Passed
Pull Request — master (#168)
by Tomáš
05:03
created
src/Phpml/NeuralNetwork/Layer.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\NeuralNetwork;
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@@ -44,7 +44,7 @@  discard block
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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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@@ -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\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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@@ -1,6 +1,6 @@
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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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src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.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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 /**
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  *	Class to obtain eigenvalues and eigenvectors of a real matrix.
@@ -128,7 +128,7 @@  discard block
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         // Always return the eigenvectors of length 1.0
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         $vectors = new Matrix($vectors);
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-        $vectors = array_map(function ($vect) {
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+        $vectors = array_map(function($vect) {
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             $sum = 0;
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             for ($i = 0; $i < count($vect); ++$i) {
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                 $sum += $vect[$i] ** 2;
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src/Phpml/Math/Distance/Manhattan.php 1 patch
Spacing   +2 added lines, -2 removed lines patch added patch discarded remove patch
@@ -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\Distance;
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             throw InvalidArgumentException::arraySizeNotMatch();
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         }
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-        return array_sum(array_map(function ($m, $n) {
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+        return array_sum(array_map(function($m, $n) {
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             return abs($m - $n);
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         }, $a, $b));
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     }
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src/Phpml/Helper/Optimizer/StochasticGD.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\Helper\Optimizer;
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      * The cost function to minimize and the gradient of the function are to be
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      * handled by the callback function provided as the third parameter of the method.
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      */
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-    public function runOptimization(array $samples, array $targets, Closure $gradientCb): ?array
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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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         $this->targets = $targets;
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     {
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         // Check for early stop: No change larger than threshold (default 1e-5)
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         $diff = array_map(
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-            function ($w1, $w2) {
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+            function($w1, $w2) {
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                 return abs($w1 - $w2) > $this->threshold ? 1 : 0;
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             },
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             $oldTheta,
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