php-ai / php-ml

src/Phpml/Classification/Linear/LogisticRegression.php

Unused Use Statements -1 removed lines

@@ -4,7 +4,6 @@
 
 namespace Phpml\Classification\Linear;
 
-use Phpml\Classification\Classifier;
 use Phpml\Helper\Optimizer\ConjugateGradient;
 
 class LogisticRegression extends Adaline

Spacing +10 added lines, -10 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -13,12 +13,12 @@ 
     /**
      * Batch training: Gradient descent algorithm (default)
      */
-    const BATCH_TRAINING    = 1;
+    const BATCH_TRAINING = 1;
 
     /**
      * Online training: Stochastic gradient descent learning
      */
-    const ONLINE_TRAINING    = 2;
+    const ONLINE_TRAINING = 2;
 
     /**
      * Conjugate Batch: Conjugate Gradient algorithm
@@ -74,14 +74,14 @@ 
         string $penalty = 'L2')
     {
         $trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
-        if (! in_array($trainingType, $trainingTypes)) {
-            throw new \Exception("Logistic regression can only be trained with " .
-                "batch (gradient descent), online (stochastic gradient descent) " .
+        if (!in_array($trainingType, $trainingTypes)) {
+            throw new \Exception("Logistic regression can only be trained with ".
+                "batch (gradient descent), online (stochastic gradient descent) ".
                 "or conjugate batch (conjugate gradients) algorithms");
         }
 
-        if (! in_array($cost, ['log', 'sse'])) {
-            throw new \Exception("Logistic regression cost function can be one of the following: \n" .
+        if (!in_array($cost, ['log', 'sse'])) {
+            throw new \Exception("Logistic regression cost function can be one of the following: \n".
                 "'log' for log-likelihood and 'sse' for sum of squared errors");
         }
 
@@ -177,7 +177,7 @@ 
                  * The gradient of the cost function to be used with gradient descent:
                  *		∇J(x) = -(y - h(x)) = (h(x) - y)
                  */
-                $callback = function ($weights, $sample, $y) use ($penalty) {
+                $callback = function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 
@@ -208,7 +208,7 @@ 
                  * The gradient of the cost function:
                  *		∇J(x) = -(h(x) - y) . h(x) . (1 - h(x))
                  */
-                $callback = function ($weights, $sample, $y) use ($penalty) {
+                $callback = function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 

src/Phpml/Classification/Linear/Perceptron.php

Indentation +2 added lines, -2 removed lines

@@ -15,7 +15,7 @@ 
 {
     use Predictable, OneVsRest;
 
-   /**
+    /**
      * @var array
      */
     protected $samples = [];
@@ -83,7 +83,7 @@ 
         $this->maxIterations = $maxIterations;
     }
 
-   /**
+    /**
      * @param array $samples
      * @param array $targets
      */

Spacing +4 added lines, -4 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -118,7 +118,7 @@ 
     protected function runTraining()
     {
         // The cost function is the sum of squares
-        $callback = function ($weights, $sample, $target) {
+        $callback = function($weights, $sample, $target) {
             $this->weights = $weights;
 
             $prediction = $this->outputClass($sample);
@@ -137,7 +137,7 @@ 
      */
     protected function runGradientDescent(\Closure $gradientFunc, bool $isBatch = false)
     {
-        $class = $isBatch ? GD::class :  StochasticGD::class;
+        $class = $isBatch ? GD::class : StochasticGD::class;
 
         $optimizer = (new $class($this->featureCount))
             ->setLearningRate($this->learningRate)
@@ -227,6 +227,6 @@ 
 
         $predictedClass = $this->outputClass($sample);
 
-        return $this->labels[ $predictedClass ];
+        return $this->labels[$predictedClass];
     }
 }

src/Phpml/Helper/Optimizer/GD.php

Spacing +5 added lines, -5 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -42,7 +42,7 @@ 
 
             $this->updateWeightsWithUpdates($updates, $totalPenalty);
 
-            $this->costValues[] = array_sum($errors)/$this->sampleCount;
+            $this->costValues[] = array_sum($errors) / $this->sampleCount;
 
             if ($this->earlyStop($theta)) {
                 break;
@@ -63,7 +63,7 @@ 
     protected function gradient(array $theta)
     {
         $costs = [];
-        $gradient= [];
+        $gradient = [];
         $totalPenalty = 0;
 
         foreach ($this->samples as $index => $sample) {
@@ -73,7 +73,7 @@ 
             list($cost, $grad, $penalty) = array_pad($result, 3, 0);
 
             $costs[] = $cost;
-            $gradient[]= $grad;
+            $gradient[] = $grad;
             $totalPenalty += $penalty;
         }
 
@@ -89,7 +89,7 @@ 
     protected function updateWeightsWithUpdates(array $updates, float $penalty)
     {
         // Updates all weights at once
-        for ($i=0; $i <= $this->dimensions; $i++) {
+        for ($i = 0; $i <= $this->dimensions; $i++) {
             if ($i == 0) {
                 $this->theta[0] -= $this->learningRate * array_sum($updates);
             } else {

src/Phpml/Helper/Optimizer/StochasticGD.php

Spacing +4 added lines, -4 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -72,7 +72,7 @@ 
      *
      * @var array
      */
-    protected $costValues= [];
+    protected $costValues = [];
 
     /**
      * Initializes the SGD optimizer for the given number of dimensions
@@ -216,7 +216,7 @@ 
             $this->theta[0] -= $this->learningRate * $gradient;
 
             // Update other values
-            for ($i=1; $i <= $this->dimensions; $i++) {
+            for ($i = 1; $i <= $this->dimensions; $i++) {
                 $this->theta[$i] -= $this->learningRate *
                     ($gradient * $sample[$i - 1] + $penalty * $this->theta[$i]);
             }
@@ -240,7 +240,7 @@ 
     {
         // Check for early stop: No change larger than threshold (default 1e-5)
         $diff = array_map(
-            function ($w1, $w2) {
+            function($w1, $w2) {
                 return abs($w1 - $w2) > $this->threshold ? 1 : 0;
             },
             $oldTheta, $this->theta);

src/Phpml/Helper/Optimizer/Optimizer.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -31,7 +31,7 @@ 
 
         // Inits the weights randomly
         $this->theta = [];
-        for ($i=0; $i < $this->dimensions; $i++) {
+        for ($i = 0; $i < $this->dimensions; $i++) {
             $this->theta[] = rand() / (float) getrandmax();
         }
     }

src/Phpml/Helper/Optimizer/ConjugateGradient.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -34,7 +34,7 @@ 
 
         $d = mp::muls($this->gradient($this->theta), -1);
 
-        for ($i=0; $i < $this->maxIterations; $i++) {
+        for ($i = 0; $i < $this->maxIterations; $i++) {
             // Obtain α that minimizes f(θ + α.d)
             $alpha = $this->getAlpha(array_sum($d));
 
@@ -161,7 +161,7 @@ 
     {
         $theta = $this->theta;
 
-        for ($i=0; $i < $this->dimensions + 1; $i++) {
+        for ($i = 0; $i < $this->dimensions + 1; $i++) {
             if ($i == 0) {
                 $theta[$i] += $alpha * array_sum($d);
             } else {

src/Phpml/Math/Matrix.php

Doc Comments +2 added lines, -2 removed lines

@@ -106,7 +106,7 @@ 
     }
 
     /**
-     * @param $column
+     * @param integer $column
      *
      * @return array
      *
@@ -278,7 +278,7 @@ 
      * Element-wise addition or substraction depending on the given sign parameter
      *
      * @param Matrix $other
-     * @param type $sign
+     * @param integer $sign
      */
     protected function _add(Matrix $other, $sign = 1)
     {

Spacing +4 added lines, -4 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Math;
 
@@ -286,8 +286,8 @@ 
         $a2 = $other->toArray();
 
         $newMatrix = [];
-        for ($i=0; $i < $this->rows; $i++) {
-            for ($k=0; $k < $this->columns; $k++) {
+        for ($i = 0; $i < $this->rows; $i++) {
+            for ($k = 0; $k < $this->columns; $k++) {
                 $newMatrix[$i][$k] = $a1[$i][$k] + $sign * $a2[$i][$k];
             }
         }
@@ -428,7 +428,7 @@ 
      */
     public static function map(array $array, $callable)
     {
-        $func = function ($row) use ($callable) {
+        $func = function($row) use ($callable) {
             return array_map($callable, $row);
         };
 

src/Phpml/Math/Statistic/Covariance.php

Doc Comments -1 removed lines

@@ -60,7 +60,6 @@
      * @param int $i
      * @param int $k
      * @param type $sample
-     * @param int $n
      * @param float $meanX
      * @param float $meanY
      */

Spacing +4 added lines, -4 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Math\Statistic;
 
@@ -133,14 +133,14 @@ 
 
         if ($means === null) {
             $means = [];
-            for ($i=0; $i < $n; $i++) {
+            for ($i = 0; $i < $n; $i++) {
                 $means[] = Mean::arithmetic(array_column($data, $i));
             }
         }
 
         $cov = [];
-        for ($i=0; $i < $n; $i++) {
-            for ($k=0; $k < $n; $k++) {
+        for ($i = 0; $i < $n; $i++) {
+            for ($k = 0; $k < $n; $k++) {
                 if ($i > $k) {
                     $cov[$i][$k] = $cov[$k][$i];
                 } else {

src/Phpml/DimensionReduction/KernelPCA.php

Indentation +3 added lines, -3 removed lines

@@ -136,9 +136,9 @@
         $N_K_N = $N->multiply($K_N);
 
         return $K->subtract($N_K)
-                 ->subtract($K_N)
-                 ->add($N_K_N)
-                 ->toArray();
+                    ->subtract($K_N)
+                    ->add($N_K_N)
+                    ->toArray();
     }
 
     /**

Spacing +10 added lines, -10 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\DimensionReduction;
 
@@ -46,7 +46,7 @@ 
      */
     public function __construct(int $kernel = self::KERNEL_RBF, $totalVariance = null, $numFeatures = null, $gamma = null)
     {
-        if (! in_array($kernel, [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN])) {
+        if (!in_array($kernel, [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN])) {
             throw new \Exception("KernelPCA can be initialized with the following kernels only: RBF, Sigmoid and Laplacian");
         }
 
@@ -100,8 +100,8 @@ 
         $kernelFunc = $this->getKernel();
 
         $matrix = [];
-        for ($i=0; $i < $numRows; $i++) {
-            for ($k=0; $k < $numRows; $k++) {
+        for ($i = 0; $i < $numRows; $i++) {
+            for ($k = 0; $k < $numRows; $k++) {
                 if ($i <= $k) {
                     $matrix[$i][$k] = $kernelFunc($data[$i], $data[$k]);
                 } else {
@@ -124,7 +124,7 @@ 
      */
     protected function centerMatrix(array $matrix, int $n)
     {
-        $N = array_fill(0, $n, array_fill(0, $n, 1.0/$n));
+        $N = array_fill(0, $n, array_fill(0, $n, 1.0 / $n));
         $N = new Matrix($N, false);
         $K = new Matrix($matrix, false);
 
@@ -157,7 +157,7 @@ 
             case self::KERNEL_RBF:
                 // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance
                 $dist = new Euclidean();
-                return function ($x, $y = null) use ($dist) {
+                return function($x, $y = null) use ($dist) {
                     if ($y === null) {
                         foreach ($x as $i => $element) {
                             $x[$i] = exp(-$this->gamma * $element);
@@ -166,12 +166,12 @@ 
                         return $x;
                     }
 
-                    return exp(-$this->gamma *    $dist->distance($x, $y));
+                    return exp(-$this->gamma * $dist->distance($x, $y));
                 };
 
             case self::KERNEL_SIGMOID:
                 // k(x,y)=tanh(γ.xT.y+c0) where c0=0
-                return function ($x, $y = null) {
+                return function($x, $y = null) {
                     if ($y === null) {
                         foreach ($x as $i => $element) {
                             $x[$i] = tanh($this->gamma * $element);
@@ -190,7 +190,7 @@ 
             case self::KERNEL_LAPLACIAN:
                 // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance
                 $dist = new Manhattan();
-                return function ($x, $y = null) use ($dist) {
+                return function($x, $y = null) use ($dist) {
                     if ($y === null) {
                         foreach ($x as $i => $element) {
                             $x[$i] = exp(-$this->gamma * $element);
@@ -228,7 +228,7 @@ 
     protected function projectSample(array $pairs)
     {
         // Normalize eigenvectors by eig = eigVectors / eigValues
-        $func = function ($eigVal, $eigVect) {
+        $func = function($eigVal, $eigVect) {
             $m = new Matrix($eigVect, false);
             $a = $m->divideByScalar(sqrt($eigVal))->toArray();