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;
     }
 
     /**