php-cs-fixer - more rules (#118)

* Add new cs-fixer rules and run them

* Do not align double arrows/equals

.php_cs

@@ -3,11 +3,25 @@
 return PhpCsFixer\Config::create()
     ->setRules([
         '@PSR2' => true,
-        'declare_strict_types' => true,
         'array_syntax' => ['syntax' => 'short'],
+        'binary_operator_spaces' => ['align_double_arrow' => false, 'align_equals' => false],
         'blank_line_after_opening_tag' => true,
+        'blank_line_before_return' => true,
+        'cast_spaces' => true,
+        'concat_space' => ['spacing' => 'none'],
+        'declare_strict_types' => true,
+        'method_separation' => true,
+        'no_blank_lines_after_class_opening' => true,
+        'no_spaces_around_offset' => ['positions' => ['inside', 'outside']],
+        'no_unneeded_control_parentheses' => true,
+        'no_unused_imports' => true,
+        'phpdoc_align' => true,
+        'phpdoc_no_access' => true,
+        'phpdoc_separation' => true,
+        'pre_increment' => true,
+        'single_quote' => true,
+        'trim_array_spaces' => true,
         'single_blank_line_before_namespace' => true,
-        'no_unused_imports' => true
     ])
     ->setFinder(
         PhpCsFixer\Finder::create()

src/Phpml/Classification/DecisionTree/DecisionTreeLeaf.php

@@ -71,6 +71,7 @@ class DecisionTreeLeaf
 
     /**
      * @param array $record
+     *
      * @return bool
      */
     public function evaluate($record)
@@ -82,6 +83,7 @@ class DecisionTreeLeaf
             $value = $this->numericValue;
             $recordField = strval($recordField);
             eval("\$result = $recordField $op $value;");
+
             return $result;
         }
         
@@ -122,6 +124,7 @@ class DecisionTreeLeaf
      * Returns HTML representation of the node including children nodes
      *
      * @param $columnNames
+     *
      * @return string
      */
     public function getHTML($columnNames = null)
@@ -135,29 +138,34 @@ class DecisionTreeLeaf
             } else {
                 $col = "col_$this->columnIndex";
             }
-            if (!preg_match("/^[<>=]{1,2}/", $value)) {
+            if (!preg_match('/^[<>=]{1,2}/', $value)) {
                 $value = "=$value";
             }
             $value = "<b>$col $value</b><br>Gini: ".number_format($this->giniIndex, 2);
         }
-        $str = "<table ><tr><td colspan=3 align=center style='border:1px solid;'>
+
-				$value</td></tr>";
+        $str = "<table ><tr><td colspan=3 align=center style='border:1px solid;'>$value</td></tr>";
+
         if ($this->leftLeaf || $this->rightLeaf) {
             $str .= '<tr>';
             if ($this->leftLeaf) {
-                $str .="<td valign=top><b>| Yes</b><br>" . $this->leftLeaf->getHTML($columnNames) . "</td>";
+                $str .= '<td valign=top><b>| Yes</b><br>'.$this->leftLeaf->getHTML($columnNames).'</td>';
             } else {
                 $str .= '<td></td>';
             }
+
             $str .= '<td>&nbsp;</td>';
             if ($this->rightLeaf) {
-                $str .="<td valign=top align=right><b>No |</b><br>" . $this->rightLeaf->getHTML($columnNames) . "</td>";
+                $str .= '<td valign=top align=right><b>No |</b><br>'.$this->rightLeaf->getHTML($columnNames).'</td>';
             } else {
                 $str .= '<td></td>';
             }
+
             $str .= '</tr>';
         }
+
         $str .= '</table>';
+
         return $str;
     }
 

src/Phpml/Classification/Ensemble/AdaBoost.php

@@ -18,6 +18,7 @@ class AdaBoost implements Classifier
 
     /**
      * Actual labels given in the targets array
+     *
      * @var array
      */
     protected $labels = [];
@@ -105,7 +106,7 @@ class AdaBoost implements Classifier
         // Initialize usual variables
         $this->labels = array_keys(array_count_values($targets));
         if (count($this->labels) != 2) {
-            throw new \Exception("AdaBoost is a binary classifier and can classify between two classes only");
+            throw new \Exception('AdaBoost is a binary classifier and can classify between two classes only');
         }
 
         // Set all target values to either -1 or 1
@@ -220,6 +221,7 @@ class AdaBoost implements Classifier
      * Calculates alpha of a classifier
      *
      * @param float $errorRate
+     *
      * @return float
      */
     protected function calculateAlpha(float $errorRate)
@@ -227,6 +229,7 @@ class AdaBoost implements Classifier
         if ($errorRate == 0) {
             $errorRate = 1e-10;
         }
+
         return 0.5 * log((1 - $errorRate) / $errorRate);
     }
 
@@ -254,6 +257,7 @@ class AdaBoost implements Classifier
 
     /**
      * @param array $sample
+     *
      * @return mixed
      */
     public function predictSample(array $sample)

src/Phpml/Classification/Ensemble/Bagging.php

@@ -84,10 +84,11 @@ class Bagging implements Classifier
     public function setSubsetRatio(float $ratio)
     {
         if ($ratio < 0.1 || $ratio > 1.0) {
-            throw new \Exception("Subset ratio should be between 0.1 and 1.0");
+            throw new \Exception('Subset ratio should be between 0.1 and 1.0');
         }
 
         $this->subsetRatio = $ratio;
+
         return $this;
     }
 
@@ -135,6 +136,7 @@ class Bagging implements Classifier
 
     /**
      * @param int $index
+     *
      * @return array
      */
     protected function getRandomSubset(int $index)
@@ -168,6 +170,7 @@ class Bagging implements Classifier
 
             $classifiers[] = $this->initSingleClassifier($obj);
         }
+
         return $classifiers;
     }
 
@@ -183,6 +186,7 @@ class Bagging implements Classifier
 
     /**
      * @param array $sample
+     *
      * @return mixed
      */
     protected function predictSample(array $sample)
@@ -196,6 +200,7 @@ class Bagging implements Classifier
         $counts = array_count_values($predictions);
         arsort($counts);
         reset($counts);
+
         return key($counts);
     }
 }

src/Phpml/Classification/Ensemble/RandomForest.php

@@ -50,7 +50,7 @@ class RandomForest extends Bagging
     public function setFeatureSubsetRatio($ratio)
     {
         if (is_float($ratio) && ($ratio < 0.1 || $ratio > 1.0)) {
-            throw new \Exception("When a float given, feature subset ratio should be between 0.1 and 1.0");
+            throw new \Exception('When a float given, feature subset ratio should be between 0.1 and 1.0');
         }
 
         if (is_string($ratio) && $ratio != 'sqrt' && $ratio != 'log') {
@@ -58,6 +58,7 @@ class RandomForest extends Bagging
         }
 
         $this->featureSubsetRatio = $ratio;
+
         return $this;
     }
 
@@ -74,7 +75,7 @@ class RandomForest extends Bagging
     public function setClassifer(string $classifier, array $classifierOptions = [])
     {
         if ($classifier != DecisionTree::class) {
-            throw new \Exception("RandomForest can only use DecisionTree as base classifier");
+            throw new \Exception('RandomForest can only use DecisionTree as base classifier');
         }
 
         return parent::setClassifer($classifier, $classifierOptions);
@@ -120,6 +121,7 @@ class RandomForest extends Bagging
      * when trying to print some information about the trees such as feature importances
      *
      * @param array $names
+     *
      * @return $this
      */
     public function setColumnNames(array $names)

src/Phpml/Classification/Linear/Adaline.php

@@ -46,7 +46,7 @@ class Adaline extends Perceptron
         int $trainingType = self::BATCH_TRAINING
     ) {
         if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) {
-            throw new \Exception("Adaline can only be trained with batch and online/stochastic gradient descent algorithm");
+            throw new \Exception('Adaline can only be trained with batch and online/stochastic gradient descent algorithm');
         }
 
         $this->trainingType = $trainingType;

src/Phpml/Classification/Linear/DecisionStump.php

@@ -106,7 +106,7 @@ class DecisionStump extends WeightedClassifier
         if ($this->weights) {
             $numWeights = count($this->weights);
             if ($numWeights != count($samples)) {
-                throw new \Exception("Number of sample weights does not match with number of samples");
+                throw new \Exception('Number of sample weights does not match with number of samples');
             }
         } else {
             $this->weights = array_fill(0, count($samples), 1);
@@ -236,7 +236,6 @@ class DecisionStump extends WeightedClassifier
         return $split;
     }
 
-
     /**
      *
      * @param mixed  $leftValue
@@ -358,7 +357,7 @@ class DecisionStump extends WeightedClassifier
     public function __toString()
     {
         return "IF $this->column $this->operator $this->value ".
-            "THEN " . $this->binaryLabels[0] . " ".
+            'THEN '.$this->binaryLabels[0].' '.
-            "ELSE " . $this->binaryLabels[1];
+            'ELSE '.$this->binaryLabels[1];
     }
 }

src/Phpml/Classification/Linear/LogisticRegression.php

@@ -76,9 +76,9 @@ class LogisticRegression extends Adaline
     ) {
         $trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
         if (!in_array($trainingType, $trainingTypes)) {
-            throw new \Exception("Logistic regression can only be trained with " .
+            throw new \Exception('Logistic regression can only be trained with '.
-                "batch (gradient descent), online (stochastic gradient descent) " .
+                'batch (gradient descent), online (stochastic gradient descent) '.
-                "or conjugate batch (conjugate gradients) algorithms");
+                'or conjugate batch (conjugate gradients) algorithms');
         }
 
         if (!in_array($cost, ['log', 'sse'])) {
@@ -290,6 +290,7 @@ class LogisticRegression extends Adaline
 
         if (strval($predicted) == strval($label)) {
             $sample = $this->checkNormalizedSample($sample);
+
             return abs($this->output($sample) - 0.5);
         }
 

src/Phpml/Classification/Linear/Perceptron.php

@@ -74,11 +74,11 @@ class Perceptron implements Classifier, IncrementalEstimator
     public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true)
     {
         if ($learningRate <= 0.0 || $learningRate > 1.0) {
-            throw new \Exception("Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)");
+            throw new \Exception('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
         }
 
         if ($maxIterations <= 0) {
-            throw new \Exception("Maximum number of iterations must be an integer greater than 0");
+            throw new \Exception('Maximum number of iterations must be an integer greater than 0');
         }
 
         if ($normalizeInputs) {
@@ -231,6 +231,7 @@ class Perceptron implements Classifier, IncrementalEstimator
      * Calculates net output of the network as a float value for the given input
      *
      * @param array $sample
+     *
      * @return int
      */
     protected function output(array $sample)
@@ -251,6 +252,7 @@ class Perceptron implements Classifier, IncrementalEstimator
      * Returns the class value (either -1 or 1) for the given input
      *
      * @param array $sample
+     *
      * @return int
      */
     protected function outputClass(array $sample)
@@ -275,6 +277,7 @@ class Perceptron implements Classifier, IncrementalEstimator
 
         if (strval($predicted) == strval($label)) {
             $sample = $this->checkNormalizedSample($sample);
+
             return abs($this->output($sample));
         }
 

src/Phpml/Classification/MLPClassifier.php

@@ -9,7 +9,6 @@ use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
 
 class MLPClassifier extends MultilayerPerceptron implements Classifier
 {
-
     /**
      * @param mixed $target
      *
@@ -22,6 +21,7 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
         if (!in_array($target, $this->classes)) {
             throw InvalidArgumentException::invalidTarget($target);
         }
+
         return array_search($target, $this->classes);
     }
 
@@ -42,6 +42,7 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
                 $max = $value;
             }
         }
+
         return $this->classes[$predictedClass];
     }
 

src/Phpml/Classification/NaiveBayes.php

@@ -80,6 +80,7 @@ class NaiveBayes implements Classifier
     /**
      * Calculates vital statistics for each label & feature. Stores these
      * values in private array in order to avoid repeated calculation
+     *
      * @param string $label
      * @param array  $samples
      */
@@ -128,6 +129,7 @@ class NaiveBayes implements Classifier
                 $this->discreteProb[$label][$feature][$value] == 0) {
                 return self::EPSILON;
             }
+
             return $this->discreteProb[$label][$feature][$value];
         }
         $std = $this->std[$label][$feature] ;
@@ -141,6 +143,7 @@ class NaiveBayes implements Classifier
         // (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
         $pdf = -0.5 * log(2.0 * pi() * $std * $std);
         $pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
+
         return $pdf;
     }
 
@@ -159,11 +162,13 @@ class NaiveBayes implements Classifier
                 $samples[] = $this->samples[$i];
             }
         }
+
         return $samples;
     }
 
     /**
      * @param array $sample
+     *
      * @return mixed
      */
     protected function predictSample(array $sample)
@@ -183,6 +188,7 @@ class NaiveBayes implements Classifier
 
         arsort($predictions, SORT_NUMERIC);
         reset($predictions);
+
         return key($predictions);
     }
 }

src/Phpml/Clustering/FuzzyCMeans.php

@@ -159,6 +159,7 @@ class FuzzyCMeans implements Clusterer
      *
      * @param int $row
      * @param int $col
+     *
      * @return float
      */
     protected function getDistanceCalc(int $row, int $col)
@@ -179,6 +180,7 @@ class FuzzyCMeans implements Clusterer
             $val = pow($dist1 / $dist2, 2.0 / ($this->fuzziness - 1));
             $sum += $val;
         }
+
         return $sum;
     }
 
@@ -212,6 +214,7 @@ class FuzzyCMeans implements Clusterer
 
     /**
      * @param array|Point[] $samples
+     *
      * @return array
      */
     public function cluster(array $samples)

src/Phpml/DimensionReduction/KernelPCA.php

@@ -55,7 +55,7 @@ class KernelPCA extends PCA
     {
         $availableKernels = [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN, self::KERNEL_LINEAR];
         if (!in_array($kernel, $availableKernels)) {
-            throw new \Exception("KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian");
+            throw new \Exception('KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian');
         }
 
         parent::__construct($totalVariance, $numFeatures);
@@ -168,6 +168,7 @@ class KernelPCA extends PCA
             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 exp(-$this->gamma * $dist->sqDistance($x, $y));
                 };
@@ -176,12 +177,14 @@ class KernelPCA extends PCA
                 // k(x,y)=tanh(γ.xT.y+c0) where c0=1
                 return function ($x, $y) {
                     $res = Matrix::dot($x, $y)[0] + 1.0;
+
                     return tanh($this->gamma * $res);
                 };
 
             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 exp(-$this->gamma * $dist->distance($x, $y));
                 };
@@ -241,11 +244,11 @@ class KernelPCA extends PCA
     public function transform(array $sample)
     {
         if (!$this->fit) {
-            throw new \Exception("KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first");
+            throw new \Exception('KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first');
         }
 
         if (is_array($sample[0])) {
-            throw new \Exception("KernelPCA::transform() accepts only one-dimensional arrays");
+            throw new \Exception('KernelPCA::transform() accepts only one-dimensional arrays');
         }
 
         $pairs = $this->getDistancePairs($sample);

src/Phpml/DimensionReduction/LDA.php

@@ -50,13 +50,13 @@ class LDA extends EigenTransformerBase
     public function __construct($totalVariance = null, $numFeatures = null)
     {
         if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
-            throw new \Exception("Total variance can be a value between 0.1 and 0.99");
+            throw new \Exception('Total variance can be a value between 0.1 and 0.99');
         }
         if ($numFeatures !== null && $numFeatures <= 0) {
-            throw new \Exception("Number of features to be preserved should be greater than 0");
+            throw new \Exception('Number of features to be preserved should be greater than 0');
         }
         if ($totalVariance !== null && $numFeatures !== null) {
-            throw new \Exception("Either totalVariance or numFeatures should be specified in order to run the algorithm");
+            throw new \Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
         }
 
         if ($numFeatures !== null) {
@@ -105,7 +105,6 @@ class LDA extends EigenTransformerBase
         return array_keys($counts);
     }
 
-
     /**
      * Calculates mean of each column for each class and returns
      * n by m matrix where n is number of labels and m is number of columns
@@ -156,7 +155,6 @@ class LDA extends EigenTransformerBase
         return $means;
     }
 
-
     /**
      * Returns in-class scatter matrix for each class, which
      * is a n by m matrix where n is number of classes and
@@ -237,7 +235,7 @@ class LDA extends EigenTransformerBase
     public function transform(array $sample)
     {
         if (!$this->fit) {
-            throw new \Exception("LDA has not been fitted with respect to original dataset, please run LDA::fit() first");
+            throw new \Exception('LDA has not been fitted with respect to original dataset, please run LDA::fit() first');
         }
 
         if (!is_array($sample[0])) {

src/Phpml/DimensionReduction/PCA.php

@@ -35,13 +35,13 @@ class PCA extends EigenTransformerBase
     public function __construct($totalVariance = null, $numFeatures = null)
     {
         if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
-            throw new \Exception("Total variance can be a value between 0.1 and 0.99");
+            throw new \Exception('Total variance can be a value between 0.1 and 0.99');
         }
         if ($numFeatures !== null && $numFeatures <= 0) {
-            throw new \Exception("Number of features to be preserved should be greater than 0");
+            throw new \Exception('Number of features to be preserved should be greater than 0');
         }
         if ($totalVariance !== null && $numFeatures !== null) {
-            throw new \Exception("Either totalVariance or numFeatures should be specified in order to run the algorithm");
+            throw new \Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
         }
 
         if ($numFeatures !== null) {
@@ -129,7 +129,7 @@ class PCA extends EigenTransformerBase
     public function transform(array $sample)
     {
         if (!$this->fit) {
-            throw new \Exception("PCA has not been fitted with respect to original dataset, please run PCA::fit() first");
+            throw new \Exception('PCA has not been fitted with respect to original dataset, please run PCA::fit() first');
         }
 
         if (!is_array($sample[0])) {

src/Phpml/Helper/OneVsRest.php

@@ -109,6 +109,7 @@ trait OneVsRest
         // multiple instances of this classifier
         $classifier = clone $this;
         $classifier->reset();
+
         return $classifier;
     }
 
@@ -121,6 +122,7 @@ trait OneVsRest
      *
      * @param array $targets
      * @param mixed $label
+     *
      * @return array Binarized targets and target's labels
      */
     private function binarizeTargets($targets, $label)
@@ -131,10 +133,10 @@ trait OneVsRest
         }
 
         $labels = [$label, $notLabel];
+
         return [$targets, $labels];
     }
 
-
     /**
      * @param array $sample
      *
@@ -153,6 +155,7 @@ trait OneVsRest
         }
 
         arsort($probs, SORT_NUMERIC);
+
         return key($probs);
     }
 

src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php

@@ -20,10 +20,12 @@ declare(strict_types=1);
  *
  *	@author  Paul Meagher
  *	@license PHP v3.0
+ *
  *	@version 1.1
  *
  *  Slightly changed to adapt the original code to PHP-ML library
  *  @date 2017/04/11
+ *
  *  @author Mustafa Karabulut
  */
 
@@ -35,18 +37,21 @@ class EigenvalueDecomposition
 {
     /**
      *	Row and column dimension (square matrix).
+     *
      *	@var int
      */
     private $n;
 
     /**
      *	Internal symmetry flag.
+     *
      *	@var bool
      */
     private $symmetric;
 
     /**
      *	Arrays for internal storage of eigenvalues.
+     *
      *	@var array
      */
     private $d = [];
@@ -54,24 +59,28 @@ class EigenvalueDecomposition
 
     /**
      *	Array for internal storage of eigenvectors.
+     *
      *	@var array
      */
     private $V = [];
 
     /**
     *	Array for internal storage of nonsymmetric Hessenberg form.
+    *
     *	@var array
     */
     private $H = [];
 
     /**
     *	Working storage for nonsymmetric algorithm.
+    *
     *	@var array
     */
     private $ort;
 
     /**
     *	Used for complex scalar division.
+    *
     *	@var float
     */
     private $cdivr;
@@ -222,7 +231,6 @@ class EigenvalueDecomposition
         $this->e[0] = 0.0;
     }
 
-
     /**
      *	Symmetric tridiagonal QL algorithm.
      *
@@ -330,7 +338,6 @@ class EigenvalueDecomposition
         }
     }
 
-
     /**
      *	Nonsymmetric reduction to Hessenberg form.
      *
@@ -823,12 +830,11 @@ class EigenvalueDecomposition
                 $this->V[$i][$j] = $z;
             }
         }
-    } // end hqr2
+    }
 
     /**
      * Return the eigenvector matrix
      *
-     * @access public
      *
      * @return array
      */
@@ -899,4 +905,4 @@ class EigenvalueDecomposition
 
         return $D;
     }
-}    //	class EigenvalueDecomposition
+}

src/Phpml/Math/LinearAlgebra/LUDecomposition.php

@@ -17,11 +17,14 @@ declare(strict_types=1);
  *	@author Paul Meagher
  *	@author Bartosz Matosiuk
  *	@author Michael Bommarito
+ *
  *	@version 1.1
+ *
  *	@license PHP v3.0
  *
  *  Slightly changed to adapt the original code to PHP-ML library
  *  @date 2017/04/24
+ *
  *  @author Mustafa Karabulut
  */
 
@@ -34,35 +37,39 @@ class LUDecomposition
 {
     /**
      *	Decomposition storage
+     *
      *	@var array
      */
     private $LU = [];
 
     /**
      *	Row dimension.
+     *
      *	@var int
      */
     private $m;
 
     /**
      *	Column dimension.
+     *
      *	@var int
      */
     private $n;
 
     /**
      *	Pivot sign.
+     *
      *	@var int
      */
     private $pivsign;
 
     /**
      *	Internal storage of pivot vector.
+     *
      *	@var array
      */
     private $piv = [];
 
-
     /**
      * Constructs Structure to access L, U and piv.
      *
@@ -128,8 +135,7 @@ class LUDecomposition
                 }
             }
         }
-    }    //	function __construct()
+    }
-
 
     /**
      * Get lower triangular factor.
@@ -150,9 +156,9 @@ class LUDecomposition
                 }
             }
         }
-        return new Matrix($L);
-    }    //	function getL()
 
+        return new Matrix($L);
+    }
 
     /**
      * Get upper triangular factor.
@@ -171,9 +177,9 @@ class LUDecomposition
                 }
             }
         }
-        return new Matrix($U);
-    }    //	function getU()
 
+        return new Matrix($U);
+    }
 
     /**
      * Return pivot permutation vector.
@@ -183,8 +189,7 @@ class LUDecomposition
     public function getPivot()
     {
         return $this->piv;
-    }    //	function getPivot()
+    }
-
 
     /**
      * Alias for getPivot
@@ -194,8 +199,7 @@ class LUDecomposition
     public function getDoublePivot()
     {
         return $this->getPivot();
-    }    //	function getDoublePivot()
+    }
-
 
     /**
      * Is the matrix nonsingular?
@@ -211,8 +215,7 @@ class LUDecomposition
         }
 
         return true;
-    }    //	function isNonsingular()
+    }
-
 
     /**
      * Count determinants
@@ -233,8 +236,7 @@ class LUDecomposition
         }
 
         return $d;
-    }    //	function det()
+    }
-
 
     /**
      * Solve A*X = B
@@ -277,8 +279,9 @@ class LUDecomposition
                 }
             }
         }
+
         return $X;
-    }    //	function solve()
+    }
 
     /**
      * @param array $matrix
@@ -302,4 +305,4 @@ class LUDecomposition
 
         return $R;
     }
-}    //	class LUDecomposition
+}

src/Phpml/Math/Matrix.php

@@ -122,7 +122,6 @@ class Matrix
         return array_column($this->matrix, $column);
     }
 
-
     /**
      * @return float|int
      *

src/Phpml/Math/Statistic/Covariance.php

@@ -80,7 +80,7 @@ class Covariance
         }
 
         if ($i < 0 || $k < 0 || $i >= $n || $k >= $n) {
-            throw new \Exception("Given indices i and k do not match with the dimensionality of data");
+            throw new \Exception('Given indices i and k do not match with the dimensionality of data');
         }
 
         if ($meanX === null || $meanY === null) {

src/Phpml/Math/Statistic/Gaussian.php

@@ -39,6 +39,7 @@ class Gaussian
         // 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());
     }
 
@@ -55,6 +56,7 @@ class Gaussian
     public static function distributionPdf(float $mean, float $std, float $value)
     {
         $normal = new self($mean, $std);
+
         return $normal->pdf($value);
     }
 }

src/Phpml/NeuralNetwork/Network/MultilayerPerceptron.php

@@ -138,6 +138,7 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
 
     /**
      * @param array $sample
+     *
      * @return mixed
      */
     abstract protected function predictSample(array $sample);

tests/Phpml/Classification/DecisionTreeTest.php

@@ -38,6 +38,7 @@ class DecisionTreeTest extends TestCase
         array_walk($input, function (&$v) {
             array_splice($v, 4, 1);
         });
+
         return [$input, $targets];
     }
 
@@ -54,6 +55,7 @@ class DecisionTreeTest extends TestCase
         $classifier->train($data, $targets);
         $this->assertEquals('Dont_play', $classifier->predict(['scorching', 95, 90, 'true']));
         $this->assertEquals('Play', $classifier->predict(['overcast', 60, 60, 'false']));
+
         return $classifier;
     }
 

tests/Phpml/Classification/Ensemble/BaggingTest.php

@@ -97,6 +97,7 @@ class BaggingTest extends TestCase
         $classifier = new Bagging($numBaseClassifiers);
         $classifier->setSubsetRatio(1.0);
         $classifier->setClassifer(DecisionTree::class, ['depth' => 10]);
+
         return $classifier;
     }
 
@@ -113,7 +114,7 @@ class BaggingTest extends TestCase
         // Populating input data to a size large enough
         // for base classifiers that they can work with a subset of it
         $populated = [];
-        for ($i=0; $i<20; $i++) {
+        for ($i = 0; $i < 20; ++$i) {
             $populated = array_merge($populated, $input);
         }
         shuffle($populated);
@@ -121,6 +122,7 @@ class BaggingTest extends TestCase
         array_walk($populated, function (&$v) {
             array_splice($v, 4, 1);
         });
+
         return [$populated, $targets];
     }
 }

tests/Phpml/Classification/Ensemble/RandomForestTest.php

@@ -14,6 +14,7 @@ class RandomForestTest extends BaggingTest
     {
         $classifier = new RandomForest($numBaseClassifiers);
         $classifier->setFeatureSubsetRatio('log');
+
         return $classifier;
     }
 

tests/Phpml/Classification/MLPClassifierTest.php

@@ -180,6 +180,7 @@ class MLPClassifierTest extends TestCase
             [0, 1, 2]
         );
     }
+
     /**
      * @expectedException \Phpml\Exception\InvalidArgumentException
      */

tests/Phpml/Clustering/FuzzyCMeansTest.php

@@ -21,6 +21,7 @@ class FuzzyCMeansTest extends TestCase
             }
         }
         $this->assertCount(0, $samples);
+
         return $fcm;
     }
 

tests/Phpml/Math/LinearAlgebra/EigenDecompositionTest.php

@@ -37,8 +37,8 @@ class EigenDecompositionTest extends TestCase
         $len = 3;
         $A = array_fill(0, $len, array_fill(0, $len, 0.0));
         srand(intval(microtime(true) * 1000));
-        for ($i=0; $i < $len; $i++) {
+        for ($i = 0; $i < $len; ++$i) {
-            for ($k=0; $k < $len; $k++) {
+            for ($k = 0; $k < $len; ++$k) {
                 if ($i > $k) {
                     $A[$i][$k] = $A[$k][$i];
                 } else {

tests/Phpml/Preprocessing/NormalizerTest.php

@@ -106,9 +106,9 @@ class NormalizerTest extends TestCase
         // Generate 10 random vectors of length 3
         $samples = [];
         srand(time());
-        for ($i=0; $i<10; $i++) {
+        for ($i = 0; $i < 10; ++$i) {
             $sample = array_fill(0, 3, 0);
-            for ($k=0; $k<3; $k++) {
+            for ($k = 0; $k < 3; ++$k) {
                 $sample[$k] = rand(1, 100);
             }
             // Last feature's value shared across samples.