php-ai / php-ml

src/Clustering/FuzzyCMeans.php

Spacing +1 added lines, -1 removed lines

@@ -143,7 +143,7 @@
                 $total += $val;
             }
 
-            $this->membership[] = array_map(function ($val) use ($total) {
+            $this->membership[] = array_map(function($val) use ($total) {
                 return $val / $total;
             }, $row);
         }

src/FeatureSelection/VarianceThreshold.php

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@@ -37,7 +37,7 @@
 
     public function fit(array $samples, ?array $targets = null): void
     {
-        $this->variances = array_map(function (array $column) {
+        $this->variances = array_map(function(array $column) {
             return Variance::population($column);
         }, Matrix::transposeArray($samples));
 

src/Helper/Optimizer/StochasticGD.php

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@@ -241,7 +241,7 @@
     {
         // 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,

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

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@@ -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

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@@ -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

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@@ -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

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@@ -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;
         });
     }