php-ai / php-ml

src/Phpml/Classification/NaiveBayes.php

Indentation +10 added lines, -10 removed lines

@@ -67,7 +67,7 @@ 
                 }, $db);
             } else {
                 $this->mean[$label][$i] = Mean::arithmetic($values);
-				// Add epsilon in order to avoid zero stdev
+                // Add epsilon in order to avoid zero stdev
                 $this->std[$label][$i] = 1e-10 + StandardDeviation::population($values, false);
             }
         }
@@ -75,7 +75,7 @@ 
 
     /**
      * Calculates the probability P(label|sample_n)
-	 *
+     *
      * @param array $sample
      * @param int $feature
      * @param string $label
@@ -94,14 +94,14 @@ 
         $mean= $this->mean[$label][$feature];
         // Calculate the probability density by use of normal/Gaussian distribution
         // Ref: https://en.wikipedia.org/wiki/Normal_distribution
-		//
-		// In order to avoid numerical errors because of small or zero values,
-		// 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);
-		$pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
-		return $pdf;
+        //
+        // In order to avoid numerical errors because of small or zero values,
+        // 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);
+        $pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
+        return $pdf;
     }
 
     /**

Spacing +14 added lines, -14 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification;
 
@@ -12,11 +12,11 @@ 
 class NaiveBayes implements Classifier
 {
     use Trainable, Predictable;
-    const CONTINUOS    = 1;
-    const NOMINAL    = 2;
+    const CONTINUOS = 1;
+    const NOMINAL = 2;
     const EPSILON = 1e-10;
     private $std = array();
-    private $mean= array();
+    private $mean = array();
     private $discreteProb = array();
     private $dataType = array();
     private $p = array();
@@ -48,10 +48,10 @@ 
     private function calculateStatistics($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);
@@ -62,7 +62,7 @@ 
                 $this->dataType[$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 {
@@ -84,14 +84,14 @@ 
     {
         $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
 		//
@@ -99,7 +99,7 @@ 
 		// 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 $pdf;
     }
@@ -112,7 +112,7 @@ 
     private function getSamplesByLabel($label)
     {
         $samples = array();
-        for ($i=0; $i<$this->sampleCount; $i++) {
+        for ($i = 0; $i < $this->sampleCount; $i++) {
             if ($this->targets[$i] == $label) {
                 $samples[] = $this->samples[$i];
             }
@@ -139,7 +139,7 @@ 
             $predictions = array();
             foreach ($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 += $Plf;
                 }
@@ -149,7 +149,7 @@ 
             reset($predictions);
             $samplePredictions[] = key($predictions);
         }
-        if (! $isArray) {
+        if (!$isArray) {
             return $samplePredictions[0];
         }
         return $samplePredictions;