php-ai / php-ml

src/Phpml/Dataset/Demo/WineDataset.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Dataset\Demo;
 

src/Phpml/Dataset/FilesDataset.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Dataset;
 

src/Phpml/Dataset/ArrayDataset.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Dataset;
 

src/Phpml/Dataset/Dataset.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Dataset;
 

src/Phpml/Metric/ClassificationReport.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Metric;
 

src/Phpml/Metric/ConfusionMatrix.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Metric;
 

src/Phpml/Classification/NaiveBayes.php

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@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification;
 
@@ -13,8 +13,8 @@ 
 {
     use Trainable, Predictable;
 
-    const CONTINUOS    = 1;
-    const NOMINAL    = 2;
+    const CONTINUOS = 1;
+    const NOMINAL = 2;
     const EPSILON = 1e-10;
 
     /**
@@ -25,7 +25,7 @@ 
     /**
      * @var array
      */
-    private $mean= [];
+    private $mean = [];
 
     /**
      * @var array
@@ -86,10 +86,10 @@ 
     private function calculateStatistics($label, $samples)
     {
         $this->std[$label] = array_fill(0, $this->featureCount, 0);
-        $this->mean[$label]= array_fill(0, $this->featureCount, 0);
+        $this->mean[$label] = array_fill(0, $this->featureCount, 0);
         $this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
         $this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
-        for ($i=0; $i<$this->featureCount; $i++) {
+        for ($i = 0; $i < $this->featureCount; $i++) {
             // Get the values of nth column in the samples array
             // Mean::arithmetic is called twice, can be optimized
             $values = array_column($samples, $i);
@@ -100,7 +100,7 @@ 
                 $this->dataType[$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 {
@@ -123,14 +123,14 @@ 
     {
         $value = $sample[$feature];
         if ($this->dataType[$label][$feature] == self::NOMINAL) {
-            if (! isset($this->discreteProb[$label][$feature][$value]) ||
+            if (!isset($this->discreteProb[$label][$feature][$value]) ||
                 $this->discreteProb[$label][$feature][$value] == 0) {
                 return self::EPSILON;
             }
             return $this->discreteProb[$label][$feature][$value];
         }
-        $std = $this->std[$label][$feature] ;
-        $mean= $this->mean[$label][$feature];
+        $std = $this->std[$label][$feature];
+        $mean = $this->mean[$label][$feature];
         // Calculate the probability density by use of normal/Gaussian distribution
         // Ref: https://en.wikipedia.org/wiki/Normal_distribution
         //
@@ -138,7 +138,7 @@ 
         // some libraries adopt taking log of calculations such as
         // scikit-learn did.
         // (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
-        $pdf  =  -0.5 * log(2.0 * pi() * $std * $std);
+        $pdf  = -0.5 * log(2.0 * pi() * $std * $std);
         $pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
         return $pdf;
     }
@@ -151,7 +151,7 @@ 
     private function getSamplesByLabel($label)
     {
         $samples = [];
-        for ($i=0; $i<$this->sampleCount; $i++) {
+        for ($i = 0; $i < $this->sampleCount; $i++) {
             if ($this->targets[$i] == $label) {
                 $samples[] = $this->samples[$i];
             }
@@ -171,7 +171,7 @@ 
         $predictions = [];
         foreach ($this->labels as $label) {
             $p = $this->p[$label];
-            for ($i=0; $i<$this->featureCount; $i++) {
+            for ($i = 0; $i < $this->featureCount; $i++) {
                 $Plf = $this->sampleProbability($sample, $i, $label);
                 $p += $Plf;
             }

src/Phpml/Exception/FileException.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Exception;
 

src/Phpml/Exception/SerializeException.php

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@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Exception;