php-ai / php-ml

src/Phpml/Classification/NaiveBayes.php

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 SMALL_VALUE = 1e-32;
 	private $std = array();
-	private $mean= array();
+	private $mean = array();
 	private $discreteProb = array();
 	private $dataType = array();
 	private $p = array();
@@ -32,7 +32,7 @@ 
 		// Get distinct targets
 		$this->labels = $targets;
 		array_unique($this->labels);
-		foreach($this->labels as $label)
+		foreach ($this->labels as $label)
 		{
 			$samples = $this->getSamplesByLabel($label);
 			$this->p[$label] = count($samples) / $this->sampleCount;
@@ -49,10 +49,10 @@ 
 	private function calculateStatistics($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
@@ -90,19 +90,19 @@ 
 		$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::SMALL_VALUE;
 			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];
 		$std2 = $std * $std;
 		if ($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));
+		return (1 / sqrt(2 * $std2 * pi())) * exp( -pow($value - $mean, 2) / (2 * $std2));
 	}
 
 	/**
@@ -113,7 +113,7 @@ 
 	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];
 		return $samples;
@@ -129,10 +129,10 @@ 
 		//	P(label|features) = P(label) * P(feature0|label) * P(feature1|label) .... P(featureN|label)
 		// Then compare probability for each class to determine which is most likely
 		$predictions = array();
-		foreach($this->labels as $label)
+		foreach ($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);
 				// Correct the value for small and zero values