php-ai / php-ml

src/Phpml/Classification/Linear/DecisionStump.php

Spacing +10 added lines, -10 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -190,8 +190,8 @@ 
             }
 
             // Try other possible points one by one
-            for ($step = $minValue; $step <= $maxValue; $step+= $stepSize) {
-                $threshold = (float)$step;
+            for ($step = $minValue; $step <= $maxValue; $step += $stepSize) {
+                $threshold = (float) $step;
                 list($errorRate, $prob) = $this->calculateErrorRate($targets, $threshold, $operator, $values);
                 if ($errorRate < $split['trainingErrorRate']) {
                     $split = ['value' => $threshold, 'operator' => $operator,
@@ -215,7 +215,7 @@ 
     {
         $values = array_column($samples, $col);
         $valueCounts = array_count_values($values);
-        $distinctVals= array_keys($valueCounts);
+        $distinctVals = array_keys($valueCounts);
 
         $split = null;
 
@@ -274,7 +274,7 @@ 
         $wrong = 0.0;
         $prob = [];
         $leftLabel = $this->binaryLabels[0];
-        $rightLabel= $this->binaryLabels[1];
+        $rightLabel = $this->binaryLabels[1];
 
         foreach ($values as $index => $value) {
             if ($this->evaluate($value, $operator, $threshold)) {
@@ -288,7 +288,7 @@ 
                 $wrong += $this->weights[$index];
             }
 
-            if (! isset($prob[$predicted][$target])) {
+            if (!isset($prob[$predicted][$target])) {
                 $prob[$predicted][$target] = 0;
             }
             $prob[$predicted][$target]++;
@@ -297,7 +297,7 @@ 
         // Calculate probabilities: Proportion of labels in each leaf
         $dist = array_combine($this->binaryLabels, array_fill(0, 2, 0.0));
         foreach ($prob as $leaf => $counts) {
-            $leafTotal = (float)array_sum($prob[$leaf]);
+            $leafTotal = (float) array_sum($prob[$leaf]);
             foreach ($counts as $label => $count) {
                 if (strval($leaf) == strval($label)) {
                     $dist[$leaf] = $count / $leafTotal;
@@ -348,8 +348,8 @@ 
      */
     public function __toString()
     {
-        return "IF $this->column $this->operator $this->value " .
-            "THEN " . $this->binaryLabels[0] . " ".
-            "ELSE " . $this->binaryLabels[1];
+        return "IF $this->column $this->operator $this->value ".
+            "THEN ".$this->binaryLabels[0]." ".
+            "ELSE ".$this->binaryLabels[1];
     }
 }

src/Phpml/Classification/Linear/Perceptron.php

Spacing +4 added lines, -4 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -167,7 +167,7 @@ 
     protected function runTraining(array $samples, array $targets)
     {
         // The cost function is the sum of squares
-        $callback = function ($weights, $sample, $target) {
+        $callback = function($weights, $sample, $target) {
             $this->weights = $weights;
 
             $prediction = $this->outputClass($sample);
@@ -189,7 +189,7 @@ 
      */
     protected function runGradientDescent(array $samples, array $targets, \Closure $gradientFunc, bool $isBatch = false)
     {
-        $class = $isBatch ? GD::class :  StochasticGD::class;
+        $class = $isBatch ? GD::class : StochasticGD::class;
 
         if (empty($this->optimizer)) {
             $this->optimizer = (new $class($this->featureCount))
@@ -284,6 +284,6 @@ 
 
         $predictedClass = $this->outputClass($sample);
 
-        return $this->labels[ $predictedClass ];
+        return $this->labels[$predictedClass];
     }
 }

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;
 
@@ -12,12 +12,12 @@ 
     /**
      * 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;
 
     /**
      * Training type may be either 'Batch' or 'Online' learning
@@ -41,7 +41,7 @@ 
     public function __construct(float $learningRate = 0.001, int $maxIterations = 1000,
         bool $normalizeInputs = true, int $trainingType = self::BATCH_TRAINING)
     {
-        if (! in_array($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");
         }
 
@@ -60,7 +60,7 @@ 
     protected function runTraining(array $samples, array $targets)
     {
         // The cost function is the sum of squares
-        $callback = function ($weights, $sample, $target) {
+        $callback = function($weights, $sample, $target) {
             $this->weights = $weights;
 
             $output = $this->output($sample);

src/Phpml/IncrementalEstimator.php

Spacing +1 added lines, -1 removed lines

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