Run newest php-cs-fixer (#108)

src/Phpml/Classification/DecisionTree.php

@@ -101,7 +101,8 @@ class DecisionTree implements Classifier
         } elseif (count($this->columnNames) > $this->featureCount) {
             $this->columnNames = array_slice($this->columnNames, 0, $this->featureCount);
         } elseif (count($this->columnNames) < $this->featureCount) {
-            $this->columnNames = array_merge($this->columnNames,
+            $this->columnNames = array_merge(
+                $this->columnNames,
                 range(count($this->columnNames), $this->featureCount - 1)
             );
         }

src/Phpml/Classification/Linear/Adaline.php

@@ -39,9 +39,12 @@ class Adaline extends Perceptron
      *
      * @throws \Exception
      */
-    public function __construct(float $learningRate = 0.001, int $maxIterations = 1000,
+    public function __construct(
-        bool $normalizeInputs = true, int $trainingType = self::BATCH_TRAINING)
+        float $learningRate = 0.001,
-    {
+        int $maxIterations = 1000,
+        bool $normalizeInputs = true,
+        int $trainingType = self::BATCH_TRAINING
+    ) {
         if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) {
             throw new \Exception("Adaline can only be trained with batch and online/stochastic gradient descent algorithm");
         }

src/Phpml/Classification/Linear/LogisticRegression.php

@@ -67,10 +67,13 @@ class LogisticRegression extends Adaline
      *
      * @throws \Exception
      */
-    public function __construct(int $maxIterations = 500, bool $normalizeInputs = true,
+    public function __construct(
-        int $trainingType = self::CONJUGATE_GRAD_TRAINING, string $cost = 'sse',
+        int $maxIterations = 500,
-        string $penalty = 'L2')
+        bool $normalizeInputs = true,
-    {
+        int $trainingType = self::CONJUGATE_GRAD_TRAINING,
+        string $cost = 'sse',
+        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 " .

src/Phpml/Classification/Linear/Perceptron.php

@@ -99,11 +99,11 @@ class Perceptron implements Classifier, IncrementalEstimator
         $this->trainByLabel($samples, $targets, $labels);
     }
 
-   /**
+    /**
-     * @param array $samples
+      * @param array $samples
-     * @param array $targets
+      * @param array $targets
-     * @param array $labels
+      * @param array $labels
-     */
+      */
     public function trainBinary(array $samples, array $targets, array $labels)
     {
         if ($this->normalizer) {

src/Phpml/Classification/SVC.php

@@ -22,8 +22,14 @@ class SVC extends SupportVectorMachine implements Classifier
      * @param bool       $probabilityEstimates
      */
     public function __construct(
-        int $kernel = Kernel::LINEAR, float $cost = 1.0, int $degree = 3, float $gamma = null, float $coef0 = 0.0,
+        int $kernel = Kernel::LINEAR,
-        float $tolerance = 0.001, int $cacheSize = 100, bool $shrinking = true,
+        float $cost = 1.0,
+        int $degree = 3,
+        float $gamma = null,
+        float $coef0 = 0.0,
+        float $tolerance = 0.001,
+        int $cacheSize = 100,
+        bool $shrinking = true,
         bool $probabilityEstimates = false
     ) {
         parent::__construct(Type::C_SVC, $kernel, $cost, 0.5, $degree, $gamma, $coef0, 0.1, $tolerance, $cacheSize, $shrinking, $probabilityEstimates);

src/Phpml/Helper/Optimizer/StochasticGD.php

@@ -243,7 +243,9 @@ class StochasticGD extends Optimizer
             function ($w1, $w2) {
                 return abs($w1 - $w2) > $this->threshold ? 1 : 0;
             },
-            $oldTheta, $this->theta);
+            $oldTheta,
+            $this->theta
+        );
 
         if (array_sum($diff) == 0) {
             return true;

src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php

@@ -301,7 +301,7 @@ class EigenvalueDecomposition
                     $p = -$s * $s2 * $c3 * $el1 * $this->e[$l] / $dl1;
                     $this->e[$l] = $s * $p;
                     $this->d[$l] = $c * $p;
-                // Check for convergence.
+                    // Check for convergence.
                 } while (abs($this->e[$l]) > $eps * $tst1);
             }
             $this->d[$l] = $this->d[$l] + $f;
@@ -493,7 +493,7 @@ class EigenvalueDecomposition
                 $this->e[$n] = 0.0;
                 --$n;
                 $iter = 0;
-            // Two roots found
+                // Two roots found
             } elseif ($l == $n - 1) {
                 $w = $this->H[$n][$n - 1] * $this->H[$n - 1][$n];
                 $p = ($this->H[$n - 1][$n - 1] - $this->H[$n][$n]) / 2.0;
@@ -541,7 +541,7 @@ class EigenvalueDecomposition
                         $this->V[$i][$n - 1] = $q * $z + $p * $this->V[$i][$n];
                         $this->V[$i][$n] = $q * $this->V[$i][$n] - $p * $z;
                     }
-                // Complex pair
+                    // Complex pair
                 } else {
                     $this->d[$n - 1] = $x + $p;
                     $this->d[$n]     = $x + $p;
@@ -550,7 +550,7 @@ class EigenvalueDecomposition
                 }
                 $n = $n - 2;
                 $iter = 0;
-            // No convergence yet
+                // No convergence yet
             } else {
                 // Form shift
                 $x = $this->H[$n][$n];
@@ -715,7 +715,7 @@ class EigenvalueDecomposition
                             } else {
                                 $this->H[$i][$n] = -$r / ($eps * $norm);
                             }
-                        // Solve real equations
+                            // Solve real equations
                         } else {
                             $x = $this->H[$i][$i + 1];
                             $y = $this->H[$i + 1][$i];
@@ -737,7 +737,7 @@ class EigenvalueDecomposition
                         }
                     }
                 }
-            // Complex vector
+                // Complex vector
             } elseif ($q < 0) {
                 $l = $n - 1;
                 // Last vector component imaginary so matrix is triangular

src/Phpml/Math/Statistic/Covariance.php

@@ -150,7 +150,12 @@ class Covariance
                     $cov[$i][$k] = $cov[$k][$i];
                 } else {
                     $cov[$i][$k] = self::fromDataset(
-                        $data, $i, $k, true, $means[$i], $means[$k]
+                        $data,
+                        $i,
+                        $k,
+                        true,
+                        $means[$i],
+                        $means[$k]
                     );
                 }
             }

src/Phpml/Regression/SVR.php

@@ -22,9 +22,15 @@ class SVR extends SupportVectorMachine implements Regression
      * @param bool       $shrinking
      */
     public function __construct(
-        int $kernel = Kernel::RBF, int $degree = 3, float $epsilon = 0.1, float $cost = 1.0,
+        int $kernel = Kernel::RBF,
-        float $gamma = null, float $coef0 = 0.0, float $tolerance = 0.001,
+        int $degree = 3,
-        int $cacheSize = 100, bool $shrinking = true
+        float $epsilon = 0.1,
+        float $cost = 1.0,
+        float $gamma = null,
+        float $coef0 = 0.0,
+        float $tolerance = 0.001,
+        int $cacheSize = 100,
+        bool $shrinking = true
     ) {
         parent::__construct(Type::EPSILON_SVR, $kernel, $cost, 0.5, $degree, $gamma, $coef0, $epsilon, $tolerance, $cacheSize, $shrinking, false);
     }

src/Phpml/SupportVectorMachine/SupportVectorMachine.php

@@ -105,9 +105,18 @@ class SupportVectorMachine
      * @param bool       $probabilityEstimates
      */
     public function __construct(
-        int $type, int $kernel, float $cost = 1.0, float $nu = 0.5, int $degree = 3,
+        int $type,
-        float $gamma = null, float $coef0 = 0.0, float $epsilon = 0.1, float $tolerance = 0.001,
+        int $kernel,
-        int $cacheSize = 100, bool $shrinking = true, bool $probabilityEstimates = false
+        float $cost = 1.0,
+        float $nu = 0.5,
+        int $degree = 3,
+        float $gamma = null,
+        float $coef0 = 0.0,
+        float $epsilon = 0.1,
+        float $tolerance = 0.001,
+        int $cacheSize = 100,
+        bool $shrinking = true,
+        bool $probabilityEstimates = false
     ) {
         $this->type = $type;
         $this->kernel = $kernel;
@@ -241,7 +250,8 @@ class SupportVectorMachine
      */
     private function buildTrainCommand(string $trainingSetFileName, string $modelFileName): string
     {
-        return sprintf('%ssvm-train%s -s %s -t %s -c %s -n %s -d %s%s -r %s -p %s -m %s -e %s -h %d -b %d %s %s',
+        return sprintf(
+            '%ssvm-train%s -s %s -t %s -c %s -n %s -d %s%s -r %s -p %s -m %s -e %s -h %d -b %d %s %s',
             $this->binPath,
             $this->getOSExtension(),
             $this->type,

tests/Phpml/Preprocessing/NormalizerTest.php

@@ -124,10 +124,12 @@ class NormalizerTest extends TestCase
         // Values in the vector should be some value between -3 and +3
         $this->assertCount(10, $samples);
         foreach ($samples as $sample) {
-            $errors = array_filter($sample,
+            $errors = array_filter(
+                $sample,
                 function ($element) {
                     return $element < -3 || $element > 3;
-                });
+                }
+            );
             $this->assertCount(0, $errors);
             $this->assertEquals(0, $sample[3]);
         }