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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 2017-08-17 08:50:37 +02:00 committed by Arkadiusz Kondas
parent ed5fc8996c
commit 3ac658c397
43 changed files with 269 additions and 201 deletions
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18
.php_cs
View File

@ -3,11 +3,25 @@
return PhpCsFixer\Config::create()
->setRules([
'@PSR2' => true,
'declare_strict_types' => true,
'array_syntax' => ['syntax' => 'short'],
'binary_operator_spaces' => ['align_double_arrow' => false, 'align_equals' => false],
'blank_line_after_opening_tag' => true,
'blank_line_before_return' => true,
'cast_spaces' => true,
'concat_space' => ['spacing' => 'none'],
'declare_strict_types' => true,
'method_separation' => true,
'no_blank_lines_after_class_opening' => true,
'no_spaces_around_offset' => ['positions' => ['inside', 'outside']],
'no_unneeded_control_parentheses' => true,
'no_unused_imports' => true,
'phpdoc_align' => true,
'phpdoc_no_access' => true,
'phpdoc_separation' => true,
'pre_increment' => true,
'single_quote' => true,
'trim_array_spaces' => true,
'single_blank_line_before_namespace' => true,
'no_unused_imports' => true
])
->setFinder(
PhpCsFixer\Finder::create()

6
src/Phpml/Classification/DecisionTree.php
View File

@ -144,7 +144,7 @@ class DecisionTree implements Classifier
// otherwise group the records so that we can classify the leaf
// in case maximum depth is reached
$leftRecords = [];
$rightRecords= [];
$rightRecords = [];
$remainingTargets = [];
$prevRecord = null;
$allSame = true;
@ -162,7 +162,7 @@ class DecisionTree implements Classifier
if ($split->evaluate($record)) {
$leftRecords[] = $recordNo;
} else {
$rightRecords[]= $recordNo;
$rightRecords[] = $recordNo;
}
// Group remaining targets
@ -183,7 +183,7 @@ class DecisionTree implements Classifier
$split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1);
}
if ($rightRecords) {
$split->rightLeaf= $this->getSplitLeaf($rightRecords, $depth + 1);
$split->rightLeaf = $this->getSplitLeaf($rightRecords, $depth + 1);
}
}

38
src/Phpml/Classification/DecisionTree/DecisionTreeLeaf.php
View File

@ -34,7 +34,7 @@ class DecisionTreeLeaf
/**
* @var DecisionTreeLeaf
*/
public $rightLeaf= null;
public $rightLeaf = null;
/**
* @var array
@ -71,6 +71,7 @@ class DecisionTreeLeaf
/**
* @param array $record
*
* @return bool
*/
public function evaluate($record)
@ -79,9 +80,10 @@ class DecisionTreeLeaf
if ($this->isContinuous) {
$op = $this->operator;
$value= $this->numericValue;
$value = $this->numericValue;
$recordField = strval($recordField);
eval("\$result = $recordField $op $value;");
return $result;
}
@ -102,16 +104,16 @@ class DecisionTreeLeaf
return 0.0;
}
$nodeSampleCount = (float)count($this->records);
$nodeSampleCount = (float) count($this->records);
$iT = $this->giniIndex;
if ($this->leftLeaf) {
$pL = count($this->leftLeaf->records)/$nodeSampleCount;
$pL = count($this->leftLeaf->records) / $nodeSampleCount;
$iT -= $pL * $this->leftLeaf->giniIndex;
}
if ($this->rightLeaf) {
$pR = count($this->rightLeaf->records)/$nodeSampleCount;
$pR = count($this->rightLeaf->records) / $nodeSampleCount;
$iT -= $pR * $this->rightLeaf->giniIndex;
}
@ -122,6 +124,7 @@ class DecisionTreeLeaf
* Returns HTML representation of the node including children nodes
*
* @param $columnNames
*
* @return string
*/
public function getHTML($columnNames = null)
@ -135,29 +138,34 @@ class DecisionTreeLeaf
} else {
$col = "col_$this->columnIndex";
}
if (!preg_match("/^[<>=]{1,2}/", $value)) {
if (!preg_match('/^[<>=]{1,2}/', $value)) {
$value = "=$value";
}
$value = "<b>$col $value</b><br>Gini: ". number_format($this->giniIndex, 2);
$value = "<b>$col $value</b><br>Gini: ".number_format($this->giniIndex, 2);
}
$str = "<table ><tr><td colspan=3 align=center style='border:1px solid;'>
$value</td></tr>";
$str = "<table ><tr><td colspan=3 align=center style='border:1px solid;'>$value</td></tr>";
if ($this->leftLeaf || $this->rightLeaf) {
$str .='<tr>';
$str .= '<tr>';
if ($this->leftLeaf) {
$str .="<td valign=top><b>| Yes</b><br>" . $this->leftLeaf->getHTML($columnNames) . "</td>";
$str .= '<td valign=top><b>| Yes</b><br>'.$this->leftLeaf->getHTML($columnNames).'</td>';
} else {
$str .='<td></td>';
$str .= '<td></td>';
}
$str .='<td>&nbsp;</td>';
$str .= '<td>&nbsp;</td>';
if ($this->rightLeaf) {
$str .="<td valign=top align=right><b>No |</b><br>" . $this->rightLeaf->getHTML($columnNames) . "</td>";
$str .= '<td valign=top align=right><b>No |</b><br>'.$this->rightLeaf->getHTML($columnNames).'</td>';
} else {
$str .='<td></td>';
$str .= '<td></td>';
}
$str .= '</tr>';
}
$str .= '</table>';
return $str;
}

18
src/Phpml/Classification/Ensemble/AdaBoost.php
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@ -18,6 +18,7 @@ class AdaBoost implements Classifier
/**
* Actual labels given in the targets array
*
* @var array
*/
protected $labels = [];
@ -86,7 +87,7 @@ class AdaBoost implements Classifier
* Sets the base classifier that will be used for boosting (default = DecisionStump)
*
* @param string $baseClassifier
* @param array $classifierOptions
* @param array $classifierOptions
*/
public function setBaseClassifier(string $baseClassifier = DecisionStump::class, array $classifierOptions = [])
{
@ -105,7 +106,7 @@ class AdaBoost implements Classifier
// Initialize usual variables
$this->labels = array_keys(array_count_values($targets));
if (count($this->labels) != 2) {
throw new \Exception("AdaBoost is a binary classifier and can classify between two classes only");
throw new \Exception('AdaBoost is a binary classifier and can classify between two classes only');
}
// Set all target values to either -1 or 1
@ -175,14 +176,14 @@ class AdaBoost implements Classifier
{
$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;
$z = (int) round(($weight - $mean) / $std) - $minZ + 1;
for ($i = 0; $i < $z; ++$i) {
if (rand(0, 1) == 0) {
continue;
@ -220,6 +221,7 @@ class AdaBoost implements Classifier
* Calculates alpha of a classifier
*
* @param float $errorRate
*
* @return float
*/
protected function calculateAlpha(float $errorRate)
@ -227,6 +229,7 @@ class AdaBoost implements Classifier
if ($errorRate == 0) {
$errorRate = 1e-10;
}
return 0.5 * log((1 - $errorRate) / $errorRate);
}
@ -234,7 +237,7 @@ class AdaBoost implements Classifier
* Updates the sample weights
*
* @param Classifier $classifier
* @param float $alpha
* @param float $alpha
*/
protected function updateWeights(Classifier $classifier, float $alpha)
{
@ -254,6 +257,7 @@ class AdaBoost implements Classifier
/**
* @param array $sample
*
* @return mixed
*/
public function predictSample(array $sample)
@ -264,6 +268,6 @@ class AdaBoost implements Classifier
$sum += $h * $alpha;
}
return $this->labels[ $sum > 0 ? 1 : -1];
return $this->labels[$sum > 0 ? 1 : -1];
}
}

9
src/Phpml/Classification/Ensemble/Bagging.php
View File

@ -84,10 +84,11 @@ class Bagging implements Classifier
public function setSubsetRatio(float $ratio)
{
if ($ratio < 0.1 || $ratio > 1.0) {
throw new \Exception("Subset ratio should be between 0.1 and 1.0");
throw new \Exception('Subset ratio should be between 0.1 and 1.0');
}
$this->subsetRatio = $ratio;
return $this;
}
@ -100,7 +101,7 @@ class Bagging implements Classifier
* names are neglected.
*
* @param string $classifier
* @param array $classifierOptions
* @param array $classifierOptions
*
* @return $this
*/
@ -135,6 +136,7 @@ class Bagging implements Classifier
/**
* @param int $index
*
* @return array
*/
protected function getRandomSubset(int $index)
@ -168,6 +170,7 @@ class Bagging implements Classifier
$classifiers[] = $this->initSingleClassifier($obj);
}
return $classifiers;
}
@ -183,6 +186,7 @@ class Bagging implements Classifier
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)
@ -196,6 +200,7 @@ class Bagging implements Classifier
$counts = array_count_values($predictions);
arsort($counts);
reset($counts);
return key($counts);
}
}

12
src/Phpml/Classification/Ensemble/RandomForest.php
View File

@ -50,7 +50,7 @@ class RandomForest extends Bagging
public function setFeatureSubsetRatio($ratio)
{
if (is_float($ratio) && ($ratio < 0.1 || $ratio > 1.0)) {
throw new \Exception("When a float given, feature subset ratio should be between 0.1 and 1.0");
throw new \Exception('When a float given, feature subset ratio should be between 0.1 and 1.0');
}
if (is_string($ratio) && $ratio != 'sqrt' && $ratio != 'log') {
@ -58,6 +58,7 @@ class RandomForest extends Bagging
}
$this->featureSubsetRatio = $ratio;
return $this;
}
@ -74,7 +75,7 @@ class RandomForest extends Bagging
public function setClassifer(string $classifier, array $classifierOptions = [])
{
if ($classifier != DecisionTree::class) {
throw new \Exception("RandomForest can only use DecisionTree as base classifier");
throw new \Exception('RandomForest can only use DecisionTree as base classifier');
}
return parent::setClassifer($classifier, $classifierOptions);
@ -120,6 +121,7 @@ class RandomForest extends Bagging
* when trying to print some information about the trees such as feature importances
*
* @param array $names
*
* @return $this
*/
public function setColumnNames(array $names)
@ -137,11 +139,11 @@ class RandomForest extends Bagging
protected function initSingleClassifier($classifier)
{
if (is_float($this->featureSubsetRatio)) {
$featureCount = (int)($this->featureSubsetRatio * $this->featureCount);
$featureCount = (int) ($this->featureSubsetRatio * $this->featureCount);
} elseif ($this->featureCount == 'sqrt') {
$featureCount = (int)sqrt($this->featureCount) + 1;
$featureCount = (int) sqrt($this->featureCount) + 1;
} else {
$featureCount = (int)log($this->featureCount, 2) + 1;
$featureCount = (int) log($this->featureCount, 2) + 1;
}
if ($featureCount >= $this->featureCount) {

6
src/Phpml/Classification/Linear/Adaline.php
View File

@ -9,12 +9,12 @@ class Adaline extends Perceptron
/**
* 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
@ -46,7 +46,7 @@ class Adaline extends Perceptron
int $trainingType = self::BATCH_TRAINING
) {
if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) {
throw new \Exception("Adaline can only be trained with batch and online/stochastic gradient descent algorithm");
throw new \Exception('Adaline can only be trained with batch and online/stochastic gradient descent algorithm');
}
$this->trainingType = $trainingType;

29
src/Phpml/Classification/Linear/DecisionStump.php
View File

@ -106,7 +106,7 @@ class DecisionStump extends WeightedClassifier
if ($this->weights) {
$numWeights = count($this->weights);
if ($numWeights != count($samples)) {
throw new \Exception("Number of sample weights does not match with number of samples");
throw new \Exception('Number of sample weights does not match with number of samples');
}
} else {
$this->weights = array_fill(0, count($samples), 1);
@ -163,7 +163,7 @@ class DecisionStump extends WeightedClassifier
*
* @param array $samples
* @param array $targets
* @param int $col
* @param int $col
*
* @return array
*/
@ -192,8 +192,8 @@ class DecisionStump extends WeightedClassifier
}
// Try other possible points one by one
for ($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,
@ -209,7 +209,7 @@ class DecisionStump extends WeightedClassifier
/**
* @param array $samples
* @param array $targets
* @param int $col
* @param int $col
*
* @return array
*/
@ -217,7 +217,7 @@ class DecisionStump extends WeightedClassifier
{
$values = array_column($samples, $col);
$valueCounts = array_count_values($values);
$distinctVals= array_keys($valueCounts);
$distinctVals = array_keys($valueCounts);
$split = null;
@ -236,7 +236,6 @@ class DecisionStump extends WeightedClassifier
return $split;
}
/**
*
* @param mixed $leftValue
@ -264,10 +263,10 @@ class DecisionStump extends WeightedClassifier
* Calculates the ratio of wrong predictions based on the new threshold
* value given as the parameter
*
* @param array $targets
* @param float $threshold
* @param array $targets
* @param float $threshold
* @param string $operator
* @param array $values
* @param array $values
*
* @return array
*/
@ -276,7 +275,7 @@ class DecisionStump extends WeightedClassifier
$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)) {
@ -299,7 +298,7 @@ class DecisionStump extends WeightedClassifier
// 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;
@ -357,8 +356,8 @@ class DecisionStump extends WeightedClassifier
*/
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];
}
}

15
src/Phpml/Classification/Linear/LogisticRegression.php
View File

@ -59,9 +59,9 @@ class LogisticRegression extends Adaline
*
* Penalty (Regularization term) can be 'L2' or empty string to cancel penalty term
*
* @param int $maxIterations
* @param bool $normalizeInputs
* @param int $trainingType
* @param int $maxIterations
* @param bool $normalizeInputs
* @param int $trainingType
* @param string $cost
* @param string $penalty
*
@ -76,13 +76,13 @@ class LogisticRegression extends Adaline
) {
$trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
if (!in_array($trainingType, $trainingTypes)) {
throw new \Exception("Logistic regression can only be trained with " .
"batch (gradient descent), online (stochastic gradient descent) " .
"or conjugate batch (conjugate gradients) algorithms");
throw new \Exception('Logistic regression can only be trained with '.
'batch (gradient descent), online (stochastic gradient descent) '.
'or conjugate batch (conjugate gradients) algorithms');
}
if (!in_array($cost, ['log', 'sse'])) {
throw new \Exception("Logistic regression cost function can be one of the following: \n" .
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");
}
@ -290,6 +290,7 @@ class LogisticRegression extends Adaline
if (strval($predicted) == strval($label)) {
$sample = $this->checkNormalizedSample($sample);
return abs($this->output($sample) - 0.5);
}

9
src/Phpml/Classification/Linear/Perceptron.php
View File

@ -74,11 +74,11 @@ class Perceptron implements Classifier, IncrementalEstimator
public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true)
{
if ($learningRate <= 0.0 || $learningRate > 1.0) {
throw new \Exception("Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)");
throw new \Exception('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
}
if ($maxIterations <= 0) {
throw new \Exception("Maximum number of iterations must be an integer greater than 0");
throw new \Exception('Maximum number of iterations must be an integer greater than 0');
}
if ($normalizeInputs) {
@ -175,7 +175,7 @@ class Perceptron implements Classifier, IncrementalEstimator
$prediction = $this->outputClass($sample);
$gradient = $prediction - $target;
$error = $gradient**2;
$error = $gradient ** 2;
return [$error, $gradient];
};
@ -231,6 +231,7 @@ class Perceptron implements Classifier, IncrementalEstimator
* Calculates net output of the network as a float value for the given input
*
* @param array $sample
*
* @return int
*/
protected function output(array $sample)
@ -251,6 +252,7 @@ class Perceptron implements Classifier, IncrementalEstimator
* Returns the class value (either -1 or 1) for the given input
*
* @param array $sample
*
* @return int
*/
protected function outputClass(array $sample)
@ -275,6 +277,7 @@ class Perceptron implements Classifier, IncrementalEstimator
if (strval($predicted) == strval($label)) {
$sample = $this->checkNormalizedSample($sample);
return abs($this->output($sample));
}

3
src/Phpml/Classification/MLPClassifier.php
View File

@ -9,7 +9,6 @@ use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
class MLPClassifier extends MultilayerPerceptron implements Classifier
{
/**
* @param mixed $target
*
@ -22,6 +21,7 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
if (!in_array($target, $this->classes)) {
throw InvalidArgumentException::invalidTarget($target);
}
return array_search($target, $this->classes);
}
@ -42,6 +42,7 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
$max = $value;
}
}
return $this->classes[$predictedClass];
}

22
src/Phpml/Classification/NaiveBayes.php
View File

@ -13,8 +13,8 @@ class NaiveBayes implements Classifier
{
use Trainable, Predictable;
const CONTINUOS = 1;
const NOMINAL = 2;
const CONTINUOS = 1;
const NOMINAL = 2;
const EPSILON = 1e-10;
/**
@ -25,7 +25,7 @@ class NaiveBayes implements Classifier
/**
* @var array
*/
private $mean= [];
private $mean = [];
/**
* @var array
@ -80,13 +80,14 @@ class NaiveBayes implements Classifier
/**
* Calculates vital statistics for each label & feature. Stores these
* values in private array in order to avoid repeated calculation
*
* @param string $label
* @param array $samples
* @param array $samples
*/
private function calculateStatistics($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) {
@ -128,10 +129,11 @@ class NaiveBayes implements Classifier
$this->discreteProb[$label][$feature][$value] == 0) {
return self::EPSILON;
}
return $this->discreteProb[$label][$feature][$value];
}
$std = $this->std[$label][$feature] ;
$mean= $this->mean[$label][$feature];
$mean = $this->mean[$label][$feature];
// Calculate the probability density by use of normal/Gaussian distribution
// Ref: https://en.wikipedia.org/wiki/Normal_distribution
//
@ -139,8 +141,9 @@ class NaiveBayes implements Classifier
// 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;
}
@ -159,11 +162,13 @@ class NaiveBayes implements Classifier
$samples[] = $this->samples[$i];
}
}
return $samples;
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)
@ -174,7 +179,7 @@ class NaiveBayes implements Classifier
$predictions = [];
foreach ($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 += $Plf;
}
@ -183,6 +188,7 @@ class NaiveBayes implements Classifier
arsort($predictions, SORT_NUMERIC);
reset($predictions);
return key($predictions);
}
}

9
src/Phpml/Clustering/FuzzyCMeans.php
View File

@ -58,10 +58,10 @@ class FuzzyCMeans implements Clusterer
private $samples;
/**
* @param int $clustersNumber
* @param int $clustersNumber
* @param float $fuzziness
* @param float $epsilon
* @param int $maxIterations
* @param int $maxIterations
*
* @throws InvalidArgumentException
*/
@ -159,6 +159,7 @@ class FuzzyCMeans implements Clusterer
*
* @param int $row
* @param int $col
*
* @return float
*/
protected function getDistanceCalc(int $row, int $col)
@ -179,6 +180,7 @@ class FuzzyCMeans implements Clusterer
$val = pow($dist1 / $dist2, 2.0 / ($this->fuzziness - 1));
$sum += $val;
}
return $sum;
}
@ -212,13 +214,14 @@ class FuzzyCMeans implements Clusterer
/**
* @param array|Point[] $samples
*
* @return array
*/
public function cluster(array $samples)
{
// 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();

2
src/Phpml/Clustering/KMeans/Space.php
View File

@ -66,7 +66,7 @@ class Space extends SplObjectStorage
/**
* @param Point $point
* @param null $data
* @param null $data
*/
public function attach($point, $data = null)
{

2
src/Phpml/DimensionReduction/EigenTransformerBase.php
View File

@ -54,7 +54,7 @@ abstract class EigenTransformerBase
{
$eig = new EigenvalueDecomposition($matrix);
$eigVals = $eig->getRealEigenvalues();
$eigVects= $eig->getEigenvectors();
$eigVects = $eig->getEigenvectors();
$totalEigVal = array_sum($eigVals);
// Sort eigenvalues in descending order

17
src/Phpml/DimensionReduction/KernelPCA.php
View File

@ -44,10 +44,10 @@ class KernelPCA extends PCA
* will initialize the algorithm with an RBF kernel having the gamma parameter as 15,0. <br>
* This transformation will return the same number of rows with only <i>2</i> columns.
*
* @param int $kernel
* @param int $kernel
* @param float $totalVariance Total variance to be preserved if numFeatures is not given
* @param int $numFeatures Number of columns to be returned
* @param float $gamma Gamma parameter is used with RBF and Sigmoid kernels
* @param int $numFeatures Number of columns to be returned
* @param float $gamma Gamma parameter is used with RBF and Sigmoid kernels
*
* @throws \Exception
*/
@ -55,7 +55,7 @@ class KernelPCA extends PCA
{
$availableKernels = [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN, self::KERNEL_LINEAR];
if (!in_array($kernel, $availableKernels)) {
throw new \Exception("KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian");
throw new \Exception('KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian');
}
parent::__construct($totalVariance, $numFeatures);
@ -133,7 +133,7 @@ class KernelPCA extends PCA
*/
protected function centerMatrix(array $matrix, int $n)
{
$N = array_fill(0, $n, array_fill(0, $n, 1.0/$n));
$N = array_fill(0, $n, array_fill(0, $n, 1.0 / $n));
$N = new Matrix($N, false);
$K = new Matrix($matrix, false);
@ -168,6 +168,7 @@ class KernelPCA extends PCA
case self::KERNEL_RBF:
// k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance
$dist = new Euclidean();
return function ($x, $y) use ($dist) {
return exp(-$this->gamma * $dist->sqDistance($x, $y));
};
@ -176,12 +177,14 @@ class KernelPCA extends PCA
// k(x,y)=tanh(γ.xT.y+c0) where c0=1
return function ($x, $y) {
$res = Matrix::dot($x, $y)[0] + 1.0;
return tanh($this->gamma * $res);
};
case self::KERNEL_LAPLACIAN:
// k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance
$dist = new Manhattan();
return function ($x, $y) use ($dist) {
return exp(-$this->gamma * $dist->distance($x, $y));
};
@ -241,11 +244,11 @@ class KernelPCA extends PCA
public function transform(array $sample)
{
if (!$this->fit) {
throw new \Exception("KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first");
throw new \Exception('KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first');
}
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);

16
src/Phpml/DimensionReduction/LDA.php
View File

@ -43,20 +43,20 @@ class LDA extends EigenTransformerBase
* or numFeatures (number of features in the dataset) to be preserved.
*
* @param float|null $totalVariance Total explained variance to be preserved
* @param int|null $numFeatures Number of features to be preserved
* @param int|null $numFeatures Number of features to be preserved
*
* @throws \Exception
*/
public function __construct($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) {
@ -78,7 +78,7 @@ class LDA extends EigenTransformerBase
public function fit(array $data, array $classes) : array
{
$this->labels = $this->getLabels($classes);
$this->means = $this->calculateMeans($data, $classes);
$this->means = $this->calculateMeans($data, $classes);
$sW = $this->calculateClassVar($data, $classes);
$sB = $this->calculateClassCov();
@ -105,7 +105,6 @@ class LDA extends EigenTransformerBase
return array_keys($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
@ -118,7 +117,7 @@ class LDA extends EigenTransformerBase
protected function calculateMeans(array $data, array $classes) : array
{
$means = [];
$counts= [];
$counts = [];
$overallMean = array_fill(0, count($data[0]), 0.0);
foreach ($data as $index => $row) {
@ -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])) {

12
src/Phpml/DimensionReduction/PCA.php
View File

@ -28,20 +28,20 @@ class PCA extends EigenTransformerBase
* within the data. It is a lossy data compression technique.<br>
*
* @param float $totalVariance Total explained variance to be preserved
* @param int $numFeatures Number of features to be preserved
* @param int $numFeatures Number of features to be preserved
*
* @throws \Exception
*/
public function __construct($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) {
@ -79,7 +79,7 @@ class PCA extends EigenTransformerBase
/**
* @param array $data
* @param int $n
* @param int $n
*/
protected function calculateMeans(array $data, int $n)
{
@ -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])) {

2
src/Phpml/Exception/InvalidArgumentException.php
View File

@ -73,7 +73,7 @@ class InvalidArgumentException extends \Exception
*/
public static function invalidTarget($target)
{
return new self('Target with value ' . $target . ' is not part of the accepted classes');
return new self('Target with value '.$target.' is not part of the accepted classes');
}
/**

2
src/Phpml/FeatureExtraction/TfIdfTransformer.php
View File

@ -32,7 +32,7 @@ class TfIdfTransformer implements Transformer
$count = count($samples);
foreach ($this->idf as &$value) {
$value = log((float)($count / $value), 10.0);
$value = log((float) ($count / $value), 10.0);
}
}

5
src/Phpml/Helper/OneVsRest.php
View File

@ -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);
}

4
src/Phpml/Helper/Optimizer/GD.php
View File

@ -42,7 +42,7 @@ class GD extends StochasticGD
$this->updateWeightsWithUpdates($updates, $totalPenalty);
$this->costValues[] = array_sum($errors)/$this->sampleCount;
$this->costValues[] = array_sum($errors) / $this->sampleCount;
if ($this->earlyStop($theta)) {
break;
@ -65,7 +65,7 @@ class GD extends StochasticGD
protected function gradient(array $theta)
{
$costs = [];
$gradient= [];
$gradient = [];
$totalPenalty = 0;
foreach ($this->samples as $index => $sample) {

6
src/Phpml/Helper/Optimizer/StochasticGD.php
View File

@ -72,7 +72,7 @@ class StochasticGD extends Optimizer
*
* @var array
*/
protected $costValues= [];
protected $costValues = [];
/**
* Initializes the SGD optimizer for the given number of dimensions
@ -151,8 +151,8 @@ class StochasticGD extends Optimizer
* The cost function to minimize and the gradient of the function are to be
* handled by the callback function provided as the third parameter of the method.
*
* @param array $samples
* @param array $targets
* @param array $samples
* @param array $targets
* @param \Closure $gradientCb
*
* @return array

2
src/Phpml/Math/Distance/Minkowski.php
View File

@ -43,6 +43,6 @@ class Minkowski implements Distance
$distance += pow(abs($a[$i] - $b[$i]), $this->lambda);
}
return (float)pow($distance, 1 / $this->lambda);
return (float) pow($distance, 1 / $this->lambda);
}
}

48
src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php
View File

@ -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.
*
@ -239,7 +247,7 @@ class EigenvalueDecomposition
$this->e[$this->n - 1] = 0.0;
$f = 0.0;
$tst1 = 0.0;
$eps = pow(2.0, -52.0);
$eps = pow(2.0, -52.0);
for ($l = 0; $l < $this->n; ++$l) {
// Find small subdiagonal element
@ -283,9 +291,9 @@ class EigenvalueDecomposition
$c3 = $c2;
$c2 = $c;
$s2 = $s;
$g = $c * $this->e[$i];
$h = $c * $p;
$r = hypot($p, $this->e[$i]);
$g = $c * $this->e[$i];
$h = $c * $p;
$r = hypot($p, $this->e[$i]);
$this->e[$i + 1] = $s * $r;
$s = $this->e[$i] / $r;
$c = $p / $r;
@ -295,7 +303,7 @@ class EigenvalueDecomposition
for ($k = 0; $k < $this->n; ++$k) {
$h = $this->V[$k][$i + 1];
$this->V[$k][$i + 1] = $s * $this->V[$k][$i] + $c * $h;
$this->V[$k][$i] = $c * $this->V[$k][$i] - $s * $h;
$this->V[$k][$i] = $c * $this->V[$k][$i] - $s * $h;
}
}
$p = -$s * $s2 * $c3 * $el1 * $this->e[$l] / $dl1;
@ -330,7 +338,6 @@ class EigenvalueDecomposition
}
}
/**
* Nonsymmetric reduction to Hessenberg form.
*
@ -341,7 +348,7 @@ class EigenvalueDecomposition
*/
private function orthes()
{
$low = 0;
$low = 0;
$high = $this->n - 1;
for ($m = $low + 1; $m <= $high - 1; ++$m) {
@ -451,7 +458,7 @@ class EigenvalueDecomposition
{
// Initialize
$nn = $this->n;
$n = $nn - 1;
$n = $nn - 1;
$low = 0;
$high = $nn - 1;
$eps = pow(2.0, -52.0);
@ -544,9 +551,9 @@ class EigenvalueDecomposition
// Complex pair
} else {
$this->d[$n - 1] = $x + $p;
$this->d[$n] = $x + $p;
$this->d[$n] = $x + $p;
$this->e[$n - 1] = $z;
$this->e[$n] = -$z;
$this->e[$n] = -$z;
}
$n = $n - 2;
$iter = 0;
@ -747,10 +754,10 @@ class EigenvalueDecomposition
} else {
$this->cdiv(0.0, -$this->H[$n - 1][$n], $this->H[$n - 1][$n - 1] - $p, $q);
$this->H[$n - 1][$n - 1] = $this->cdivr;
$this->H[$n - 1][$n] = $this->cdivi;
$this->H[$n - 1][$n] = $this->cdivi;
}
$this->H[$n][$n - 1] = 0.0;
$this->H[$n][$n] = 1.0;
$this->H[$n][$n] = 1.0;
for ($i = $n - 2; $i >= 0; --$i) {
// double ra,sa,vr,vi;
$ra = 0.0;
@ -769,7 +776,7 @@ class EigenvalueDecomposition
if ($this->e[$i] == 0) {
$this->cdiv(-$ra, -$sa, $w, $q);
$this->H[$i][$n - 1] = $this->cdivr;
$this->H[$i][$n] = $this->cdivi;
$this->H[$i][$n] = $this->cdivi;
} else {
// Solve complex equations
$x = $this->H[$i][$i + 1];
@ -781,14 +788,14 @@ class EigenvalueDecomposition
}
$this->cdiv($x * $r - $z * $ra + $q * $sa, $x * $s - $z * $sa - $q * $ra, $vr, $vi);
$this->H[$i][$n - 1] = $this->cdivr;
$this->H[$i][$n] = $this->cdivi;
$this->H[$i][$n] = $this->cdivi;
if (abs($x) > (abs($z) + abs($q))) {
$this->H[$i + 1][$n - 1] = (-$ra - $w * $this->H[$i][$n - 1] + $q * $this->H[$i][$n]) / $x;
$this->H[$i + 1][$n] = (-$sa - $w * $this->H[$i][$n] - $q * $this->H[$i][$n - 1]) / $x;
$this->H[$i + 1][$n] = (-$sa - $w * $this->H[$i][$n] - $q * $this->H[$i][$n - 1]) / $x;
} else {
$this->cdiv(-$r - $y * $this->H[$i][$n - 1], -$s - $y * $this->H[$i][$n], $z, $q);
$this->H[$i + 1][$n - 1] = $this->cdivr;
$this->H[$i + 1][$n] = $this->cdivi;
$this->H[$i + 1][$n] = $this->cdivi;
}
}
// Overflow control
@ -796,7 +803,7 @@ class EigenvalueDecomposition
if (($eps * $t) * $t > 1) {
for ($j = $i; $j <= $n; ++$j) {
$this->H[$j][$n - 1] = $this->H[$j][$n - 1] / $t;
$this->H[$j][$n] = $this->H[$j][$n] / $t;
$this->H[$j][$n] = $this->H[$j][$n] / $t;
}
}
} // end else
@ -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
}

43
src/Phpml/Math/LinearAlgebra/LUDecomposition.php
View File

@ -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.
*
@ -78,8 +85,8 @@ class LUDecomposition
// Use a "left-looking", dot-product, Crout/Doolittle algorithm.
$this->LU = $A->toArray();
$this->m = $A->getRows();
$this->n = $A->getColumns();
$this->m = $A->getRows();
$this->n = $A->getColumns();
for ($i = 0; $i < $this->m; ++$i) {
$this->piv[$i] = $i;
}
@ -128,8 +135,7 @@ class LUDecomposition
}
}
}
} // function __construct()
}
/**
* Get lower triangular factor.
@ -150,9 +156,9 @@ class LUDecomposition
}
}
}
return new Matrix($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
@ -257,7 +259,7 @@ class LUDecomposition
// Copy right hand side with pivoting
$nx = $B->getColumns();
$X = $this->getSubMatrix($B->toArray(), $this->piv, 0, $nx - 1);
$X = $this->getSubMatrix($B->toArray(), $this->piv, 0, $nx - 1);
// Solve L*Y = B(piv,:)
for ($k = 0; $k < $this->n; ++$k) {
for ($i = $k + 1; $i < $this->n; ++$i) {
@ -277,8 +279,9 @@ class LUDecomposition
}
}
}
return $X;
} // function solve()
}
/**
* @param array $matrix
@ -302,4 +305,4 @@ class LUDecomposition
return $R;
}
} // class LUDecomposition
}

1
src/Phpml/Math/Matrix.php
View File

@ -122,7 +122,6 @@ class Matrix
return array_column($this->matrix, $column);
}
/**
* @return float|int
*

2
src/Phpml/Math/Statistic/Covariance.php
View File

@ -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) {

4
src/Phpml/Math/Statistic/Gaussian.php
View File

@ -39,7 +39,8 @@ 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 * $std2 * pi());
return exp(-(($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);
}
}

2
src/Phpml/Math/Statistic/Mean.php
View File

@ -34,7 +34,7 @@ class Mean
self::checkArrayLength($numbers);
$count = count($numbers);
$middleIndex = (int)floor($count / 2);
$middleIndex = (int) floor($count / 2);
sort($numbers, SORT_NUMERIC);
$median = $numbers[$middleIndex];

1
src/Phpml/NeuralNetwork/Network/MultilayerPerceptron.php
View File

@ -138,6 +138,7 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
/**
* @param array $sample
*
* @return mixed
*/
abstract protected function predictSample(array $sample);

6
src/Phpml/Preprocessing/Normalizer.php
View File

@ -12,7 +12,7 @@ class Normalizer implements Preprocessor
{
const NORM_L1 = 1;
const NORM_L2 = 2;
const NORM_STD= 3;
const NORM_STD = 3;
/**
* @var int
@ -77,7 +77,7 @@ class Normalizer implements Preprocessor
$methods = [
self::NORM_L1 => 'normalizeL1',
self::NORM_L2 => 'normalizeL2',
self::NORM_STD=> 'normalizeSTD'
self::NORM_STD => 'normalizeSTD'
];
$method = $methods[$this->norm];
@ -117,7 +117,7 @@ class Normalizer implements Preprocessor
foreach ($sample as $feature) {
$norm2 += $feature * $feature;
}
$norm2 = sqrt((float)$norm2);
$norm2 = sqrt((float) $norm2);
if (0 == $norm2) {
$sample = array_fill(0, count($sample), 1);

30
tests/Phpml/Classification/DecisionTreeTest.php
View File

@ -11,20 +11,20 @@ use PHPUnit\Framework\TestCase;
class DecisionTreeTest extends TestCase
{
private $data = [
['sunny', 85, 85, 'false', 'Dont_play' ],
['sunny', 80, 90, 'true', 'Dont_play' ],
['overcast', 83, 78, 'false', 'Play' ],
['rain', 70, 96, 'false', 'Play' ],
['rain', 68, 80, 'false', 'Play' ],
['rain', 65, 70, 'true', 'Dont_play' ],
['overcast', 64, 65, 'true', 'Play' ],
['sunny', 72, 95, 'false', 'Dont_play' ],
['sunny', 69, 70, 'false', 'Play' ],
['rain', 75, 80, 'false', 'Play' ],
['sunny', 75, 70, 'true', 'Play' ],
['overcast', 72, 90, 'true', 'Play' ],
['overcast', 81, 75, 'false', 'Play' ],
['rain', 71, 80, 'true', 'Dont_play' ]
['sunny', 85, 85, 'false', 'Dont_play'],
['sunny', 80, 90, 'true', 'Dont_play'],
['overcast', 83, 78, 'false', 'Play'],
['rain', 70, 96, 'false', 'Play'],
['rain', 68, 80, 'false', 'Play'],
['rain', 65, 70, 'true', 'Dont_play'],
['overcast', 64, 65, 'true', 'Play'],
['sunny', 72, 95, 'false', 'Dont_play'],
['sunny', 69, 70, 'false', 'Play'],
['rain', 75, 80, 'false', 'Play'],
['sunny', 75, 70, 'true', 'Play'],
['overcast', 72, 90, 'true', 'Play'],
['overcast', 81, 75, 'false', 'Play'],
['rain', 71, 80, 'true', 'Dont_play']
];
private $extraData = [
@ -38,6 +38,7 @@ class DecisionTreeTest extends TestCase
array_walk($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;
}

38
tests/Phpml/Classification/Ensemble/BaggingTest.php
View File

@ -13,25 +13,25 @@ use PHPUnit\Framework\TestCase;
class BaggingTest extends TestCase
{
private $data = [
['sunny', 85, 85, 'false', 'Dont_play' ],
['sunny', 80, 90, 'true', 'Dont_play' ],
['overcast', 83, 78, 'false', 'Play' ],
['rain', 70, 96, 'false', 'Play' ],
['rain', 68, 80, 'false', 'Play' ],
['rain', 65, 70, 'true', 'Dont_play' ],
['overcast', 64, 65, 'true', 'Play' ],
['sunny', 72, 95, 'false', 'Dont_play' ],
['sunny', 69, 70, 'false', 'Play' ],
['rain', 75, 80, 'false', 'Play' ],
['sunny', 75, 70, 'true', 'Play' ],
['overcast', 72, 90, 'true', 'Play' ],
['overcast', 81, 75, 'false', 'Play' ],
['rain', 71, 80, 'true', 'Dont_play' ]
['sunny', 85, 85, 'false', 'Dont_play'],
['sunny', 80, 90, 'true', 'Dont_play'],
['overcast', 83, 78, 'false', 'Play'],
['rain', 70, 96, 'false', 'Play'],
['rain', 68, 80, 'false', 'Play'],
['rain', 65, 70, 'true', 'Dont_play'],
['overcast', 64, 65, 'true', 'Play'],
['sunny', 72, 95, 'false', 'Dont_play'],
['sunny', 69, 70, 'false', 'Play'],
['rain', 75, 80, 'false', 'Play'],
['sunny', 75, 70, 'true', 'Play'],
['overcast', 72, 90, 'true', 'Play'],
['overcast', 81, 75, 'false', 'Play'],
['rain', 71, 80, 'true', 'Dont_play']
];
private $extraData = [
['scorching', 90, 95, 'false', 'Dont_play'],
['scorching', 0, 0, 'false', 'Dont_play'],
['scorching', 90, 95, 'false', 'Dont_play'],
['scorching', 0, 0, 'false', 'Dont_play'],
];
public function testPredictSingleSample()
@ -97,6 +97,7 @@ class BaggingTest extends TestCase
$classifier = new Bagging($numBaseClassifiers);
$classifier->setSubsetRatio(1.0);
$classifier->setClassifer(DecisionTree::class, ['depth' => 10]);
return $classifier;
}
@ -104,7 +105,7 @@ class BaggingTest extends TestCase
{
return [
DecisionTree::class => ['depth' => 5],
NaiveBayes::class => []
NaiveBayes::class => []
];
}
@ -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];
}
}

3
tests/Phpml/Classification/Ensemble/RandomForestTest.php
View File

@ -14,12 +14,13 @@ class RandomForestTest extends BaggingTest
{
$classifier = new RandomForest($numBaseClassifiers);
$classifier->setFeatureSubsetRatio('log');
return $classifier;
}
protected function getAvailableBaseClassifiers()
{
return [ DecisionTree::class => ['depth' => 5] ];
return [DecisionTree::class => ['depth' => 5]];
}
public function testOtherBaseClassifier()

1
tests/Phpml/Classification/MLPClassifierTest.php
View File

@ -180,6 +180,7 @@ class MLPClassifierTest extends TestCase
[0, 1, 2]
);
}
/**
* @expectedException \Phpml\Exception\InvalidArgumentException
*/

1
tests/Phpml/Clustering/FuzzyCMeansTest.php
View File

@ -21,6 +21,7 @@ class FuzzyCMeansTest extends TestCase
}
}
$this->assertCount(0, $samples);
return $fcm;
}

2
tests/Phpml/DimensionReduction/LDATest.php
View File

@ -57,7 +57,7 @@ class LDATest extends TestCase
// for each projected row
foreach ($data as $i => $row) {
$newRow = [$transformed2[$i]];
$newRow2= $lda->transform($row);
$newRow2 = $lda->transform($row);
array_map($check, $newRow, $newRow2);
}

2
tests/Phpml/DimensionReduction/PCATest.php
View File

@ -47,7 +47,7 @@ class PCATest extends TestCase
// same dimensionality with the original dataset
foreach ($data as $i => $row) {
$newRow = [[$transformed[$i]]];
$newRow2= $pca->transform($row);
$newRow2 = $pca->transform($row);
array_map(function ($val1, $val2) use ($epsilon) {
$this->assertEquals(abs($val1), abs($val2), '', $epsilon);

10
tests/Phpml/Math/LinearAlgebra/EigenDecompositionTest.php
View File

@ -22,7 +22,7 @@ class EigenDecompositionTest extends TestCase
[0.614444444, 0.716555556]
];
$knownEigvalues = [0.0490833989, 1.28402771];
$knownEigvectors= [[-0.735178656, 0.677873399], [-0.677873399, -0.735178656]];
$knownEigvectors = [[-0.735178656, 0.677873399], [-0.677873399, -0.735178656]];
$decomp = new EigenvalueDecomposition($matrix);
$eigVectors = $decomp->getEigenvectors();
@ -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 {
@ -49,7 +49,7 @@ class EigenDecompositionTest extends TestCase
$decomp = new EigenvalueDecomposition($A);
$eigValues = $decomp->getRealEigenvalues();
$eigVectors= $decomp->getEigenvectors();
$eigVectors = $decomp->getEigenvectors();
foreach ($eigValues as $index => $lambda) {
$m1 = new Matrix($A);
@ -57,7 +57,7 @@ class EigenDecompositionTest extends TestCase
// A.v=λ.v
$leftSide = $m1->multiply($m2)->toArray();
$rightSide= $m2->multiplyByScalar($lambda)->toArray();
$rightSide = $m2->multiplyByScalar($lambda)->toArray();
$this->assertEquals($leftSide, $rightSide, '', $epsilon);
}

4
tests/Phpml/Math/Statistic/GaussianTest.php
View File

@ -12,12 +12,12 @@ class GaussianTest extends TestCase
public function testPdf()
{
$std = 1.0;
$mean= 0.0;
$mean = 0.0;
$g = new Gaussian($mean, $std);
// Allowable error
$delta = 0.001;
$x = [0, 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0];
$x = [0, 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0];
$pdf = [0.3989, 0.3969, 0.3520, 0.2419, 0.1295, 0.0539, 0.0175, 0.0044];
foreach ($x as $i => $v) {
$this->assertEquals($pdf[$i], $g->pdf($v), '', $delta);

4
tests/Phpml/ModelManagerTest.php
View File

@ -13,7 +13,7 @@ class ModelManagerTest extends TestCase
public function testSaveAndRestore()
{
$filename = uniqid();
$filepath = sys_get_temp_dir() . DIRECTORY_SEPARATOR . $filename;
$filepath = sys_get_temp_dir().DIRECTORY_SEPARATOR.$filename;
$estimator = new LeastSquares();
$modelManager = new ModelManager();
@ -28,7 +28,7 @@ class ModelManagerTest extends TestCase
*/
public function testRestoreWrongFile()
{
$filepath = sys_get_temp_dir() . DIRECTORY_SEPARATOR . 'unexisting';
$filepath = sys_get_temp_dir().DIRECTORY_SEPARATOR.'unexisting';
$modelManager = new ModelManager();
$modelManager->restoreFromFile($filepath);
}

4
tests/Phpml/Preprocessing/NormalizerTest.php
View File

@ -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.