php-ai / php-ml

src/Phpml/DimensionReduction/EigenTransformerBase.php

Spacing +1 added lines, -1 removed lines

@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\DimensionReduction;
 

src/Phpml/Math/Statistic/Gaussian.php

Spacing +1 added lines, -1 removed lines

@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Math\Statistic;
 

src/Phpml/NeuralNetwork/ActivationFunction/ThresholdedReLU.php

Spacing +1 added lines, -1 removed lines

@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\NeuralNetwork\ActivationFunction;
 

src/Phpml/Math/Comparison.php

Spacing +1 added lines, -1 removed lines

@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Math;
 

src/Phpml/DimensionReduction/KernelPCA.php

Spacing +6 added lines, -6 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\DimensionReduction;
 
@@ -161,20 +161,20 @@ 
         switch ($this->kernel) {
             case self::KERNEL_LINEAR:
                 // k(x,y) = xT.y
-                return function ($x, $y) {
+                return function($x, $y) {
                     return Matrix::dot($x, $y)[0];
                 };
             case self::KERNEL_RBF:
                 // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance
                 $dist = new Euclidean();
 
-                return function ($x, $y) use ($dist) {
+                return function($x, $y) use ($dist) {
                     return exp(-$this->gamma * $dist->sqDistance($x, $y));
                 };
 
             case self::KERNEL_SIGMOID:
                 // k(x,y)=tanh(γ.xT.y+c0) where c0=1
-                return function ($x, $y) {
+                return function($x, $y) {
                     $res = Matrix::dot($x, $y)[0] + 1.0;
 
                     return tanh($this->gamma * $res);
@@ -184,7 +184,7 @@ 
                 // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance
                 $dist = new Manhattan();
 
-                return function ($x, $y) use ($dist) {
+                return function($x, $y) use ($dist) {
                     return exp(-$this->gamma * $dist->distance($x, $y));
                 };
 
@@ -218,7 +218,7 @@ 
     protected function projectSample(array $pairs): array
     {
         // Normalize eigenvectors by eig = eigVectors / eigValues
-        $func = function ($eigVal, $eigVect) {
+        $func = function($eigVal, $eigVect) {
             $m = new Matrix($eigVect, false);
             $a = $m->divideByScalar($eigVal)->toArray();
 

src/Phpml/FeatureExtraction/TokenCountVectorizer.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\FeatureExtraction;
 
@@ -73,7 +73,7 @@ 
         }
     }
 
-    private function transformSample(string &$sample): void
+    private function transformSample(string & $sample): void
     {
         $counts = [];
         $tokens = $this->tokenizer->tokenize($sample);

src/Phpml/NeuralNetwork/Network/MultilayerPerceptron.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\NeuralNetwork\Network;
 
@@ -132,7 +132,7 @@ 
         $this->addLayer(new Layer($nodes, Input::class));
     }
 
-    private function addNeuronLayers(array $layers, ?ActivationFunction $activationFunction = null): void
+    private function addNeuronLayers(array $layers, ?ActivationFunction $activationFunction = null) : void
     {
         foreach ($layers as $neurons) {
             $this->addLayer(new Layer($neurons, Neuron::class, $activationFunction));

src/Phpml/SupportVectorMachine/SupportVectorMachine.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\SupportVectorMachine;
 
@@ -234,7 +234,7 @@ 
         );
     }
 
-    private function ensureDirectorySeparator(string &$path): void
+    private function ensureDirectorySeparator(string & $path): void
     {
         if (substr($path, -1) !== DIRECTORY_SEPARATOR) {
             $path .= DIRECTORY_SEPARATOR;

src/Phpml/CrossValidation/Split.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\CrossValidation;
 
@@ -62,7 +62,7 @@ 
 
     abstract protected function splitDataset(Dataset $dataset, float $testSize);
 
-    protected function seedGenerator(?int $seed = null): void
+    protected function seedGenerator(?int $seed = null) : void
     {
         if ($seed === null) {
             mt_srand();