php-ai / php-ml

src/Phpml/Classification/Linear/LogisticRegression.php

Doc Comments +1 added lines, -1 removed lines

@@ -63,7 +63,7 @@
 
      * @param int $maxIterations
      * @param \Phpml\Classification\Linear\type $normalizeInputs
-     * @param \Phpml\Classification\Linear\type $trainingType
+     * @param integer $trainingType
      * @param \Phpml\Classification\Linear\type $cost
      * @param string $penalty
      *

Unused Use Statements -1 removed lines

@@ -4,7 +4,6 @@
 
 namespace Phpml\Classification\Linear;
 
-use Phpml\Classification\Classifier;
 use Phpml\Helper\Optimizer\ConjugateGradient;
 
 class LogisticRegression extends Adaline

Indentation -1 removed lines

@@ -60,7 +60,6 @@
      * Cost function can be 'log' for log-likelihood and 'sse' for sum of squared errors <br>
      *
      * Penalty (Regularization term) can be 'L2' or empty string to cancel penalty term
-
      * @param int $maxIterations
      * @param \Phpml\Classification\Linear\type $normalizeInputs
      * @param \Phpml\Classification\Linear\type $trainingType

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 Descent 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 gradient descent) 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");
         }
 
@@ -176,7 +176,7 @@ 
                  * 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);
 
@@ -207,7 +207,7 @@ 
                  * 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/Helper/Optimizer/GD.php

Spacing +5 added lines, -5 removed lines

@@ -1,4 +1,4 @@ 
-<?php declare(strict_types=1);
+<?php declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -40,7 +40,7 @@ 
 
             $this->updateWeightsWithUpdates($updates, $totalPenalty);
 
-            $this->costValues[] = array_sum($errors)/$this->sampleCount;
+            $this->costValues[] = array_sum($errors) / $this->sampleCount;
 
             if ($this->earlyStop($theta)) {
                 break;
@@ -61,7 +61,7 @@ 
     protected function gradient(array $theta)
     {
         $costs = [];
-        $gradient= [];
+        $gradient = [];
         $totalPenalty = 0;
 
         foreach ($this->samples as $index => $sample) {
@@ -71,7 +71,7 @@ 
             list($cost, $grad, $penalty) = array_pad($result, 3, 0);
 
             $costs[] = $cost;
-            $gradient[]= $grad;
+            $gradient[] = $grad;
             $totalPenalty += $penalty;
         }
 
@@ -87,7 +87,7 @@ 
     protected function updateWeightsWithUpdates(array $updates, float $penalty)
     {
         // Updates all weights at once
-        for ($i=0; $i <= $this->dimensions; $i++) {
+        for ($i = 0; $i <= $this->dimensions; $i++) {
             if ($i == 0) {
                 $this->theta[0] -= $this->learningRate * array_sum($updates);
             } else {

src/Phpml/Helper/Optimizer/StochasticGD.php

Spacing +4 added lines, -4 removed lines

@@ -1,4 +1,4 @@ 
-<?php declare(strict_types=1);
+<?php declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -63,7 +63,7 @@ 
      *
      * @var array
      */
-    protected $costValues= [];
+    protected $costValues = [];
 
     /**
      * Initializes the SGD optimizer for the given number of dimensions
@@ -192,7 +192,7 @@ 
             $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]);
             }
@@ -216,7 +216,7 @@ 
     {
         // 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

@@ -1,4 +1,4 @@ 
-<?php declare(strict_types=1);
+<?php declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -29,7 +29,7 @@ 
 
         // 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

@@ -1,4 +1,4 @@ 
-<?php declare(strict_types=1);
+<?php declare(strict_types = 1);
 
 namespace Phpml\Helper\Optimizer;
 
@@ -32,7 +32,7 @@ 
 
         $d = mp::muls($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));
 
@@ -159,7 +159,7 @@ 
     {
         $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/Perceptron.php

Indentation +2 added lines, -2 removed lines

@@ -15,7 +15,7 @@ 
 {
     use Predictable, OneVsRest;
 
-   /**
+    /**
      * @var array
      */
     protected $samples = [];
@@ -83,7 +83,7 @@ 
         $this->maxIterations = $maxIterations;
     }
 
-   /**
+    /**
      * @param array $samples
      * @param array $targets
      */

Spacing +4 added lines, -4 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Linear;
 
@@ -118,7 +118,7 @@ 
     protected function runTraining()
     {
         // 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);
@@ -137,7 +137,7 @@ 
      */
     protected function runGradientDescent(\Closure $gradientFunc, bool $isBatch = false)
     {
-        $class = $isBatch ? GD::class :  StochasticGD::class;
+        $class = $isBatch ? GD::class : StochasticGD::class;
 
         $optimizer = (new $class($this->featureCount))
             ->setLearningRate($this->learningRate)
@@ -227,6 +227,6 @@ 
 
         $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");
         }
 
@@ -57,7 +57,7 @@ 
     protected function runTraining()
     {
         // 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);