php-ai / php-ml

src/Phpml/Regression/MLPRegressor.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/Tokenization/WhitespaceTokenizer.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/Tokenization/Tokenizer.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/Tokenization/WordTokenizer.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/Association/Associator.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/Regression/SVR.php

Spacing +1 added lines, -1 removed lines

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

src/Phpml/Classification/NaiveBayes.php

Spacing +14 added lines, -14 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification;
 
@@ -13,8 +13,8 @@ 
 {
     use Trainable, Predictable;
 
-    const CONTINUOS    = 1;
-    const NOMINAL    = 2;
+    const CONTINUOS = 1;
+    const NOMINAL = 2;
     const EPSILON = 1e-10;
 
     /**
@@ -25,7 +25,7 @@ 
     /**
      * @var array
      */
-    private $mean= [];
+    private $mean = [];
 
     /**
      * @var array
@@ -86,10 +86,10 @@ 
     private function calculateStatistics($label, $samples)
     {
         $this->std[$label] = array_fill(0, $this->featureCount, 0);
-        $this->mean[$label]= array_fill(0, $this->featureCount, 0);
+        $this->mean[$label] = array_fill(0, $this->featureCount, 0);
         $this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
         $this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
-        for ($i=0; $i<$this->featureCount; $i++) {
+        for ($i = 0; $i < $this->featureCount; $i++) {
             // Get the values of nth column in the samples array
             // Mean::arithmetic is called twice, can be optimized
             $values = array_column($samples, $i);
@@ -100,7 +100,7 @@ 
                 $this->dataType[$label][$i] = self::NOMINAL;
                 $this->discreteProb[$label][$i] = array_count_values($values);
                 $db = &$this->discreteProb[$label][$i];
-                $db = array_map(function ($el) use ($numValues) {
+                $db = array_map(function($el) use ($numValues) {
                     return $el / $numValues;
                 }, $db);
             } else {
@@ -123,14 +123,14 @@ 
     {
         $value = $sample[$feature];
         if ($this->dataType[$label][$feature] == self::NOMINAL) {
-            if (! isset($this->discreteProb[$label][$feature][$value]) ||
+            if (!isset($this->discreteProb[$label][$feature][$value]) ||
                 $this->discreteProb[$label][$feature][$value] == 0) {
                 return self::EPSILON;
             }
             return $this->discreteProb[$label][$feature][$value];
         }
-        $std = $this->std[$label][$feature] ;
-        $mean= $this->mean[$label][$feature];
+        $std = $this->std[$label][$feature];
+        $mean = $this->mean[$label][$feature];
         // Calculate the probability density by use of normal/Gaussian distribution
         // Ref: https://en.wikipedia.org/wiki/Normal_distribution
         //
@@ -138,7 +138,7 @@ 
         // some libraries adopt taking log of calculations such as
         // scikit-learn did.
         // (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
-        $pdf  =  -0.5 * log(2.0 * pi() * $std * $std);
+        $pdf  = -0.5 * log(2.0 * pi() * $std * $std);
         $pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
         return $pdf;
     }
@@ -151,7 +151,7 @@ 
     private function getSamplesByLabel($label)
     {
         $samples = array();
-        for ($i=0; $i<$this->sampleCount; $i++) {
+        for ($i = 0; $i < $this->sampleCount; $i++) {
             if ($this->targets[$i] == $label) {
                 $samples[] = $this->samples[$i];
             }
@@ -178,7 +178,7 @@ 
             $predictions = array();
             foreach ($this->labels as $label) {
                 $p = $this->p[$label];
-                for ($i=0; $i<$this->featureCount; $i++) {
+                for ($i = 0; $i < $this->featureCount; $i++) {
                     $Plf = $this->sampleProbability($sample, $i, $label);
                     $p += $Plf;
                 }
@@ -188,7 +188,7 @@ 
             reset($predictions);
             $samplePredictions[] = key($predictions);
         }
-        if (! $isArray) {
+        if (!$isArray) {
             return $samplePredictions[0];
         }
         return $samplePredictions;

src/Phpml/Clustering/FuzzyCMeans.php

Spacing +10 added lines, -10 removed lines

@@ -1,5 +1,5 @@ 
 <?php
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Clustering;
 
@@ -85,15 +85,15 @@ 
     protected function generateRandomMembership(int $rows, int $cols)
     {
         $this->membership = [];
-        for ($i=0; $i < $rows; $i++) {
+        for ($i = 0; $i < $rows; $i++) {
             $row = [];
             $total = 0.0;
-            for ($k=0; $k < $cols; $k++) {
+            for ($k = 0; $k < $cols; $k++) {
                 $val = rand(1, 5) / 10.0;
                 $row[] = $val;
                 $total += $val;
             }
-            $this->membership[] = array_map(function ($val) use ($total) {
+            $this->membership[] = array_map(function($val) use ($total) {
                 return $val / $total;
             }, $row);
         }
@@ -102,17 +102,17 @@ 
     protected function updateClusters()
     {
         $dim = $this->space->getDimension();
-        if (! $this->clusters) {
+        if (!$this->clusters) {
             $this->clusters = [];
-            for ($i=0; $i<$this->clustersNumber; $i++) {
+            for ($i = 0; $i < $this->clustersNumber; $i++) {
                 $this->clusters[] = new Cluster($this->space, array_fill(0, $dim, 0.0));
             }
         }
 
-        for ($i=0; $i<$this->clustersNumber; $i++) {
+        for ($i = 0; $i < $this->clustersNumber; $i++) {
             $cluster = $this->clusters[$i];
             $center = $cluster->getCoordinates();
-            for ($k=0; $k<$dim; $k++) {
+            for ($k = 0; $k < $dim; $k++) {
                 $a = $this->getMembershipRowTotal($i, $k, true);
                 $b = $this->getMembershipRowTotal($i, $k, false);
                 $center[$k] = $a / $b;
@@ -202,7 +202,7 @@ 
     {
         // 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();
 
@@ -223,7 +223,7 @@ 
         } while ($difference > $this->epsilon && $iterations++ <= $this->maxIterations);
 
         // Attach (hard cluster) each data point to the nearest cluster
-        for ($k=0; $k<$this->sampleCount; $k++) {
+        for ($k = 0; $k < $this->sampleCount; $k++) {
             $column = array_column($this->membership, $k);
             arsort($column);
             reset($column);