php-cs-fixer - more rules (#118)
* Add new cs-fixer rules and run them * Do not align double arrows/equals
This commit is contained in:
Marcin Michalski
Arkadiusz Kondas
parent
ed5fc8996c
commit
3ac658c397
18
.php_cs
18
.php_cs
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@ -3,11 +3,25 @@
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return PhpCsFixer\Config::create()
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->setRules([
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'@PSR2' => true,
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'declare_strict_types' => true,
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'array_syntax' => ['syntax' => 'short'],
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'binary_operator_spaces' => ['align_double_arrow' => false, 'align_equals' => false],
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'blank_line_after_opening_tag' => true,
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'blank_line_before_return' => true,
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'cast_spaces' => true,
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'concat_space' => ['spacing' => 'none'],
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'declare_strict_types' => true,
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'method_separation' => true,
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'no_blank_lines_after_class_opening' => true,
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'no_spaces_around_offset' => ['positions' => ['inside', 'outside']],
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'no_unneeded_control_parentheses' => true,
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'no_unused_imports' => true,
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'phpdoc_align' => true,
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'phpdoc_no_access' => true,
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'phpdoc_separation' => true,
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'pre_increment' => true,
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'single_quote' => true,
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'trim_array_spaces' => true,
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'single_blank_line_before_namespace' => true,
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'no_unused_imports' => true
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])
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->setFinder(
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PhpCsFixer\Finder::create()
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@ -71,6 +71,7 @@ class DecisionTreeLeaf
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/**
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* @param array $record
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*
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* @return bool
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*/
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public function evaluate($record)
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@ -82,6 +83,7 @@ class DecisionTreeLeaf
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$value = $this->numericValue;
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$recordField = strval($recordField);
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eval("\$result = $recordField $op $value;");
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return $result;
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}
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@ -122,6 +124,7 @@ class DecisionTreeLeaf
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* Returns HTML representation of the node including children nodes
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*
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* @param $columnNames
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*
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* @return string
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*/
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public function getHTML($columnNames = null)
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@ -135,29 +138,34 @@ class DecisionTreeLeaf
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} else {
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$col = "col_$this->columnIndex";
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}
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if (!preg_match("/^[<>=]{1,2}/", $value)) {
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if (!preg_match('/^[<>=]{1,2}/', $value)) {
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$value = "=$value";
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}
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$value = "<b>$col $value</b><br>Gini: ".number_format($this->giniIndex, 2);
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}
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$str = "<table ><tr><td colspan=3 align=center style='border:1px solid;'>
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$value</td></tr>";
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$str = "<table ><tr><td colspan=3 align=center style='border:1px solid;'>$value</td></tr>";
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if ($this->leftLeaf || $this->rightLeaf) {
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$str .= '<tr>';
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if ($this->leftLeaf) {
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$str .="<td valign=top><b>| Yes</b><br>" . $this->leftLeaf->getHTML($columnNames) . "</td>";
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$str .= '<td valign=top><b>| Yes</b><br>'.$this->leftLeaf->getHTML($columnNames).'</td>';
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} else {
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$str .= '<td></td>';
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}
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$str .= '<td> </td>';
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if ($this->rightLeaf) {
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$str .="<td valign=top align=right><b>No |</b><br>" . $this->rightLeaf->getHTML($columnNames) . "</td>";
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$str .= '<td valign=top align=right><b>No |</b><br>'.$this->rightLeaf->getHTML($columnNames).'</td>';
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} else {
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$str .= '<td></td>';
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}
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$str .= '</tr>';
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}
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$str .= '</table>';
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return $str;
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}
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@ -18,6 +18,7 @@ class AdaBoost implements Classifier
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/**
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* Actual labels given in the targets array
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*
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* @var array
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*/
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protected $labels = [];
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@ -105,7 +106,7 @@ class AdaBoost implements Classifier
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// Initialize usual variables
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$this->labels = array_keys(array_count_values($targets));
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if (count($this->labels) != 2) {
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throw new \Exception("AdaBoost is a binary classifier and can classify between two classes only");
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throw new \Exception('AdaBoost is a binary classifier and can classify between two classes only');
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}
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// Set all target values to either -1 or 1
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@ -220,6 +221,7 @@ class AdaBoost implements Classifier
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* Calculates alpha of a classifier
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*
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* @param float $errorRate
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*
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* @return float
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*/
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protected function calculateAlpha(float $errorRate)
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@ -227,6 +229,7 @@ class AdaBoost implements Classifier
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if ($errorRate == 0) {
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$errorRate = 1e-10;
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}
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return 0.5 * log((1 - $errorRate) / $errorRate);
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}
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@ -254,6 +257,7 @@ class AdaBoost implements Classifier
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/**
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* @param array $sample
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*
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* @return mixed
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*/
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public function predictSample(array $sample)
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@ -84,10 +84,11 @@ class Bagging implements Classifier
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public function setSubsetRatio(float $ratio)
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{
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if ($ratio < 0.1 || $ratio > 1.0) {
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throw new \Exception("Subset ratio should be between 0.1 and 1.0");
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throw new \Exception('Subset ratio should be between 0.1 and 1.0');
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}
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$this->subsetRatio = $ratio;
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return $this;
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}
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@ -135,6 +136,7 @@ class Bagging implements Classifier
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/**
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* @param int $index
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*
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* @return array
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*/
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protected function getRandomSubset(int $index)
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@ -168,6 +170,7 @@ class Bagging implements Classifier
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$classifiers[] = $this->initSingleClassifier($obj);
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}
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return $classifiers;
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}
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@ -183,6 +186,7 @@ class Bagging implements Classifier
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/**
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* @param array $sample
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*
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* @return mixed
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*/
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protected function predictSample(array $sample)
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@ -196,6 +200,7 @@ class Bagging implements Classifier
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$counts = array_count_values($predictions);
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arsort($counts);
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reset($counts);
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return key($counts);
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}
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}
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@ -50,7 +50,7 @@ class RandomForest extends Bagging
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public function setFeatureSubsetRatio($ratio)
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{
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if (is_float($ratio) && ($ratio < 0.1 || $ratio > 1.0)) {
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throw new \Exception("When a float given, feature subset ratio should be between 0.1 and 1.0");
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throw new \Exception('When a float given, feature subset ratio should be between 0.1 and 1.0');
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}
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if (is_string($ratio) && $ratio != 'sqrt' && $ratio != 'log') {
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@ -58,6 +58,7 @@ class RandomForest extends Bagging
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}
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$this->featureSubsetRatio = $ratio;
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return $this;
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}
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@ -74,7 +75,7 @@ class RandomForest extends Bagging
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public function setClassifer(string $classifier, array $classifierOptions = [])
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{
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if ($classifier != DecisionTree::class) {
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throw new \Exception("RandomForest can only use DecisionTree as base classifier");
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throw new \Exception('RandomForest can only use DecisionTree as base classifier');
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}
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return parent::setClassifer($classifier, $classifierOptions);
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@ -120,6 +121,7 @@ class RandomForest extends Bagging
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* when trying to print some information about the trees such as feature importances
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*
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* @param array $names
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*
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* @return $this
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*/
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public function setColumnNames(array $names)
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@ -46,7 +46,7 @@ class Adaline extends Perceptron
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int $trainingType = self::BATCH_TRAINING
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) {
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if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) {
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throw new \Exception("Adaline can only be trained with batch and online/stochastic gradient descent algorithm");
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throw new \Exception('Adaline can only be trained with batch and online/stochastic gradient descent algorithm');
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}
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$this->trainingType = $trainingType;
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@ -106,7 +106,7 @@ class DecisionStump extends WeightedClassifier
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if ($this->weights) {
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$numWeights = count($this->weights);
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if ($numWeights != count($samples)) {
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throw new \Exception("Number of sample weights does not match with number of samples");
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throw new \Exception('Number of sample weights does not match with number of samples');
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}
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} else {
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$this->weights = array_fill(0, count($samples), 1);
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@ -236,7 +236,6 @@ class DecisionStump extends WeightedClassifier
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return $split;
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}
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/**
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*
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* @param mixed $leftValue
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@ -358,7 +357,7 @@ class DecisionStump extends WeightedClassifier
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public function __toString()
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{
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return "IF $this->column $this->operator $this->value ".
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"THEN " . $this->binaryLabels[0] . " ".
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"ELSE " . $this->binaryLabels[1];
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'THEN '.$this->binaryLabels[0].' '.
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'ELSE '.$this->binaryLabels[1];
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}
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}
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@ -76,9 +76,9 @@ class LogisticRegression extends Adaline
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) {
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$trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
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if (!in_array($trainingType, $trainingTypes)) {
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throw new \Exception("Logistic regression can only be trained with " .
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"batch (gradient descent), online (stochastic gradient descent) " .
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"or conjugate batch (conjugate gradients) algorithms");
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throw new \Exception('Logistic regression can only be trained with '.
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'batch (gradient descent), online (stochastic gradient descent) '.
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'or conjugate batch (conjugate gradients) algorithms');
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}
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if (!in_array($cost, ['log', 'sse'])) {
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@ -290,6 +290,7 @@ class LogisticRegression extends Adaline
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if (strval($predicted) == strval($label)) {
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$sample = $this->checkNormalizedSample($sample);
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return abs($this->output($sample) - 0.5);
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}
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@ -74,11 +74,11 @@ class Perceptron implements Classifier, IncrementalEstimator
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public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true)
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{
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if ($learningRate <= 0.0 || $learningRate > 1.0) {
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throw new \Exception("Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)");
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throw new \Exception('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
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}
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if ($maxIterations <= 0) {
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throw new \Exception("Maximum number of iterations must be an integer greater than 0");
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throw new \Exception('Maximum number of iterations must be an integer greater than 0');
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}
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if ($normalizeInputs) {
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@ -231,6 +231,7 @@ class Perceptron implements Classifier, IncrementalEstimator
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* Calculates net output of the network as a float value for the given input
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*
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* @param array $sample
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*
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* @return int
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*/
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protected function output(array $sample)
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@ -251,6 +252,7 @@ class Perceptron implements Classifier, IncrementalEstimator
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* Returns the class value (either -1 or 1) for the given input
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*
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* @param array $sample
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*
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* @return int
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*/
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protected function outputClass(array $sample)
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@ -275,6 +277,7 @@ class Perceptron implements Classifier, IncrementalEstimator
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if (strval($predicted) == strval($label)) {
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$sample = $this->checkNormalizedSample($sample);
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return abs($this->output($sample));
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}
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@ -9,7 +9,6 @@ use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
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class MLPClassifier extends MultilayerPerceptron implements Classifier
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{
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/**
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* @param mixed $target
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*
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@ -22,6 +21,7 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
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if (!in_array($target, $this->classes)) {
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throw InvalidArgumentException::invalidTarget($target);
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}
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return array_search($target, $this->classes);
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}
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@ -42,6 +42,7 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
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$max = $value;
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}
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}
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return $this->classes[$predictedClass];
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}
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@ -80,6 +80,7 @@ class NaiveBayes implements Classifier
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/**
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* Calculates vital statistics for each label & feature. Stores these
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* values in private array in order to avoid repeated calculation
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*
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* @param string $label
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* @param array $samples
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*/
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@ -128,6 +129,7 @@ class NaiveBayes implements Classifier
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$this->discreteProb[$label][$feature][$value] == 0) {
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return self::EPSILON;
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}
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return $this->discreteProb[$label][$feature][$value];
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}
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$std = $this->std[$label][$feature] ;
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@ -141,6 +143,7 @@ class NaiveBayes implements Classifier
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// (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
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$pdf = -0.5 * log(2.0 * pi() * $std * $std);
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$pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
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return $pdf;
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}
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@ -159,11 +162,13 @@ class NaiveBayes implements Classifier
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$samples[] = $this->samples[$i];
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}
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}
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return $samples;
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}
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/**
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* @param array $sample
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*
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* @return mixed
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*/
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protected function predictSample(array $sample)
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@ -183,6 +188,7 @@ class NaiveBayes implements Classifier
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arsort($predictions, SORT_NUMERIC);
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reset($predictions);
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return key($predictions);
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}
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}
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@ -159,6 +159,7 @@ class FuzzyCMeans implements Clusterer
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*
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* @param int $row
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* @param int $col
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*
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* @return float
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*/
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protected function getDistanceCalc(int $row, int $col)
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@ -179,6 +180,7 @@ class FuzzyCMeans implements Clusterer
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$val = pow($dist1 / $dist2, 2.0 / ($this->fuzziness - 1));
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$sum += $val;
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}
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return $sum;
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}
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@ -212,6 +214,7 @@ class FuzzyCMeans implements Clusterer
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/**
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* @param array|Point[] $samples
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*
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* @return array
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*/
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public function cluster(array $samples)
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@ -55,7 +55,7 @@ class KernelPCA extends PCA
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{
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$availableKernels = [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN, self::KERNEL_LINEAR];
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if (!in_array($kernel, $availableKernels)) {
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throw new \Exception("KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian");
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throw new \Exception('KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian');
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}
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parent::__construct($totalVariance, $numFeatures);
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@ -168,6 +168,7 @@ class KernelPCA extends PCA
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case self::KERNEL_RBF:
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// k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance
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$dist = new Euclidean();
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return function ($x, $y) use ($dist) {
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return exp(-$this->gamma * $dist->sqDistance($x, $y));
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};
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@ -176,12 +177,14 @@ class KernelPCA extends PCA
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// k(x,y)=tanh(γ.xT.y+c0) where c0=1
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return function ($x, $y) {
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$res = Matrix::dot($x, $y)[0] + 1.0;
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return tanh($this->gamma * $res);
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};
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case self::KERNEL_LAPLACIAN:
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// k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance
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$dist = new Manhattan();
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return function ($x, $y) use ($dist) {
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return exp(-$this->gamma * $dist->distance($x, $y));
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};
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@ -241,11 +244,11 @@ class KernelPCA extends PCA
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public function transform(array $sample)
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{
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if (!$this->fit) {
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throw new \Exception("KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first");
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throw new \Exception('KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first');
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}
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if (is_array($sample[0])) {
|
||||
throw new \Exception("KernelPCA::transform() accepts only one-dimensional arrays");
|
||||
throw new \Exception('KernelPCA::transform() accepts only one-dimensional arrays');
|
||||
}
|
||||
|
||||
$pairs = $this->getDistancePairs($sample);
|
||||
|
|
|
|||
|
|
@ -50,13 +50,13 @@ class LDA extends EigenTransformerBase
|
|||
public function __construct($totalVariance = null, $numFeatures = null)
|
||||
{
|
||||
if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
|
||||
throw new \Exception("Total variance can be a value between 0.1 and 0.99");
|
||||
throw new \Exception('Total variance can be a value between 0.1 and 0.99');
|
||||
}
|
||||
if ($numFeatures !== null && $numFeatures <= 0) {
|
||||
throw new \Exception("Number of features to be preserved should be greater than 0");
|
||||
throw new \Exception('Number of features to be preserved should be greater than 0');
|
||||
}
|
||||
if ($totalVariance !== null && $numFeatures !== null) {
|
||||
throw new \Exception("Either totalVariance or numFeatures should be specified in order to run the algorithm");
|
||||
throw new \Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
|
||||
}
|
||||
|
||||
if ($numFeatures !== null) {
|
||||
|
|
@ -105,7 +105,6 @@ class LDA extends EigenTransformerBase
|
|||
return array_keys($counts);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Calculates mean of each column for each class and returns
|
||||
* n by m matrix where n is number of labels and m is number of columns
|
||||
|
|
@ -156,7 +155,6 @@ class LDA extends EigenTransformerBase
|
|||
return $means;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Returns in-class scatter matrix for each class, which
|
||||
* is a n by m matrix where n is number of classes and
|
||||
|
|
@ -237,7 +235,7 @@ class LDA extends EigenTransformerBase
|
|||
public function transform(array $sample)
|
||||
{
|
||||
if (!$this->fit) {
|
||||
throw new \Exception("LDA has not been fitted with respect to original dataset, please run LDA::fit() first");
|
||||
throw new \Exception('LDA has not been fitted with respect to original dataset, please run LDA::fit() first');
|
||||
}
|
||||
|
||||
if (!is_array($sample[0])) {
|
||||
|
|
|
|||
|
|
@ -35,13 +35,13 @@ class PCA extends EigenTransformerBase
|
|||
public function __construct($totalVariance = null, $numFeatures = null)
|
||||
{
|
||||
if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
|
||||
throw new \Exception("Total variance can be a value between 0.1 and 0.99");
|
||||
throw new \Exception('Total variance can be a value between 0.1 and 0.99');
|
||||
}
|
||||
if ($numFeatures !== null && $numFeatures <= 0) {
|
||||
throw new \Exception("Number of features to be preserved should be greater than 0");
|
||||
throw new \Exception('Number of features to be preserved should be greater than 0');
|
||||
}
|
||||
if ($totalVariance !== null && $numFeatures !== null) {
|
||||
throw new \Exception("Either totalVariance or numFeatures should be specified in order to run the algorithm");
|
||||
throw new \Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
|
||||
}
|
||||
|
||||
if ($numFeatures !== null) {
|
||||
|
|
@ -129,7 +129,7 @@ class PCA extends EigenTransformerBase
|
|||
public function transform(array $sample)
|
||||
{
|
||||
if (!$this->fit) {
|
||||
throw new \Exception("PCA has not been fitted with respect to original dataset, please run PCA::fit() first");
|
||||
throw new \Exception('PCA has not been fitted with respect to original dataset, please run PCA::fit() first');
|
||||
}
|
||||
|
||||
if (!is_array($sample[0])) {
|
||||
|
|
|
|||
|
|
@ -109,6 +109,7 @@ trait OneVsRest
|
|||
// multiple instances of this classifier
|
||||
$classifier = clone $this;
|
||||
$classifier->reset();
|
||||
|
||||
return $classifier;
|
||||
}
|
||||
|
||||
|
|
@ -121,6 +122,7 @@ trait OneVsRest
|
|||
*
|
||||
* @param array $targets
|
||||
* @param mixed $label
|
||||
*
|
||||
* @return array Binarized targets and target's labels
|
||||
*/
|
||||
private function binarizeTargets($targets, $label)
|
||||
|
|
@ -131,10 +133,10 @@ trait OneVsRest
|
|||
}
|
||||
|
||||
$labels = [$label, $notLabel];
|
||||
|
||||
return [$targets, $labels];
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* @param array $sample
|
||||
*
|
||||
|
|
@ -153,6 +155,7 @@ trait OneVsRest
|
|||
}
|
||||
|
||||
arsort($probs, SORT_NUMERIC);
|
||||
|
||||
return key($probs);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -20,10 +20,12 @@ declare(strict_types=1);
|
|||
*
|
||||
* @author Paul Meagher
|
||||
* @license PHP v3.0
|
||||
*
|
||||
* @version 1.1
|
||||
*
|
||||
* Slightly changed to adapt the original code to PHP-ML library
|
||||
* @date 2017/04/11
|
||||
*
|
||||
* @author Mustafa Karabulut
|
||||
*/
|
||||
|
||||
|
|
@ -35,18 +37,21 @@ class EigenvalueDecomposition
|
|||
{
|
||||
/**
|
||||
* Row and column dimension (square matrix).
|
||||
*
|
||||
* @var int
|
||||
*/
|
||||
private $n;
|
||||
|
||||
/**
|
||||
* Internal symmetry flag.
|
||||
*
|
||||
* @var bool
|
||||
*/
|
||||
private $symmetric;
|
||||
|
||||
/**
|
||||
* Arrays for internal storage of eigenvalues.
|
||||
*
|
||||
* @var array
|
||||
*/
|
||||
private $d = [];
|
||||
|
|
@ -54,24 +59,28 @@ class EigenvalueDecomposition
|
|||
|
||||
/**
|
||||
* Array for internal storage of eigenvectors.
|
||||
*
|
||||
* @var array
|
||||
*/
|
||||
private $V = [];
|
||||
|
||||
/**
|
||||
* Array for internal storage of nonsymmetric Hessenberg form.
|
||||
*
|
||||
* @var array
|
||||
*/
|
||||
private $H = [];
|
||||
|
||||
/**
|
||||
* Working storage for nonsymmetric algorithm.
|
||||
*
|
||||
* @var array
|
||||
*/
|
||||
private $ort;
|
||||
|
||||
/**
|
||||
* Used for complex scalar division.
|
||||
*
|
||||
* @var float
|
||||
*/
|
||||
private $cdivr;
|
||||
|
|
@ -222,7 +231,6 @@ class EigenvalueDecomposition
|
|||
$this->e[0] = 0.0;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Symmetric tridiagonal QL algorithm.
|
||||
*
|
||||
|
|
@ -330,7 +338,6 @@ class EigenvalueDecomposition
|
|||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Nonsymmetric reduction to Hessenberg form.
|
||||
*
|
||||
|
|
@ -823,12 +830,11 @@ class EigenvalueDecomposition
|
|||
$this->V[$i][$j] = $z;
|
||||
}
|
||||
}
|
||||
} // end hqr2
|
||||
}
|
||||
|
||||
/**
|
||||
* Return the eigenvector matrix
|
||||
*
|
||||
* @access public
|
||||
*
|
||||
* @return array
|
||||
*/
|
||||
|
|
@ -899,4 +905,4 @@ class EigenvalueDecomposition
|
|||
|
||||
return $D;
|
||||
}
|
||||
} // class EigenvalueDecomposition
|
||||
}
|
||||
|
|
|
|||
|
|
@ -17,11 +17,14 @@ declare(strict_types=1);
|
|||
* @author Paul Meagher
|
||||
* @author Bartosz Matosiuk
|
||||
* @author Michael Bommarito
|
||||
*
|
||||
* @version 1.1
|
||||
*
|
||||
* @license PHP v3.0
|
||||
*
|
||||
* Slightly changed to adapt the original code to PHP-ML library
|
||||
* @date 2017/04/24
|
||||
*
|
||||
* @author Mustafa Karabulut
|
||||
*/
|
||||
|
||||
|
|
@ -34,35 +37,39 @@ class LUDecomposition
|
|||
{
|
||||
/**
|
||||
* Decomposition storage
|
||||
*
|
||||
* @var array
|
||||
*/
|
||||
private $LU = [];
|
||||
|
||||
/**
|
||||
* Row dimension.
|
||||
*
|
||||
* @var int
|
||||
*/
|
||||
private $m;
|
||||
|
||||
/**
|
||||
* Column dimension.
|
||||
*
|
||||
* @var int
|
||||
*/
|
||||
private $n;
|
||||
|
||||
/**
|
||||
* Pivot sign.
|
||||
*
|
||||
* @var int
|
||||
*/
|
||||
private $pivsign;
|
||||
|
||||
/**
|
||||
* Internal storage of pivot vector.
|
||||
*
|
||||
* @var array
|
||||
*/
|
||||
private $piv = [];
|
||||
|
||||
|
||||
/**
|
||||
* Constructs Structure to access L, U and piv.
|
||||
*
|
||||
|
|
@ -128,8 +135,7 @@ class LUDecomposition
|
|||
}
|
||||
}
|
||||
}
|
||||
} // function __construct()
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Get lower triangular factor.
|
||||
|
|
@ -150,9 +156,9 @@ class LUDecomposition
|
|||
}
|
||||
}
|
||||
}
|
||||
return new Matrix($L);
|
||||
} // function getL()
|
||||
|
||||
return new Matrix($L);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get upper triangular factor.
|
||||
|
|
@ -171,9 +177,9 @@ class LUDecomposition
|
|||
}
|
||||
}
|
||||
}
|
||||
return new Matrix($U);
|
||||
} // function getU()
|
||||
|
||||
return new Matrix($U);
|
||||
}
|
||||
|
||||
/**
|
||||
* Return pivot permutation vector.
|
||||
|
|
@ -183,8 +189,7 @@ class LUDecomposition
|
|||
public function getPivot()
|
||||
{
|
||||
return $this->piv;
|
||||
} // function getPivot()
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Alias for getPivot
|
||||
|
|
@ -194,8 +199,7 @@ class LUDecomposition
|
|||
public function getDoublePivot()
|
||||
{
|
||||
return $this->getPivot();
|
||||
} // function getDoublePivot()
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Is the matrix nonsingular?
|
||||
|
|
@ -211,8 +215,7 @@ class LUDecomposition
|
|||
}
|
||||
|
||||
return true;
|
||||
} // function isNonsingular()
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Count determinants
|
||||
|
|
@ -233,8 +236,7 @@ class LUDecomposition
|
|||
}
|
||||
|
||||
return $d;
|
||||
} // function det()
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Solve A*X = B
|
||||
|
|
@ -277,8 +279,9 @@ class LUDecomposition
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
return $X;
|
||||
} // function solve()
|
||||
}
|
||||
|
||||
/**
|
||||
* @param array $matrix
|
||||
|
|
@ -302,4 +305,4 @@ class LUDecomposition
|
|||
|
||||
return $R;
|
||||
}
|
||||
} // class LUDecomposition
|
||||
}
|
||||
|
|
|
|||
|
|
@ -122,7 +122,6 @@ class Matrix
|
|||
return array_column($this->matrix, $column);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* @return float|int
|
||||
*
|
||||
|
|
|
|||
|
|
@ -80,7 +80,7 @@ class Covariance
|
|||
}
|
||||
|
||||
if ($i < 0 || $k < 0 || $i >= $n || $k >= $n) {
|
||||
throw new \Exception("Given indices i and k do not match with the dimensionality of data");
|
||||
throw new \Exception('Given indices i and k do not match with the dimensionality of data');
|
||||
}
|
||||
|
||||
if ($meanX === null || $meanY === null) {
|
||||
|
|
|
|||
|
|
@ -39,6 +39,7 @@ class Gaussian
|
|||
// Ref: https://en.wikipedia.org/wiki/Normal_distribution
|
||||
$std2 = $this->std ** 2;
|
||||
$mean = $this->mean;
|
||||
|
||||
return exp(-(($value - $mean) ** 2) / (2 * $std2)) / sqrt(2 * $std2 * pi());
|
||||
}
|
||||
|
||||
|
|
@ -55,6 +56,7 @@ class Gaussian
|
|||
public static function distributionPdf(float $mean, float $std, float $value)
|
||||
{
|
||||
$normal = new self($mean, $std);
|
||||
|
||||
return $normal->pdf($value);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -138,6 +138,7 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
|
|||
|
||||
/**
|
||||
* @param array $sample
|
||||
*
|
||||
* @return mixed
|
||||
*/
|
||||
abstract protected function predictSample(array $sample);
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@ class DecisionTreeTest extends TestCase
|
|||
array_walk($input, function (&$v) {
|
||||
array_splice($v, 4, 1);
|
||||
});
|
||||
|
||||
return [$input, $targets];
|
||||
}
|
||||
|
||||
|
|
@ -54,6 +55,7 @@ class DecisionTreeTest extends TestCase
|
|||
$classifier->train($data, $targets);
|
||||
$this->assertEquals('Dont_play', $classifier->predict(['scorching', 95, 90, 'true']));
|
||||
$this->assertEquals('Play', $classifier->predict(['overcast', 60, 60, 'false']));
|
||||
|
||||
return $classifier;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -97,6 +97,7 @@ class BaggingTest extends TestCase
|
|||
$classifier = new Bagging($numBaseClassifiers);
|
||||
$classifier->setSubsetRatio(1.0);
|
||||
$classifier->setClassifer(DecisionTree::class, ['depth' => 10]);
|
||||
|
||||
return $classifier;
|
||||
}
|
||||
|
||||
|
|
@ -113,7 +114,7 @@ class BaggingTest extends TestCase
|
|||
// Populating input data to a size large enough
|
||||
// for base classifiers that they can work with a subset of it
|
||||
$populated = [];
|
||||
for ($i=0; $i<20; $i++) {
|
||||
for ($i = 0; $i < 20; ++$i) {
|
||||
$populated = array_merge($populated, $input);
|
||||
}
|
||||
shuffle($populated);
|
||||
|
|
@ -121,6 +122,7 @@ class BaggingTest extends TestCase
|
|||
array_walk($populated, function (&$v) {
|
||||
array_splice($v, 4, 1);
|
||||
});
|
||||
|
||||
return [$populated, $targets];
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ class RandomForestTest extends BaggingTest
|
|||
{
|
||||
$classifier = new RandomForest($numBaseClassifiers);
|
||||
$classifier->setFeatureSubsetRatio('log');
|
||||
|
||||
return $classifier;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -180,6 +180,7 @@ class MLPClassifierTest extends TestCase
|
|||
[0, 1, 2]
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* @expectedException \Phpml\Exception\InvalidArgumentException
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ class FuzzyCMeansTest extends TestCase
|
|||
}
|
||||
}
|
||||
$this->assertCount(0, $samples);
|
||||
|
||||
return $fcm;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -37,8 +37,8 @@ class EigenDecompositionTest extends TestCase
|
|||
$len = 3;
|
||||
$A = array_fill(0, $len, array_fill(0, $len, 0.0));
|
||||
srand(intval(microtime(true) * 1000));
|
||||
for ($i=0; $i < $len; $i++) {
|
||||
for ($k=0; $k < $len; $k++) {
|
||||
for ($i = 0; $i < $len; ++$i) {
|
||||
for ($k = 0; $k < $len; ++$k) {
|
||||
if ($i > $k) {
|
||||
$A[$i][$k] = $A[$k][$i];
|
||||
} else {
|
||||
|
|
|
|||
|
|
@ -106,9 +106,9 @@ class NormalizerTest extends TestCase
|
|||
// Generate 10 random vectors of length 3
|
||||
$samples = [];
|
||||
srand(time());
|
||||
for ($i=0; $i<10; $i++) {
|
||||
for ($i = 0; $i < 10; ++$i) {
|
||||
$sample = array_fill(0, 3, 0);
|
||||
for ($k=0; $k<3; $k++) {
|
||||
for ($k = 0; $k < 3; ++$k) {
|
||||
$sample[$k] = rand(1, 100);
|
||||
}
|
||||
// Last feature's value shared across samples.
|
||||
|
|
|
|||
Loading…
Reference in New Issue