coords[] = [
+                $ti * cos($ti),
+                $y[$i],
+                $ti * sin($ti),
+            ];
+        }
+
+        $noise = [];
+
+        if ($this->noise > 0.0) {
+            for ($i = 0; $i < $n; ++$i) {
+                $row = [];
+
+                for ($j = 0; $j < 3; ++$j) {
+                    $u1 = mt_rand() / mt_getrandmax();
+                    $u2 = mt_rand() / mt_getrandmax();
+                    $u1 = $u1 > 0.0 ? $u1 : 1e-12;
+
+                    $z0 = sqrt(-2.0 * log($u1)) * cos(2.0 * M_PI * $u2);
+
+                    $row[] = $z0 * $this->noise;
+                }
+
+                $noise[] = $row;
+            }
+        } else {
+            for ($i = 0; $i < $n; ++$i) {
+                $noise[] = [0.0, 0.0, 0.0];
+            }
+        }
+
+        $center = [];
+
+        for ($i = 0; $i < $n; ++$i) {
+            $center[] = $this->center;
+        }
+
+        $coords = NumPower::array($coords);
+        $noise = NumPower::array($noise);
+        $center = NumPower::array($center);
+
+        $samples = NumPower::add(
+            NumPower::add(
+                NumPower::multiply($coords, $this->scale),
+                $center
+            ),
+            $noise
+        );
+
+        return Labeled::quick($samples->toArray(), $t);
+    }
+}

src/NeuralNet/FeedForward.php

@@ -27,7 +27,7 @@
  * @package     Rubix/ML
  * @author      Andrew DalPino
  */
-class FeedForward extends Network
+class FeedForward implements Network
 {
     /**
      * The input layer to the network.