php-ai / php-ml

src/Phpml/Classification/Ensemble/RandomForest.php

Spacing +4 added lines, -4 removed lines

@@ -1,5 +1,5 @@ 
 <?php
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Ensemble;
 
@@ -70,11 +70,11 @@ 
     protected function initSingleClassifier($classifier, $index)
     {
         if (is_float($this->featureSubsetRatio)) {
-            $featureCount = (int)($this->featureSubsetRatio * $this->featureCount);
+            $featureCount = (int) ($this->featureSubsetRatio * $this->featureCount);
         } elseif ($this->featureCount == 'sqrt') {
-            $featureCount = (int)sqrt($this->featureCount) + 1;
+            $featureCount = (int) sqrt($this->featureCount) + 1;
         } else {
-            $featureCount = (int)log($this->featureCount, 2) + 1;
+            $featureCount = (int) log($this->featureCount, 2) + 1;
         }
 
         if ($featureCount >= $this->featureCount) {

src/Phpml/Classification/DecisionTree/DecisionTreeLeaf.php

Spacing +14 added lines, -14 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\DecisionTree;
 
@@ -24,7 +24,7 @@ 
     /**
      * @var DecisionTreeLeaf
      */
-    public $rightLeaf= null;
+    public $rightLeaf = null;
 
     /**
      * @var array
@@ -68,7 +68,7 @@ 
         $recordField = $record[$this->columnIndex];
         if ($this->isContinuous && preg_match("/^([<>=]{1,2})\s*(.*)/", strval($this->value), $matches)) {
             $op = $matches[1];
-            $value= floatval($matches[2]);
+            $value = floatval($matches[2]);
             $recordField = strval($recordField);
             eval("\$result = $recordField $op $value;");
             return $result;
@@ -88,16 +88,16 @@ 
             return 0.0;
         }
 
-        $nodeSampleCount = (float)count($this->records);
+        $nodeSampleCount = (float) count($this->records);
         $iT = $this->giniIndex;
 
         if ($this->leftLeaf) {
-            $pL = count($this->leftLeaf->records)/$nodeSampleCount;
+            $pL = count($this->leftLeaf->records) / $nodeSampleCount;
             $iT -= $pL * $this->leftLeaf->giniIndex;
         }
 
         if ($this->rightLeaf) {
-            $pR = count($this->rightLeaf->records)/$nodeSampleCount;
+            $pR = count($this->rightLeaf->records) / $nodeSampleCount;
             $iT -= $pR * $this->rightLeaf->giniIndex;
         }
 
@@ -121,25 +121,25 @@ 
             } else {
                 $col = "col_$this->columnIndex";
             }
-            if (! preg_match("/^[<>=]{1,2}/", $value)) {
+            if (!preg_match("/^[<>=]{1,2}/", $value)) {
                 $value = "=$value";
             }
-            $value = "<b>$col $value</b><br>Gini: ". number_format($this->giniIndex, 2);
+            $value = "<b>$col $value</b><br>Gini: ".number_format($this->giniIndex, 2);
         }
         $str = "<table ><tr><td colspan=3 align=center style='border:1px solid;'>
 				$value</td></tr>";
         if ($this->leftLeaf || $this->rightLeaf) {
-            $str .='<tr>';
+            $str .= '<tr>';
             if ($this->leftLeaf) {
-                $str .="<td valign=top><b>| Yes</b><br>" . $this->leftLeaf->getHTML($columnNames) . "</td>";
+                $str .= "<td valign=top><b>| Yes</b><br>".$this->leftLeaf->getHTML($columnNames)."</td>";
             } else {
-                $str .='<td></td>';
+                $str .= '<td></td>';
             }
-            $str .='<td>&nbsp;</td>';
+            $str .= '<td>&nbsp;</td>';
             if ($this->rightLeaf) {
-                $str .="<td valign=top align=right><b>No |</b><br>" . $this->rightLeaf->getHTML($columnNames) . "</td>";
+                $str .= "<td valign=top align=right><b>No |</b><br>".$this->rightLeaf->getHTML($columnNames)."</td>";
             } else {
-                $str .='<td></td>';
+                $str .= '<td></td>';
             }
             $str .= '</tr>';
         }

src/Phpml/Classification/Ensemble/Bagging.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Ensemble;
 
@@ -140,7 +140,7 @@ 
         $targets = [];
         srand($index);
         $bootstrapSize = $this->subsetRatio * $this->numSamples;
-        for ($i=0; $i < $bootstrapSize; $i++) {
+        for ($i = 0; $i < $bootstrapSize; $i++) {
             $rand = rand(0, $this->numSamples - 1);
             $samples[] = $this->samples[$rand];
             $targets[] = $this->targets[$rand];
@@ -154,7 +154,7 @@ 
     protected function initClassifiers()
     {
         $classifiers = [];
-        for ($i=0; $i<$this->numClassifier; $i++) {
+        for ($i = 0; $i < $this->numClassifier; $i++) {
             $ref = new \ReflectionClass($this->classifier);
             if ($this->classifierOptions) {
                 $obj = $ref->newInstanceArgs($this->classifierOptions);

src/Phpml/Preprocessing/Normalizer.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Preprocessing;
 
@@ -12,7 +12,7 @@ 
 {
     const NORM_L1 = 1;
     const NORM_L2 = 2;
-    const NORM_STD= 3;
+    const NORM_STD = 3;
 
     /**
      * @var int
@@ -117,7 +117,7 @@ 
         foreach ($sample as $feature) {
             $norm2 += $feature * $feature;
         }
-        $norm2 = sqrt((float)$norm2);
+        $norm2 = sqrt((float) $norm2);
 
         if (0 == $norm2) {
             $sample = array_fill(0, count($sample), 1);

src/Phpml/Classification/Linear/Adaline.php

Spacing +5 added lines, -5 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -16,12 +16,12 @@ 
     /**
      * Batch training is the default Adaline training algorithm
      */
-    const BATCH_TRAINING    = 1;
+    const BATCH_TRAINING = 1;
 
     /**
      * Online training: Stochastic gradient descent learning
      */
-    const ONLINE_TRAINING    = 2;
+    const ONLINE_TRAINING = 2;
 
     /**
      * The function whose result will be used to calculate the network error
@@ -62,7 +62,7 @@ 
             $this->normalizer = new Normalizer(Normalizer::NORM_STD);
         }
 
-        if (! in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) {
+        if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) {
             throw new \Exception("Adaline can only be trained with batch and online/stochastic gradient descent algorithm");
         }
         $this->trainingType = $trainingType;
@@ -103,7 +103,7 @@ 
             $sum = array_sum($updates);
 
             // Updates all weights at once
-            for ($i=0; $i <= $this->featureCount; $i++) {
+            for ($i = 0; $i <= $this->featureCount; $i++) {
                 if ($i == 0) {
                     $this->weights[0] += $this->learningRate * $sum;
                 } else {

src/Phpml/Classification/Linear/Perceptron.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -123,7 +123,7 @@ 
                 // Update bias
                 $this->weights[0] += $update * $this->learningRate; // Bias
                 // Update other weights
-                for ($i=1; $i <= $this->featureCount; $i++) {
+                for ($i = 1; $i <= $this->featureCount; $i++) {
                     $this->weights[$i] += $update * $sample[$i - 1] * $this->learningRate;
                 }
             }
@@ -169,6 +169,6 @@ 
     {
         $predictedClass = $this->outputClass($sample);
 
-        return $this->labels[ $predictedClass ];
+        return $this->labels[$predictedClass];
     }
 }

src/Phpml/Classification/Linear/DecisionStump.php

Spacing +1 added lines, -1 removed lines

@@ -1,6 +1,6 @@
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 

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;
 
@@ -112,7 +112,7 @@ 
     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;
@@ -136,7 +136,7 @@ 
         // otherwise group the records so that we can classify the leaf
         // in case maximum depth is reached
         $leftRecords = [];
-        $rightRecords= [];
+        $rightRecords = [];
         $remainingTargets = [];
         $prevRecord = null;
         $allSame = true;
@@ -154,12 +154,12 @@ 
             if ($split->evaluate($record)) {
                 $leftRecords[] = $recordNo;
             } else {
-                $rightRecords[]= $recordNo;
+                $rightRecords[] = $recordNo;
             }
 
             // Group remaining targets
             $target = $this->targets[$recordNo];
-            if (! array_key_exists($target, $remainingTargets)) {
+            if (!array_key_exists($target, $remainingTargets)) {
                 $remainingTargets[$target] = 1;
             } else {
                 $remainingTargets[$target]++;
@@ -175,7 +175,7 @@ 
                 $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;
@@ -234,7 +234,7 @@ 
     protected function getSelectedFeatures()
     {
         $allFeatures = range(0, $this->featureCount - 1);
-        if ($this->numUsableFeatures == 0 && ! $this->selectedFeatures) {
+        if ($this->numUsableFeatures == 0 && !$this->selectedFeatures) {
             return $allFeatures;
         }
 
@@ -270,7 +270,7 @@ 
             $countMatrix[$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) {
@@ -292,7 +292,7 @@ 
         // 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);