396 convert regressors to num power

.github/workflows/ci.yml

@@ -78,9 +78,10 @@ jobs:
         run: composer phplint
 
       - name: Static Analysis
-        run: composer analyze
+        run: composer analyze-ci
 
       - name: Unit Tests
+        #run: vendor/bin/phpunit --display-warning --display-deprecations --display-notices --testsuite="Anomaly Detectors,Backends,Base,Classifiers,Clusterers,Cross Validation,Datasets,Extractors,Graph,Helpers,Kernels,Loggers,NeuralNet,Persisters,Regressors,Serializers,Specifications,Strategies,Tokenizers,Transformers"
         run: composer test
 
       - name: Check Coding Style

.php-cs-fixer.dist.php

@@ -58,6 +58,7 @@
             ],
             'native_function_casing' => true,
             'native_type_declaration_casing' => true,
+            'new_expression_parentheses' => false,
             'new_with_parentheses' => true,
             'no_alternative_syntax' => true,
             'no_blank_lines_after_class_opening' => true,

CHANGELOG.md

@@ -9,6 +9,10 @@
     - RBX Serializer only tracks major library version number
     - Convert NeuralNet classes to use NDArray instead of Matrix
     - Converted Network back from a class to an interface
+    - Added array_pack() function to replace array_map('array_values', $samples)
+    - Converted Regressor classes to use NDArray instead of Matrix
+    - Added benchmark tests for Activation Functions, based on NDArray
+    - Added benchmark tests for Regressors, based on NDArray
 
 - 2.5.0
     - Added Vantage Point Spatial tree

benchmarks/NeuralNet/ActivationFunctions/ELUBench.php

@@ -0,0 +1,58 @@
+<?php
+
+namespace Rubix\ML\Benchmarks\NeuralNet\ActivationFunctions;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\NeuralNet\ActivationFunctions\ELU;
+
+/**
+ * @Groups({"ActivationFunctions"})
+ * @BeforeMethods({"setUp"})
+ */
+class ELUBench
+{
+    /**
+     * @var NDArray
+     */
+    protected NDArray $z;
+
+    /**
+     * @var NDArray
+     */
+    protected NDArray $computed;
+
+    /**
+     * @var ELU
+     */
+    protected ELU $activationFn;
+
+    public function setUp() : void
+    {
+        $this->z = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->computed = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->activationFn = new ELU();
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function activate() : void
+    {
+        $this->activationFn->activate($this->z);
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function differentiate() : void
+    {
+        $this->activationFn->differentiate($this->z, $this->computed);
+    }
+}

benchmarks/NeuralNet/ActivationFunctions/GELUBench.php

@@ -0,0 +1,58 @@
+<?php
+
+namespace Rubix\ML\Benchmarks\NeuralNet\ActivationFunctions;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\NeuralNet\ActivationFunctions\GELU;
+
+/**
+ * @Groups({"ActivationFunctions"})
+ * @BeforeMethods({"setUp"})
+ */
+class GELUBench
+{
+    /**
+     * @var NDArray
+     */
+    protected NDArray $z;
+
+    /**
+     * @var NDArray
+     */
+    protected NDArray $computed;
+
+    /**
+     * @var GELU
+     */
+    protected GELU $activationFn;
+
+    public function setUp() : void
+    {
+        $this->z = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->computed = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->activationFn = new GELU();
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function activate() : void
+    {
+        $this->activationFn->activate($this->z);
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function differentiate() : void
+    {
+        $this->activationFn->differentiate($this->z);
+    }
+}

benchmarks/NeuralNet/ActivationFunctions/HyperbolicTangentBench.php

@@ -0,0 +1,58 @@
+<?php
+
+namespace Rubix\ML\Benchmarks\NeuralNet\ActivationFunctions;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\NeuralNet\ActivationFunctions\HyperbolicTangent;
+
+/**
+ * @Groups({"ActivationFunctions"})
+ * @BeforeMethods({"setUp"})
+ */
+class HyperbolicTangentBench
+{
+    /**
+     * @var NDArray
+     */
+    protected NDArray $z;
+
+    /**
+     * @var NDArray
+     */
+    protected NDArray $computed;
+
+    /**
+     * @var HyperbolicTangent
+     */
+    protected HyperbolicTangent $activationFn;
+
+    public function setUp() : void
+    {
+        $this->z = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->computed = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->activationFn = new HyperbolicTangent();
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function activate() : void
+    {
+        $this->activationFn->activate($this->z);
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function differentiate() : void
+    {
+        $this->activationFn->differentiate($this->computed);
+    }
+}

benchmarks/NeuralNet/ActivationFunctions/LeakyReLUBench.php

@@ -0,0 +1,58 @@
+<?php
+
+namespace Rubix\ML\Benchmarks\NeuralNet\ActivationFunctions;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\NeuralNet\ActivationFunctions\LeakyReLU;
+
+/**
+ * @Groups({"ActivationFunctions"})
+ * @BeforeMethods({"setUp"})
+ */
+class LeakyReLUBench
+{
+    /**
+     * @var NDArray
+     */
+    protected NDArray $z;
+
+    /**
+     * @var NDArray
+     */
+    protected NDArray $computed;
+
+    /**
+     * @var LeakyReLU
+     */
+    protected LeakyReLU $activationFn;
+
+    public function setUp() : void
+    {
+        $this->z = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->computed = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->activationFn = new LeakyReLU();
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function activate() : void
+    {
+        $this->activationFn->activate($this->z);
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function differentiate() : void
+    {
+        $this->activationFn->differentiate($this->z);
+    }
+}

benchmarks/NeuralNet/ActivationFunctions/ReLUBench.php

@@ -0,0 +1,58 @@
+<?php
+
+namespace Rubix\ML\Benchmarks\NeuralNet\ActivationFunctions;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\NeuralNet\ActivationFunctions\ReLU;
+
+/**
+ * @Groups({"ActivationFunctions"})
+ * @BeforeMethods({"setUp"})
+ */
+class ReLUBench
+{
+    /**
+     * @var NDArray
+     */
+    protected NDArray $z;
+
+    /**
+     * @var NDArray
+     */
+    protected NDArray $computed;
+
+    /**
+     * @var ReLU
+     */
+    protected ReLU $activationFn;
+
+    public function setUp() : void
+    {
+        $this->z = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->computed = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->activationFn = new ReLU();
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function activate() : void
+    {
+        $this->activationFn->activate($this->z);
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function differentiate() : void
+    {
+        $this->activationFn->differentiate($this->z);
+    }
+}

benchmarks/NeuralNet/ActivationFunctions/SELUBench.php

@@ -0,0 +1,58 @@
+<?php
+
+namespace Rubix\ML\Benchmarks\NeuralNet\ActivationFunctions;
+
+use NDArray;
+use NumPower;
+use Rubix\ML\NeuralNet\ActivationFunctions\SELU;
+
+/**
+ * @Groups({"ActivationFunctions"})
+ * @BeforeMethods({"setUp"})
+ */
+class SELUBench
+{
+    /**
+     * @var NDArray
+     */
+    protected NDArray $z;
+
+    /**
+     * @var NDArray
+     */
+    protected NDArray $computed;
+
+    /**
+     * @var SELU
+     */
+    protected SELU $activationFn;
+
+    public function setUp() : void
+    {
+        $this->z = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->computed = NumPower::uniform([500, 500], low: -1.0, high: 1.0);
+
+        $this->activationFn = new SELU();
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function activate() : void
+    {
+        $this->activationFn->activate($this->z);
+    }
+
+    /**
+     * @Subject
+     * @Iterations(3)
+     * @OutputTimeUnit("milliseconds", precision=3)
+     */
+    public function differentiate() : void
+    {
+        $this->activationFn->differentiate($this->z);
+    }
+}