php-ai / php-ml

src/Phpml/Math/Statistic/Gaussian.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Math\Statistic;
 
@@ -39,7 +39,7 @@ 
         // Ref: https://en.wikipedia.org/wiki/Normal_distribution
         $std2 = $this->std ** 2;
         $mean = $this->mean;
-        return exp(- (($value - $mean) ** 2) / (2 * $std2)) / sqrt(2 * $std2 * pi());
+        return exp(-(($value - $mean) ** 2) / (2 * $std2)) / sqrt(2 * $std2 * pi());
     }
 
     /**

src/Phpml/Helper/OneVsRest.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/IncrementalEstimator.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/DimensionReduction/KernelPCA.php

Indentation +3 added lines, -3 removed lines

@@ -136,9 +136,9 @@
         $N_K_N = $N->multiply($K_N);
 
         return $K->subtract($N_K)
-                 ->subtract($K_N)
-                 ->add($N_K_N)
-                 ->toArray();
+                    ->subtract($K_N)
+                    ->add($N_K_N)
+                    ->toArray();
     }
 
     /**

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@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\DimensionReduction;
 
@@ -133,7 +133,7 @@ 
      */
     protected function centerMatrix(array $matrix, int $n)
     {
-        $N = array_fill(0, $n, array_fill(0, $n, 1.0/$n));
+        $N = array_fill(0, $n, array_fill(0, $n, 1.0 / $n));
         $N = new Matrix($N, false);
         $K = new Matrix($matrix, false);
 
@@ -162,19 +162,19 @@ 
         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);
                 };
@@ -182,7 +182,7 @@ 
             case self::KERNEL_LAPLACIAN:
                 // 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));
                 };
 
@@ -216,7 +216,7 @@ 
     protected function projectSample(array $pairs)
     {
         // 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/EigenvalueDecomposition.php

Indentation +9 added lines, -9 removed lines

@@ -58,21 +58,21 @@
     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;
 

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 /**
  *
  *	Class to obtain eigenvalues and eigenvectors of a real matrix.
@@ -838,7 +838,7 @@ 
 
         // 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/LinearAlgebra/LUDecomposition.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/Math/Statistic/Covariance.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/DimensionReduction/PCA.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/DimensionReduction/LDA.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\DimensionReduction;
 
@@ -118,7 +118,7 @@ 
     protected function calculateMeans(array $data, array $classes) : array
     {
         $means = [];
-        $counts= [];
+        $counts = [];
         $overallMean = array_fill(0, count($data[0]), 0.0);
 
         foreach ($data as $index => $row) {
@@ -147,7 +147,7 @@ 
 
         // 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);