php-ai / php-ml

src/Classification/Ensemble/RandomForest.php

Spacing +1 added lines, -1 removed lines

@@ -100,7 +100,7 @@
 
         // Normalize & sort the importance values
         $total = array_sum($sum);
-        array_walk($sum, function (&$importance) use ($total): void {
+        array_walk($sum, function(&$importance) use ($total): void {
             $importance /= $total;
         });
         arsort($sum);

src/Classification/DecisionTree.php

Spacing +1 added lines, -1 removed lines

@@ -220,7 +220,7 @@
         // Normalize & sort the importances
         $total = array_sum($this->featureImportances);
         if ($total > 0) {
-            array_walk($this->featureImportances, function (&$importance) use ($total): void {
+            array_walk($this->featureImportances, function(&$importance) use ($total): void {
                 $importance /= $total;
             });
             arsort($this->featureImportances);

src/Preprocessing/Normalizer.php

Spacing +2 added lines, -2 removed lines

@@ -93,7 +93,7 @@ 
             $count = count($sample);
             $sample = array_fill(0, $count, 1.0 / $count);
         } else {
-            array_walk($sample, function (&$feature) use ($norm1): void {
+            array_walk($sample, function(&$feature) use ($norm1): void {
                 $feature /= $norm1;
             });
         }
@@ -111,7 +111,7 @@ 
         if ($norm2 == 0) {
             $sample = array_fill(0, count($sample), 1);
         } else {
-            array_walk($sample, function (&$feature) use ($norm2): void {
+            array_walk($sample, function(&$feature) use ($norm2): void {
                 $feature /= $norm2;
             });
         }

src/FeatureExtraction/TokenCountVectorizer.php

Spacing +1 added lines, -1 removed lines

@@ -48,7 +48,7 @@
 
     public function transform(array &$samples): void
     {
-        array_walk($samples, function (string &$sample): void {
+        array_walk($samples, function(string &$sample): void {
             $this->transformSample($sample);
         });
 

src/FeatureSelection/ScoringFunction/UnivariateLinearRegression.php

Spacing +2 added lines, -2 removed lines

@@ -52,7 +52,7 @@ 
 
         $degreesOfFreedom = count($targets) - ($this->center ? 2 : 1);
 
-        return array_map(function (float $correlation) use ($degreesOfFreedom): float {
+        return array_map(function(float $correlation) use ($degreesOfFreedom): float {
             return $correlation ** 2 / (1 - $correlation ** 2) * $degreesOfFreedom;
         }, $correlations);
     }
@@ -60,7 +60,7 @@ 
     private function centerTargets(array &$targets): void
     {
         $mean = Mean::arithmetic($targets);
-        array_walk($targets, function (&$target) use ($mean): void {
+        array_walk($targets, function(&$target) use ($mean): void {
             $target -= $mean;
         });
     }

src/Clustering/KMeans/Space.php

Doc Comments +2 added lines, -2 removed lines

@@ -59,7 +59,7 @@ 
     }
 
     /**
-     * @param object $point
+     * @param Point $point
      * @param mixed  $data
      */
     public function attach($point, $data = null): void
@@ -129,7 +129,7 @@ 
     }
 
     /**
-     * @return array|Cluster[]
+     * @return Cluster[]
      */
     protected function initializeClusters(int $clustersNumber, int $initMethod): array
     {

src/Classification/Linear/Adaline.php

Spacing +1 added lines, -1 removed lines

@@ -58,7 +58,7 @@
     protected function runTraining(array $samples, array $targets): void
     {
         // The cost function is the sum of squares
-        $callback = function ($weights, $sample, $target) {
+        $callback = function($weights, $sample, $target) {
             $this->weights = $weights;
 
             $output = $this->output($sample);

src/Classification/Linear/Perceptron.php

Spacing +1 added lines, -1 removed lines

@@ -154,7 +154,7 @@
     protected function runTraining(array $samples, array $targets): void
     {
         // 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);

src/DimensionReduction/KernelPCA.php

Spacing +5 added lines, -5 removed lines

@@ -172,20 +172,20 @@ 
         switch ($this->kernel) {
             case self::KERNEL_LINEAR:
                 // k(x,y) = xT.y
-                return function ($x, $y) {
+                return function($x, $y) {
                     return Matrix::dot($x, $y)[0];
                 };
             case self::KERNEL_RBF:
                 // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance
                 $dist = new Euclidean();
 
-                return function ($x, $y) use ($dist) {
+                return function($x, $y) use ($dist) {
                     return exp(-$this->gamma * $dist->sqDistance($x, $y));
                 };
 
             case self::KERNEL_SIGMOID:
                 // k(x,y)=tanh(γ.xT.y+c0) where c0=1
-                return function ($x, $y) {
+                return function($x, $y) {
                     $res = Matrix::dot($x, $y)[0] + 1.0;
 
                     return tanh((float) $this->gamma * $res);
@@ -195,7 +195,7 @@ 
                 // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance
                 $dist = new Manhattan();
 
-                return function ($x, $y) use ($dist) {
+                return function($x, $y) use ($dist) {
                     return exp(-$this->gamma * $dist->distance($x, $y));
                 };
 
@@ -220,7 +220,7 @@ 
     protected function projectSample(array $pairs): array
     {
         // Normalize eigenvectors by eig = eigVectors / eigValues
-        $func = function ($eigVal, $eigVect) {
+        $func = function($eigVal, $eigVect) {
             $m = new Matrix($eigVect, false);
             $a = $m->divideByScalar($eigVal)->toArray();