php-ai / php-ml

src/Phpml/Classification/Ensemble/RandomForest.php

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

src/Phpml/Classification/Ensemble/AdaBoost.php

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

src/Phpml/Classification/Linear/Adaline.php

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@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -64,7 +64,7 @@ 
     protected function runTraining(array $samples, array $targets)
     {
         // The cost function is the sum of squares
-        $callback = function ($weights, $sample, $target) {
+        $callback = function($weights, $sample, $target) {
             $this->weights = $weights;
 
             $output = $this->output($sample);

src/Phpml/Classification/Linear/LogisticRegression.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -193,7 +193,7 @@ 
                  * The gradient of the cost function to be used with gradient descent:
                  *		∇J(x) = -(y - h(x)) = (h(x) - y)
                  */
-                $callback = function ($weights, $sample, $y) use ($penalty) {
+                $callback = function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 
@@ -224,7 +224,7 @@ 
                  * The gradient of the cost function:
                  *		∇J(x) = -(h(x) - y) . h(x) . (1 - h(x))
                  */
-                $callback = function ($weights, $sample, $y) use ($penalty) {
+                $callback = function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 

Indentation -1 removed lines

@@ -256,7 +256,6 @@
      *
      * The probability is simply taken as the distance of the sample
      * to the decision plane.
-
      * @param mixed $label
      */
     protected function predictProbability(array $sample, $label) : float

src/Phpml/DimensionReduction/LDA.php

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@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\DimensionReduction;
 
@@ -146,7 +146,7 @@ 
 
         // Calculate overall mean of the dataset for each column
         $numElements = array_sum($counts);
-        $map = function ($el) use ($numElements) {
+        $map = function($el) use ($numElements) {
             return $el / $numElements;
         };
         $this->overallMean = array_map($map, $overallMean);

src/Phpml/DimensionReduction/EigenTransformerBase.php

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

src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php

Indentation +12 added lines, -12 removed lines

@@ -65,24 +65,24 @@
     private $V = [];
 
     /**
-    *	Array for internal storage of nonsymmetric Hessenberg form.
-    *
-    *	@var array
-    */
+     *	Array for internal storage of nonsymmetric Hessenberg form.
+     *
+     *	@var array
+     */
     private $H = [];
 
     /**
-    *	Working storage for nonsymmetric algorithm.
-    *
-    *	@var array
-    */
+     *	Working storage for nonsymmetric algorithm.
+     *
+     *	@var array
+     */
     private $ort;
 
     /**
-    *	Used for complex scalar division.
-    *
-    *	@var float
-    */
+     *	Used for complex scalar division.
+     *
+     *	@var float
+     */
     private $cdivr;
     private $cdivi;
 

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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/Math/Statistic/Gaussian.php

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@@ -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;