Passed
Branch master (83f3e8)
by Arkadiusz
02:29
created
src/NeuralNetwork/Node/Neuron.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\Node;
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src/NeuralNetwork/Node/Input.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\Node;
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src/NeuralNetwork/Node/Neuron/Synapse.php 1 patch
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\NeuralNetwork\Node\Neuron;
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src/NeuralNetwork/Training/Backpropagation.php 1 patch
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\NeuralNetwork\Training;
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src/NeuralNetwork/Training/Backpropagation/Sigma.php 1 patch
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-declare(strict_types=1);
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+declare(strict_types = 1);
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 namespace Phpml\NeuralNetwork\Training\Backpropagation;
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src/DimensionReduction/PCA.php 1 patch
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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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src/DimensionReduction/EigenTransformerBase.php 1 patch
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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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src/DimensionReduction/KernelPCA.php 1 patch
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@@ -1,6 +1,6 @@  discard block
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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/DimensionReduction/LDA.php 1 patch
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@@ -1,6 +1,6 @@  discard block
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-declare(strict_types=1);
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 namespace Phpml\DimensionReduction;
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         // Calculate overall mean of the dataset for each column
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         $numElements = array_sum($counts);
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-        $map = function ($el) use ($numElements) {
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+        $map = function($el) use ($numElements) {
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             return $el / $numElements;
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         };
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         $this->overallMean = array_map($map, $overallMean);
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