php-ai / php-ml

src/Clustering/KMeans/Cluster.php

Doc Comments +1 added lines, -1 removed lines

@@ -128,7 +128,7 @@
     }
 
     /**
-     * @return mixed
+     * @return integer
      */
     public function count()
     {

src/NeuralNetwork/Network/MultilayerPerceptron.php

Doc Comments +3 added lines

@@ -142,6 +142,9 @@
         $this->addLayer(new Layer($nodes, Input::class));
     }
 
+    /**
+     * @param ActivationFunction $defaultActivationFunction
+     */
     private function addNeuronLayers(array $layers, ?ActivationFunction $defaultActivationFunction = null): void
     {
         foreach ($layers as $layer) {

src/Math/Distance/Manhattan.php

Spacing +1 added lines, -1 removed lines

@@ -18,7 +18,7 @@
             throw new InvalidArgumentException('Size of given arrays does not match');
         }
 
-        return array_sum(array_map(function ($m, $n) {
+        return array_sum(array_map(function($m, $n) {
             return abs($m - $n);
         }, $a, $b));
     }

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/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(function (array $class): int {
+        $samplesPerClass = array_map(function(array $class): int {
             return count($class);
         }, $samples);
         $allSamples = array_sum($samplesPerClass);
@@ -41,10 +41,10 @@ 
         $dfbn = $classes - 1;
         $dfwn = $allSamples - $classes;
 
-        $msb = array_map(function ($s) use ($dfbn) {
+        $msb = array_map(function($s) use ($dfbn) {
             return $s / $dfbn;
         }, $ssbn);
-        $msw = array_map(function ($s) use ($dfwn) {
+        $msw = array_map(function($s) use ($dfwn) {
             return $s / $dfwn;
         }, $sswn);
 
@@ -72,7 +72,7 @@ 
 
     private static function sumOfFeaturesPerClass(array $samples): array
     {
-        return array_map(function (array $class) {
+        return array_map(function(array $class) {
             $sum = array_fill(0, count($class[0]), 0);
             foreach ($class as $sample) {
                 foreach ($sample as $index => $feature) {
@@ -93,7 +93,7 @@ 
             }
         }
 
-        return array_map(function ($sum) {
+        return array_map(function($sum) {
             return $sum ** 2;
         }, $squares);
     }

src/Math/Matrix.php

Spacing +1 added lines, -1 removed lines

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

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/Classification/Linear/Perceptron.php

Spacing +1 added lines, -1 removed lines

@@ -157,7 +157,7 @@
     protected function runTraining(array $samples, array $targets)
     {
         // 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);