php-ai / php-ml

src/Phpml/Math/Matrix.php

Doc Comments +1 added lines, -1 removed lines

@@ -91,7 +91,7 @@
     }
 
     /**
-     * @param $column
+     * @param integer $column
      *
      * @return array
      *

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Math;
 
@@ -157,7 +157,7 @@ 
     public function transpose()
     {
         if ($this->rows == 1) {
-            $matrix = array_map(function ($el) {
+            $matrix = array_map(function($el) {
                 return [$el];
             }, $this->matrix[0]);
         } else {

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/LinearAlgebra/EigenvalueDecomposition.php

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/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/KernelPCA.php

Spacing +7 added lines, -7 removed lines

@@ -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/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);

src/Phpml/DimensionReduction/EigenTransformerBase.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\DimensionReduction;
 
@@ -54,7 +54,7 @@ 
     {
         $eig = new EigenvalueDecomposition($matrix);
         $eigVals = $eig->getRealEigenvalues();
-        $eigVects= $eig->getEigenvectors();
+        $eigVects = $eig->getEigenvectors();
 
         $totalEigVal = array_sum($eigVals);
         // Sort eigenvalues in descending order

src/Phpml/Classification/NaiveBayes.php

Spacing +10 added lines, -10 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification;
 
@@ -13,8 +13,8 @@ 
 {
     use Trainable, Predictable;
 
-    const CONTINUOS    = 1;
-    const NOMINAL    = 2;
+    const CONTINUOS = 1;
+    const NOMINAL = 2;
     const EPSILON = 1e-10;
 
     /**
@@ -25,7 +25,7 @@ 
     /**
      * @var array
      */
-    private $mean= [];
+    private $mean = [];
 
     /**
      * @var array
@@ -86,7 +86,7 @@ 
     private function calculateStatistics($label, $samples)
     {
         $this->std[$label] = array_fill(0, $this->featureCount, 0);
-        $this->mean[$label]= array_fill(0, $this->featureCount, 0);
+        $this->mean[$label] = array_fill(0, $this->featureCount, 0);
         $this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
         $this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
         for ($i = 0; $i < $this->featureCount; ++$i) {
@@ -100,7 +100,7 @@ 
                 $this->dataType[$label][$i] = self::NOMINAL;
                 $this->discreteProb[$label][$i] = array_count_values($values);
                 $db = &$this->discreteProb[$label][$i];
-                $db = array_map(function ($el) use ($numValues) {
+                $db = array_map(function($el) use ($numValues) {
                     return $el / $numValues;
                 }, $db);
             } else {
@@ -130,8 +130,8 @@ 
             }
             return $this->discreteProb[$label][$feature][$value];
         }
-        $std = $this->std[$label][$feature] ;
-        $mean= $this->mean[$label][$feature];
+        $std = $this->std[$label][$feature];
+        $mean = $this->mean[$label][$feature];
         // Calculate the probability density by use of normal/Gaussian distribution
         // Ref: https://en.wikipedia.org/wiki/Normal_distribution
         //
@@ -139,7 +139,7 @@ 
         // some libraries adopt taking log of calculations such as
         // scikit-learn did.
         // (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
-        $pdf  =  -0.5 * log(2.0 * pi() * $std * $std);
+        $pdf  = -0.5 * log(2.0 * pi() * $std * $std);
         $pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
         return $pdf;
     }
@@ -174,7 +174,7 @@ 
         $predictions = [];
         foreach ($this->labels as $label) {
             $p = $this->p[$label];
-            for ($i = 0; $i<$this->featureCount; ++$i) {
+            for ($i = 0; $i < $this->featureCount; ++$i) {
                 $Plf = $this->sampleProbability($sample, $i, $label);
                 $p += $Plf;
             }