mirror of
https://github.com/Llewellynvdm/php-ml.git
synced 2024-11-05 04:57:52 +00:00
[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
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@ -72,10 +72,6 @@ class DecisionTree implements Classifier
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$this->maxDepth = $maxDepth;
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}
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/**
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* @param array $samples
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* @param array $targets
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*/
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public function train(array $samples, array $targets)
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{
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$this->samples = array_merge($this->samples, $samples);
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@ -104,11 +100,6 @@ class DecisionTree implements Classifier
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}
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}
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/**
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* @param array $samples
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*
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* @return array
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*/
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public static function getColumnTypes(array $samples) : array
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{
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$types = [];
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@ -122,10 +113,6 @@ class DecisionTree implements Classifier
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return $types;
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}
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/**
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* @param array $records
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* @param int $depth
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*/
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protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf
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{
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$split = $this->getBestSplit($records);
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@ -239,8 +226,6 @@ class DecisionTree implements Classifier
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*
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* If any of above methods were not called beforehand, then all features
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* are returned by default.
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*
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* @return array
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*/
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protected function getSelectedFeatures() : array
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{
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@ -296,11 +281,6 @@ class DecisionTree implements Classifier
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return array_sum($giniParts) / count($colValues);
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}
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/**
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* @param array $samples
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*
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* @return array
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*/
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protected function preprocess(array $samples) : array
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{
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// Detect and convert continuous data column values into
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@ -325,9 +305,6 @@ class DecisionTree implements Classifier
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return array_map(null, ...$columns);
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}
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/**
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* @param array $columnValues
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*/
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protected static function isCategoricalColumn(array $columnValues) : bool
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{
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$count = count($columnValues);
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@ -52,7 +52,6 @@ class Adaline extends Perceptron
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/**
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* Adapts the weights with respect to given samples and targets
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* by use of gradient descent learning rule
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* @param array $targets
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*/
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protected function runTraining(array $samples, array $targets)
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{
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@ -138,9 +138,6 @@ class LogisticRegression extends Adaline
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/**
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* Executes Conjugate Gradient method to optimize the weights of the LogReg model
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*
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* @param array $samples
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* @param array $targets
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*/
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protected function runConjugateGradient(array $samples, array $targets, \Closure $gradientFunc)
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{
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@ -13,8 +13,6 @@ abstract class WeightedClassifier implements Classifier
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/**
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* Sets the array including a weight for each sample
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*
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* @param array $weights
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*/
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public function setSampleWeights(array $weights)
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{
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@ -19,9 +19,6 @@ class ArrayDataset implements Dataset
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protected $targets = [];
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/**
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* @param array $samples
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* @param array $targets
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*
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* @throws InvalidArgumentException
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*/
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public function __construct(array $samples, array $targets)
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@ -34,17 +31,11 @@ class ArrayDataset implements Dataset
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$this->targets = $targets;
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}
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/**
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* @return array
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*/
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public function getSamples() : array
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{
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return $this->samples;
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}
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/**
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* @return array
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*/
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public function getTargets() : array
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{
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return $this->targets;
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@ -47,8 +47,6 @@ abstract class EigenTransformerBase
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* Calculates eigenValues and eigenVectors of the given matrix. Returns
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* top eigenVectors along with the largest eigenValues. The total explained variance
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* of these eigenVectors will be no less than desired $totalVariance value
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*
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* @param array $matrix
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*/
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protected function eigenDecomposition(array $matrix)
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{
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@ -85,10 +83,6 @@ abstract class EigenTransformerBase
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/**
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* Returns the reduced data
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*
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* @param array $data
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*
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* @return array
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*/
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protected function reduce(array $data) : array
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{
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@ -13,9 +13,6 @@ class TfIdfTransformer implements Transformer
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*/
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private $idf;
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/**
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* @param array $samples
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*/
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public function __construct(array $samples = null)
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{
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if ($samples) {
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@ -23,9 +20,6 @@ class TfIdfTransformer implements Transformer
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}
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}
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/**
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* @param array $samples
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*/
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public function fit(array $samples)
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{
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$this->countTokensFrequency($samples);
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@ -36,9 +30,6 @@ class TfIdfTransformer implements Transformer
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}
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}
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/**
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* @param array $samples
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*/
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public function transform(array &$samples)
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{
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foreach ($samples as &$sample) {
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@ -48,9 +39,6 @@ class TfIdfTransformer implements Transformer
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}
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}
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/**
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* @param array $samples
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*/
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private function countTokensFrequency(array $samples)
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{
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$this->idf = array_fill_keys(array_keys($samples[0]), 0);
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@ -17,12 +17,6 @@ namespace Phpml\Helper\Optimizer;
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*/
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class ConjugateGradient extends GD
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{
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/**
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* @param array $samples
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* @param array $targets
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*
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* @return array
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*/
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public function runOptimization(array $samples, array $targets, \Closure $gradientCb) : array
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{
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$this->samples = $samples;
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@ -2,7 +2,6 @@
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declare(strict_types=1);
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/**
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*
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* Class to obtain eigenvalues and eigenvectors of a real matrix.
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*
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* If A is symmetric, then A = V*D*V' where the eigenvalue matrix D
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@ -88,8 +87,6 @@ class EigenvalueDecomposition
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/**
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* Constructor: Check for symmetry, then construct the eigenvalue decomposition
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*
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* @param array $Arg
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*/
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public function __construct(array $Arg)
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{
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@ -7,9 +7,6 @@ namespace Phpml\Math;
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class Product
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{
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/**
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* @param array $a
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* @param array $b
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*
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* @return mixed
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*/
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public static function scalar(array $a, array $b)
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@ -9,8 +9,6 @@ use Phpml\Exception\InvalidArgumentException;
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class Mean
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{
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/**
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* @param array $numbers
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*
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* @throws InvalidArgumentException
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*/
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public static function arithmetic(array $numbers) : float
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@ -21,8 +19,6 @@ class Mean
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}
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/**
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* @param array $numbers
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*
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* @return float|mixed
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*
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* @throws InvalidArgumentException
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@ -44,8 +40,6 @@ class Mean
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}
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/**
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* @param array $numbers
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*
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* @return mixed
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*
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* @throws InvalidArgumentException
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@ -60,8 +54,6 @@ class Mean
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}
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/**
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* @param array $array
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*
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* @throws InvalidArgumentException
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*/
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private static function checkArrayLength(array $array)
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@ -9,10 +9,6 @@ use Phpml\Exception\InvalidArgumentException;
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class Accuracy
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{
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/**
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* @param array $actualLabels
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* @param array $predictedLabels
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* @param bool $normalize
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*
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* @return float|int
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*
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* @throws InvalidArgumentException
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@ -130,12 +130,6 @@ class ClassificationReport
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return 2.0 * (($precision * $recall) / $divider);
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}
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/**
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* @param array $actualLabels
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* @param array $predictedLabels
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*
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* @return array
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*/
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private static function getLabelIndexedArray(array $actualLabels, array $predictedLabels) : array
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{
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$labels = array_values(array_unique(array_merge($actualLabels, $predictedLabels)));
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@ -6,13 +6,6 @@ namespace Phpml\Metric;
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class ConfusionMatrix
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{
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/**
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* @param array $actualLabels
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* @param array $predictedLabels
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* @param array $labels
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*
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* @return array
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*/
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public static function compute(array $actualLabels, array $predictedLabels, array $labels = null) : array
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{
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$labels = $labels ? array_flip($labels) : self::getUniqueLabels($actualLabels);
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@ -38,11 +31,6 @@ class ConfusionMatrix
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return $matrix;
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}
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/**
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* @param array $labels
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*
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* @return array
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*/
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private static function generateMatrixWithZeros(array $labels) : array
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{
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$count = count($labels);
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@ -55,11 +43,6 @@ class ConfusionMatrix
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return $matrix;
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}
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/**
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* @param array $labels
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*
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* @return array
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*/
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private static function getUniqueLabels(array $labels) : array
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{
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$labels = array_values(array_unique($labels));
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@ -39,9 +39,6 @@ abstract class LayeredNetwork implements Network
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return $this->layers[count($this->layers) - 1];
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}
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/**
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* @return array
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*/
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public function getOutput() : array
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{
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$result = [];
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@ -19,7 +19,6 @@ class Synapse
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protected $node;
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/**
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* @param Node $node
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* @param float|null $weight
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*/
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public function __construct(Node $node, float $weight = null)
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@ -18,7 +18,6 @@ class Pipeline implements Estimator
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/**
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* @param array|Transformer[] $transformers
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* @param Estimator $estimator
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*/
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public function __construct(array $transformers, Estimator $estimator)
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{
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@ -52,10 +51,6 @@ class Pipeline implements Estimator
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return $this->estimator;
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}
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/**
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* @param array $samples
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* @param array $targets
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*/
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public function train(array $samples, array $targets)
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{
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foreach ($this->transformers as $transformer) {
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@ -67,8 +62,6 @@ class Pipeline implements Estimator
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}
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/**
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* @param array $samples
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*
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* @return mixed
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*/
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public function predict(array $samples)
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@ -78,9 +71,6 @@ class Pipeline implements Estimator
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return $this->estimator->predict($samples);
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}
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/**
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* @param array $samples
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*/
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private function transformSamples(array &$samples)
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{
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foreach ($this->transformers as $transformer) {
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@ -33,8 +33,6 @@ class Imputer implements Preprocessor
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/**
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* @param mixed $missingValue
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* @param Strategy $strategy
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* @param int $axis
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* @param array|null $samples
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*/
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public function __construct($missingValue, Strategy $strategy, int $axis = self::AXIS_COLUMN, array $samples = [])
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@ -45,17 +43,11 @@ class Imputer implements Preprocessor
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$this->samples = $samples;
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}
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/**
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* @param array $samples
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*/
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public function fit(array $samples)
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{
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$this->samples = $samples;
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}
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/**
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* @param array $samples
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*/
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public function transform(array &$samples)
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{
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foreach ($samples as &$sample) {
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@ -63,9 +55,6 @@ class Imputer implements Preprocessor
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}
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}
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/**
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* @param array $sample
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*/
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private function preprocessSample(array &$sample)
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{
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foreach ($sample as $column => &$value) {
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@ -75,12 +64,6 @@ class Imputer implements Preprocessor
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}
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}
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/**
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* @param int $column
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* @param array $currentSample
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*
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* @return array
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*/
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private function getAxis(int $column, array $currentSample) : array
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{
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if (self::AXIS_ROW === $this->axis) {
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@ -9,9 +9,6 @@ use Phpml\Preprocessing\Imputer\Strategy;
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class MeanStrategy implements Strategy
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{
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/**
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* @param array $currentAxis
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*/
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public function replaceValue(array $currentAxis) : float
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{
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return Mean::arithmetic($currentAxis);
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@ -9,9 +9,6 @@ use Phpml\Preprocessing\Imputer\Strategy;
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class MedianStrategy implements Strategy
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{
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/**
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* @param array $currentAxis
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*/
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public function replaceValue(array $currentAxis) : float
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{
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return Mean::median($currentAxis);
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@ -10,8 +10,6 @@ use Phpml\Preprocessing\Imputer\Strategy;
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class MostFrequentStrategy implements Strategy
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{
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/**
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* @param array $currentAxis
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*
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* @return float|mixed
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*/
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public function replaceValue(array $currentAxis)
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@ -46,9 +46,6 @@ class Normalizer implements Preprocessor
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$this->norm = $norm;
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}
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/**
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* @param array $samples
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*/
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public function fit(array $samples)
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{
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if ($this->fitted) {
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@ -67,9 +64,6 @@ class Normalizer implements Preprocessor
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$this->fitted = true;
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}
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/**
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* @param array $samples
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*/
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public function transform(array &$samples)
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{
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$methods = [
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@ -86,9 +80,6 @@ class Normalizer implements Preprocessor
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}
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}
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/**
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* @param array $sample
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*/
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private function normalizeL1(array &$sample)
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{
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$norm1 = 0;
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@ -106,9 +97,6 @@ class Normalizer implements Preprocessor
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}
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}
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/**
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* @param array $sample
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*/
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private function normalizeL2(array &$sample)
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{
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$norm2 = 0;
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@ -126,9 +114,6 @@ class Normalizer implements Preprocessor
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}
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}
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/**
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* @param array $sample
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*/
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private function normalizeSTD(array &$sample)
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{
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foreach ($sample as $i => $val) {
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|
@ -189,11 +189,6 @@ class MLPClassifierTest extends TestCase
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new MLPClassifier(2, [2], [0]);
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}
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/**
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* @param array $synapses
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*
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* @return array
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*/
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private function getSynapsesNodes(array $synapses) : array
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{
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$nodes = [];
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|
@ -46,12 +46,6 @@ class StratifiedRandomSplitTest extends TestCase
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$this->assertEquals(1, $this->countSamplesByTarget($split->getTestLabels(), 2));
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}
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/**
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* @param $splitTargets
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* @param $countTarget
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*
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* @return int
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*/
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private function countSamplesByTarget($splitTargets, $countTarget): int
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{
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$count = 0;
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|
@ -12,8 +12,6 @@ class ComparisonTest extends TestCase
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/**
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* @param mixed $a
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* @param mixed $b
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* @param string $operator
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* @param bool $expected
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*
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* @dataProvider provideData
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*/
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|
@ -261,7 +261,6 @@ class MatrixTest extends TestCase
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$matrix1 = [[1, 1], [2, 2]];
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$matrix2 = [[3, 3], [3, 3], [3, 3]];
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$dot = [6, 12];
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$this->assertEquals($dot, Matrix::dot($matrix2, $matrix1));
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}
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}
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|
@ -10,9 +10,6 @@ use PHPUnit\Framework\TestCase;
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class BinaryStepTest extends TestCase
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{
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/**
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* @param $expected
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* @param $value
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*
|
||||
* @dataProvider binaryStepProvider
|
||||
*/
|
||||
public function testBinaryStepActivationFunction($expected, $value)
|
||||
|
@ -10,9 +10,6 @@ use PHPUnit\Framework\TestCase;
|
||||
class GaussianTest extends TestCase
|
||||
{
|
||||
/**
|
||||
* @param $expected
|
||||
* @param $value
|
||||
*
|
||||
* @dataProvider gaussianProvider
|
||||
*/
|
||||
public function testGaussianActivationFunction($expected, $value)
|
||||
|
@ -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)
|
||||
|
@ -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)
|
||||
|
@ -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)
|
||||
|
@ -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)
|
||||
|
Loading…
Reference in New Issue
Block a user