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[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
This commit is contained in:
Tomáš Votruba 2017-11-13 11:42:40 +01:00 committed by Arkadiusz Kondas
parent f4650c696c
commit d85bfed468
32 changed files with 0 additions and 193 deletions
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23
src/Phpml/Classification/DecisionTree.php
View File

@ -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);

1
src/Phpml/Classification/Linear/Adaline.php
View File

@ -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)
{

3
src/Phpml/Classification/Linear/LogisticRegression.php
View File

@ -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)
{

2
src/Phpml/Classification/WeightedClassifier.php
View File

@ -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)
{

9
src/Phpml/Dataset/ArrayDataset.php
View File

@ -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;

6
src/Phpml/DimensionReduction/EigenTransformerBase.php
View File

@ -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
{

12
src/Phpml/FeatureExtraction/TfIdfTransformer.php
View File

@ -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);

6
src/Phpml/Helper/Optimizer/ConjugateGradient.php
View File

@ -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;

3
src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php
View File

@ -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)
{

3
src/Phpml/Math/Product.php
View File

@ -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)

8
src/Phpml/Math/Statistic/Mean.php
View File

@ -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)

4
src/Phpml/Metric/Accuracy.php
View File

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

6
src/Phpml/Metric/ClassificationReport.php
View File

@ -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)));

17
src/Phpml/Metric/ConfusionMatrix.php
View File

@ -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));

3
src/Phpml/NeuralNetwork/Network/LayeredNetwork.php
View File

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

1
src/Phpml/NeuralNetwork/Node/Neuron/Synapse.php
View File

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

10
src/Phpml/Pipeline.php
View File

@ -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) {

17
src/Phpml/Preprocessing/Imputer.php
View File

@ -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) {

3
src/Phpml/Preprocessing/Imputer/Strategy/MeanStrategy.php
View File

@ -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);

3
src/Phpml/Preprocessing/Imputer/Strategy/MedianStrategy.php
View File

@ -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);

2
src/Phpml/Preprocessing/Imputer/Strategy/MostFrequentStrategy.php
View File

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

15
src/Phpml/Preprocessing/Normalizer.php
View File

@ -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) {

5
tests/Phpml/Classification/MLPClassifierTest.php
View File

@ -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 = [];

6
tests/Phpml/CrossValidation/StratifiedRandomSplitTest.php
View File

@ -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;

2
tests/Phpml/Math/ComparisonTest.php
View File

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

1
tests/Phpml/Math/MatrixTest.php
View File

@ -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));
}
}

3
tests/Phpml/NeuralNetwork/ActivationFunction/BinaryStepTest.php
View File

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

3
tests/Phpml/NeuralNetwork/ActivationFunction/GaussianTest.php
View File

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

4
tests/Phpml/NeuralNetwork/ActivationFunction/HyperboliTangentTest.php
View File

@ -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)

4
tests/Phpml/NeuralNetwork/ActivationFunction/PReLUTest.php
View File

@ -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)

4
tests/Phpml/NeuralNetwork/ActivationFunction/SigmoidTest.php
View File

@ -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)

4
tests/Phpml/NeuralNetwork/ActivationFunction/ThresholdedReLUTest.php
View File

@ -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)