php-ai / php-ml

src/Math/Matrix.php

Spacing +1 added lines, -1 removed lines

@@ -126,7 +126,7 @@
     public function transpose(): self
     {
         if ($this->rows === 1) {
-            $matrix = array_map(static function ($el): array {
+            $matrix = array_map(static function($el): array {
                 return [$el];
             }, $this->matrix[0]);
         } else {

src/Math/Statistic/ANOVA.php

Spacing +5 added lines, -5 removed lines

@@ -28,7 +28,7 @@ 
             throw new InvalidArgumentException('The array must have at least 2 elements');
         }
 
-        $samplesPerClass = array_map(static function (array $class): int {
+        $samplesPerClass = array_map(static function(array $class): int {
             return count($class);
         }, $samples);
         $allSamples = (int) array_sum($samplesPerClass);
@@ -41,10 +41,10 @@ 
         $dfbn = $classes - 1;
         $dfwn = $allSamples - $classes;
 
-        $msb = array_map(static function ($s) use ($dfbn) {
+        $msb = array_map(static function($s) use ($dfbn) {
             return $s / $dfbn;
         }, $ssbn);
-        $msw = array_map(static function ($s) use ($dfwn) {
+        $msw = array_map(static function($s) use ($dfwn) {
             if ($dfwn === 0) {
                 return 1;
             }
@@ -76,7 +76,7 @@ 
 
     private static function sumOfFeaturesPerClass(array $samples): array
     {
-        return array_map(static function (array $class): array {
+        return array_map(static function(array $class): array {
             $sum = array_fill(0, count($class[0]), 0);
             foreach ($class as $sample) {
                 foreach ($sample as $index => $feature) {
@@ -97,7 +97,7 @@ 
             }
         }
 
-        return array_map(static function ($sum) {
+        return array_map(static function($sum) {
             return $sum ** 2;
         }, $squares);
     }

src/Math/Statistic/StandardDeviation.php

Spacing +1 added lines, -1 removed lines

@@ -50,7 +50,7 @@
         $mean = Mean::arithmetic($numbers);
 
         return array_sum(array_map(
-            static function ($val) use ($mean): float {
+            static function($val) use ($mean): float {
                 return ($val - $mean) ** 2;
             },
             $numbers

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(static function (array $column): float {
+        $this->variances = array_map(static function(array $column): float {
             return Variance::population($column);
         }, Matrix::transposeArray($samples));
 

src/Tree/Node/DecisionNode.php

Spacing +1 added lines, -1 removed lines

@@ -50,7 +50,7 @@
         $this->value = $value;
         $this->groups = $groups;
         $this->impurity = $impurity;
-        $this->samplesCount = (int) array_sum(array_map(static function (array $group): int {
+        $this->samplesCount = (int) array_sum(array_map(static function(array $group): int {
             return count($group[0]);
         }, $groups));
     }

src/Clustering/FuzzyCMeans.php

Spacing +1 added lines, -1 removed lines

@@ -139,7 +139,7 @@
                 $total += $val;
             }
 
-            $this->membership[] = array_map(static function ($val) use ($total): float {
+            $this->membership[] = array_map(static function($val) use ($total): float {
                 return $val / $total;
             }, $row);
         }

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): float {
+                return function($x, $y) use ($dist): float {
                     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, $y): float {
+                return function($x, $y): float {
                     $res = Matrix::dot($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): float {
+                return function($x, $y) use ($dist): float {
                     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();
 

src/Association/Apriori.php

Spacing +7 added lines, -7 removed lines

@@ -104,11 +104,11 @@ 
      */
     protected function predictSample(array $sample): array
     {
-        $predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample): bool {
+        $predicts = array_values(array_filter($this->getRules(), function($rule) use ($sample): bool {
             return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample);
         }));
 
-        return array_map(static function ($rule) {
+        return array_map(static function($rule) {
             return $rule[self::ARRAY_KEY_CONSEQUENT];
         }, $predicts);
     }
@@ -177,7 +177,7 @@ 
         $cardinality = count($sample);
         $antecedents = $this->powerSet($sample);
 
-        return array_filter($antecedents, static function ($antecedent) use ($cardinality): bool {
+        return array_filter($antecedents, static function($antecedent) use ($cardinality): bool {
             return (count($antecedent) != $cardinality) && ($antecedent != []);
         });
     }
@@ -199,7 +199,7 @@ 
             }
         }
 
-        return array_map(static function ($entry): array {
+        return array_map(static function($entry): array {
             return [$entry];
         }, $items);
     }
@@ -213,7 +213,7 @@ 
      */
     private function frequent(array $samples): array
     {
-        return array_values(array_filter($samples, function ($entry): bool {
+        return array_values(array_filter($samples, function($entry): bool {
             return $this->support($entry) >= $this->support;
         }));
     }
@@ -288,7 +288,7 @@ 
      */
     private function frequency(array $sample): int
     {
-        return count(array_filter($this->samples, function ($entry) use ($sample): bool {
+        return count(array_filter($this->samples, function($entry) use ($sample): bool {
             return $this->subset($entry, $sample);
         }));
     }
@@ -303,7 +303,7 @@ 
      */
     private function contains(array $system, array $set): bool
     {
-        return (bool) array_filter($system, function ($entry) use ($set): bool {
+        return (bool) array_filter($system, function($entry) use ($set): bool {
             return $this->equals($entry, $set);
         });
     }

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): array {
+        $callback = function($weights, $sample, $target): array {
             $this->weights = $weights;
 
             $output = $this->output($sample);