php-ai / php-ml

src/Phpml/Classification/DecisionTree.php

Spacing +9 added lines, -9 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification;
 
@@ -75,7 +75,7 @@ 
     protected function getColumnTypes(array $samples)
     {
         $types = [];
-        for ($i=0; $i<$this->featureCount; $i++) {
+        for ($i = 0; $i < $this->featureCount; $i++) {
             $values = array_column($samples, $i);
             $isCategorical = $this->isCategoricalColumn($values);
             $types[] = $isCategorical ? self::NOMINAL : self::CONTINUOS;
@@ -95,7 +95,7 @@ 
             $this->actualDepth = $depth;
         }
         $leftRecords = [];
-        $rightRecords= [];
+        $rightRecords = [];
         $remainingTargets = [];
         $prevRecord = null;
         $allSame = true;
@@ -108,10 +108,10 @@ 
             if ($split->evaluate($record)) {
                 $leftRecords[] = $recordNo;
             } else {
-                $rightRecords[]= $recordNo;
+                $rightRecords[] = $recordNo;
             }
             $target = $this->targets[$recordNo];
-            if (! in_array($target, $remainingTargets)) {
+            if (!in_array($target, $remainingTargets)) {
                 $remainingTargets[] = $target;
             }
         }
@@ -126,7 +126,7 @@ 
                 $split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1);
             }
             if ($rightRecords) {
-                $split->rightLeaf= $this->getSplitLeaf($rightRecords, $depth + 1);
+                $split->rightLeaf = $this->getSplitLeaf($rightRecords, $depth + 1);
             }
         }
         return $split;
@@ -143,7 +143,7 @@ 
         $samples = array_combine($records, $this->preprocess($samples));
         $bestGiniVal = 1;
         $bestSplit = null;
-        for ($i=0; $i<$this->featureCount; $i++) {
+        for ($i = 0; $i < $this->featureCount; $i++) {
             $colValues = [];
             $baseValue = null;
             foreach ($samples as $index => $row) {
@@ -183,7 +183,7 @@ 
             $countMatrix[$label][$rowIndex]++;
         }
         $giniParts = [0, 0];
-        for ($i=0; $i<=1; $i++) {
+        for ($i = 0; $i <= 1; $i++) {
             $part = 0;
             $sum = array_sum(array_column($countMatrix, $i));
             if ($sum > 0) {
@@ -205,7 +205,7 @@ 
         // Detect and convert continuous data column values into
         // discrete values by using the median as a threshold value
         $columns = [];
-        for ($i=0; $i<$this->featureCount; $i++) {
+        for ($i = 0; $i < $this->featureCount; $i++) {
             $values = array_column($samples, $i);
             if ($this->columnTypes[$i] == self::CONTINUOS) {
                 $median = Mean::median($values);

src/Phpml/Classification/NaiveBayes.php

Spacing +13 added lines, -13 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification;
 
@@ -13,8 +13,8 @@ 
 {
     use Trainable, Predictable;
 
-    const CONTINUOS    = 1;
-    const NOMINAL    = 2;
+    const CONTINUOS = 1;
+    const NOMINAL = 2;
     const EPSILON = 1e-10;
 
     /**
@@ -25,7 +25,7 @@ 
     /**
      * @var array
      */
-    private $mean= [];
+    private $mean = [];
 
     /**
      * @var array
@@ -86,10 +86,10 @@ 
     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);
@@ -100,7 +100,7 @@ 
                 $this->dataType[$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 {
@@ -123,14 +123,14 @@ 
     {
         $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
         //
@@ -138,7 +138,7 @@ 
         // 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 $pdf;
     }
@@ -151,7 +151,7 @@ 
     private function getSamplesByLabel($label)
     {
         $samples = [];
-        for ($i=0; $i<$this->sampleCount; $i++) {
+        for ($i = 0; $i < $this->sampleCount; $i++) {
             if ($this->targets[$i] == $label) {
                 $samples[] = $this->samples[$i];
             }
@@ -171,7 +171,7 @@ 
         $predictions = [];
         foreach ($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;
             }