Passed
Pull Request — master (#156)
by Tomáš
07:08
created
src/Phpml/DimensionReduction/KernelPCA.php 1 patch
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@@ -1,6 +1,6 @@  discard block
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\DimensionReduction;
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@@ -161,20 +161,20 @@  discard block
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         switch ($this->kernel) {
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             case self::KERNEL_LINEAR:
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                 // k(x,y) = xT.y
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-                return function ($x, $y) {
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+                return function($x, $y) {
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                     return Matrix::dot($x, $y)[0];
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                 };
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             case self::KERNEL_RBF:
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                 // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance
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                 $dist = new Euclidean();
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-                return function ($x, $y) use ($dist) {
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+                return function($x, $y) use ($dist) {
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                     return exp(-$this->gamma * $dist->sqDistance($x, $y));
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                 };
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             case self::KERNEL_SIGMOID:
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                 // k(x,y)=tanh(γ.xT.y+c0) where c0=1
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-                return function ($x, $y) {
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+                return function($x, $y) {
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                     $res = Matrix::dot($x, $y)[0] + 1.0;
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                     return tanh($this->gamma * $res);
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                 // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance
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                 $dist = new Manhattan();
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-                return function ($x, $y) use ($dist) {
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+                return function($x, $y) use ($dist) {
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                     return exp(-$this->gamma * $dist->distance($x, $y));
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                 };
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     protected function projectSample(array $pairs): array
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     {
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         // Normalize eigenvectors by eig = eigVectors / eigValues
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-        $func = function ($eigVal, $eigVect) {
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+        $func = function($eigVal, $eigVect) {
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             $m = new Matrix($eigVect, false);
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             $a = $m->divideByScalar($eigVal)->toArray();
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src/Phpml/CrossValidation/Split.php 1 patch
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@@ -1,6 +1,6 @@  discard block
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\CrossValidation;
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@@ -62,7 +62,7 @@  discard block
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     abstract protected function splitDataset(Dataset $dataset, float $testSize);
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-    protected function seedGenerator(?int $seed = null): void
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+    protected function seedGenerator(?int $seed = null) : void
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     {
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         if ($seed === null) {
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             mt_srand();
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src/Phpml/FeatureExtraction/TfIdfTransformer.php 1 patch
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@@ -1,6 +1,6 @@  discard block
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\FeatureExtraction;
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      */
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     private $idf = [];
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-    public function __construct(?array $samples = null)
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+    public function __construct(? array $samples = null)
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     {
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         if ($samples) {
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             $this->fit($samples);
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src/Phpml/NeuralNetwork/Layer.php 1 patch
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\NeuralNetwork;
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     /**
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      * @return Neuron
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      */
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-    private function createNode(string $nodeClass, ?ActivationFunction $activationFunction = null): Node
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+    private function createNode(string $nodeClass, ?ActivationFunction $activationFunction = null) : Node
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     {
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         if ($nodeClass == Neuron::class) {
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             return new Neuron($activationFunction);
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src/Phpml/Metric/ConfusionMatrix.php 1 patch
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@@ -1,12 +1,12 @@
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\Metric;
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 class ConfusionMatrix
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 {
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-    public static function compute(array $actualLabels, array $predictedLabels, ?array $labels = null): array
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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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         $matrix = self::generateMatrixWithZeros($labels);
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src/Phpml/Association/Apriori.php 1 patch
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\Association;
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      */
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     protected function predictSample(array $sample): array
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     {
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-        $predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample) {
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+        $predicts = array_values(array_filter($this->getRules(), function($rule) use ($sample) {
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             return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample);
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         }));
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-        return array_map(function ($rule) {
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+        return array_map(function($rule) {
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             return $rule[self::ARRAY_KEY_CONSEQUENT];
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         }, $predicts);
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     }
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         $cardinality = count($sample);
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         $antecedents = $this->powerSet($sample);
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-        return array_filter($antecedents, function ($antecedent) use ($cardinality) {
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+        return array_filter($antecedents, function($antecedent) use ($cardinality) {
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             return (count($antecedent) != $cardinality) && ($antecedent != []);
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         });
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     }
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             }
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         }
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-        return array_map(function ($entry) {
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+        return array_map(function($entry) {
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             return [$entry];
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         }, $items);
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     }
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      */
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     private function frequent(array $samples): array
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     {
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-        return array_filter($samples, function ($entry) {
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+        return array_filter($samples, function($entry) {
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             return $this->support($entry) >= $this->support;
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         });
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     }
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      */
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     private function frequency(array $sample): int
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     {
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-        return count(array_filter($this->samples, function ($entry) use ($sample) {
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+        return count(array_filter($this->samples, function($entry) use ($sample) {
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             return $this->subset($entry, $sample);
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         }));
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     }
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      */
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     private function contains(array $system, array $set): bool
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     {
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-        return (bool) array_filter($system, function ($entry) use ($set) {
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+        return (bool) array_filter($system, function($entry) use ($set) {
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             return $this->equals($entry, $set);
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         });
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     }
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src/Phpml/Math/Matrix.php 1 patch
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@@ -1,6 +1,6 @@  discard block
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\Math;
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     public function transpose(): self
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     {
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         if ($this->rows == 1) {
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-            $matrix = array_map(function ($el) {
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+            $matrix = array_map(function($el) {
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                 return [$el];
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             }, $this->matrix[0]);
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         } else {
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src/Phpml/Math/Statistic/Covariance.php 1 patch
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@@ -1,6 +1,6 @@  discard block
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\Math\Statistic;
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      *
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      * @throws InvalidArgumentException
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      */
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-    public static function fromXYArrays(array $x, array $y, bool $sample = true, ?float $meanX = null, ?float $meanY = null): float
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+    public static function fromXYArrays(array $x, array $y, bool $sample = true, ?float $meanX = null, ?float $meanY = null) : float
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     {
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         if (empty($x) || empty($y)) {
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             throw InvalidArgumentException::arrayCantBeEmpty();
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      * @throws InvalidArgumentException
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      * @throws \Exception
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      */
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-    public static function fromDataset(array $data, int $i, int $k, bool $sample = true, ?float $meanX = null, ?float $meanY = null): float
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+    public static function fromDataset(array $data, int $i, int $k, bool $sample = true, ?float $meanX = null, ?float $meanY = null) : float
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     {
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         if (empty($data)) {
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             throw InvalidArgumentException::arrayCantBeEmpty();
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      *
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      * @param array|null $means
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      */
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-    public static function covarianceMatrix(array $data, ?array $means = null): array
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+    public static function covarianceMatrix(array $data, ? array $means = null) : array
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     {
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         $n = count($data[0]);
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src/Phpml/Math/LinearAlgebra/LUDecomposition.php 1 patch
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 <?php
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 /**
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  * @package JAMA
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