php-ai / php-ml

src/Phpml/Classification/WeightedClassifier.php

Spacing +1 added lines, -1 removed lines

@@ -1,4 +1,4 @@
-<?php declare(strict_types=1);
+<?php declare(strict_types = 1);
 
 namespace Phpml\Classification;
 

src/Phpml/Classification/DecisionTree.php

Spacing +9 added lines, -9 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification;
 
@@ -114,7 +114,7 @@ 
     {
         $types = [];
         $featureCount = count($samples[0]);
-        for ($i=0; $i < $featureCount; $i++) {
+        for ($i = 0; $i < $featureCount; $i++) {
             $values = array_column($samples, $i);
             $isCategorical = self::isCategoricalColumn($values);
             $types[] = $isCategorical ? self::NOMINAL : self::CONTINUOS;
@@ -138,7 +138,7 @@ 
         // otherwise group the records so that we can classify the leaf
         // in case maximum depth is reached
         $leftRecords = [];
-        $rightRecords= [];
+        $rightRecords = [];
         $remainingTargets = [];
         $prevRecord = null;
         $allSame = true;
@@ -156,12 +156,12 @@ 
             if ($split->evaluate($record)) {
                 $leftRecords[] = $recordNo;
             } else {
-                $rightRecords[]= $recordNo;
+                $rightRecords[] = $recordNo;
             }
 
             // Group remaining targets
             $target = $this->targets[$recordNo];
-            if (! array_key_exists($target, $remainingTargets)) {
+            if (!array_key_exists($target, $remainingTargets)) {
                 $remainingTargets[$target] = 1;
             } else {
                 $remainingTargets[$target]++;
@@ -177,7 +177,7 @@ 
                 $split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1);
             }
             if ($rightRecords) {
-                $split->rightLeaf= $this->getSplitLeaf($rightRecords, $depth + 1);
+                $split->rightLeaf = $this->getSplitLeaf($rightRecords, $depth + 1);
             }
         }
         return $split;
@@ -247,7 +247,7 @@ 
     protected function getSelectedFeatures()
     {
         $allFeatures = range(0, $this->featureCount - 1);
-        if ($this->numUsableFeatures == 0 && ! $this->selectedFeatures) {
+        if ($this->numUsableFeatures == 0 && !$this->selectedFeatures) {
             return $allFeatures;
         }
 
@@ -283,7 +283,7 @@ 
             $countMatrix[$label][$rowIndex]++;
         }
         $giniParts = [0, 0];
-        for ($i=0; $i<=1; $i++) {
+        for ($i = 0; $i <= 1; $i++) {
             $part = 0;
             $sum = array_sum(array_column($countMatrix, $i));
             if ($sum > 0) {
@@ -305,7 +305,7 @@ 
         // Detect and convert continuous data column values into
         // discrete values by using the median as a threshold value
         $columns = [];
-        for ($i=0; $i<$this->featureCount; $i++) {
+        for ($i = 0; $i < $this->featureCount; $i++) {
             $values = array_column($samples, $i);
             if ($this->columnTypes[$i] == self::CONTINUOS) {
                 $median = Mean::median($values);

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;
 
@@ -169,7 +169,7 @@ 
                 // Update bias
                 $this->weights[0] += $update * $this->learningRate; // Bias
                 // Update other weights
-                for ($i=1; $i <= $this->featureCount; $i++) {
+                for ($i = 1; $i <= $this->featureCount; $i++) {
                     $this->weights[$i] += $update * $sample[$i - 1] * $this->learningRate;
                 }
             }
@@ -202,7 +202,7 @@ 
     {
         // Check for early stop: No change larger than 1e-5
         $diff = array_map(
-            function ($w1, $w2) {
+            function($w1, $w2) {
                 return abs($w1 - $w2) > 1e-5 ? 1 : 0;
             },
             $oldWeights, $this->weights);
@@ -259,6 +259,6 @@ 
 
         $predictedClass = $this->outputClass($sample);
 
-        return $this->labels[ $predictedClass ];
+        return $this->labels[$predictedClass];
     }
 }

src/Phpml/Classification/Linear/DecisionStump.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;
 
@@ -83,7 +83,7 @@ 
         $labels = array_count_values($this->targets);
         $this->labels = array_keys($labels);
         if (count($this->labels) != 2) {
-            throw new \Exception("DecisionStump can classify between two classes only:" . implode(',', $this->labels));
+            throw new \Exception("DecisionStump can classify between two classes only:".implode(',', $this->labels));
         }
 
         // If a column index is given, it should be among the existing columns
@@ -160,8 +160,8 @@ 
             }
 
             // 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;
                 $errorRate = $this->calculateErrorRate($threshold, $operator, $values);
                 if ($errorRate < $split['trainingErrorRate']) {
                     $split = ['value' => $threshold, 'operator' => $operator,
@@ -183,7 +183,7 @@ 
     {
         $values = array_column($this->samples, $col);
         $valueCounts = array_count_values($values);
-        $distinctVals= array_keys($valueCounts);
+        $distinctVals = array_keys($valueCounts);
 
         $split = null;
 
@@ -238,7 +238,7 @@ 
         $total = (float) array_sum($this->weights);
         $wrong = 0.0;
         $leftLabel = $this->labels[0];
-        $rightLabel= $this->labels[1];
+        $rightLabel = $this->labels[1];
         foreach ($values as $index => $value) {
             if ($this->evaluate($threshold, $operator, $value)) {
                 $predicted = $leftLabel;

src/Phpml/Classification/Ensemble/AdaBoost.php

Spacing +6 added lines, -6 removed lines

@@ -1,6 +1,6 @@ 
 <?php
 
-declare(strict_types=1);
+declare(strict_types = 1);
 
 namespace Phpml\Classification\Ensemble;
 
@@ -173,15 +173,15 @@ 
     {
         $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) - $minZ + 1;
-            for ($i=0; $i < $z; $i++) {
+            $z = (int) round(($weight - $mean) / $std) - $minZ + 1;
+            for ($i = 0; $i < $z; $i++) {
                 if (rand(0, 1) == 0) {
                     continue;
                 }
@@ -260,6 +260,6 @@ 
             $sum += $h * $alpha;
         }
 
-        return $this->labels[ $sum > 0 ? 1 : -1];
+        return $this->labels[$sum > 0 ? 1 : -1];
     }
 }