[cs] remove more unused comments (#146)

* [cs] remove more unused comments

* [cs] remove unused array phpdocs

* [cs] remove empty lines in docs

* [cs] space-proof useless docs

* [cs] remove empty @param lines

* [cs] remove references arrays

src/Phpml/Classification/DecisionTree.php

@@ -72,10 +72,6 @@ class DecisionTree implements Classifier
         $this->maxDepth = $maxDepth;
     }
 
-    /**
-     * @param array $samples
-     * @param array $targets
-     */
     public function train(array $samples, array $targets)
     {
         $this->samples = array_merge($this->samples, $samples);
@@ -104,11 +100,6 @@ class DecisionTree implements Classifier
         }
     }
 
-    /**
-     * @param array $samples
-     *
-     * @return array
-     */
     public static function getColumnTypes(array $samples) : array
     {
         $types = [];
@@ -122,10 +113,6 @@ class DecisionTree implements Classifier
         return $types;
     }
 
-    /**
-     * @param array $records
-     * @param int   $depth
-     */
     protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf
     {
         $split = $this->getBestSplit($records);
@@ -239,8 +226,6 @@ class DecisionTree implements Classifier
      *
      * If any of above methods were not called beforehand, then all features
      * are returned by default.
-     *
-     * @return array
      */
     protected function getSelectedFeatures() : array
     {
@@ -296,11 +281,6 @@ class DecisionTree implements Classifier
         return array_sum($giniParts) / count($colValues);
     }
 
-    /**
-     * @param array $samples
-     *
-     * @return array
-     */
     protected function preprocess(array $samples) : array
     {
         // Detect and convert continuous data column values into
@@ -325,9 +305,6 @@ class DecisionTree implements Classifier
         return array_map(null, ...$columns);
     }
 
-    /**
-     * @param array $columnValues
-     */
     protected static function isCategoricalColumn(array $columnValues) : bool
     {
         $count = count($columnValues);

src/Phpml/Classification/Linear/Adaline.php

@@ -52,7 +52,6 @@ class Adaline extends Perceptron
     /**
      * Adapts the weights with respect to given samples and targets
      * by use of gradient descent learning rule
-     * @param array $targets
      */
     protected function runTraining(array $samples, array $targets)
     {

src/Phpml/Classification/Linear/LogisticRegression.php

@@ -138,9 +138,6 @@ class LogisticRegression extends Adaline
 
     /**
      * Executes Conjugate Gradient method to optimize the weights of the LogReg model
-     *
-     * @param array    $samples
-     * @param array    $targets
      */
     protected function runConjugateGradient(array $samples, array $targets, \Closure $gradientFunc)
     {

src/Phpml/Classification/WeightedClassifier.php

@@ -13,8 +13,6 @@ abstract class WeightedClassifier implements Classifier
 
     /**
      * Sets the array including a weight for each sample
-     *
-     * @param array $weights
      */
     public function setSampleWeights(array $weights)
     {

src/Phpml/Dataset/ArrayDataset.php

@@ -19,9 +19,6 @@ class ArrayDataset implements Dataset
     protected $targets = [];
 
     /**
-     * @param array $samples
-     * @param array $targets
-     *
      * @throws InvalidArgumentException
      */
     public function __construct(array $samples, array $targets)
@@ -34,17 +31,11 @@ class ArrayDataset implements Dataset
         $this->targets = $targets;
     }
 
-    /**
-     * @return array
-     */
     public function getSamples() : array
     {
         return $this->samples;
     }
 
-    /**
-     * @return array
-     */
     public function getTargets() : array
     {
         return $this->targets;

src/Phpml/DimensionReduction/EigenTransformerBase.php

@@ -47,8 +47,6 @@ abstract class EigenTransformerBase
      * Calculates eigenValues and eigenVectors of the given matrix. Returns
      * top eigenVectors along with the largest eigenValues. The total explained variance
      * of these eigenVectors will be no less than desired $totalVariance value
-     *
-     * @param array $matrix
      */
     protected function eigenDecomposition(array $matrix)
     {
@@ -85,10 +83,6 @@ abstract class EigenTransformerBase
 
     /**
      * Returns the reduced data
-     *
-     * @param array $data
-     *
-     * @return array
      */
     protected function reduce(array $data) : array
     {

src/Phpml/FeatureExtraction/TfIdfTransformer.php

@@ -13,9 +13,6 @@ class TfIdfTransformer implements Transformer
      */
     private $idf;
 
-    /**
-     * @param array $samples
-     */
     public function __construct(array $samples = null)
     {
         if ($samples) {
@@ -23,9 +20,6 @@ class TfIdfTransformer implements Transformer
         }
     }
 
-    /**
-     * @param array $samples
-     */
     public function fit(array $samples)
     {
         $this->countTokensFrequency($samples);
@@ -36,9 +30,6 @@ class TfIdfTransformer implements Transformer
         }
     }
 
-    /**
-     * @param array $samples
-     */
     public function transform(array &$samples)
     {
         foreach ($samples as &$sample) {
@@ -48,9 +39,6 @@ class TfIdfTransformer implements Transformer
         }
     }
 
-    /**
-     * @param array $samples
-     */
     private function countTokensFrequency(array $samples)
     {
         $this->idf = array_fill_keys(array_keys($samples[0]), 0);

src/Phpml/Helper/Optimizer/ConjugateGradient.php

@@ -17,12 +17,6 @@ namespace Phpml\Helper\Optimizer;
  */
 class ConjugateGradient extends GD
 {
-    /**
-     * @param array    $samples
-     * @param array    $targets
-     *
-     * @return array
-     */
     public function runOptimization(array $samples, array $targets, \Closure $gradientCb) : array
     {
         $this->samples = $samples;

src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php

@@ -2,7 +2,6 @@
 
 declare(strict_types=1);
 /**
- *
  *	Class to obtain eigenvalues and eigenvectors of a real matrix.
  *
  *	If A is symmetric, then A = V*D*V' where the eigenvalue matrix D
@@ -88,8 +87,6 @@ class EigenvalueDecomposition
 
     /**
      *	Constructor: Check for symmetry, then construct the eigenvalue decomposition
-     *
-     * @param array $Arg
      */
     public function __construct(array $Arg)
     {

src/Phpml/Math/Product.php

@@ -7,9 +7,6 @@ namespace Phpml\Math;
 class Product
 {
     /**
-     * @param array $a
-     * @param array $b
-     *
      * @return mixed
      */
     public static function scalar(array $a, array $b)

src/Phpml/Math/Statistic/Mean.php

@@ -9,8 +9,6 @@ use Phpml\Exception\InvalidArgumentException;
 class Mean
 {
     /**
-     * @param array $numbers
-     *
      * @throws InvalidArgumentException
      */
     public static function arithmetic(array $numbers) : float
@@ -21,8 +19,6 @@ class Mean
     }
 
     /**
-     * @param array $numbers
-     *
      * @return float|mixed
      *
      * @throws InvalidArgumentException
@@ -44,8 +40,6 @@ class Mean
     }
 
     /**
-     * @param array $numbers
-     *
      * @return mixed
      *
      * @throws InvalidArgumentException
@@ -60,8 +54,6 @@ class Mean
     }
 
     /**
-     * @param array $array
-     *
      * @throws InvalidArgumentException
      */
     private static function checkArrayLength(array $array)

src/Phpml/Metric/Accuracy.php

@@ -9,10 +9,6 @@ use Phpml\Exception\InvalidArgumentException;
 class Accuracy
 {
     /**
-     * @param array $actualLabels
-     * @param array $predictedLabels
-     * @param bool  $normalize
-     *
      * @return float|int
      *
      * @throws InvalidArgumentException

src/Phpml/Metric/ClassificationReport.php

@@ -130,12 +130,6 @@ class ClassificationReport
         return 2.0 * (($precision * $recall) / $divider);
     }
 
-    /**
-     * @param array $actualLabels
-     * @param array $predictedLabels
-     *
-     * @return array
-     */
     private static function getLabelIndexedArray(array $actualLabels, array $predictedLabels) : array
     {
         $labels = array_values(array_unique(array_merge($actualLabels, $predictedLabels)));

src/Phpml/Metric/ConfusionMatrix.php

@@ -6,13 +6,6 @@ namespace Phpml\Metric;
 
 class ConfusionMatrix
 {
-    /**
-     * @param array $actualLabels
-     * @param array $predictedLabels
-     * @param array $labels
-     *
-     * @return array
-     */
     public static function compute(array $actualLabels, array $predictedLabels, array $labels = null) : array
     {
         $labels = $labels ? array_flip($labels) : self::getUniqueLabels($actualLabels);
@@ -38,11 +31,6 @@ class ConfusionMatrix
         return $matrix;
     }
 
-    /**
-     * @param array $labels
-     *
-     * @return array
-     */
     private static function generateMatrixWithZeros(array $labels) : array
     {
         $count = count($labels);
@@ -55,11 +43,6 @@ class ConfusionMatrix
         return $matrix;
     }
 
-    /**
-     * @param array $labels
-     *
-     * @return array
-     */
     private static function getUniqueLabels(array $labels) : array
     {
         $labels = array_values(array_unique($labels));

src/Phpml/NeuralNetwork/Network/LayeredNetwork.php

@@ -39,9 +39,6 @@ abstract class LayeredNetwork implements Network
         return $this->layers[count($this->layers) - 1];
     }
 
-    /**
-     * @return array
-     */
     public function getOutput() : array
     {
         $result = [];

src/Phpml/NeuralNetwork/Node/Neuron/Synapse.php

@@ -19,7 +19,6 @@ class Synapse
     protected $node;
 
     /**
-     * @param Node       $node
      * @param float|null $weight
      */
     public function __construct(Node $node, float $weight = null)

src/Phpml/Pipeline.php

@@ -18,7 +18,6 @@ class Pipeline implements Estimator
 
     /**
      * @param array|Transformer[] $transformers
-     * @param Estimator           $estimator
      */
     public function __construct(array $transformers, Estimator $estimator)
     {
@@ -52,10 +51,6 @@ class Pipeline implements Estimator
         return $this->estimator;
     }
 
-    /**
-     * @param array $samples
-     * @param array $targets
-     */
     public function train(array $samples, array $targets)
     {
         foreach ($this->transformers as $transformer) {
@@ -67,8 +62,6 @@ class Pipeline implements Estimator
     }
 
     /**
-     * @param array $samples
-     *
      * @return mixed
      */
     public function predict(array $samples)
@@ -78,9 +71,6 @@ class Pipeline implements Estimator
         return $this->estimator->predict($samples);
     }
 
-    /**
-     * @param array $samples
-     */
     private function transformSamples(array &$samples)
     {
         foreach ($this->transformers as $transformer) {

src/Phpml/Preprocessing/Imputer.php

@@ -33,8 +33,6 @@ class Imputer implements Preprocessor
 
     /**
      * @param mixed      $missingValue
-     * @param Strategy   $strategy
-     * @param int        $axis
      * @param array|null $samples
      */
     public function __construct($missingValue, Strategy $strategy, int $axis = self::AXIS_COLUMN, array $samples = [])
@@ -45,17 +43,11 @@ class Imputer implements Preprocessor
         $this->samples = $samples;
     }
 
-    /**
-     * @param array $samples
-     */
     public function fit(array $samples)
     {
         $this->samples = $samples;
     }
 
-    /**
-     * @param array $samples
-     */
     public function transform(array &$samples)
     {
         foreach ($samples as &$sample) {
@@ -63,9 +55,6 @@ class Imputer implements Preprocessor
         }
     }
 
-    /**
-     * @param array $sample
-     */
     private function preprocessSample(array &$sample)
     {
         foreach ($sample as $column => &$value) {
@@ -75,12 +64,6 @@ class Imputer implements Preprocessor
         }
     }
 
-    /**
-     * @param int   $column
-     * @param array $currentSample
-     *
-     * @return array
-     */
     private function getAxis(int $column, array $currentSample) : array
     {
         if (self::AXIS_ROW === $this->axis) {

src/Phpml/Preprocessing/Imputer/Strategy/MeanStrategy.php

@@ -9,9 +9,6 @@ use Phpml\Preprocessing\Imputer\Strategy;
 
 class MeanStrategy implements Strategy
 {
-    /**
-     * @param array $currentAxis
-     */
     public function replaceValue(array $currentAxis) : float
     {
         return Mean::arithmetic($currentAxis);

src/Phpml/Preprocessing/Imputer/Strategy/MedianStrategy.php

@@ -9,9 +9,6 @@ use Phpml\Preprocessing\Imputer\Strategy;
 
 class MedianStrategy implements Strategy
 {
-    /**
-     * @param array $currentAxis
-     */
     public function replaceValue(array $currentAxis) : float
     {
         return Mean::median($currentAxis);

src/Phpml/Preprocessing/Imputer/Strategy/MostFrequentStrategy.php

@@ -10,8 +10,6 @@ use Phpml\Preprocessing\Imputer\Strategy;
 class MostFrequentStrategy implements Strategy
 {
     /**
-     * @param array $currentAxis
-     *
      * @return float|mixed
      */
     public function replaceValue(array $currentAxis)

src/Phpml/Preprocessing/Normalizer.php

@@ -46,9 +46,6 @@ class Normalizer implements Preprocessor
         $this->norm = $norm;
     }
 
-    /**
-     * @param array $samples
-     */
     public function fit(array $samples)
     {
         if ($this->fitted) {
@@ -67,9 +64,6 @@ class Normalizer implements Preprocessor
         $this->fitted = true;
     }
 
-    /**
-     * @param array $samples
-     */
     public function transform(array &$samples)
     {
         $methods = [
@@ -86,9 +80,6 @@ class Normalizer implements Preprocessor
         }
     }
 
-    /**
-     * @param array $sample
-     */
     private function normalizeL1(array &$sample)
     {
         $norm1 = 0;
@@ -106,9 +97,6 @@ class Normalizer implements Preprocessor
         }
     }
 
-    /**
-     * @param array $sample
-     */
     private function normalizeL2(array &$sample)
     {
         $norm2 = 0;
@@ -126,9 +114,6 @@ class Normalizer implements Preprocessor
         }
     }
 
-    /**
-     * @param array $sample
-     */
     private function normalizeSTD(array &$sample)
     {
         foreach ($sample as $i => $val) {

tests/Phpml/Classification/MLPClassifierTest.php

@@ -189,11 +189,6 @@ class MLPClassifierTest extends TestCase
         new MLPClassifier(2, [2], [0]);
     }
 
-    /**
-     * @param array $synapses
-     *
-     * @return array
-     */
     private function getSynapsesNodes(array $synapses) : array
     {
         $nodes = [];

tests/Phpml/CrossValidation/StratifiedRandomSplitTest.php

@@ -46,12 +46,6 @@ class StratifiedRandomSplitTest extends TestCase
         $this->assertEquals(1, $this->countSamplesByTarget($split->getTestLabels(), 2));
     }
 
-    /**
-     * @param $splitTargets
-     * @param $countTarget
-     *
-     * @return int
-     */
     private function countSamplesByTarget($splitTargets, $countTarget): int
     {
         $count = 0;

tests/Phpml/Math/ComparisonTest.php

@@ -12,8 +12,6 @@ class ComparisonTest extends TestCase
     /**
      * @param mixed  $a
      * @param mixed  $b
-     * @param string $operator
-     * @param bool   $expected
      *
      * @dataProvider provideData
      */

tests/Phpml/Math/MatrixTest.php

@@ -261,7 +261,6 @@ class MatrixTest extends TestCase
         $matrix1 = [[1, 1], [2, 2]];
         $matrix2 = [[3, 3], [3, 3], [3, 3]];
         $dot = [6, 12];
-        
         $this->assertEquals($dot, Matrix::dot($matrix2, $matrix1));
     }
 }

tests/Phpml/NeuralNetwork/ActivationFunction/BinaryStepTest.php

@@ -10,9 +10,6 @@ use PHPUnit\Framework\TestCase;
 class BinaryStepTest extends TestCase
 {
     /**
-     * @param $expected
-     * @param $value
-     *
      * @dataProvider binaryStepProvider
      */
     public function testBinaryStepActivationFunction($expected, $value)

tests/Phpml/NeuralNetwork/ActivationFunction/GaussianTest.php

@@ -10,9 +10,6 @@ use PHPUnit\Framework\TestCase;
 class GaussianTest extends TestCase
 {
     /**
-     * @param $expected
-     * @param $value
-     *
      * @dataProvider gaussianProvider
      */
     public function testGaussianActivationFunction($expected, $value)

tests/Phpml/NeuralNetwork/ActivationFunction/HyperboliTangentTest.php

@@ -10,10 +10,6 @@ use PHPUnit\Framework\TestCase;
 class HyperboliTangentTest extends TestCase
 {
     /**
-     * @param $beta
-     * @param $expected
-     * @param $value
-     *
      * @dataProvider tanhProvider
      */
     public function testHyperbolicTangentActivationFunction($beta, $expected, $value)

tests/Phpml/NeuralNetwork/ActivationFunction/PReLUTest.php

@@ -10,10 +10,6 @@ use PHPUnit\Framework\TestCase;
 class PReLUTest extends TestCase
 {
     /**
-     * @param $beta
-     * @param $expected
-     * @param $value
-     *
      * @dataProvider preluProvider
      */
     public function testPReLUActivationFunction($beta, $expected, $value)

tests/Phpml/NeuralNetwork/ActivationFunction/SigmoidTest.php

@@ -10,10 +10,6 @@ use PHPUnit\Framework\TestCase;
 class SigmoidTest extends TestCase
 {
     /**
-     * @param $beta
-     * @param $expected
-     * @param $value
-     *
      * @dataProvider sigmoidProvider
      */
     public function testSigmoidActivationFunction($beta, $expected, $value)

tests/Phpml/NeuralNetwork/ActivationFunction/ThresholdedReLUTest.php

@@ -10,10 +10,6 @@ use PHPUnit\Framework\TestCase;
 class ThresholdedReLUTest extends TestCase
 {
     /**
-     * @param $theta
-     * @param $expected
-     * @param $value
-     *
      * @dataProvider thresholdProvider
      */
     public function testThresholdedReLUActivationFunction($theta, $expected, $value)