php-ai / php-ml

src/Phpml/Classification/DecisionTree.php

Doc Comments +1 added lines, -4 removed lines

@@ -183,7 +183,7 @@ 
 
     /**
      * @param array $records
-     * @return DecisionTreeLeaf[]
+     * @return null|DecisionTreeLeaf
      */
     protected function getBestSplit($records)
     {
@@ -359,7 +359,6 @@ 
     /**
      * Used to set predefined features to consider while deciding which column to use for a split,
      *
-     * @param array $features
      */
     protected function setSelectedFeatures(array $selectedFeatures)
     {
@@ -397,7 +396,6 @@ 
      * each column in the given dataset. The importance values are
      * normalized and their total makes 1.<br/>
      *
-     * @param array $labels
      * @return array
      */
     public function getFeatureImportances()
@@ -437,7 +435,6 @@ 
      *
      * @param int $column
      * @param DecisionTreeLeaf
-     * @param array $collected
      *
      * @return array
      */

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);

src/Phpml/Classification/Linear/Adaline.php

Doc Comments +1 added lines, -1 removed lines

@@ -52,7 +52,7 @@
      * If normalizeInputs is set to true, then every input given to the algorithm will be standardized
      * by use of standard deviation and mean calculation
      *
-     * @param int $learningRate
+     * @param double $learningRate
      * @param int $maxIterations
      */
     public function __construct(float $learningRate = 0.001, int $maxIterations = 1000,

Unused Use Statements -3 removed lines

@@ -4,9 +4,6 @@
 
 namespace Phpml\Classification\Linear;
 
-use Phpml\Helper\Predictable;
-use Phpml\Helper\Trainable;
-use Phpml\Classification\Classifier;
 use Phpml\Classification\Linear\Perceptron;
 use Phpml\Preprocessing\Normalizer;
 

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/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/Perceptron.php

Indentation +2 added lines, -2 removed lines

@@ -20,7 +20,7 @@ 
      */
     protected static $errorFunction = 'outputClass';
 
-   /**
+    /**
      * @var array
      */
     protected $samples = [];
@@ -78,7 +78,7 @@ 
         $this->maxIterations = $maxIterations;
     }
 
-   /**
+    /**
      * @param array $samples
      * @param array $targets
      */

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;