php-ai / php-ml

src/Phpml/Classification/NaiveBayes.php

Braces +13 added lines, -10 removed lines

@@ -69,8 +69,7 @@ 
 				$db = array_map(function($el) use ($numValues) {
 					return $el / $numValues;
 				}, $db);
-			}
-			else
+			} else
 			{
 				$this->mean[$label][$i] = Mean::arithmetic($values);
 				$this->std[$label][$i] = StandardDeviation::population($values, false);
@@ -91,15 +90,17 @@ 
 		if ($this->dataType[$label][$feature] == self::NOMINAL)
 		{
 			if (! isset($this->discreteProb[$label][$feature][$value]) ||
-				$this->discreteProb[$label][$feature][$value] == 0)
-				return self::SMALL_VALUE;
+				$this->discreteProb[$label][$feature][$value] == 0) {
+							return self::SMALL_VALUE;
+			}
 			return $this->discreteProb[$label][$feature][$value];
 		}
 		$std = $this->std[$label][$feature] ;
 		$mean= $this->mean[$label][$feature];
 		$std2 = $std * $std;
-		if ($std2 == 0)
-			$std2 = self::SMALL_VALUE;
+		if ($std2 == 0) {
+					$std2 = self::SMALL_VALUE;
+		}
 		// Calculate the probability density by use of normal/Gaussian distribution
 		// Ref: https://en.wikipedia.org/wiki/Normal_distribution
 		return (1 / sqrt(2 * $std2 * pi())) * exp( - pow($value - $mean, 2) / (2 * $std2));
@@ -113,9 +114,10 @@ 
 	private function getSamplesByLabel($label)
 	{
 		$samples = array();
-		for($i=0; $i<$this->sampleCount; $i++)
-			if ($this->targets[$i] == $label)
+		for($i=0; $i<$this->sampleCount; $i++) {
+					if ($this->targets[$i] == $label)
 				$samples[] = $this->samples[$i];
+		}
 		return $samples;
 	}
 
@@ -136,8 +138,9 @@ 
 			{
 				$Plf = $this->sampleProbability($sample, $i, $label);
 				// Correct the value for small and zero values
-				if (is_nan($Plf) || $Plf < self::SMALL_VALUE)
-					$Plf = self::SMALL_VALUE;
+				if (is_nan($Plf) || $Plf < self::SMALL_VALUE) {
+									$Plf = self::SMALL_VALUE;
+				}
 				$p *= $Plf;
 			}
 			$predictions[$label] = $p;