php-ai / php-ml

src/Classification/Linear/LogisticRegression.php

Spacing +2 added lines, -2 removed lines

@@ -188,7 +188,7 @@ 
                  * The gradient of the cost function to be used with gradient descent:
                  *		∇J(x) = -(y - h(x)) = (h(x) - y)
                  */
-                return function ($weights, $sample, $y) use ($penalty) {
+                return function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 
@@ -220,7 +220,7 @@ 
                  * The gradient of the cost function:
                  *		∇J(x) = -(h(x) - y) . h(x) . (1 - h(x))
                  */
-                return function ($weights, $sample, $y) use ($penalty) {
+                return function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 

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;
         });
     }