php-ai / php-ml

src/Math/Statistic/StandardDeviation.php

Spacing +1 added lines, -1 removed lines

@@ -51,7 +51,7 @@
         $mean = Mean::arithmetic($numbers);
 
         return array_sum(array_map(
-            function ($val) use ($mean) {
+            function($val) use ($mean) {
                 return ($val - $mean) ** 2;
             },
             $numbers

src/Association/Apriori.php

Spacing +7 added lines, -7 removed lines

@@ -103,11 +103,11 @@ 
      */
     protected function predictSample(array $sample): array
     {
-        $predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample) {
+        $predicts = array_values(array_filter($this->getRules(), function($rule) use ($sample) {
             return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample);
         }));
 
-        return array_map(function ($rule) {
+        return array_map(function($rule) {
             return $rule[self::ARRAY_KEY_CONSEQUENT];
         }, $predicts);
     }
@@ -176,7 +176,7 @@ 
         $cardinality = count($sample);
         $antecedents = $this->powerSet($sample);
 
-        return array_filter($antecedents, function ($antecedent) use ($cardinality) {
+        return array_filter($antecedents, function($antecedent) use ($cardinality) {
             return (count($antecedent) != $cardinality) && ($antecedent != []);
         });
     }
@@ -198,7 +198,7 @@ 
             }
         }
 
-        return array_map(function ($entry) {
+        return array_map(function($entry) {
             return [$entry];
         }, $items);
     }
@@ -212,7 +212,7 @@ 
      */
     private function frequent(array $samples): array
     {
-        return array_values(array_filter($samples, function ($entry) {
+        return array_values(array_filter($samples, function($entry) {
             return $this->support($entry) >= $this->support;
         }));
     }
@@ -287,7 +287,7 @@ 
      */
     private function frequency(array $sample): int
     {
-        return count(array_filter($this->samples, function ($entry) use ($sample) {
+        return count(array_filter($this->samples, function($entry) use ($sample) {
             return $this->subset($entry, $sample);
         }));
     }
@@ -302,7 +302,7 @@ 
      */
     private function contains(array $system, array $set): bool
     {
-        return (bool) array_filter($system, function ($entry) use ($set) {
+        return (bool) array_filter($system, function($entry) use ($set) {
             return $this->equals($entry, $set);
         });
     }

src/Classification/NaiveBayes.php

Spacing +1 added lines, -1 removed lines

@@ -120,7 +120,7 @@
                 $this->dataType[$label][$i] = self::NOMINAL;
                 $this->discreteProb[$label][$i] = array_count_values($values);
                 $db = &$this->discreteProb[$label][$i];
-                $db = array_map(function ($el) use ($numValues) {
+                $db = array_map(function($el) use ($numValues) {
                     return $el / $numValues;
                 }, $db);
             } else {

src/DimensionReduction/LDA.php

Spacing +1 added lines, -1 removed lines

@@ -157,7 +157,7 @@
 
         // Calculate overall mean of the dataset for each column
         $numElements = array_sum($counts);
-        $map = function ($el) use ($numElements) {
+        $map = function($el) use ($numElements) {
             return $el / $numElements;
         };
         $this->overallMean = array_map($map, $overallMean);

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

Spacing +1 added lines, -1 removed lines

@@ -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

Spacing +1 added lines, -1 removed lines

@@ -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

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);