php-ai / php-ml

src/Phpml/Helper/Optimizer/StochasticGD.php

Spacing +4 added lines, -4 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
@@ -216,7 +216,7 @@ 
             $this->theta[0] -= $this->learningRate * $gradient;
 
             // Update other values
-            for ($i=1; $i <= $this->dimensions; $i++) {
+            for ($i = 1; $i <= $this->dimensions; $i++) {
                 $this->theta[$i] -= $this->learningRate *
                     ($gradient * $sample[$i - 1] + $penalty * $this->theta[$i]);
             }
@@ -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/Optimizer.php

Spacing +2 added lines, -2 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -31,7 +31,7 @@ 
 
         // Inits the weights randomly
         $this->theta = [];
-        for ($i=0; $i < $this->dimensions; $i++) {
+        for ($i = 0; $i < $this->dimensions; $i++) {
             $this->theta[] = rand() / (float) getrandmax();
         }
     }

src/Phpml/Helper/Optimizer/ConjugateGradient.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;
 
@@ -34,7 +34,7 @@ 
 
         $d = mp::muls($this->gradient($this->theta), -1);
 
-        for ($i=0; $i < $this->maxIterations; $i++) {
+        for ($i = 0; $i < $this->maxIterations; $i++) {
             // Obtain α that minimizes f(θ + α.d)
             $alpha = $this->getAlpha(array_sum($d));
 
@@ -161,7 +161,7 @@ 
     {
         $theta = $this->theta;
 
-        for ($i=0; $i < $this->dimensions + 1; $i++) {
+        for ($i = 0; $i < $this->dimensions + 1; $i++) {
             if ($i == 0) {
                 $theta[$i] += $alpha * array_sum($d);
             } else {

src/Phpml/Classification/Linear/LogisticRegression.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;
 
@@ -13,12 +13,12 @@ 
     /**
      * Batch training: Gradient descent algorithm (default)
      */
-    const BATCH_TRAINING    = 1;
+    const BATCH_TRAINING = 1;
 
     /**
      * Online training: Stochastic gradient descent learning
      */
-    const ONLINE_TRAINING    = 2;
+    const ONLINE_TRAINING = 2;
 
     /**
      * Conjugate Batch: Conjugate Gradient algorithm
@@ -74,14 +74,14 @@ 
         string $penalty = 'L2')
     {
         $trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
-        if (! in_array($trainingType, $trainingTypes)) {
-            throw new \Exception("Logistic regression can only be trained with " .
-                "batch (gradient descent), online (stochastic gradient descent) " .
+        if (!in_array($trainingType, $trainingTypes)) {
+            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($cost, ['log', 'sse'])) {
-            throw new \Exception("Logistic regression cost function can be one of the following: \n" .
+        if (!in_array($cost, ['log', 'sse'])) {
+            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");
         }
 
@@ -177,7 +177,7 @@ 
                  * 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);
 
@@ -208,7 +208,7 @@ 
                  * 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);
 

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;