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

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

src/Phpml/Math/Comparison.php

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@@ -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;
 
@@ -145,20 +145,20 @@ 
         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);
@@ -168,7 +168,7 @@ 
                 // 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));
                 };
 
@@ -192,7 +192,7 @@ 
     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/Math/LinearAlgebra/LUDecomposition.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 /**
  * @package JAMA
  *

src/Phpml/Math/Matrix.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Math;
 
@@ -126,7 +126,7 @@ 
     public function transpose() : Matrix
     {
         if ($this->rows == 1) {
-            $matrix = array_map(function ($el) {
+            $matrix = array_map(function($el) {
                 return [$el];
             }, $this->matrix[0]);
         } else {

src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php

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@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 /**
  *	Class to obtain eigenvalues and eigenvectors of a real matrix.
  *
@@ -842,7 +842,7 @@ 
 
         // Always return the eigenvectors of length 1.0
         $vectors = new Matrix($vectors);
-        $vectors = array_map(function ($vect) {
+        $vectors = array_map(function($vect) {
             $sum = 0;
             for ($i = 0; $i < count($vect); ++$i) {
                 $sum += $vect[$i] ** 2;

src/Phpml/FeatureExtraction/TfIdfTransformer.php

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@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\FeatureExtraction;
 
@@ -13,7 +13,7 @@ 
      */
     private $idf;
 
-    public function __construct(?array $samples = null)
+    public function __construct(? array $samples = null)
     {
         if ($samples) {
             $this->fit($samples);