php-ai / php-ml

src/Phpml/Classification/Linear/DecisionStump.php

Spacing +9 added lines, -9 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -192,8 +192,8 @@ 
             }
 
             // 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,
@@ -217,7 +217,7 @@ 
     {
         $values = array_column($samples, $col);
         $valueCounts = array_count_values($values);
-        $distinctVals= array_keys($valueCounts);
+        $distinctVals = array_keys($valueCounts);
 
         $split = null;
 
@@ -276,7 +276,7 @@ 
         $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)) {
@@ -299,7 +299,7 @@ 
         // 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;
@@ -357,8 +357,8 @@ 
      */
     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/Adaline.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;
 
@@ -9,12 +9,12 @@ 
     /**
      * 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
@@ -61,7 +61,7 @@ 
     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/Classification/Linear/Perceptron.php

Indentation +1 added lines, -1 removed lines

@@ -99,7 +99,7 @@
         $this->trainByLabel($samples, $targets, $labels);
     }
 
-   /**
+    /**
      * @param array $samples
      * @param array $targets
      * @param array $labels

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -170,7 +170,7 @@ 
     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);

src/Phpml/Classification/Ensemble/AdaBoost.php

Spacing +5 added lines, -5 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Ensemble;
 
@@ -175,14 +175,14 @@ 
     {
         $weights = $this->weights;
         $std = StandardDeviation::population($weights);
-        $mean= Mean::arithmetic($weights);
+        $mean = Mean::arithmetic($weights);
         $min = min($weights);
-        $minZ= (int)round(($min - $mean) / $std);
+        $minZ = (int) round(($min - $mean) / $std);
 
         $samples = [];
         $targets = [];
         foreach ($weights as $index => $weight) {
-            $z = (int)round(($weight - $mean) / $std) - $minZ + 1;
+            $z = (int) round(($weight - $mean) / $std) - $minZ + 1;
             for ($i = 0; $i < $z; ++$i) {
                 if (rand(0, 1) == 0) {
                     continue;
@@ -264,6 +264,6 @@ 
             $sum += $h * $alpha;
         }
 
-        return $this->labels[ $sum > 0 ? 1 : -1];
+        return $this->labels[$sum > 0 ? 1 : -1];
     }
 }

src/Phpml/Classification/Ensemble/Bagging.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/Classification/Ensemble/RandomForest.php

Spacing +4 added lines, -4 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Ensemble;
 
@@ -137,11 +137,11 @@ 
     protected function initSingleClassifier($classifier)
     {
         if (is_float($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/Clustering/FuzzyCMeans.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Clustering;
 
@@ -101,7 +101,7 @@ 
                 $total += $val;
             }
 
-            $this->membership[] = array_map(function ($val) use ($total) {
+            $this->membership[] = array_map(function($val) use ($total) {
                 return $val / $total;
             }, $row);
         }
@@ -218,7 +218,7 @@ 
     {
         // Initialize variables, clusters and membership matrix
         $this->sampleCount = count($samples);
-        $this->samples =& $samples;
+        $this->samples = & $samples;
         $this->space = new Space(count($samples[0]));
         $this->initClusters();
 

src/Phpml/Helper/Optimizer/StochasticGD.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -72,7 +72,7 @@ 
      *
      * @var array
      */
-    protected $costValues= [];
+    protected $costValues = [];
 
     /**
      * Initializes the SGD optimizer for the given number of dimensions
@@ -240,7 +240,7 @@ 
     {
         // Check for early stop: No change larger than threshold (default 1e-5)
         $diff = array_map(
-            function ($w1, $w2) {
+            function($w1, $w2) {
                 return abs($w1 - $w2) > $this->threshold ? 1 : 0;
             },
             $oldTheta, $this->theta);

src/Phpml/Helper/Optimizer/GD.php

Spacing +3 added lines, -3 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -42,7 +42,7 @@ 
 
             $this->updateWeightsWithUpdates($updates, $totalPenalty);
 
-            $this->costValues[] = array_sum($errors)/$this->sampleCount;
+            $this->costValues[] = array_sum($errors) / $this->sampleCount;
 
             if ($this->earlyStop($theta)) {
                 break;
@@ -65,7 +65,7 @@ 
     protected function gradient(array $theta)
     {
         $costs = [];
-        $gradient= [];
+        $gradient = [];
         $totalPenalty = 0;
 
         foreach ($this->samples as $index => $sample) {