php-ai / php-ml

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,

src/Phpml/Classification/Linear/Perceptron.php

Indentation +4 added lines, -4 removed lines

@@ -100,10 +100,10 @@
     }
 
     /**
-      * @param array $samples
-      * @param array $targets
-      * @param array $labels
-      */
+     * @param array $samples
+     * @param array $targets
+     * @param array $labels
+     */
     public function trainBinary(array $samples, array $targets, array $labels)
     {
         if ($this->normalizer) {

src/Phpml/Classification/Linear/LogisticRegression.php

Spacing +6 added lines, -6 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -76,13 +76,13 @@ 
     ) {
         $trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
         if (!in_array($trainingType, $trainingTypes)) {
-            throw new \Exception("Logistic regression can only be trained with " .
-                "batch (gradient descent), online (stochastic gradient descent) " .
+            throw new \Exception("Logistic regression can only be trained with ".
+                "batch (gradient descent), online (stochastic gradient descent) ".
                 "or conjugate batch (conjugate gradients) algorithms");
         }
 
         if (!in_array($cost, ['log', 'sse'])) {
-            throw new \Exception("Logistic regression cost function can be one of the following: \n" .
+            throw new \Exception("Logistic regression cost function can be one of the following: \n".
                 "'log' for log-likelihood and 'sse' for sum of squared errors");
         }
 
@@ -193,7 +193,7 @@ 
                  * The gradient of the cost function to be used with gradient descent:
                  *		∇J(x) = -(y - h(x)) = (h(x) - y)
                  */
-                $callback = function ($weights, $sample, $y) use ($penalty) {
+                $callback = function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 
@@ -224,7 +224,7 @@ 
                  * The gradient of the cost function:
                  *		∇J(x) = -(h(x) - y) . h(x) . (1 - h(x))
                  */
-                $callback = function ($weights, $sample, $y) use ($penalty) {
+                $callback = function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);