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by Arkadiusz
06:54
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src/Phpml/Classification/NaiveBayes.php 1 patch
Spacing   +14 added lines, -14 removed lines patch added patch discarded remove patch 
@@ -1,6 +1,6 @@  discard block
 
 block discarded – undo
1 1 
 <?php
2 2
3 
-declare(strict_types=1);
3 
+declare(strict_types = 1);
4 4
5 5 
 namespace Phpml\Classification;
6 6 
 
@@ -12,11 +12,11 @@  discard block
 
 block discarded – undo
12 12 
 class NaiveBayes implements Classifier
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 {
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     use Trainable, Predictable;
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-    const CONTINUOS    = 1;
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-    const NOMINAL    = 2;
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+    const CONTINUOS = 1;
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+    const NOMINAL = 2;
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     const EPSILON = 1e-10;
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     private $std = array();
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-    private $mean= array();
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+    private $mean = array();
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     private $discreteProb = array();
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     private $dataType = array();
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     private $p = array();
 
@@ -48,10 +48,10 @@  discard block
 
 block discarded – undo
48 48 
     private function calculateStatistics($label, $samples)
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     {
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         $this->std[$label] = array_fill(0, $this->featureCount, 0);
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-        $this->mean[$label]= array_fill(0, $this->featureCount, 0);
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+        $this->mean[$label] = array_fill(0, $this->featureCount, 0);
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         $this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
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         $this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
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-        for ($i=0; $i<$this->featureCount; $i++) {
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+        for ($i = 0; $i < $this->featureCount; $i++) {
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             // Get the values of nth column in the samples array
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             // Mean::arithmetic is called twice, can be optimized
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             $values = array_column($samples, $i);
 
@@ -62,7 +62,7 @@  discard block
 
 block discarded – undo
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                 $this->dataType[$label][$i] = self::NOMINAL;
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                 $this->discreteProb[$label][$i] = array_count_values($values);
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                 $db = &$this->discreteProb[$label][$i];
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-                $db = array_map(function ($el) use ($numValues) {
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+                $db = array_map(function($el) use ($numValues) {
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                     return $el / $numValues;
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                 }, $db);
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             } else {
 
@@ -84,14 +84,14 @@  discard block
 
 block discarded – undo
84 84 
     {
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         $value = $sample[$feature];
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         if ($this->dataType[$label][$feature] == self::NOMINAL) {
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-            if (! isset($this->discreteProb[$label][$feature][$value]) ||
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+            if (!isset($this->discreteProb[$label][$feature][$value]) ||
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                 $this->discreteProb[$label][$feature][$value] == 0) {
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                 return self::EPSILON;
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             }
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             return $this->discreteProb[$label][$feature][$value];
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         }
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-        $std = $this->std[$label][$feature] ;
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-        $mean= $this->mean[$label][$feature];
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+        $std = $this->std[$label][$feature];
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+        $mean = $this->mean[$label][$feature];
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         // Calculate the probability density by use of normal/Gaussian distribution
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         // Ref: https://en.wikipedia.org/wiki/Normal_distribution
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 		//
 
@@ -99,7 +99,7 @@  discard block
 
 block discarded – undo
99 99 
 		// some libraries adopt taking log of calculations such as
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 		// scikit-learn did.
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 		// (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
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-		$pdf  =  -0.5 * log(2.0 * pi() * $std * $std);
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+		$pdf  = -0.5 * log(2.0 * pi() * $std * $std);
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 		$pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
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 		return $pdf;
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     }
 
@@ -112,7 +112,7 @@  discard block
 
 block discarded – undo
112 112 
     private function getSamplesByLabel($label)
113 113 
     {
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         $samples = array();
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-        for ($i=0; $i<$this->sampleCount; $i++) {
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+        for ($i = 0; $i < $this->sampleCount; $i++) {
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             if ($this->targets[$i] == $label) {
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                 $samples[] = $this->samples[$i];
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             }
 
@@ -139,7 +139,7 @@  discard block
 
 block discarded – undo
139 139 
             $predictions = array();
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             foreach ($this->labels as $label) {
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                 $p = $this->p[$label];
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-                for ($i=0; $i<$this->featureCount; $i++) {
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+                for ($i = 0; $i < $this->featureCount; $i++) {
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                     $Plf = $this->sampleProbability($sample, $i, $label);
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                     $p += $Plf;
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                 }
 
@@ -149,7 +149,7 @@  discard block
 
 block discarded – undo
149 149 
             reset($predictions);
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             $samplePredictions[] = key($predictions);
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         }
152 
-        if (! $isArray) {
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+        if (!$isArray) {
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             return $samplePredictions[0];
154 154 
         }
155 155 
         return $samplePredictions;
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