octoleo/php-ml
1
0
Fork 0
mirror of https://github.com/Llewellynvdm/php-ml.git synced 2024-11-21 20:45:10 +00:00
Code Issues Packages Projects Releases Wiki Activity

[coding standard] fix imports order and drop unused docs typehints (#145)

Browse Source 
* fix imports order

* drop unused docs typehints, make use of return types where possible
This commit is contained in:
Tomáš Votruba 2017-11-06 08:56:37 +01:00 committed by Arkadiusz Kondas
parent a0772658bf
commit f4650c696c
106 changed files with 302 additions and 1734 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
docs/math/distance.md
View File

@ -94,7 +94,7 @@ class CustomDistance implements Distance
*
* @return float
*/
public function distance(array $a, array $b): float
public function distance(array $a, array $b) : float
{
$distance = [];
$count = count($a);

15
src/Phpml/Association/Apriori.php
View File

@ -49,9 +49,6 @@ class Apriori implements Associator
/**
* Apriori constructor.
*
* @param float $support
* @param float $confidence
*/
public function __construct(float $support = 0.0, float $confidence = 0.0)
{
@ -261,8 +258,6 @@ class Apriori implements Associator
*
* @param mixed[] $set
* @param mixed[] $subset
*
* @return float
*/
private function confidence(array $set, array $subset) : float
{
@ -276,8 +271,6 @@ class Apriori implements Associator
* @see \Phpml\Association\Apriori::samples
*
* @param mixed[] $sample
*
* @return float
*/
private function support(array $sample) : float
{
@ -290,8 +283,6 @@ class Apriori implements Associator
* @see \Phpml\Association\Apriori::samples
*
* @param mixed[] $sample
*
* @return int
*/
private function frequency(array $sample) : int
{
@ -307,8 +298,6 @@ class Apriori implements Associator
*
* @param mixed[][] $system
* @param mixed[] $set
*
* @return bool
*/
private function contains(array $system, array $set) : bool
{
@ -322,8 +311,6 @@ class Apriori implements Associator
*
* @param mixed[] $set
* @param mixed[] $subset
*
* @return bool
*/
private function subset(array $set, array $subset) : bool
{
@ -335,8 +322,6 @@ class Apriori implements Associator
*
* @param mixed[] $set1
* @param mixed[] $set2
*
* @return bool
*/
private function equals(array $set1, array $set2) : bool
{

41
src/Phpml/Classification/DecisionTree.php
View File

@ -4,11 +4,11 @@ declare(strict_types=1);
namespace Phpml\Classification;
use Phpml\Classification\DecisionTree\DecisionTreeLeaf;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
use Phpml\Math\Statistic\Mean;
use Phpml\Classification\DecisionTree\DecisionTreeLeaf;
class DecisionTree implements Classifier
{
@ -63,14 +63,10 @@ class DecisionTree implements Classifier
private $featureImportances = null;
/**
*
* @var array
*/
private $columnNames = null;
/**
* @param int $maxDepth
*/
public function __construct(int $maxDepth = 10)
{
$this->maxDepth = $maxDepth;
@ -129,8 +125,6 @@ class DecisionTree implements Classifier
/**
* @param array $records
* @param int $depth
*
* @return DecisionTreeLeaf
*/
protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf
{
@ -190,11 +184,6 @@ class DecisionTree implements Classifier
return $split;
}
/**
* @param array $records
*
* @return DecisionTreeLeaf
*/
protected function getBestSplit(array $records) : DecisionTreeLeaf
{
$targets = array_intersect_key($this->targets, array_flip($records));
@ -277,10 +266,6 @@ class DecisionTree implements Classifier
/**
* @param mixed $baseValue
* @param array $colValues
* @param array $targets
*
* @return float
*/
public function getGiniIndex($baseValue, array $colValues, array $targets) : float
{
@ -342,8 +327,6 @@ class DecisionTree implements Classifier
/**
* @param array $columnValues
*
* @return bool
*/
protected static function isCategoricalColumn(array $columnValues) : bool
{
@ -376,8 +359,6 @@ class DecisionTree implements Classifier
* otherwise the given value will be used as a maximum for number of columns
* randomly selected for each split operation.
*
* @param int $numFeatures
*
* @return $this
*
* @throws InvalidArgumentException
@ -395,8 +376,6 @@ class DecisionTree implements Classifier
/**
* Used to set predefined features to consider while deciding which column to use for a split
*
* @param array $selectedFeatures
*/
protected function setSelectedFeatures(array $selectedFeatures)
{
@ -407,8 +386,6 @@ class DecisionTree implements Classifier
* A string array to represent columns. Useful when HTML output or
* column importances are desired to be inspected.
*
* @param array $names
*
* @return $this
*
* @throws InvalidArgumentException
@ -424,10 +401,7 @@ class DecisionTree implements Classifier
return $this;
}
/**
* @return string
*/
public function getHtml()
public function getHtml() : string
{
return $this->tree->getHTML($this->columnNames);
}
@ -436,10 +410,8 @@ class DecisionTree implements Classifier
* This will return an array including an importance value for
* each column in the given dataset. The importance values are
* normalized and their total makes 1.<br/>
*
* @return array
*/
public function getFeatureImportances()
public function getFeatureImportances() : array
{
if ($this->featureImportances !== null) {
return $this->featureImportances;
@ -473,11 +445,6 @@ class DecisionTree implements Classifier
/**
* Collects and returns an array of internal nodes that use the given
* column as a split criterion
*
* @param int $column
* @param DecisionTreeLeaf $node
*
* @return array
*/
protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) : array
{
@ -506,8 +473,6 @@ class DecisionTree implements Classifier
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)

25
src/Phpml/Classification/DecisionTree/DecisionTreeLeaf.php
View File

@ -71,31 +71,22 @@ class DecisionTreeLeaf
*/
public $level = 0;
/**
* @param array $record
*
* @return bool
*/
public function evaluate($record)
public function evaluate(array $record) : bool
{
$recordField = $record[$this->columnIndex];
if ($this->isContinuous) {
return Comparison::compare((string) $recordField, $this->numericValue, $this->operator);
}
return $recordField == $this->value;
}
/**
* Returns Mean Decrease Impurity (MDI) in the node.
* For terminal nodes, this value is equal to 0
*
* @param int $parentRecordCount
*
* @return float
*/
public function getNodeImpurityDecrease(int $parentRecordCount)
public function getNodeImpurityDecrease(int $parentRecordCount) : float
{
if ($this->isTerminal) {
return 0.0;
@ -119,12 +110,8 @@ class DecisionTreeLeaf
/**
* Returns HTML representation of the node including children nodes
*
* @param $columnNames
*
* @return string
*/
public function getHTML($columnNames = null)
public function getHTML($columnNames = null) : string
{
if ($this->isTerminal) {
$value = "<b>$this->classValue</b>";
@ -170,10 +157,8 @@ class DecisionTreeLeaf
/**
* HTML representation of the tree without column names
*
* @return string
*/
public function __toString()
public function __toString() : string
{
return $this->getHTML();
}

39
src/Phpml/Classification/Ensemble/AdaBoost.php
View File

@ -4,13 +4,13 @@ declare(strict_types=1);
namespace Phpml\Classification\Ensemble;
use Phpml\Classification\Classifier;
use Phpml\Classification\Linear\DecisionStump;
use Phpml\Classification\WeightedClassifier;
use Phpml\Math\Statistic\Mean;
use Phpml\Math\Statistic\StandardDeviation;
use Phpml\Classification\Classifier;
use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
use Phpml\Math\Statistic\Mean;
use Phpml\Math\Statistic\StandardDeviation;
class AdaBoost implements Classifier
{
@ -75,8 +75,6 @@ class AdaBoost implements Classifier
* ADAptive BOOSTing (AdaBoost) is an ensemble algorithm to
* improve classification performance of 'weak' classifiers such as
* DecisionStump (default base classifier of AdaBoost).
*
* @param int $maxIterations
*/
public function __construct(int $maxIterations = 50)
{
@ -85,9 +83,6 @@ class AdaBoost implements Classifier
/**
* Sets the base classifier that will be used for boosting (default = DecisionStump)
*
* @param string $baseClassifier
* @param array $classifierOptions
*/
public function setBaseClassifier(string $baseClassifier = DecisionStump::class, array $classifierOptions = [])
{
@ -96,9 +91,6 @@ class AdaBoost implements Classifier
}
/**
* @param array $samples
* @param array $targets
*
* @throws \Exception
*/
public function train(array $samples, array $targets)
@ -143,10 +135,8 @@ class AdaBoost implements Classifier
/**
* Returns the classifier with the lowest error rate with the
* consideration of current sample weights
*
* @return Classifier
*/
protected function getBestClassifier()
protected function getBestClassifier() : Classifier
{
$ref = new \ReflectionClass($this->baseClassifier);
if ($this->classifierOptions) {
@ -169,10 +159,8 @@ class AdaBoost implements Classifier
/**
* Resamples the dataset in accordance with the weights and
* returns the new dataset
*
* @return array
*/
protected function resample()
protected function resample() : array
{
$weights = $this->weights;
$std = StandardDeviation::population($weights);
@ -198,12 +186,8 @@ class AdaBoost implements Classifier
/**
* Evaluates the classifier and returns the classification error rate
*
* @param Classifier $classifier
*
* @return float
*/
protected function evaluateClassifier(Classifier $classifier)
protected function evaluateClassifier(Classifier $classifier) : float
{
$total = (float) array_sum($this->weights);
$wrong = 0;
@ -219,12 +203,8 @@ class AdaBoost implements Classifier
/**
* Calculates alpha of a classifier
*
* @param float $errorRate
*
* @return float
*/
protected function calculateAlpha(float $errorRate)
protected function calculateAlpha(float $errorRate) : float
{
if ($errorRate == 0) {
$errorRate = 1e-10;
@ -235,9 +215,6 @@ class AdaBoost implements Classifier
/**
* Updates the sample weights
*
* @param Classifier $classifier
* @param float $alpha
*/
protected function updateWeights(Classifier $classifier, float $alpha)
{
@ -256,8 +233,6 @@ class AdaBoost implements Classifier
}
/**
* @param array $sample
*
* @return mixed
*/
public function predictSample(array $sample)

29
src/Phpml/Classification/Ensemble/Bagging.php
View File

@ -4,10 +4,10 @@ declare(strict_types=1);
namespace Phpml\Classification\Ensemble;
use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
use Phpml\Classification\Classifier;
use Phpml\Classification\DecisionTree;
use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
class Bagging implements Classifier
{
@ -62,8 +62,6 @@ class Bagging implements Classifier
* Creates an ensemble classifier with given number of base classifiers
* Default number of base classifiers is 50.
* The more number of base classifiers, the better performance but at the cost of procesing time
*
* @param int $numClassifier
*/
public function __construct(int $numClassifier = 50)
{
@ -75,8 +73,6 @@ class Bagging implements Classifier
* e.g., random samples drawn from the original dataset with replacement (allow repeats),
* to train each base classifier.
*
* @param float $ratio
*
* @return $this
*
* @throws \Exception
@ -100,9 +96,6 @@ class Bagging implements Classifier
* given in the order they are in the constructor of the classifier and parameter
* names are neglected.
*
* @param string $classifier
* @param array $classifierOptions
*
* @return $this
*/
public function setClassifer(string $classifier, array $classifierOptions = [])
@ -113,10 +106,6 @@ class Bagging implements Classifier
return $this;
}
/**
* @param array $samples
* @param array $targets
*/
public function train(array $samples, array $targets)
{
$this->samples = array_merge($this->samples, $samples);
@ -134,12 +123,7 @@ class Bagging implements Classifier
}
}
/**
* @param int $index
*
* @return array
*/
protected function getRandomSubset(int $index)
protected function getRandomSubset(int $index) : array
{
$samples = [];
$targets = [];
@ -154,10 +138,7 @@ class Bagging implements Classifier
return [$samples, $targets];
}
/**
* @return array
*/
protected function initClassifiers()
protected function initClassifiers() : array
{
$classifiers = [];
for ($i = 0; $i < $this->numClassifier; ++$i) {
@ -185,8 +166,6 @@ class Bagging implements Classifier
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)

11
src/Phpml/Classification/Ensemble/RandomForest.php
View File

@ -22,8 +22,6 @@ class RandomForest extends Bagging
* Initializes RandomForest with the given number of trees. More trees
* may increase the prediction performance while it will also substantially
* increase the processing time and the required memory
*
* @param int $numClassifier
*/
public function __construct(int $numClassifier = 50)
{
@ -65,9 +63,6 @@ class RandomForest extends Bagging
/**
* RandomForest algorithm is usable *only* with DecisionTree
*
* @param string $classifier
* @param array $classifierOptions
*
* @return $this
*
* @throws \Exception
@ -85,10 +80,8 @@ class RandomForest extends Bagging
* This will return an array including an importance value for
* each column in the given dataset. Importance values for a column
* is the average importance of that column in all trees in the forest
*
* @return array
*/
public function getFeatureImportances()
public function getFeatureImportances() : array
{
// Traverse each tree and sum importance of the columns
$sum = [];
@ -120,8 +113,6 @@ class RandomForest extends Bagging
* A string array to represent the columns is given. They are useful
* when trying to print some information about the trees such as feature importances
*
* @param array $names
*
* @return $this
*/
public function setColumnNames(array $names)

9
src/Phpml/Classification/KNearestNeighbors.php
View File

@ -24,7 +24,6 @@ class KNearestNeighbors implements Classifier
private $distanceMetric;
/**
* @param int $k
* @param Distance|null $distanceMetric (if null then Euclidean distance as default)
*/
public function __construct(int $k = 3, Distance $distanceMetric = null)
@ -40,8 +39,6 @@ class KNearestNeighbors implements Classifier
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)
@ -61,13 +58,9 @@ class KNearestNeighbors implements Classifier
}
/**
* @param array $sample
*
* @return array
*
* @throws \Phpml\Exception\InvalidArgumentException
*/
private function kNeighborsDistances(array $sample)
private function kNeighborsDistances(array $sample) : array
{
$distances = [];

7
src/Phpml/Classification/Linear/Adaline.php
View File

@ -32,11 +32,6 @@ class Adaline extends Perceptron
* If normalizeInputs is set to true, then every input given to the algorithm will be standardized
* by use of standard deviation and mean calculation
*
* @param float $learningRate
* @param int $maxIterations
* @param bool $normalizeInputs
* @param int $trainingType
*
* @throws \Exception
*/
public function __construct(
@ -57,8 +52,6 @@ class Adaline extends Perceptron
/**
* Adapts the weights with respect to given samples and targets
* by use of gradient descent learning rule
*
* @param array $samples
* @param array $targets
*/
protected function runTraining(array $samples, array $targets)

49
src/Phpml/Classification/Linear/DecisionStump.php
View File

@ -4,10 +4,10 @@ declare(strict_types=1);
namespace Phpml\Classification\Linear;
use Phpml\Helper\Predictable;
use Phpml\Helper\OneVsRest;
use Phpml\Classification\WeightedClassifier;
use Phpml\Classification\DecisionTree;
use Phpml\Classification\WeightedClassifier;
use Phpml\Helper\OneVsRest;
use Phpml\Helper\Predictable;
use Phpml\Math\Comparison;
class DecisionStump extends WeightedClassifier
@ -77,8 +77,6 @@ class DecisionStump extends WeightedClassifier
* If columnIndex is given, then the stump tries to produce a decision node
* on this column, otherwise in cases given the value of -1, the stump itself
* decides which column to take for the decision (Default DecisionTree behaviour)
*
* @param int $columnIndex
*/
public function __construct(int $columnIndex = self::AUTO_SELECT)
{
@ -86,10 +84,6 @@ class DecisionStump extends WeightedClassifier
}
/**
* @param array $samples
* @param array $targets
* @param array $labels
*
* @throws \Exception
*/
protected function trainBinary(array $samples, array $targets, array $labels)
@ -151,8 +145,6 @@ class DecisionStump extends WeightedClassifier
* values in the column. Given <i>$count</i> value determines how many split
* points to be probed. The more split counts, the better performance but
* worse processing time (Default value is 10.0)
*
* @param float $count
*/
public function setNumericalSplitCount(float $count)
{
@ -161,14 +153,8 @@ class DecisionStump extends WeightedClassifier
/**
* Determines best split point for the given column
*
* @param array $samples
* @param array $targets
* @param int $col
*
* @return array
*/
protected function getBestNumericalSplit(array $samples, array $targets, int $col)
protected function getBestNumericalSplit(array $samples, array $targets, int $col) : array
{
$values = array_column($samples, $col);
// Trying all possible points may be accomplished in two general ways:
@ -207,13 +193,6 @@ class DecisionStump extends WeightedClassifier
return $split;
}
/**
* @param array $samples
* @param array $targets
* @param int $col
*
* @return array
*/
protected function getBestNominalSplit(array $samples, array $targets, int $col) : array
{
$values = array_column($samples, $col);
@ -240,13 +219,6 @@ 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 string $operator
* @param array $values
*
* @return array
*/
protected function calculateErrorRate(array $targets, float $threshold, string $operator, array $values) : array
{
@ -293,10 +265,7 @@ class DecisionStump extends WeightedClassifier
* Probability of a sample is calculated as the proportion of the label
* within the labels of the training samples in the decision node
*
* @param array $sample
* @param mixed $label
*
* @return float
*/
protected function predictProbability(array $sample, $label) : float
{
@ -309,8 +278,6 @@ class DecisionStump extends WeightedClassifier
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSampleBinary(array $sample)
@ -322,17 +289,11 @@ class DecisionStump extends WeightedClassifier
return $this->binaryLabels[1];
}
/**
* @return void
*/
protected function resetBinary()
{
}
/**
* @return string
*/
public function __toString()
public function __toString() : string
{
return "IF $this->column $this->operator $this->value ".
'THEN '.$this->binaryLabels[0].' '.

35
src/Phpml/Classification/Linear/LogisticRegression.php
View File

@ -59,12 +59,6 @@ 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 string $cost
* @param string $penalty
*
* @throws \Exception
*/
public function __construct(
@ -102,8 +96,6 @@ class LogisticRegression extends Adaline
/**
* Sets the learning rate if gradient descent algorithm is
* selected for training
*
* @param float $learningRate
*/
public function setLearningRate(float $learningRate)
{
@ -113,8 +105,6 @@ class LogisticRegression extends Adaline
/**
* Lambda (λ) parameter of regularization term. If 0 is given,
* then the regularization term is cancelled
*
* @param float $lambda
*/
public function setLambda(float $lambda)
{
@ -125,9 +115,6 @@ class LogisticRegression extends Adaline
* Adapts the weights with respect to given samples and targets
* by use of selected solver
*
* @param array $samples
* @param array $targets
*
* @throws \Exception
*/
protected function runTraining(array $samples, array $targets)
@ -154,7 +141,6 @@ class LogisticRegression extends Adaline
*
* @param array $samples
* @param array $targets
* @param \Closure $gradientFunc
*/
protected function runConjugateGradient(array $samples, array $targets, \Closure $gradientFunc)
{
@ -170,11 +156,9 @@ class LogisticRegression extends Adaline
/**
* Returns the appropriate callback function for the selected cost function
*
* @return \Closure
*
* @throws \Exception
*/
protected function getCostFunction()
protected function getCostFunction() : \Closure
{
$penalty = 0;
if ($this->penalty == 'L2') {
@ -244,8 +228,6 @@ class LogisticRegression extends Adaline
/**
* Returns the output of the network, a float value between 0.0 and 1.0
*
* @param array $sample
*
* @return float
*/
protected function output(array $sample)
@ -257,12 +239,8 @@ class LogisticRegression extends Adaline
/**
* Returns the class value (either -1 or 1) for the given input
*
* @param array $sample
*
* @return int
*/
protected function outputClass(array $sample)
protected function outputClass(array $sample) : int
{
$output = $this->output($sample);
@ -278,20 +256,17 @@ class LogisticRegression extends Adaline
*
* The probability is simply taken as the distance of the sample
* to the decision plane.
*
* @param array $sample
* @param mixed $label
*
* @return float
*/
protected function predictProbability(array $sample, $label)
protected function predictProbability(array $sample, $label) : float
{
$predicted = $this->predictSampleBinary($sample);
if ((string) $predicted == (string) $label) {
$sample = $this->checkNormalizedSample($sample);
return abs($this->output($sample) - 0.5);
return (float) abs($this->output($sample) - 0.5);
}
return 0.0;

58
src/Phpml/Classification/Linear/Perceptron.php
View File

@ -4,13 +4,13 @@ declare(strict_types=1);
namespace Phpml\Classification\Linear;
use Phpml\Helper\Predictable;
use Phpml\Helper\OneVsRest;
use Phpml\Helper\Optimizer\StochasticGD;
use Phpml\Helper\Optimizer\GD;
use Phpml\Classification\Classifier;
use Phpml\Preprocessing\Normalizer;
use Phpml\Helper\OneVsRest;
use Phpml\Helper\Optimizer\GD;
use Phpml\Helper\Optimizer\StochasticGD;
use Phpml\Helper\Predictable;
use Phpml\IncrementalEstimator;
use Phpml\Preprocessing\Normalizer;
class Perceptron implements Classifier, IncrementalEstimator
{
@ -67,7 +67,6 @@ class Perceptron implements Classifier, IncrementalEstimator
*
* @param float $learningRate Value between 0.0(exclusive) and 1.0(inclusive)
* @param int $maxIterations Must be at least 1
* @param bool $normalizeInputs
*
* @throws \Exception
*/
@ -89,21 +88,11 @@ class Perceptron implements Classifier, IncrementalEstimator
$this->maxIterations = $maxIterations;
}
/**
* @param array $samples
* @param array $targets
* @param array $labels
*/
public function partialTrain(array $samples, array $targets, array $labels = [])
{
$this->trainByLabel($samples, $targets, $labels);
}
/**
* @param array $samples
* @param array $targets
* @param array $labels
*/
public function trainBinary(array $samples, array $targets, array $labels)
{
if ($this->normalizer) {
@ -139,8 +128,6 @@ class Perceptron implements Classifier, IncrementalEstimator
* If "false" is given, the optimization procedure will always be executed
* for $maxIterations times
*
* @param bool $enable
*
* @return $this
*/
public function setEarlyStop(bool $enable = true)
@ -152,10 +139,8 @@ class Perceptron implements Classifier, IncrementalEstimator
/**
* Returns the cost values obtained during the training.
*
* @return array
*/
public function getCostValues()
public function getCostValues() : array
{
return $this->costValues;
}
@ -163,9 +148,6 @@ class Perceptron implements Classifier, IncrementalEstimator
/**
* Trains the perceptron model with Stochastic Gradient Descent optimization
* to get the correct set of weights
*
* @param array $samples
* @param array $targets
*/
protected function runTraining(array $samples, array $targets)
{
@ -186,11 +168,6 @@ class Perceptron implements Classifier, IncrementalEstimator
/**
* Executes a Gradient Descent algorithm for
* the given cost function
*
* @param array $samples
* @param array $targets
* @param \Closure $gradientFunc
* @param bool $isBatch
*/
protected function runGradientDescent(array $samples, array $targets, \Closure $gradientFunc, bool $isBatch = false)
{
@ -211,12 +188,8 @@ class Perceptron implements Classifier, IncrementalEstimator
/**
* Checks if the sample should be normalized and if so, returns the
* normalized sample
*
* @param array $sample
*
* @return array
*/
protected function checkNormalizedSample(array $sample)
protected function checkNormalizedSample(array $sample) : array
{
if ($this->normalizer) {
$samples = [$sample];
@ -230,8 +203,6 @@ 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)
@ -250,12 +221,8 @@ 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)
protected function outputClass(array $sample) : int
{
return $this->output($sample) > 0 ? 1 : -1;
}
@ -266,27 +233,22 @@ class Perceptron implements Classifier, IncrementalEstimator
* The probability is simply taken as the distance of the sample
* to the decision plane.
*
* @param array $sample
* @param mixed $label
*
* @return float
*/
protected function predictProbability(array $sample, $label)
protected function predictProbability(array $sample, $label) : float
{
$predicted = $this->predictSampleBinary($sample);
if ((string) $predicted == (string) $label) {
$sample = $this->checkNormalizedSample($sample);
return abs($this->output($sample));
return (float) abs($this->output($sample));
}
return 0.0;
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSampleBinary(array $sample)

7
src/Phpml/Classification/MLPClassifier.php
View File

@ -13,10 +13,8 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
* @param mixed $target
*
* @throws InvalidArgumentException
*
* @return int
*/
public function getTargetClass($target): int
public function getTargetClass($target) : int
{
if (!in_array($target, $this->classes)) {
throw InvalidArgumentException::invalidTarget($target);
@ -26,8 +24,6 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)
@ -47,7 +43,6 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
}
/**
* @param array $sample
* @param mixed $target
*/
protected function trainSample(array $sample, $target)

25
src/Phpml/Classification/NaiveBayes.php
View File

@ -57,10 +57,6 @@ class NaiveBayes implements Classifier
*/
private $labels = [];
/**
* @param array $samples
* @param array $targets
*/
public function train(array $samples, array $targets)
{
$this->samples = array_merge($this->samples, $samples);
@ -80,11 +76,8 @@ 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 $samples
*/
private function calculateStatistics($label, $samples)
private function calculateStatistics(string $label, array $samples)
{
$this->std[$label] = array_fill(0, $this->featureCount, 0);
$this->mean[$label] = array_fill(0, $this->featureCount, 0);
@ -114,14 +107,8 @@ class NaiveBayes implements Classifier
/**
* Calculates the probability P(label|sample_n)
*
* @param array $sample
* @param int $feature
* @param string $label
*
* @return float
*/
private function sampleProbability($sample, $feature, $label)
private function sampleProbability(array $sample, int $feature, string $label) : float
{
$value = $sample[$feature];
if ($this->dataType[$label][$feature] == self::NOMINAL) {
@ -149,12 +136,8 @@ class NaiveBayes implements Classifier
/**
* Return samples belonging to specific label
*
* @param string $label
*
* @return array
*/
private function getSamplesByLabel($label)
private function getSamplesByLabel(string $label) : array
{
$samples = [];
for ($i = 0; $i < $this->sampleCount; ++$i) {
@ -167,8 +150,6 @@ class NaiveBayes implements Classifier
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)

11
src/Phpml/Classification/SVC.php
View File

@ -10,17 +10,6 @@ use Phpml\SupportVectorMachine\Type;
class SVC extends SupportVectorMachine implements Classifier
{
/**
* @param int $kernel
* @param float $cost
* @param int $degree
* @param float|null $gamma
* @param float $coef0
* @param float $tolerance
* @param int $cacheSize
* @param bool $shrinking
* @param bool $probabilityEstimates
*/
public function __construct(
int $kernel = Kernel::LINEAR,
float $cost = 1.0,

7
src/Phpml/Clustering/Clusterer.php
View File

@ -6,10 +6,5 @@ namespace Phpml\Clustering;
interface Clusterer
{
/**
* @param array $samples
*
* @return array
*/
public function cluster(array $samples);
public function cluster(array $samples) : array;
}

30
src/Phpml/Clustering/DBSCAN.php
View File

@ -24,12 +24,7 @@ class DBSCAN implements Clusterer
*/
private $distanceMetric;
/**
* @param float $epsilon
* @param int $minSamples
* @param Distance $distanceMetric
*/
public function __construct($epsilon = 0.5, $minSamples = 3, Distance $distanceMetric = null)
public function __construct(float $epsilon = 0.5, int $minSamples = 3, Distance $distanceMetric = null)
{
if (null === $distanceMetric) {
$distanceMetric = new Euclidean();
@ -40,12 +35,7 @@ class DBSCAN implements Clusterer
$this->distanceMetric = $distanceMetric;
}
/**
* @param array $samples
*
* @return array
*/
public function cluster(array $samples)
public function cluster(array $samples) : array
{
$clusters = [];
$visited = [];
@ -65,13 +55,7 @@ class DBSCAN implements Clusterer
return $clusters;
}
/**
* @param array $localSample
* @param array $samples
*
* @return array
*/
private function getSamplesInRegion($localSample, $samples)
private function getSamplesInRegion(array $localSample, array $samples) : array
{
$region = [];
@ -84,13 +68,7 @@ class DBSCAN implements Clusterer
return $region;
}
/**
* @param array $samples
* @param array $visited
*
* @return array
*/
private function expandCluster($samples, &$visited)
private function expandCluster(array $samples, array &$visited) : array
{
$cluster = [];

29
src/Phpml/Clustering/FuzzyCMeans.php
View File

@ -4,8 +4,8 @@ declare(strict_types=1);
namespace Phpml\Clustering;
use Phpml\Clustering\KMeans\Point;
use Phpml\Clustering\KMeans\Cluster;
use Phpml\Clustering\KMeans\Point;
use Phpml\Clustering\KMeans\Space;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Math\Distance\Euclidean;
@ -58,11 +58,6 @@ class FuzzyCMeans implements Clusterer
private $samples;
/**
* @param int $clustersNumber
* @param float $fuzziness
* @param float $epsilon
* @param int $maxIterations
*
* @throws InvalidArgumentException
*/
public function __construct(int $clustersNumber, float $fuzziness = 2.0, float $epsilon = 1e-2, int $maxIterations = 100)
@ -85,10 +80,6 @@ class FuzzyCMeans implements Clusterer
$this->updateClusters();
}
/**
* @param int $rows
* @param int $cols
*/
protected function generateRandomMembership(int $rows, int $cols)
{
$this->membership = [];
@ -155,14 +146,7 @@ class FuzzyCMeans implements Clusterer
}
}
/**
*
* @param int $row
* @param int $col
*
* @return float
*/
protected function getDistanceCalc(int $row, int $col)
protected function getDistanceCalc(int $row, int $col) : float
{
$sum = 0.0;
$distance = new Euclidean();
@ -204,20 +188,15 @@ class FuzzyCMeans implements Clusterer
return $sum;
}
/**
* @return array
*/
public function getMembershipMatrix()
public function getMembershipMatrix() : array
{
return $this->membership;
}
/**
* @param array|Point[] $samples
*
* @return array
*/
public function cluster(array $samples)
public function cluster(array $samples) : array
{
// Initialize variables, clusters and membership matrix
$this->sampleCount = count($samples);

13
src/Phpml/Clustering/KMeans.php
View File

@ -22,12 +22,6 @@ class KMeans implements Clusterer
*/
private $initialization;
/**
* @param int $clustersNumber
* @param int $initialization
*
* @throws InvalidArgumentException
*/
public function __construct(int $clustersNumber, int $initialization = self::INIT_KMEANS_PLUS_PLUS)
{
if ($clustersNumber <= 0) {
@ -38,12 +32,7 @@ class KMeans implements Clusterer
$this->initialization = $initialization;
}
/**
* @param array $samples
*
* @return array
*/
public function cluster(array $samples)
public function cluster(array $samples) : array
{
$space = new Space(count($samples[0]));
foreach ($samples as $sample) {

45
src/Phpml/Clustering/KMeans/Cluster.php
View File

@ -4,10 +4,10 @@ declare(strict_types=1);
namespace Phpml\Clustering\KMeans;
use IteratorAggregate;
use Countable;
use SplObjectStorage;
use IteratorAggregate;
use LogicException;
use SplObjectStorage;
class Cluster extends Point implements IteratorAggregate, Countable
{
@ -21,10 +21,6 @@ class Cluster extends Point implements IteratorAggregate, Countable
*/
protected $points;
/**
* @param Space $space
* @param array $coordinates
*/
public function __construct(Space $space, array $coordinates)
{
parent::__construct($coordinates);
@ -32,10 +28,7 @@ class Cluster extends Point implements IteratorAggregate, Countable
$this->points = new SplObjectStorage();
}
/**
* @return array
*/
public function getPoints()
public function getPoints() : array
{
$points = [];
foreach ($this->points as $point) {
@ -45,10 +38,7 @@ class Cluster extends Point implements IteratorAggregate, Countable
return $points;
}
/**
* @return array
*/
public function toArray()
public function toArray() : array
{
return [
'centroid' => parent::toArray(),
@ -56,14 +46,7 @@ class Cluster extends Point implements IteratorAggregate, Countable
];
}
/**
* @param Point $point
*
* @return Point
*
* @throws \LogicException
*/
public function attach(Point $point)
public function attach(Point $point) : Point
{
if ($point instanceof self) {
throw new LogicException('cannot attach a cluster to another');
@ -74,29 +57,18 @@ class Cluster extends Point implements IteratorAggregate, Countable
return $point;
}
/**
* @param Point $point
*
* @return Point
*/
public function detach(Point $point)
public function detach(Point $point) : Point
{
$this->points->detach($point);
return $point;
}
/**
* @param SplObjectStorage $points
*/
public function attachAll(SplObjectStorage $points)
{
$this->points->addAll($points);
}
/**
* @param SplObjectStorage $points
*/
public function detachAll(SplObjectStorage $points)
{
$this->points->removeAll($points);
@ -136,10 +108,7 @@ class Cluster extends Point implements IteratorAggregate, Countable
{
return count($this->points);
}
/**
* @param array $newCoordinates
*/
public function setCoordinates(array $newCoordinates)
{
$this->coordinates = $newCoordinates;

24
src/Phpml/Clustering/KMeans/Point.php
View File

@ -18,30 +18,21 @@ class Point implements ArrayAccess
*/
protected $coordinates;
/**
* @param array $coordinates
*/
public function __construct(array $coordinates)
{
$this->dimension = count($coordinates);
$this->coordinates = $coordinates;
}
/**
* @return array
*/
public function toArray()
public function toArray() : array
{
return $this->coordinates;
}
/**
* @param Point $point
* @param bool $precise
*
* @return int|mixed
*/
public function getDistanceWith(self $point, $precise = true)
public function getDistanceWith(self $point, bool $precise = true)
{
$distance = 0;
for ($n = 0; $n < $this->dimension; ++$n) {
@ -53,8 +44,6 @@ class Point implements ArrayAccess
}
/**
* @param array $points
*
* @return mixed
*/
public function getClosest(array $points)
@ -77,20 +66,15 @@ class Point implements ArrayAccess
return $minPoint;
}
/**
* @return array
*/
public function getCoordinates()
public function getCoordinates() : array
{
return $this->coordinates;
}
/**
* @param mixed $offset
*
* @return bool
*/
public function offsetExists($offset)
public function offsetExists($offset): bool
{
return isset($this->coordinates[$offset]);
}

64
src/Phpml/Clustering/KMeans/Space.php
View File

@ -4,10 +4,10 @@ declare(strict_types=1);
namespace Phpml\Clustering\KMeans;
use InvalidArgumentException;
use LogicException;
use Phpml\Clustering\KMeans;
use SplObjectStorage;
use LogicException;
use InvalidArgumentException;
class Space extends SplObjectStorage
{
@ -16,9 +16,6 @@ class Space extends SplObjectStorage
*/
protected $dimension;
/**
* @param $dimension
*/
public function __construct($dimension)
{
if ($dimension < 1) {
@ -28,10 +25,7 @@ class Space extends SplObjectStorage
$this->dimension = $dimension;
}
/**
* @return array
*/
public function toArray()
public function toArray() : array
{
$points = [];
foreach ($this as $point) {
@ -41,12 +35,7 @@ class Space extends SplObjectStorage
return ['points' => $points];
}
/**
* @param array $coordinates
*
* @return Point
*/
public function newPoint(array $coordinates)
public function newPoint(array $coordinates) : Point
{
if (count($coordinates) != $this->dimension) {
throw new LogicException('('.implode(',', $coordinates).') is not a point of this space');
@ -56,7 +45,6 @@ class Space extends SplObjectStorage
}
/**
* @param array $coordinates
* @param null $data
*/
public function addPoint(array $coordinates, $data = null)
@ -77,10 +65,7 @@ class Space extends SplObjectStorage
parent::attach($point, $data);
}
/**
* @return int
*/
public function getDimension()
public function getDimension() : int
{
return $this->dimension;
}
@ -107,13 +92,7 @@ class Space extends SplObjectStorage
return [$min, $max];
}
/**
* @param Point $min
* @param Point $max
*
* @return Point
*/
public function getRandomPoint(Point $min, Point $max)
public function getRandomPoint(Point $min, Point $max) : Point
{
$point = $this->newPoint(array_fill(0, $this->dimension, null));
@ -125,12 +104,9 @@ class Space extends SplObjectStorage
}
/**
* @param int $clustersNumber
* @param int $initMethod
*
* @return array|Cluster[]
*/
public function cluster(int $clustersNumber, int $initMethod = KMeans::INIT_RANDOM)
public function cluster(int $clustersNumber, int $initMethod = KMeans::INIT_RANDOM) : array
{
$clusters = $this->initializeClusters($clustersNumber, $initMethod);
@ -141,12 +117,9 @@ class Space extends SplObjectStorage
}
/**
* @param $clustersNumber
* @param $initMethod
*
* @return array|Cluster[]
*/
protected function initializeClusters(int $clustersNumber, int $initMethod)
protected function initializeClusters(int $clustersNumber, int $initMethod) : array
{
switch ($initMethod) {
case KMeans::INIT_RANDOM:
@ -166,12 +139,7 @@ class Space extends SplObjectStorage
return $clusters;
}
/**
* @param $clusters
*
* @return bool
*/
protected function iterate($clusters)
protected function iterate($clusters) : bool
{
$convergence = true;
@ -209,12 +177,7 @@ class Space extends SplObjectStorage
return $convergence;
}
/**
* @param int $clustersNumber
*
* @return array
*/
private function initializeRandomClusters(int $clustersNumber)
private function initializeRandomClusters(int $clustersNumber) : array
{
$clusters = [];
list($min, $max) = $this->getBoundaries();
@ -226,12 +189,7 @@ class Space extends SplObjectStorage
return $clusters;
}
/**
* @param int $clustersNumber
*
* @return array
*/
protected function initializeKMPPClusters(int $clustersNumber)
protected function initializeKMPPClusters(int $clustersNumber) : array
{
$clusters = [];
$this->rewind();

4
src/Phpml/CrossValidation/RandomSplit.php
View File

@ -8,10 +8,6 @@ use Phpml\Dataset\Dataset;
class RandomSplit extends Split
{
/**
* @param Dataset $dataset
* @param float $testSize
*/
protected function splitDataset(Dataset $dataset, float $testSize)
{
$samples = $dataset->getSamples();

30
src/Phpml/CrossValidation/Split.php
View File

@ -29,13 +29,6 @@ abstract class Split
*/
protected $testLabels = [];
/**
* @param Dataset $dataset
* @param float $testSize
* @param int $seed
*
* @throws InvalidArgumentException
*/
public function __construct(Dataset $dataset, float $testSize = 0.3, int $seed = null)
{
if (0 >= $testSize || 1 <= $testSize) {
@ -48,41 +41,26 @@ abstract class Split
abstract protected function splitDataset(Dataset $dataset, float $testSize);
/**
* @return array
*/
public function getTrainSamples()
public function getTrainSamples() : array
{
return $this->trainSamples;
}
/**
* @return array
*/
public function getTestSamples()
public function getTestSamples() : array
{
return $this->testSamples;
}
/**
* @return array
*/
public function getTrainLabels()
public function getTrainLabels() : array
{
return $this->trainLabels;
}
/**
* @return array
*/
public function getTestLabels()
public function getTestLabels() : array
{
return $this->testLabels;
}
/**
* @param int|null $seed
*/
protected function seedGenerator(int $seed = null)
{
if (null === $seed) {

16
src/Phpml/CrossValidation/StratifiedRandomSplit.php
View File

@ -9,10 +9,6 @@ use Phpml\Dataset\Dataset;
class StratifiedRandomSplit extends RandomSplit
{
/**
* @param Dataset $dataset
* @param float $testSize
*/
protected function splitDataset(Dataset $dataset, float $testSize)
{
$datasets = $this->splitByTarget($dataset);
@ -23,11 +19,9 @@ class StratifiedRandomSplit extends RandomSplit
}
/**
* @param Dataset $dataset
*
* @return Dataset[]|array
*/
private function splitByTarget(Dataset $dataset): array
private function splitByTarget(Dataset $dataset) : array
{
$targets = $dataset->getTargets();
$samples = $dataset->getSamples();
@ -44,13 +38,7 @@ class StratifiedRandomSplit extends RandomSplit
return $datasets;
}
/**
* @param array $uniqueTargets
* @param array $split
*
* @return array
*/
private function createDatasets(array $uniqueTargets, array $split): array
private function createDatasets(array $uniqueTargets, array $split) : array
{
$datasets = [];
foreach ($uniqueTargets as $target) {

4
src/Phpml/Dataset/ArrayDataset.php
View File

@ -37,7 +37,7 @@ class ArrayDataset implements Dataset
/**
* @return array
*/
public function getSamples(): array
public function getSamples() : array
{
return $this->samples;
}
@ -45,7 +45,7 @@ class ArrayDataset implements Dataset
/**
* @return array
*/
public function getTargets(): array
public function getTargets() : array
{
return $this->targets;
}

11
src/Phpml/Dataset/CsvDataset.php
View File

@ -14,12 +14,6 @@ class CsvDataset extends ArrayDataset
protected $columnNames;
/**
* @param string $filepath
* @param int $features
* @param bool $headingRow
* @param string $delimiter
* @param int $maxLineLength
*
* @throws FileException
*/
public function __construct(string $filepath, int $features, bool $headingRow = true, string $delimiter = ',', int $maxLineLength = 0)
@ -50,10 +44,7 @@ class CsvDataset extends ArrayDataset
parent::__construct($samples, $targets);
}
/**
* @return array
*/
public function getColumnNames()
public function getColumnNames() : array
{
return $this->columnNames;
}

4
src/Phpml/Dataset/Dataset.php
View File

@ -9,10 +9,10 @@ interface Dataset
/**
* @return array
*/
public function getSamples(): array;
public function getSamples() : array;
/**
* @return array
*/
public function getTargets(): array;
public function getTargets() : array;
}

11
src/Phpml/Dataset/FilesDataset.php
View File

@ -8,11 +8,6 @@ use Phpml\Exception\DatasetException;
class FilesDataset extends ArrayDataset
{
/**
* @param string $rootPath
*
* @throws DatasetException
*/
public function __construct(string $rootPath)
{
if (!is_dir($rootPath)) {
@ -22,9 +17,6 @@ class FilesDataset extends ArrayDataset
$this->scanRootPath($rootPath);
}
/**
* @param string $rootPath
*/
private function scanRootPath(string $rootPath)
{
foreach (glob($rootPath.DIRECTORY_SEPARATOR.'*', GLOB_ONLYDIR) as $dir) {
@ -32,9 +24,6 @@ class FilesDataset extends ArrayDataset
}
}
/**
* @param string $dir
*/
private function scanDir(string $dir)
{
$target = basename($dir);

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

@ -90,7 +90,7 @@ abstract class EigenTransformerBase
*
* @return array
*/
protected function reduce(array $data)
protected function reduce(array $data) : array
{
$m1 = new Matrix($data);
$m2 = new Matrix($this->eigVectors);

47
src/Phpml/DimensionReduction/KernelPCA.php
View File

@ -44,14 +44,13 @@ 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 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
*
* @throws \Exception
*/
public function __construct(int $kernel = self::KERNEL_RBF, $totalVariance = null, $numFeatures = null, $gamma = null)
public function __construct(int $kernel = self::KERNEL_RBF, float $totalVariance = null, int $numFeatures = null, float $gamma = null)
{
$availableKernels = [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN, self::KERNEL_LINEAR];
if (!in_array($kernel, $availableKernels)) {
@ -69,12 +68,8 @@ class KernelPCA extends PCA
* of this data while preserving $totalVariance or $numFeatures. <br>
* $data is an n-by-m matrix and returned array is
* n-by-k matrix where k <= m
*
* @param array $data
*
* @return array
*/
public function fit(array $data)
public function fit(array $data) : array
{
$numRows = count($data);
$this->data = $data;
@ -96,13 +91,8 @@ class KernelPCA extends PCA
/**
* Calculates similarity matrix by use of selected kernel function<br>
* An n-by-m matrix is given and an n-by-n matrix is returned
*
* @param array $data
* @param int $numRows
*
* @return array
*/
protected function calculateKernelMatrix(array $data, int $numRows)
protected function calculateKernelMatrix(array $data, int $numRows) : array
{
$kernelFunc = $this->getKernel();
@ -125,13 +115,8 @@ class KernelPCA extends PCA
* conversion:
*
* K′ = K − N.K − K.N + N.K.N where N is n-by-n matrix filled with 1/n
*
* @param array $matrix
* @param int $n
*
* @return array
*/
protected function centerMatrix(array $matrix, int $n)
protected function centerMatrix(array $matrix, int $n) : array
{
$N = array_fill(0, $n, array_fill(0, $n, 1.0 / $n));
$N = new Matrix($N, false);
@ -153,11 +138,9 @@ class KernelPCA extends PCA
/**
* Returns the callable kernel function
*
* @return \Closure
*
* @throws \Exception
*/
protected function getKernel()
protected function getKernel(): \Closure
{
switch ($this->kernel) {
case self::KERNEL_LINEAR:
@ -194,12 +177,7 @@ class KernelPCA extends PCA
}
}
/**
* @param array $sample
*
* @return array
*/
protected function getDistancePairs(array $sample)
protected function getDistancePairs(array $sample) : array
{
$kernel = $this->getKernel();
@ -211,12 +189,7 @@ class KernelPCA extends PCA
return $pairs;
}
/**
* @param array $pairs
*
* @return array
*/
protected function projectSample(array $pairs)
protected function projectSample(array $pairs) : array
{
// Normalize eigenvectors by eig = eigVectors / eigValues
$func = function ($eigVal, $eigVect) {
@ -235,13 +208,9 @@ class KernelPCA extends PCA
* Transforms the given sample to a lower dimensional vector by using
* the variables obtained during the last run of <code>fit</code>.
*
* @param array $sample
*
* @return array
*
* @throws \Exception
*/
public function transform(array $sample)
public function transform(array $sample) : array
{
if (!$this->fit) {
throw new \Exception('KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first');

42
src/Phpml/DimensionReduction/LDA.php
View File

@ -47,7 +47,7 @@ class LDA extends EigenTransformerBase
*
* @throws \Exception
*/
public function __construct($totalVariance = null, $numFeatures = null)
public function __construct(float $totalVariance = null, int $numFeatures = null)
{
if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
throw new \Exception('Total variance can be a value between 0.1 and 0.99');
@ -69,11 +69,6 @@ class LDA extends EigenTransformerBase
/**
* Trains the algorithm to transform the given data to a lower dimensional space.
*
* @param array $data
* @param array $classes
*
* @return array
*/
public function fit(array $data, array $classes) : array
{
@ -93,12 +88,8 @@ class LDA extends EigenTransformerBase
/**
* Returns unique labels in the dataset
*
* @param array $classes
*
* @return array
*/
protected function getLabels(array $classes): array
protected function getLabels(array $classes) : array
{
$counts = array_count_values($classes);
@ -108,11 +99,6 @@ class LDA extends EigenTransformerBase
/**
* 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
*
* @param array $data
* @param array $classes
*
* @return array
*/
protected function calculateMeans(array $data, array $classes) : array
{
@ -159,13 +145,8 @@ class LDA extends EigenTransformerBase
* Returns in-class scatter matrix for each class, which
* is a n by m matrix where n is number of classes and
* m is number of columns
*
* @param array $data
* @param array $classes
*
* @return Matrix
*/
protected function calculateClassVar($data, $classes)
protected function calculateClassVar(array $data, array $classes) : Matrix
{
// s is an n (number of classes) by m (number of column) matrix
$s = array_fill(0, count($data[0]), array_fill(0, count($data[0]), 0));
@ -187,10 +168,8 @@ class LDA extends EigenTransformerBase
* Returns between-class scatter matrix for each class, which
* is an n by m matrix where n is number of classes and
* m is number of columns
*
* @return Matrix
*/
protected function calculateClassCov()
protected function calculateClassCov() : Matrix
{
// s is an n (number of classes) by m (number of column) matrix
$s = array_fill(0, count($this->overallMean), array_fill(0, count($this->overallMean), 0));
@ -207,13 +186,8 @@ class LDA extends EigenTransformerBase
/**
* Returns the result of the calculation (x - m)T.(x - m)
*
* @param array $row
* @param array $means
*
* @return Matrix
*/
protected function calculateVar(array $row, array $means)
protected function calculateVar(array $row, array $means) : Matrix
{
$x = new Matrix($row, false);
$m = new Matrix($means, false);
@ -226,13 +200,9 @@ class LDA extends EigenTransformerBase
* Transforms the given sample to a lower dimensional vector by using
* the eigenVectors obtained in the last run of <code>fit</code>.
*
* @param array $sample
*
* @return array
*
* @throws \Exception
*/
public function transform(array $sample)
public function transform(array $sample) : array
{
if (!$this->fit) {
throw new \Exception('LDA has not been fitted with respect to original dataset, please run LDA::fit() first');

25
src/Phpml/DimensionReduction/PCA.php
View File

@ -32,7 +32,7 @@ class PCA extends EigenTransformerBase
*
* @throws \Exception
*/
public function __construct($totalVariance = null, $numFeatures = null)
public function __construct(float $totalVariance = null, int $numFeatures = null)
{
if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
throw new \Exception('Total variance can be a value between 0.1 and 0.99');
@ -57,12 +57,8 @@ class PCA extends EigenTransformerBase
* of this data while preserving $totalVariance or $numFeatures. <br>
* $data is an n-by-m matrix and returned array is
* n-by-k matrix where k <= m
*
* @param array $data
*
* @return array
*/
public function fit(array $data)
public function fit(array $data) : array
{
$n = count($data[0]);
@ -77,10 +73,6 @@ class PCA extends EigenTransformerBase
return $this->reduce($data);
}
/**
* @param array $data
* @param int $n
*/
protected function calculateMeans(array $data, int $n)
{
// Calculate means for each dimension
@ -94,13 +86,8 @@ class PCA extends EigenTransformerBase
/**
* Normalization of the data includes subtracting mean from
* each dimension therefore dimensions will be centered to zero
*
* @param array $data
* @param int $n
*
* @return array
*/
protected function normalize(array $data, int $n)
protected function normalize(array $data, int $n) : array
{
if (empty($this->means)) {
$this->calculateMeans($data, $n);
@ -120,13 +107,9 @@ class PCA extends EigenTransformerBase
* Transforms the given sample to a lower dimensional vector by using
* the eigenVectors obtained in the last run of <code>fit</code>.
*
* @param array $sample
*
* @return array
*
* @throws \Exception
*/
public function transform(array $sample)
public function transform(array $sample) : array
{
if (!$this->fit) {
throw new \Exception('PCA has not been fitted with respect to original dataset, please run PCA::fit() first');

7
src/Phpml/Exception/DatasetException.php
View File

@ -6,12 +6,7 @@ namespace Phpml\Exception;
class DatasetException extends \Exception
{
/**
* @param string $path
*
* @return DatasetException
*/
public static function missingFolder(string $path)
public static function missingFolder(string $path) : DatasetException
{
return new self(sprintf('Dataset root folder "%s" missing.', $path));
}

21
src/Phpml/Exception/FileException.php
View File

@ -6,32 +6,17 @@ namespace Phpml\Exception;
class FileException extends \Exception
{
/**
* @param string $filepath
*
* @return FileException
*/
public static function missingFile(string $filepath)
public static function missingFile(string $filepath) : FileException
{
return new self(sprintf('File "%s" missing.', $filepath));
}
/**
* @param string $filepath
*
* @return FileException
*/
public static function cantOpenFile(string $filepath)
public static function cantOpenFile(string $filepath) : FileException
{
return new self(sprintf('File "%s" can\'t be open.', $filepath));
}
/**
* @param string $filepath
*
* @return FileException
*/
public static function cantSaveFile(string $filepath)
public static function cantSaveFile(string $filepath) : FileException
{
return new self(sprintf('File "%s" can\'t be saved.', $filepath));
}

105
src/Phpml/Exception/InvalidArgumentException.php
View File

@ -6,164 +6,95 @@ namespace Phpml\Exception;
class InvalidArgumentException extends \Exception
{
/**
* @return InvalidArgumentException
*/
public static function arraySizeNotMatch()
public static function arraySizeNotMatch() : InvalidArgumentException
{
return new self('Size of given arrays does not match');
}
/**
* @param $name
*
* @return InvalidArgumentException
*/
public static function percentNotInRange($name)
public static function percentNotInRange($name) : InvalidArgumentException
{
return new self(sprintf('%s must be between 0.0 and 1.0', $name));
}
/**
* @return InvalidArgumentException
*/
public static function arrayCantBeEmpty()
public static function arrayCantBeEmpty() : InvalidArgumentException
{
return new self('The array has zero elements');
}
/**
* @param int $minimumSize
*
* @return InvalidArgumentException
*/
public static function arraySizeToSmall($minimumSize = 2)
public static function arraySizeToSmall(int $minimumSize = 2) : InvalidArgumentException
{
return new self(sprintf('The array must have at least %d elements', $minimumSize));
}
/**
* @return InvalidArgumentException
*/
public static function matrixDimensionsDidNotMatch()
public static function matrixDimensionsDidNotMatch() : InvalidArgumentException
{
return new self('Matrix dimensions did not match');
}
/**
* @return InvalidArgumentException
*/
public static function inconsistentMatrixSupplied()
public static function inconsistentMatrixSupplied() : InvalidArgumentException
{
return new self('Inconsistent matrix supplied');
}
/**
* @return InvalidArgumentException
*/
public static function invalidClustersNumber()
public static function invalidClustersNumber() : InvalidArgumentException
{
return new self('Invalid clusters number');
}
/**
* @param mixed $target
*
* @return InvalidArgumentException
*/
public static function invalidTarget($target)
public static function invalidTarget($target) : InvalidArgumentException
{
return new self(sprintf('Target with value "%s" is not part of the accepted classes', $target));
}
/**
* @param string $language
*
* @return InvalidArgumentException
*/
public static function invalidStopWordsLanguage(string $language)
public static function invalidStopWordsLanguage(string $language) : InvalidArgumentException
{
return new self(sprintf('Can\'t find "%s" language for StopWords', $language));
}
/**
* @return InvalidArgumentException
*/
public static function invalidLayerNodeClass()
public static function invalidLayerNodeClass() : InvalidArgumentException
{
return new self('Layer node class must implement Node interface');
}
/**
* @return InvalidArgumentException
*/
public static function invalidLayersNumber()
public static function invalidLayersNumber() : InvalidArgumentException
{
return new self('Provide at least 1 hidden layer');
}
/**
* @return InvalidArgumentException
*/
public static function invalidClassesNumber()
public static function invalidClassesNumber() : InvalidArgumentException
{
return new self('Provide at least 2 different classes');
}
/**
* @return InvalidArgumentException
*/
public static function inconsistentClasses()
public static function inconsistentClasses() : InvalidArgumentException
{
return new self('The provided classes don\'t match the classes provided in the constructor');
}
/**
* @param string $file
*
* @return InvalidArgumentException
*/
public static function fileNotFound(string $file)
public static function fileNotFound(string $file) : InvalidArgumentException
{
return new self(sprintf('File "%s" not found', $file));
}
/**
* @param string $file
*
* @return InvalidArgumentException
*/
public static function fileNotExecutable(string $file)
public static function fileNotExecutable(string $file) : InvalidArgumentException
{
return new self(sprintf('File "%s" is not executable', $file));
}
/**
* @param string $path
*
* @return InvalidArgumentException
*/
public static function pathNotFound(string $path)
public static function pathNotFound(string $path) : InvalidArgumentException
{
return new self(sprintf('The specified path "%s" does not exist', $path));
}
/**
* @param string $path
*
* @return InvalidArgumentException
*/
public static function pathNotWritable(string $path)
public static function pathNotWritable(string $path) : InvalidArgumentException
{
return new self(sprintf('The specified path "%s" is not writable', $path));
}
/**
* @param string $operator
*
* @return InvalidArgumentException
*/
public static function invalidOperator(string $operator)
public static function invalidOperator(string $operator) : InvalidArgumentException
{
return new self(sprintf('Invalid operator "%s" provided', $operator));
}

15
src/Phpml/Exception/MatrixException.php
View File

@ -6,26 +6,17 @@ namespace Phpml\Exception;
class MatrixException extends \Exception
{
/**
* @return MatrixException
*/
public static function notSquareMatrix()
public static function notSquareMatrix() : MatrixException
{
return new self('Matrix is not square matrix');
}
/**
* @return MatrixException
*/
public static function columnOutOfRange()
public static function columnOutOfRange() : MatrixException
{
return new self('Column out of range');
}
/**
* @return MatrixException
*/
public static function singularMatrix()
public static function singularMatrix() : MatrixException
{
return new self('Matrix is singular');
}

5
src/Phpml/Exception/NormalizerException.php
View File

@ -6,10 +6,7 @@ namespace Phpml\Exception;
class NormalizerException extends \Exception
{
/**
* @return NormalizerException
*/
public static function unknownNorm()
public static function unknownNorm() : NormalizerException
{
return new self('Unknown norm supplied.');
}

14
src/Phpml/Exception/SerializeException.php
View File

@ -6,22 +6,12 @@ namespace Phpml\Exception;
class SerializeException extends \Exception
{
/**
* @param string $filepath
*
* @return SerializeException
*/
public static function cantUnserialize(string $filepath)
public static function cantUnserialize(string $filepath) : SerializeException
{
return new self(sprintf('"%s" can not be unserialized.', $filepath));
}
/**
* @param string $classname
*
* @return SerializeException
*/
public static function cantSerialize(string $classname)
public static function cantSerialize(string $classname) : SerializeException
{
return new self(sprintf('Class "%s" can not be serialized.', $classname));
}

19
src/Phpml/FeatureExtraction/StopWords.php
View File

@ -13,32 +13,17 @@ class StopWords
*/
protected $stopWords;
/**
* @param array $stopWords
*/
public function __construct(array $stopWords)
{
$this->stopWords = array_fill_keys($stopWords, true);
}
/**
* @param string $token
*
* @return bool
*/
public function isStopWord(string $token): bool
public function isStopWord(string $token) : bool
{
return isset($this->stopWords[$token]);
}
/**
* @param string $language
*
* @return StopWords
*
* @throws InvalidArgumentException
*/
public static function factory($language = 'English'): StopWords
public static function factory(string $language = 'English') : StopWords
{
$className = __NAMESPACE__."\\StopWords\\$language";

49
src/Phpml/FeatureExtraction/TokenCountVectorizer.php
View File

@ -34,11 +34,6 @@ class TokenCountVectorizer implements Transformer
*/
private $frequencies;
/**
* @param Tokenizer $tokenizer
* @param StopWords $stopWords
* @param float $minDF
*/
public function __construct(Tokenizer $tokenizer, StopWords $stopWords = null, float $minDF = 0.0)
{
$this->tokenizer = $tokenizer;
@ -49,17 +44,11 @@ class TokenCountVectorizer implements Transformer
$this->frequencies = [];
}
/**
* @param array $samples
*/
public function fit(array $samples)
{
$this->buildVocabulary($samples);
}
/**
* @param array $samples
*/
public function transform(array &$samples)
{
foreach ($samples as &$sample) {
@ -69,17 +58,11 @@ class TokenCountVectorizer implements Transformer
$this->checkDocumentFrequency($samples);
}
/**
* @return array
*/
public function getVocabulary()
public function getVocabulary() : array
{
return array_flip($this->vocabulary);
}
/**
* @param array $samples
*/
private function buildVocabulary(array &$samples)
{
foreach ($samples as $index => $sample) {
@ -90,9 +73,6 @@ class TokenCountVectorizer implements Transformer
}
}
/**
* @param string $sample
*/
private function transformSample(string &$sample)
{
$counts = [];
@ -122,8 +102,6 @@ class TokenCountVectorizer implements Transformer
}
/**
* @param string $token
*
* @return int|bool
*/
private function getTokenIndex(string $token)
@ -135,9 +113,6 @@ class TokenCountVectorizer implements Transformer
return $this->vocabulary[$token] ?? false;
}
/**
* @param string $token
*/
private function addTokenToVocabulary(string $token)
{
if ($this->isStopWord($token)) {
@ -149,19 +124,11 @@ class TokenCountVectorizer implements Transformer
}
}
/**
* @param string $token
*
* @return bool
*/
private function isStopWord(string $token): bool
{
return $this->stopWords && $this->stopWords->isStopWord($token);
}
/**
* @param string $token
*/
private function updateFrequency(string $token)
{
if (!isset($this->frequencies[$token])) {
@ -171,9 +138,6 @@ class TokenCountVectorizer implements Transformer
++$this->frequencies[$token];
}
/**
* @param array $samples
*/
private function checkDocumentFrequency(array &$samples)
{
if ($this->minDF > 0) {
@ -184,10 +148,6 @@ class TokenCountVectorizer implements Transformer
}
}
/**
* @param array $sample
* @param array $beyondMinimum
*/
private function resetBeyondMinimum(array &$sample, array $beyondMinimum)
{
foreach ($beyondMinimum as $index) {
@ -195,12 +155,7 @@ class TokenCountVectorizer implements Transformer
}
}
/**
* @param int $samplesCount
*
* @return array
*/
private function getBeyondMinimumIndexes(int $samplesCount)
private function getBeyondMinimumIndexes(int $samplesCount) : array
{
$indexes = [];
foreach ($this->frequencies as $token => $frequency) {

24
src/Phpml/Helper/OneVsRest.php
View File

@ -27,9 +27,6 @@ trait OneVsRest
/**
* Train a binary classifier in the OvR style
*
* @param array $samples
* @param array $targets
*/
public function train(array $samples, array $targets)
{
@ -39,13 +36,6 @@ trait OneVsRest
$this->trainBylabel($samples, $targets);
}
/**
* @param array $samples
* @param array $targets
* @param array $allLabels All training set labels
*
* @return void
*/
protected function trainByLabel(array $samples, array $targets, array $allLabels = [])
{
// Overwrites the current value if it exist. $allLabels must be provided for each partialTrain run.
@ -122,12 +112,11 @@ trait OneVsRest
* $targets is not passed by reference nor contains objects so this method
* changes will not affect the caller $targets array.
*
* @param array $targets
* @param mixed $label
*
* @return array Binarized targets and target's labels
*/
private function binarizeTargets($targets, $label)
private function binarizeTargets(array $targets, $label) : array
{
$notLabel = "not_$label";
foreach ($targets as $key => $target) {
@ -140,8 +129,6 @@ trait OneVsRest
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)
@ -163,10 +150,6 @@ trait OneVsRest
/**
* Each classifier should implement this method instead of train(samples, targets)
*
* @param array $samples
* @param array $targets
* @param array $labels
*/
abstract protected function trainBinary(array $samples, array $targets, array $labels);
@ -181,9 +164,6 @@ trait OneVsRest
* Each classifier that make use of OvR approach should be able to
* return a probability for a sample to belong to the given label.
*
* @param array $sample
* @param string $label
*
* @return mixed
*/
abstract protected function predictProbability(array $sample, string $label);
@ -191,8 +171,6 @@ trait OneVsRest
/**
* Each classifier should implement this method instead of predictSample()
*
* @param array $sample
*
* @return mixed
*/
abstract protected function predictSampleBinary(array $sample);

100
src/Phpml/Helper/Optimizer/ConjugateGradient.php
View File

@ -20,11 +20,10 @@ class ConjugateGradient extends GD
/**
* @param array $samples
* @param array $targets
* @param \Closure $gradientCb
*
* @return array
*/
public function runOptimization(array $samples, array $targets, \Closure $gradientCb)
public function runOptimization(array $samples, array $targets, \Closure $gradientCb) : array
{
$this->samples = $samples;
$this->targets = $targets;
@ -65,12 +64,8 @@ class ConjugateGradient extends GD
/**
* Executes the callback function for the problem and returns
* sum of the gradient for all samples & targets.
*
* @param array $theta
*
* @return array
*/
protected function gradient(array $theta)
protected function gradient(array $theta) : array
{
list(, $gradient) = parent::gradient($theta);
@ -79,12 +74,8 @@ class ConjugateGradient extends GD
/**
* Returns the value of f(x) for given solution
*
* @param array $theta
*
* @return float
*/
protected function cost(array $theta)
protected function cost(array $theta) : float
{
list($cost) = parent::gradient($theta);
@ -104,12 +95,8 @@ class ConjugateGradient extends GD
* b) Probe a larger alpha (0.01) and calculate cost function
* b-1) If cost function decreases, continue enlarging alpha
* b-2) If cost function increases, take the midpoint and try again
*
* @param float $d
*
* @return float
*/
protected function getAlpha(float $d)
protected function getAlpha(float $d) : float
{
$small = 0.0001 * $d;
$large = 0.01 * $d;
@ -153,13 +140,8 @@ class ConjugateGradient extends GD
* gradient direction.
*
* θ(k+1) = θ(k) + α.d
*
* @param float $alpha
* @param array $d
*
* @return array
*/
protected function getNewTheta(float $alpha, array $d)
protected function getNewTheta(float $alpha, array $d) : array
{
$theta = $this->theta;
@ -187,12 +169,8 @@ class ConjugateGradient extends GD
*
* See:
* R. Fletcher and C. M. Reeves, "Function minimization by conjugate gradients", Comput. J. 7 (1964), 149–154.
*
* @param array $newTheta
*
* @return float
*/
protected function getBeta(array $newTheta)
protected function getBeta(array $newTheta) : float
{
$dNew = array_sum($this->gradient($newTheta));
$dOld = array_sum($this->gradient($this->theta)) + 1e-100;
@ -204,14 +182,8 @@ class ConjugateGradient extends GD
* Calculates the new conjugate direction
*
* d(k+1) =–∇f(x(k+1)) + β(k).d(k)
*
* @param array $theta
* @param float $beta
* @param array $d
*
* @return array
*/
protected function getNewDirection(array $theta, float $beta, array $d)
protected function getNewDirection(array $theta, float $beta, array $d) : array
{
$grad = $this->gradient($theta);
@ -227,13 +199,8 @@ class mp
{
/**
* Element-wise <b>multiplication</b> of two vectors of the same size
*
* @param array $m1
* @param array $m2
*
* @return array
*/
public static function mul(array $m1, array $m2)
public static function mul(array $m1, array $m2) : array
{
$res = [];
foreach ($m1 as $i => $val) {
@ -245,13 +212,8 @@ class mp
/**
* Element-wise <b>division</b> of two vectors of the same size
*
* @param array $m1
* @param array $m2
*
* @return array
*/
public static function div(array $m1, array $m2)
public static function div(array $m1, array $m2) : array
{
$res = [];
foreach ($m1 as $i => $val) {
@ -263,14 +225,8 @@ class mp
/**
* Element-wise <b>addition</b> of two vectors of the same size
*
* @param array $m1
* @param array $m2
* @param int $mag
*
* @return array
*/
public static function add(array $m1, array $m2, int $mag = 1)
public static function add(array $m1, array $m2, int $mag = 1) : array
{
$res = [];
foreach ($m1 as $i => $val) {
@ -282,26 +238,16 @@ class mp
/**
* Element-wise <b>subtraction</b> of two vectors of the same size
*
* @param array $m1
* @param array $m2
*
* @return array
*/
public static function sub(array $m1, array $m2)
public static function sub(array $m1, array $m2) : array
{
return self::add($m1, $m2, -1);
}
/**
* Element-wise <b>multiplication</b> of a vector with a scalar
*
* @param array $m1
* @param float $m2
*
* @return array
*/
public static function muls(array $m1, float $m2)
public static function muls(array $m1, float $m2) : array
{
$res = [];
foreach ($m1 as $val) {
@ -313,13 +259,8 @@ class mp
/**
* Element-wise <b>division</b> of a vector with a scalar
*
* @param array $m1
* @param float $m2
*
* @return array
*/
public static function divs(array $m1, float $m2)
public static function divs(array $m1, float $m2) : array
{
$res = [];
foreach ($m1 as $val) {
@ -331,14 +272,8 @@ class mp
/**
* Element-wise <b>addition</b> of a vector with a scalar
*
* @param array $m1
* @param float $m2
* @param int $mag
*
* @return array
*/
public static function adds(array $m1, float $m2, int $mag = 1)
public static function adds(array $m1, float $m2, int $mag = 1) : array
{
$res = [];
foreach ($m1 as $val) {
@ -350,13 +285,8 @@ class mp
/**
* Element-wise <b>subtraction</b> of a vector with a scalar
*
* @param array $m1
* @param float $m2
*
* @return array
*/
public static function subs(array $m1, float $m2)
public static function subs(array $m1, float $m2) : array
{
return self::adds($m1, $m2, -1);
}

21
src/Phpml/Helper/Optimizer/GD.php
View File

@ -17,14 +17,7 @@ class GD extends StochasticGD
*/
protected $sampleCount = null;
/**
* @param array $samples
* @param array $targets
* @param \Closure $gradientCb
*
* @return array
*/
public function runOptimization(array $samples, array $targets, \Closure $gradientCb)
public function runOptimization(array $samples, array $targets, \Closure $gradientCb) : array
{
$this->samples = $samples;
$this->targets = $targets;
@ -57,12 +50,8 @@ class GD extends StochasticGD
/**
* Calculates gradient, cost function and penalty term for each sample
* then returns them as an array of values
*
* @param array $theta
*
* @return array
*/
protected function gradient(array $theta)
protected function gradient(array $theta) : array
{
$costs = [];
$gradient = [];
@ -84,10 +73,6 @@ class GD extends StochasticGD
return [$costs, $gradient, $totalPenalty];
}
/**
* @param array $updates
* @param float $penalty
*/
protected function updateWeightsWithUpdates(array $updates, float $penalty)
{
// Updates all weights at once
@ -110,8 +95,6 @@ class GD extends StochasticGD
/**
* Clears the optimizer internal vars after the optimization process.
*
* @return void
*/
protected function clear()
{

8
src/Phpml/Helper/Optimizer/Optimizer.php
View File

@ -22,8 +22,6 @@ abstract class Optimizer
/**
* Inits a new instance of Optimizer for the given number of dimensions
*
* @param int $dimensions
*/
public function __construct(int $dimensions)
{
@ -39,8 +37,6 @@ abstract class Optimizer
/**
* Sets the weights manually
*
* @param array $theta
*
* @return $this
*
* @throws \Exception
@ -59,10 +55,6 @@ abstract class Optimizer
/**
* Executes the optimization with the given samples & targets
* and returns the weights
*
* @param array $samples
* @param array $targets
* @param \Closure $gradientCb
*/
abstract public function runOptimization(array $samples, array $targets, \Closure $gradientCb);
}

35
src/Phpml/Helper/Optimizer/StochasticGD.php
View File

@ -76,8 +76,6 @@ class StochasticGD extends Optimizer
/**
* Initializes the SGD optimizer for the given number of dimensions
*
* @param int $dimensions
*/
public function __construct(int $dimensions)
{
@ -94,8 +92,6 @@ class StochasticGD extends Optimizer
* If change in the theta is less than given value then the
* algorithm will stop training
*
* @param float $threshold
*
* @return $this
*/
public function setChangeThreshold(float $threshold = 1e-5)
@ -109,8 +105,6 @@ class StochasticGD extends Optimizer
* Enable/Disable early stopping by checking at each iteration
* whether changes in theta or cost value are not large enough
*
* @param bool $enable
*
* @return $this
*/
public function setEarlyStop(bool $enable = true)
@ -121,8 +115,6 @@ class StochasticGD extends Optimizer
}
/**
* @param float $learningRate
*
* @return $this
*/
public function setLearningRate(float $learningRate)
@ -133,8 +125,6 @@ class StochasticGD extends Optimizer
}
/**
* @param int $maxIterations
*
* @return $this
*/
public function setMaxIterations(int $maxIterations)
@ -150,14 +140,8 @@ 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 \Closure $gradientCb
*
* @return array
*/
public function runOptimization(array $samples, array $targets, \Closure $gradientCb)
public function runOptimization(array $samples, array $targets, \Closure $gradientCb) : array
{
$this->samples = $samples;
$this->targets = $targets;
@ -197,10 +181,7 @@ class StochasticGD extends Optimizer
return $this->theta = $bestTheta;
}
/**
* @return float
*/
protected function updateTheta()
protected function updateTheta() : float
{
$jValue = 0.0;
$theta = $this->theta;
@ -231,12 +212,8 @@ class StochasticGD extends Optimizer
/**
* Checks if the optimization is not effective enough and can be stopped
* in case large enough changes in the solution do not happen
*
* @param array $oldTheta
*
* @return boolean
*/
protected function earlyStop($oldTheta)
protected function earlyStop(array $oldTheta): bool
{
// Check for early stop: No change larger than threshold (default 1e-5)
$diff = array_map(
@ -263,18 +240,14 @@ class StochasticGD extends Optimizer
/**
* Returns the list of cost values for each iteration executed in
* last run of the optimization
*
* @return array
*/
public function getCostValues()
public function getCostValues() : array
{
return $this->costValues;
}
/**
* Clears the optimizer internal vars after the optimization process.
*
* @return void
*/
protected function clear()
{

6
src/Phpml/Math/Comparison.php
View File

@ -9,12 +9,6 @@ use Phpml\Exception\InvalidArgumentException;
class Comparison
{
/**
* @param mixed $a
* @param mixed $b
* @param string $operator
*
* @return bool
*
* @throws InvalidArgumentException
*/
public static function compare($a, $b, string $operator): bool

4
src/Phpml/Math/Distance.php
View File

@ -9,8 +9,6 @@ interface Distance
/**
* @param array $a
* @param array $b
*
* @return float
*/
public function distance(array $a, array $b): float;
public function distance(array $a, array $b) : float;
}

7
src/Phpml/Math/Distance/Chebyshev.php
View File

@ -10,14 +10,9 @@ use Phpml\Math\Distance;
class Chebyshev implements Distance
{
/**
* @param array $a
* @param array $b
*
* @return float
*
* @throws InvalidArgumentException
*/
public function distance(array $a, array $b): float
public function distance(array $a, array $b) : float
{
if (count($a) !== count($b)) {
throw InvalidArgumentException::arraySizeNotMatch();

14
src/Phpml/Math/Distance/Euclidean.php
View File

@ -10,14 +10,9 @@ use Phpml\Math\Distance;
class Euclidean implements Distance
{
/**
* @param array $a
* @param array $b
*
* @return float
*
* @throws InvalidArgumentException
*/
public function distance(array $a, array $b): float
public function distance(array $a, array $b) : float
{
if (count($a) !== count($b)) {
throw InvalidArgumentException::arraySizeNotMatch();
@ -34,13 +29,8 @@ class Euclidean implements Distance
/**
* Square of Euclidean distance
*
* @param array $a
* @param array $b
*
* @return float
*/
public function sqDistance(array $a, array $b): float
public function sqDistance(array $a, array $b) : float
{
return $this->distance($a, $b) ** 2;
}

7
src/Phpml/Math/Distance/Manhattan.php
View File

@ -10,14 +10,9 @@ use Phpml\Math\Distance;
class Manhattan implements Distance
{
/**
* @param array $a
* @param array $b
*
* @return float
*
* @throws InvalidArgumentException
*/
public function distance(array $a, array $b): float
public function distance(array $a, array $b) : float
{
if (count($a) !== count($b)) {
throw InvalidArgumentException::arraySizeNotMatch();

10
src/Phpml/Math/Distance/Minkowski.php
View File

@ -14,23 +14,15 @@ class Minkowski implements Distance
*/
private $lambda;
/**
* @param float $lambda
*/
public function __construct(float $lambda = 3.0)
{
$this->lambda = $lambda;
}
/**
* @param array $a
* @param array $b
*
* @return float
*
* @throws InvalidArgumentException
*/
public function distance(array $a, array $b): float
public function distance(array $a, array $b) : float
{
if (count($a) !== count($b)) {
throw InvalidArgumentException::arraySizeNotMatch();

5
src/Phpml/Math/Kernel/RBF.php
View File

@ -14,9 +14,6 @@ class RBF implements Kernel
*/
private $gamma;
/**
* @param float $gamma
*/
public function __construct(float $gamma)
{
$this->gamma = $gamma;
@ -25,8 +22,6 @@ class RBF implements Kernel
/**
* @param array $a
* @param array $b
*
* @return float
*/
public function compute($a, $b)
{

1
src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php
View File

@ -835,7 +835,6 @@ class EigenvalueDecomposition
/**
* Return the eigenvector matrix
*
*
* @return array
*/
public function getEigenvectors()

68
src/Phpml/Math/LinearAlgebra/LUDecomposition.php
View File

@ -2,25 +2,25 @@
declare(strict_types=1);
/**
* @package JAMA
* @package JAMA
*
* For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n
* unit lower triangular matrix L, an n-by-n upper triangular matrix U,
* and a permutation vector piv of length m so that A(piv,:) = L*U.
* If m < n, then L is m-by-m and U is m-by-n.
* For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n
* unit lower triangular matrix L, an n-by-n upper triangular matrix U,
* and a permutation vector piv of length m so that A(piv,:) = L*U.
* If m < n, then L is m-by-m and U is m-by-n.
*
* The LU decompostion with pivoting always exists, even if the matrix is
* singular, so the constructor will never fail. The primary use of the
* LU decomposition is in the solution of square systems of simultaneous
* linear equations. This will fail if isNonsingular() returns false.
* The LU decompostion with pivoting always exists, even if the matrix is
* singular, so the constructor will never fail. The primary use of the
* LU decomposition is in the solution of square systems of simultaneous
* linear equations. This will fail if isNonsingular() returns false.
*
* @author Paul Meagher
* @author Bartosz Matosiuk
* @author Michael Bommarito
* @author Paul Meagher
* @author Bartosz Matosiuk
* @author Michael Bommarito
*
* @version 1.1
* @version 1.1
*
* @license PHP v3.0
* @license PHP v3.0
*
* Slightly changed to adapt the original code to PHP-ML library
* @date 2017/04/24
@ -30,43 +30,43 @@ declare(strict_types=1);
namespace Phpml\Math\LinearAlgebra;
use Phpml\Math\Matrix;
use Phpml\Exception\MatrixException;
use Phpml\Math\Matrix;
class LUDecomposition
{
/**
* Decomposition storage
* Decomposition storage
*
* @var array
* @var array
*/
private $LU = [];
/**
* Row dimension.
* Row dimension.
*
* @var int
* @var int
*/
private $m;
/**
* Column dimension.
* Column dimension.
*
* @var int
* @var int
*/
private $n;
/**
* Pivot sign.
* Pivot sign.
*
* @var int
* @var int
*/
private $pivsign;
/**
* Internal storage of pivot vector.
* Internal storage of pivot vector.
*
* @var array
* @var array
*/
private $piv = [];
@ -142,7 +142,7 @@ class LUDecomposition
*
* @return Matrix Lower triangular factor
*/
public function getL()
public function getL() : Matrix
{
$L = [];
for ($i = 0; $i < $this->m; ++$i) {
@ -165,7 +165,7 @@ class LUDecomposition
*
* @return Matrix Upper triangular factor
*/
public function getU()
public function getU() : Matrix
{
$U = [];
for ($i = 0; $i < $this->n; ++$i) {
@ -186,7 +186,7 @@ class LUDecomposition
*
* @return array Pivot vector
*/
public function getPivot()
public function getPivot() : array
{
return $this->piv;
}
@ -247,7 +247,7 @@ class LUDecomposition
*
* @throws MatrixException
*/
public function solve(Matrix $B)
public function solve(Matrix $B) : array
{
if ($B->getRows() != $this->m) {
throw MatrixException::notSquareMatrix();
@ -283,15 +283,7 @@ class LUDecomposition
return $X;
}
/**
* @param array $matrix
* @param array $RL
* @param int $j0
* @param int $jF
*
* @return array
*/
protected function getSubMatrix(array $matrix, array $RL, int $j0, int $jF)
protected function getSubMatrix(array $matrix, array $RL, int $j0, int $jF) : array
{
$m = count($RL);
$n = $jF - $j0;

125
src/Phpml/Math/Matrix.php
View File

@ -4,9 +4,9 @@ declare(strict_types=1);
namespace Phpml\Math;
use Phpml\Math\LinearAlgebra\LUDecomposition;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Exception\MatrixException;
use Phpml\Math\LinearAlgebra\LUDecomposition;
class Matrix
{
@ -31,9 +31,6 @@ class Matrix
private $determinant;
/**
* @param array $matrix
* @param bool $validate
*
* @throws InvalidArgumentException
*/
public function __construct(array $matrix, bool $validate = true)
@ -59,12 +56,7 @@ class Matrix
$this->matrix = $matrix;
}
/**
* @param array $array
*
* @return Matrix
*/
public static function fromFlatArray(array $array)
public static function fromFlatArray(array $array) : Matrix
{
$matrix = [];
foreach ($array as $value) {
@ -74,46 +66,30 @@ class Matrix
return new self($matrix);
}
/**
* @return array
*/
public function toArray()
public function toArray() : array
{
return $this->matrix;
}
/**
* @return float
*/
public function toScalar()
public function toScalar() : float
{
return $this->matrix[0][0];
}
/**
* @return int
*/
public function getRows()
public function getRows(): int
{
return $this->rows;
}
/**
* @return int
*/
public function getColumns()
public function getColumns(): int
{
return $this->columns;
}
/**
* @param $column
*
* @return array
*
* @throws MatrixException
*/
public function getColumnValues($column)
public function getColumnValues($column) : array
{
if ($column >= $this->columns) {
throw MatrixException::columnOutOfRange();
@ -142,18 +118,12 @@ class Matrix
return $this->determinant = $lu->det();
}
/**
* @return bool
*/
public function isSquare()
public function isSquare(): bool
{
return $this->columns === $this->rows;
}
/**
* @return Matrix
*/
public function transpose()
public function transpose() : Matrix
{
if ($this->rows == 1) {
$matrix = array_map(function ($el) {
@ -166,14 +136,7 @@ class Matrix
return new self($matrix, false);
}
/**
* @param Matrix $matrix
*
* @return Matrix
*
* @throws InvalidArgumentException
*/
public function multiply(Matrix $matrix)
public function multiply(Matrix $matrix) : Matrix
{
if ($this->columns != $matrix->getRows()) {
throw InvalidArgumentException::inconsistentMatrixSupplied();
@ -194,12 +157,7 @@ class Matrix
return new self($product, false);
}
/**
* @param $value
*
* @return Matrix
*/
public function divideByScalar($value)
public function divideByScalar($value) : Matrix
{
$newMatrix = [];
for ($i = 0; $i < $this->rows; ++$i) {
@ -211,12 +169,7 @@ class Matrix
return new self($newMatrix, false);
}
/**
* @param $value
*
* @return Matrix
*/
public function multiplyByScalar($value)
public function multiplyByScalar($value) : Matrix
{
$newMatrix = [];
for ($i = 0; $i < $this->rows; ++$i) {
@ -230,37 +183,24 @@ class Matrix
/**
* Element-wise addition of the matrix with another one
*
* @param Matrix $other
*
* @return Matrix
*/
public function add(Matrix $other)
public function add(Matrix $other) : Matrix
{
return $this->_add($other);
}
/**
* Element-wise subtracting of another matrix from this one
*
* @param Matrix $other
*
* @return Matrix
*/
public function subtract(Matrix $other)
public function subtract(Matrix $other) : Matrix
{
return $this->_add($other, -1);
}
/**
* Element-wise addition or substraction depending on the given sign parameter
*
* @param Matrix $other
* @param int $sign
*
* @return Matrix
*/
protected function _add(Matrix $other, $sign = 1)
protected function _add(Matrix $other, int $sign = 1) : Matrix
{
$a1 = $this->toArray();
$a2 = $other->toArray();
@ -275,12 +215,7 @@ class Matrix
return new self($newMatrix, false);
}
/**
* @return Matrix
*
* @throws MatrixException
*/
public function inverse()
public function inverse() : Matrix
{
if (!$this->isSquare()) {
throw MatrixException::notSquareMatrix();
@ -295,10 +230,8 @@ class Matrix
/**
* Returns diagonal identity matrix of the same size of this matrix
*
* @return Matrix
*/
protected function getIdentity()
protected function getIdentity() : Matrix
{
$array = array_fill(0, $this->rows, array_fill(0, $this->columns, 0));
for ($i = 0; $i < $this->rows; ++$i) {
@ -308,13 +241,7 @@ class Matrix
return new self($array, false);
}
/**
* @param int $row
* @param int $column
*
* @return Matrix
*/
public function crossOut(int $row, int $column)
public function crossOut(int $row, int $column) : Matrix
{
$newMatrix = [];
$r = 0;
@ -334,9 +261,6 @@ class Matrix
return new self($newMatrix, false);
}
/**
* @return bool
*/
public function isSingular() : bool
{
return 0 == $this->getDeterminant();
@ -344,12 +268,8 @@ class Matrix
/**
* Returns the transpose of given array
*
* @param array $array
*
* @return array
*/
public static function transposeArray(array $array)
public static function transposeArray(array $array) : array
{
return (new self($array, false))->transpose()->toArray();
}
@ -357,13 +277,8 @@ class Matrix
/**
* Returns the dot product of two arrays<br>
* Matrix::dot(x, y) ==> x.y'
*
* @param array $array1
* @param array $array2
*
* @return array
*/
public static function dot(array $array1, array $array2)
public static function dot(array $array1, array $array2) : array
{
$m1 = new self($array1, false);
$m2 = new self($array2, false);

41
src/Phpml/Math/Set.php
View File

@ -21,11 +21,6 @@ class Set implements \IteratorAggregate
/**
* Creates the union of A and B.
*
* @param Set $a
* @param Set $b
*
* @return Set
*/
public static function union(Set $a, Set $b) : Set
{
@ -34,11 +29,6 @@ class Set implements \IteratorAggregate
/**
* Creates the intersection of A and B.
*
* @param Set $a
* @param Set $b
*
* @return Set
*/
public static function intersection(Set $a, Set $b) : Set
{
@ -47,11 +37,6 @@ class Set implements \IteratorAggregate
/**
* Creates the difference of A and B.
*
* @param Set $a
* @param Set $b
*
* @return Set
*/
public static function difference(Set $a, Set $b) : Set
{
@ -61,9 +46,6 @@ class Set implements \IteratorAggregate
/**
* Creates the Cartesian product of A and B.
*
* @param Set $a
* @param Set $b
*
* @return Set[]
*/
public static function cartesian(Set $a, Set $b) : array
@ -82,8 +64,6 @@ class Set implements \IteratorAggregate
/**
* Creates the power set of A.
*
* @param Set $a
*
* @return Set[]
*/
public static function power(Set $a) : array
@ -115,8 +95,6 @@ class Set implements \IteratorAggregate
/**
* @param string|int|float $element
*
* @return Set
*/
public function add($element) : Set
{
@ -125,8 +103,6 @@ class Set implements \IteratorAggregate
/**
* @param string[]|int[]|float[] $elements
*
* @return Set
*/
public function addAll(array $elements) : Set
{
@ -137,8 +113,6 @@ class Set implements \IteratorAggregate
/**
* @param string|int|float $element
*
* @return Set
*/
public function remove($element) : Set
{
@ -147,8 +121,6 @@ class Set implements \IteratorAggregate
/**
* @param string[]|int[]|float[] $elements
*
* @return Set
*/
public function removeAll(array $elements) : Set
{
@ -159,8 +131,6 @@ class Set implements \IteratorAggregate
/**
* @param string|int|float $element
*
* @return bool
*/
public function contains($element) : bool
{
@ -169,8 +139,6 @@ class Set implements \IteratorAggregate
/**
* @param string[]|int[]|float[] $elements
*
* @return bool
*/
public function containsAll(array $elements) : bool
{
@ -185,25 +153,16 @@ class Set implements \IteratorAggregate
return $this->elements;
}
/**
* @return \ArrayIterator
*/
public function getIterator() : \ArrayIterator
{
return new \ArrayIterator($this->elements);
}
/**
* @return bool
*/
public function isEmpty() : bool
{
return $this->cardinality() == 0;
}
/**
* @return int
*/
public function cardinality() : int
{
return count($this->elements);

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

@ -12,11 +12,9 @@ class Correlation
* @param array|int[]|float[] $x
* @param array|int[]|float[] $y
*
* @return float
*
* @throws InvalidArgumentException
*/
public static function pearson(array $x, array $y)
public static function pearson(array $x, array $y) : float
{
if (count($x) !== count($y)) {
throw InvalidArgumentException::arraySizeNotMatch();

26
src/Phpml/Math/Statistic/Covariance.php
View File

@ -11,17 +11,9 @@ class Covariance
/**
* Calculates covariance from two given arrays, x and y, respectively
*
* @param array $x
* @param array $y
* @param bool $sample
* @param float $meanX
* @param float $meanY
*
* @return float
*
* @throws InvalidArgumentException
*/
public static function fromXYArrays(array $x, array $y, $sample = true, float $meanX = null, float $meanY = null)
public static function fromXYArrays(array $x, array $y, bool $sample = true, float $meanX = null, float $meanY = null) : float
{
if (empty($x) || empty($y)) {
throw InvalidArgumentException::arrayCantBeEmpty();
@ -56,19 +48,10 @@ class Covariance
/**
* Calculates covariance of two dimensions, i and k in the given data.
*
* @param array $data
* @param int $i
* @param int $k
* @param bool $sample
* @param float $meanX
* @param float $meanY
*
* @return float
*
* @throws InvalidArgumentException
* @throws \Exception
*/
public static function fromDataset(array $data, int $i, int $k, bool $sample = true, float $meanX = null, float $meanY = null)
public static function fromDataset(array $data, int $i, int $k, bool $sample = true, float $meanX = null, float $meanY = null) : float
{
if (empty($data)) {
throw InvalidArgumentException::arrayCantBeEmpty();
@ -127,12 +110,9 @@ class Covariance
/**
* Returns the covariance matrix of n-dimensional data
*
* @param array $data
* @param array|null $means
*
* @return array
*/
public static function covarianceMatrix(array $data, array $means = null)
public static function covarianceMatrix(array $data, array $means = null) : array
{
$n = count($data[0]);

14
src/Phpml/Math/Statistic/Gaussian.php
View File

@ -16,10 +16,6 @@ class Gaussian
*/
protected $std;
/**
* @param float $mean
* @param float $std
*/
public function __construct(float $mean, float $std)
{
$this->mean = $mean;
@ -29,8 +25,6 @@ class Gaussian
/**
* Returns probability density of the given <i>$value</i>
*
* @param float $value
*
* @return float|int
*/
public function pdf(float $value)
@ -46,14 +40,8 @@ class Gaussian
/**
* Returns probability density value of the given <i>$value</i> based on
* given standard deviation and the mean
*
* @param float $mean
* @param float $std
* @param float $value
*
* @return float
*/
public static function distributionPdf(float $mean, float $std, float $value)
public static function distributionPdf(float $mean, float $std, float $value) : float
{
$normal = new self($mean, $std);

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

@ -11,11 +11,9 @@ class Mean
/**
* @param array $numbers
*
* @return float
*
* @throws InvalidArgumentException
*/
public static function arithmetic(array $numbers)
public static function arithmetic(array $numbers) : float
{
self::checkArrayLength($numbers);

5
src/Phpml/Math/Statistic/StandardDeviation.php
View File

@ -10,13 +10,10 @@ class StandardDeviation
{
/**
* @param array|float[] $a
* @param bool $sample
*
* @return float
*
* @throws InvalidArgumentException
*/
public static function population(array $a, $sample = true)
public static function population(array $a, bool $sample = true) : float
{
if (empty($a)) {
throw InvalidArgumentException::arrayCantBeEmpty();

50
src/Phpml/Metric/ClassificationReport.php
View File

@ -31,10 +31,6 @@ class ClassificationReport
*/
private $average = [];
/**
* @param array $actualLabels
* @param array $predictedLabels
*/
public function __construct(array $actualLabels, array $predictedLabels)
{
$truePositive = $falsePositive = $falseNegative = $this->support = self::getLabelIndexedArray($actualLabels, $predictedLabels);
@ -55,51 +51,31 @@ class ClassificationReport
$this->computeAverage();
}
/**
* @return array
*/
public function getPrecision()
public function getPrecision() : array
{
return $this->precision;
}
/**
* @return array
*/
public function getRecall()
public function getRecall() : array
{
return $this->recall;
}
/**
* @return array
*/
public function getF1score()
public function getF1score() : array
{
return $this->f1score;
}
/**
* @return array
*/
public function getSupport()
public function getSupport() : array
{
return $this->support;
}
/**
* @return array
*/
public function getAverage()
public function getAverage() : array
{
return $this->average;
}
/**
* @param array $truePositive
* @param array $falsePositive
* @param array $falseNegative
*/
private function computeMetrics(array $truePositive, array $falsePositive, array $falseNegative)
{
foreach ($truePositive as $label => $tp) {
@ -122,9 +98,6 @@ class ClassificationReport
}
/**
* @param int $truePositive
* @param int $falsePositive
*
* @return float|string
*/
private function computePrecision(int $truePositive, int $falsePositive)
@ -137,9 +110,6 @@ class ClassificationReport
}
/**
* @param int $truePositive
* @param int $falseNegative
*
* @return float|string
*/
private function computeRecall(int $truePositive, int $falseNegative)
@ -151,13 +121,7 @@ class ClassificationReport
return $truePositive / $divider;
}
/**
* @param float $precision
* @param float $recall
*
* @return float
*/
private function computeF1Score(float $precision, float $recall): float
private function computeF1Score(float $precision, float $recall) : float
{
if (0 == ($divider = $precision + $recall)) {
return 0.0;
@ -172,7 +136,7 @@ class ClassificationReport
*
* @return array
*/
private static function getLabelIndexedArray(array $actualLabels, array $predictedLabels): array
private static function getLabelIndexedArray(array $actualLabels, array $predictedLabels) : array
{
$labels = array_values(array_unique(array_merge($actualLabels, $predictedLabels)));
sort($labels);

6
src/Phpml/Metric/ConfusionMatrix.php
View File

@ -13,7 +13,7 @@ class ConfusionMatrix
*
* @return array
*/
public static function compute(array $actualLabels, array $predictedLabels, array $labels = null): array
public static function compute(array $actualLabels, array $predictedLabels, array $labels = null) : array
{
$labels = $labels ? array_flip($labels) : self::getUniqueLabels($actualLabels);
$matrix = self::generateMatrixWithZeros($labels);
@ -43,7 +43,7 @@ class ConfusionMatrix
*
* @return array
*/
private static function generateMatrixWithZeros(array $labels): array
private static function generateMatrixWithZeros(array $labels) : array
{
$count = count($labels);
$matrix = [];
@ -60,7 +60,7 @@ class ConfusionMatrix
*
* @return array
*/
private static function getUniqueLabels(array $labels): array
private static function getUniqueLabels(array $labels) : array
{
$labels = array_values(array_unique($labels));
sort($labels);

17
src/Phpml/ModelManager.php
View File

@ -4,18 +4,11 @@ declare(strict_types=1);
namespace Phpml;
use Phpml\Exception\SerializeException;
use Phpml\Exception\FileException;
use Phpml\Exception\SerializeException;
class ModelManager
{
/**
* @param Estimator $estimator
* @param string $filepath
*
* @throws FileException
* @throws SerializeException
*/
public function saveToFile(Estimator $estimator, string $filepath)
{
if (!is_writable(dirname($filepath))) {
@ -33,14 +26,6 @@ class ModelManager
}
}
/**
* @param string $filepath
*
* @return Estimator
*
* @throws FileException
* @throws SerializeException
*/
public function restoreFromFile(string $filepath) : Estimator
{
if (!file_exists($filepath) || !is_readable($filepath)) {

4
src/Phpml/NeuralNetwork/ActivationFunction.php
View File

@ -8,8 +8,6 @@ interface ActivationFunction
{
/**
* @param float|int $value
*
* @return float
*/
public function compute($value): float;
public function compute($value) : float;
}

4
src/Phpml/NeuralNetwork/ActivationFunction/BinaryStep.php
View File

@ -10,10 +10,8 @@ class BinaryStep implements ActivationFunction
{
/**
* @param float|int $value
*
* @return float
*/
public function compute($value): float
public function compute($value) : float
{
return $value >= 0 ? 1.0 : 0.0;
}

4
src/Phpml/NeuralNetwork/ActivationFunction/Gaussian.php
View File

@ -10,10 +10,8 @@ class Gaussian implements ActivationFunction
{
/**
* @param float|int $value
*
* @return float
*/
public function compute($value): float
public function compute($value) : float
{
return exp(-pow($value, 2));
}

9
src/Phpml/NeuralNetwork/ActivationFunction/HyperbolicTangent.php
View File

@ -13,20 +13,15 @@ class HyperbolicTangent implements ActivationFunction
*/
private $beta;
/**
* @param float $beta
*/
public function __construct($beta = 1.0)
public function __construct(float $beta = 1.0)
{
$this->beta = $beta;
}
/**
* @param float|int $value
*
* @return float
*/
public function compute($value): float
public function compute($value) : float
{
return tanh($this->beta * $value);
}

9
src/Phpml/NeuralNetwork/ActivationFunction/PReLU.php
View File

@ -13,20 +13,15 @@ class PReLU implements ActivationFunction
*/
private $beta;
/**
* @param float $beta
*/
public function __construct($beta = 0.01)
public function __construct(float $beta = 0.01)
{
$this->beta = $beta;
}
/**
* @param float|int $value
*
* @return float
*/
public function compute($value): float
public function compute($value) : float
{
return $value >= 0 ? $value : $this->beta * $value;
}

9
src/Phpml/NeuralNetwork/ActivationFunction/Sigmoid.php
View File

@ -13,20 +13,15 @@ class Sigmoid implements ActivationFunction
*/
private $beta;
/**
* @param float $beta
*/
public function __construct($beta = 1.0)
public function __construct(float $beta = 1.0)
{
$this->beta = $beta;
}
/**
* @param float|int $value
*
* @return float
*/
public function compute($value): float
public function compute($value) : float
{
return 1 / (1 + exp(-$this->beta * $value));
}

9
src/Phpml/NeuralNetwork/ActivationFunction/ThresholdedReLU.php
View File

@ -13,20 +13,15 @@ class ThresholdedReLU implements ActivationFunction
*/
private $theta;
/**
* @param float $theta
*/
public function __construct($theta = 1.0)
public function __construct(float $theta = 1.0)
{
$this->theta = $theta;
}
/**
* @param float|int $value
*
* @return float
*/
public function compute($value): float
public function compute($value) : float
{
return $value > $this->theta ? $value : 0.0;
}

10
src/Phpml/NeuralNetwork/Layer.php
View File

@ -15,10 +15,6 @@ class Layer
private $nodes = [];
/**
* @param int $nodesNumber
* @param string $nodeClass
* @param ActivationFunction|null $activationFunction
*
* @throws InvalidArgumentException
*/
public function __construct(int $nodesNumber = 0, string $nodeClass = Neuron::class, ActivationFunction $activationFunction = null)
@ -33,7 +29,6 @@ class Layer
}
/**
* @param string $nodeClass
* @param ActivationFunction|null $activationFunction
*
* @return Neuron
@ -47,9 +42,6 @@ class Layer
return new $nodeClass();
}
/**
* @param Node $node
*/
public function addNode(Node $node)
{
$this->nodes[] = $node;
@ -58,7 +50,7 @@ class Layer
/**
* @return Node[]
*/
public function getNodes()
public function getNodes() : array
{
return $this->nodes;
}

7
src/Phpml/NeuralNetwork/Network.php
View File

@ -16,15 +16,12 @@ interface Network
/**
* @return array
*/
public function getOutput(): array;
public function getOutput() : array;
/**
* @param Layer $layer
*/
public function addLayer(Layer $layer);
/**
* @return Layer[]
*/
public function getLayers(): array;
public function getLayers() : array;
}

13
src/Phpml/NeuralNetwork/Network/LayeredNetwork.php
View File

@ -16,9 +16,6 @@ abstract class LayeredNetwork implements Network
*/
protected $layers;
/**
* @param Layer $layer
*/
public function addLayer(Layer $layer)
{
$this->layers[] = $layer;
@ -27,22 +24,16 @@ abstract class LayeredNetwork implements Network
/**
* @return Layer[]
*/
public function getLayers(): array
public function getLayers() : array
{
return $this->layers;
}
/**
* @return void
*/
public function removeLayers()
{
unset($this->layers);
}
/**
* @return Layer
*/
public function getOutputLayer(): Layer
{
return $this->layers[count($this->layers) - 1];
@ -51,7 +42,7 @@ abstract class LayeredNetwork implements Network
/**
* @return array
*/
public function getOutput(): array
public function getOutput() : array
{
$result = [];
foreach ($this->getOutputLayer()->getNodes() as $neuron) {

49
src/Phpml/NeuralNetwork/Network/MultilayerPerceptron.php
View File

@ -5,16 +5,16 @@ declare(strict_types=1);
namespace Phpml\NeuralNetwork\Network;
use Phpml\Estimator;
use Phpml\IncrementalEstimator;
use Phpml\Exception\InvalidArgumentException;
use Phpml\NeuralNetwork\Training\Backpropagation;
use Phpml\Helper\Predictable;
use Phpml\IncrementalEstimator;
use Phpml\NeuralNetwork\ActivationFunction;
use Phpml\NeuralNetwork\Layer;
use Phpml\NeuralNetwork\Node\Bias;
use Phpml\NeuralNetwork\Node\Input;
use Phpml\NeuralNetwork\Node\Neuron;
use Phpml\NeuralNetwork\Node\Neuron\Synapse;
use Phpml\Helper\Predictable;
use Phpml\NeuralNetwork\Training\Backpropagation;
abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator, IncrementalEstimator
{
@ -56,13 +56,6 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
protected $backpropagation = null;
/**
* @param int $inputLayerFeatures
* @param array $hiddenLayers
* @param array $classes
* @param int $iterations
* @param ActivationFunction|null $activationFunction
* @param int $theta
*
* @throws InvalidArgumentException
*/
public function __construct(int $inputLayerFeatures, array $hiddenLayers, array $classes, int $iterations = 10000, ActivationFunction $activationFunction = null, int $theta = 1)
@ -85,9 +78,6 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
$this->initNetwork();
}
/**
* @return void
*/
private function initNetwork()
{
$this->addInputLayer($this->inputLayerFeatures);
@ -100,10 +90,6 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
$this->backpropagation = new Backpropagation($this->theta);
}
/**
* @param array $samples
* @param array $targets
*/
public function train(array $samples, array $targets)
{
$this->reset();
@ -112,10 +98,6 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
}
/**
* @param array $samples
* @param array $targets
* @param array $classes
*
* @throws InvalidArgumentException
*/
public function partialTrain(array $samples, array $targets, array $classes = [])
@ -131,38 +113,25 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
}
/**
* @param array $sample
* @param mixed $target
*/
abstract protected function trainSample(array $sample, $target);
/**
* @param array $sample
*
* @return mixed
*/
abstract protected function predictSample(array $sample);
/**
* @return void
*/
protected function reset()
{
$this->removeLayers();
}
/**
* @param int $nodes
*/
private function addInputLayer(int $nodes)
{
$this->addLayer(new Layer($nodes, Input::class));
}
/**
* @param array $layers
* @param ActivationFunction|null $activationFunction
*/
private function addNeuronLayers(array $layers, ActivationFunction $activationFunction = null)
{
foreach ($layers as $neurons) {
@ -188,10 +157,6 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
}
}
/**
* @param Layer $nextLayer
* @param Layer $currentLayer
*/
private function generateLayerSynapses(Layer $nextLayer, Layer $currentLayer)
{
foreach ($nextLayer->getNodes() as $nextNeuron) {
@ -201,10 +166,6 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
}
}
/**
* @param Layer $currentLayer
* @param Neuron $nextNeuron
*/
private function generateNeuronSynapses(Layer $currentLayer, Neuron $nextNeuron)
{
foreach ($currentLayer->getNodes() as $currentNeuron) {
@ -212,10 +173,6 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
}
}
/**
* @param array $samples
* @param array $targets
*/
private function trainSamples(array $samples, array $targets)
{
foreach ($targets as $key => $target) {

5
src/Phpml/NeuralNetwork/Node.php
View File

@ -6,8 +6,5 @@ namespace Phpml\NeuralNetwork;
interface Node
{
/**
* @return float
*/
public function getOutput(): float;
public function getOutput() : float;
}

5
src/Phpml/NeuralNetwork/Node/Bias.php
View File

@ -8,10 +8,7 @@ use Phpml\NeuralNetwork\Node;
class Bias implements Node
{
/**
* @return float
*/
public function getOutput(): float
public function getOutput() : float
{
return 1.0;
}

11
src/Phpml/NeuralNetwork/Node/Input.php
View File

@ -13,25 +13,16 @@ class Input implements Node
*/
private $input;
/**
* @param float $input
*/
public function __construct(float $input = 0.0)
{
$this->input = $input;
}
/**
* @return float
*/
public function getOutput(): float
public function getOutput() : float
{
return $this->input;
}
/**
* @param float $input
*/
public function setInput(float $input)
{
$this->input = $input;

13
src/Phpml/NeuralNetwork/Node/Neuron.php
View File

@ -5,8 +5,8 @@ declare(strict_types=1);
namespace Phpml\NeuralNetwork\Node;
use Phpml\NeuralNetwork\ActivationFunction;
use Phpml\NeuralNetwork\Node\Neuron\Synapse;
use Phpml\NeuralNetwork\Node;
use Phpml\NeuralNetwork\Node\Neuron\Synapse;
class Neuron implements Node
{
@ -25,9 +25,6 @@ class Neuron implements Node
*/
protected $output;
/**
* @param ActivationFunction|null $activationFunction
*/
public function __construct(ActivationFunction $activationFunction = null)
{
$this->activationFunction = $activationFunction ?: new ActivationFunction\Sigmoid();
@ -35,9 +32,6 @@ class Neuron implements Node
$this->output = 0;
}
/**
* @param Synapse $synapse
*/
public function addSynapse(Synapse $synapse)
{
$this->synapses[] = $synapse;
@ -51,10 +45,7 @@ class Neuron implements Node
return $this->synapses;
}
/**
* @return float
*/
public function getOutput(): float
public function getOutput() : float
{
if (0 === $this->output) {
$sum = 0;

25
src/Phpml/NeuralNetwork/Node/Neuron/Synapse.php
View File

@ -28,42 +28,27 @@ class Synapse
$this->weight = $weight ?: $this->generateRandomWeight();
}
/**
* @return float
*/
protected function generateRandomWeight(): float
protected function generateRandomWeight() : float
{
return 1 / random_int(5, 25) * (random_int(0, 1) ? -1 : 1);
}
/**
* @return float
*/
public function getOutput(): float
public function getOutput() : float
{
return $this->weight * $this->node->getOutput();
}
/**
* @param float $delta
*/
public function changeWeight($delta)
public function changeWeight(float $delta)
{
$this->weight += $delta;
}
/**
* @return float
*/
public function getWeight()
public function getWeight() : float
{
return $this->weight;
}
/**
* @return Node
*/
public function getNode()
public function getNode(): Node
{
return $this->node;
}

21
src/Phpml/NeuralNetwork/Training/Backpropagation.php
View File

@ -24,16 +24,12 @@ class Backpropagation
*/
private $prevSigmas = null;
/**
* @param int $theta
*/
public function __construct(int $theta)
{
$this->theta = $theta;
}
/**
* @param array $layers
* @param mixed $targetClass
*/
public function backpropagate(array $layers, $targetClass)
@ -59,15 +55,7 @@ class Backpropagation
$this->prevSigmas = null;
}
/**
* @param Neuron $neuron
* @param int $targetClass
* @param int $key
* @param bool $lastLayer
*
* @return float
*/
private function getSigma(Neuron $neuron, int $targetClass, int $key, bool $lastLayer): float
private function getSigma(Neuron $neuron, int $targetClass, int $key, bool $lastLayer) : float
{
$neuronOutput = $neuron->getOutput();
$sigma = $neuronOutput * (1 - $neuronOutput);
@ -87,12 +75,7 @@ class Backpropagation
return $sigma;
}
/**
* @param Neuron $neuron
*
* @return float
*/
private function getPrevSigma(Neuron $neuron): float
private function getPrevSigma(Neuron $neuron) : float
{
$sigma = 0.0;

23
src/Phpml/NeuralNetwork/Training/Backpropagation/Sigma.php
View File

@ -18,38 +18,23 @@ class Sigma
*/
private $sigma;
/**
* @param Neuron $neuron
* @param float $sigma
*/
public function __construct(Neuron $neuron, $sigma)
public function __construct(Neuron $neuron, float $sigma)
{
$this->neuron = $neuron;
$this->sigma = $sigma;
}
/**
* @return Neuron
*/
public function getNeuron()
public function getNeuron(): Neuron
{
return $this->neuron;
}
/**
* @return float
*/
public function getSigma()
public function getSigma() : float
{
return $this->sigma;
}
/**
* @param Neuron $neuron
*
* @return float
*/
public function getSigmaForNeuron(Neuron $neuron): float
public function getSigmaForNeuron(Neuron $neuron) : float
{
$sigma = 0.0;

13
src/Phpml/Pipeline.php
View File

@ -29,17 +29,11 @@ class Pipeline implements Estimator
$this->estimator = $estimator;
}
/**
* @param Transformer $transformer
*/
public function addTransformer(Transformer $transformer)
{
$this->transformers[] = $transformer;
}
/**
* @param Estimator $estimator
*/
public function setEstimator(Estimator $estimator)
{
$this->estimator = $estimator;
@ -48,15 +42,12 @@ class Pipeline implements Estimator
/**
* @return array|Transformer[]
*/
public function getTransformers()
public function getTransformers() : array
{
return $this->transformers;
}
/**
* @return Estimator
*/
public function getEstimator()
public function getEstimator(): Estimator
{
return $this->estimator;
}

2
src/Phpml/Preprocessing/Imputer.php
View File

@ -81,7 +81,7 @@ class Imputer implements Preprocessor
*
* @return array
*/
private function getAxis(int $column, array $currentSample): array
private function getAxis(int $column, array $currentSample) : array
{
if (self::AXIS_ROW === $this->axis) {
return array_diff($currentSample, [$this->missingValue]);

6
src/Phpml/Preprocessing/Imputer/Strategy/MeanStrategy.php
View File

@ -4,17 +4,15 @@ declare(strict_types=1);
namespace Phpml\Preprocessing\Imputer\Strategy;
use Phpml\Preprocessing\Imputer\Strategy;
use Phpml\Math\Statistic\Mean;
use Phpml\Preprocessing\Imputer\Strategy;
class MeanStrategy implements Strategy
{
/**
* @param array $currentAxis
*
* @return float
*/
public function replaceValue(array $currentAxis)
public function replaceValue(array $currentAxis) : float
{
return Mean::arithmetic($currentAxis);
}

6
src/Phpml/Preprocessing/Imputer/Strategy/MedianStrategy.php
View File

@ -4,17 +4,15 @@ declare(strict_types=1);
namespace Phpml\Preprocessing\Imputer\Strategy;
use Phpml\Preprocessing\Imputer\Strategy;
use Phpml\Math\Statistic\Mean;
use Phpml\Preprocessing\Imputer\Strategy;
class MedianStrategy implements Strategy
{
/**
* @param array $currentAxis
*
* @return float
*/
public function replaceValue(array $currentAxis)
public function replaceValue(array $currentAxis) : float
{
return Mean::median($currentAxis);
}

2
src/Phpml/Preprocessing/Imputer/Strategy/MostFrequentStrategy.php
View File

@ -4,8 +4,8 @@ declare(strict_types=1);
namespace Phpml\Preprocessing\Imputer\Strategy;
use Phpml\Preprocessing\Imputer\Strategy;
use Phpml\Math\Statistic\Mean;
use Phpml\Preprocessing\Imputer\Strategy;
class MostFrequentStrategy implements Strategy
{

4
src/Phpml/Preprocessing/Normalizer.php
View File

@ -5,8 +5,8 @@ declare(strict_types=1);
namespace Phpml\Preprocessing;
use Phpml\Exception\NormalizerException;
use Phpml\Math\Statistic\StandardDeviation;
use Phpml\Math\Statistic\Mean;
use Phpml\Math\Statistic\StandardDeviation;
class Normalizer implements Preprocessor
{
@ -35,8 +35,6 @@ class Normalizer implements Preprocessor
private $mean;
/**
* @param int $norm
*
* @throws NormalizerException
*/
public function __construct(int $norm = self::NORM_L2)

25
src/Phpml/Regression/LeastSquares.php
View File

@ -30,10 +30,6 @@ class LeastSquares implements Regression
*/
private $coefficients;
/**
* @param array $samples
* @param array $targets
*/
public function train(array $samples, array $targets)
{
$this->samples = array_merge($this->samples, $samples);
@ -43,8 +39,6 @@ class LeastSquares implements Regression
}
/**
* @param array $sample
*
* @return mixed
*/
public function predictSample(array $sample)
@ -57,18 +51,12 @@ class LeastSquares implements Regression
return $result;
}
/**
* @return array
*/
public function getCoefficients()
public function getCoefficients() : array
{
return $this->coefficients;
}
/**
* @return float
*/
public function getIntercept()
public function getIntercept() : float
{
return $this->intercept;
}
@ -90,10 +78,8 @@ class LeastSquares implements Regression
/**
* Add one dimension for intercept calculation.
*
* @return Matrix
*/
private function getSamplesMatrix()
private function getSamplesMatrix() : Matrix
{
$samples = [];
foreach ($this->samples as $sample) {
@ -104,10 +90,7 @@ class LeastSquares implements Regression
return new Matrix($samples);
}
/**
* @return Matrix
*/
private function getTargetsMatrix()
private function getTargetsMatrix() : Matrix
{
if (is_array($this->targets[0])) {
return new Matrix($this->targets);

11
src/Phpml/Regression/SVR.php
View File

@ -10,17 +10,6 @@ use Phpml\SupportVectorMachine\Type;
class SVR extends SupportVectorMachine implements Regression
{
/**
* @param int $kernel
* @param int $degree
* @param float $epsilon
* @param float $cost
* @param float|null $gamma
* @param float $coef0
* @param float $tolerance
* @param int $cacheSize
* @param bool $shrinking
*/
public function __construct(
int $kernel = Kernel::RBF,
int $degree = 3,

32
src/Phpml/SupportVectorMachine/DataTransformer.php
View File

@ -6,13 +6,6 @@ namespace Phpml\SupportVectorMachine;
class DataTransformer
{
/**
* @param array $samples
* @param array $labels
* @param bool $targets
*
* @return string
*/
public static function trainingSet(array $samples, array $labels, bool $targets = false): string
{
$set = '';
@ -27,11 +20,6 @@ class DataTransformer
return $set;
}
/**
* @param array $samples
*
* @return string
*/
public static function testSet(array $samples): string
{
if (!is_array($samples[0])) {
@ -46,13 +34,7 @@ class DataTransformer
return $set;
}
/**
* @param string $rawPredictions
* @param array $labels
*
* @return array
*/
public static function predictions(string $rawPredictions, array $labels): array
public static function predictions(string $rawPredictions, array $labels) : array
{
$numericLabels = self::numericLabels($labels);
$results = [];
@ -65,12 +47,7 @@ class DataTransformer
return $results;
}
/**
* @param array $labels
*
* @return array
*/
public static function numericLabels(array $labels): array
public static function numericLabels(array $labels) : array
{
$numericLabels = [];
foreach ($labels as $label) {
@ -84,11 +61,6 @@ class DataTransformer
return $numericLabels;
}
/**
* @param array $sample
*
* @return string
*/
private static function sampleRow(array $sample): string
{
$row = [];

54
src/Phpml/SupportVectorMachine/SupportVectorMachine.php
View File

@ -91,20 +91,6 @@ class SupportVectorMachine
*/
private $targets = [];
/**
* @param int $type
* @param int $kernel
* @param float $cost
* @param float $nu
* @param int $degree
* @param float|null $gamma
* @param float $coef0
* @param float $epsilon
* @param float $tolerance
* @param int $cacheSize
* @param bool $shrinking
* @param bool $probabilityEstimates
*/
public function __construct(
int $type,
int $kernel,
@ -138,11 +124,6 @@ class SupportVectorMachine
$this->varPath = $rootPath.'var'.DIRECTORY_SEPARATOR;
}
/**
* @param string $binPath
*
* @throws InvalidArgumentException
*/
public function setBinPath(string $binPath)
{
$this->ensureDirectorySeparator($binPath);
@ -151,11 +132,6 @@ class SupportVectorMachine
$this->binPath = $binPath;
}
/**
* @param string $varPath
*
* @throws InvalidArgumentException
*/
public function setVarPath(string $varPath)
{
if (!is_writable($varPath)) {
@ -166,10 +142,6 @@ class SupportVectorMachine
$this->varPath = $varPath;
}
/**
* @param array $samples
* @param array $targets
*/
public function train(array $samples, array $targets)
{
$this->samples = array_merge($this->samples, $samples);
@ -189,17 +161,12 @@ class SupportVectorMachine
unlink($modelFileName);
}
/**
* @return string
*/
public function getModel()
public function getModel(): string
{
return $this->model;
}
/**
* @param array $samples
*
* @return array
*/
public function predict(array $samples)
@ -232,10 +199,7 @@ class SupportVectorMachine
return $predictions;
}
/**
* @return string
*/
private function getOSExtension()
private function getOSExtension(): string
{
$os = strtoupper(substr(PHP_OS, 0, 3));
if ($os === 'WIN') {
@ -247,12 +211,6 @@ class SupportVectorMachine
return '';
}
/**
* @param string $trainingSetFileName
* @param string $modelFileName
*
* @return string
*/
private function buildTrainCommand(string $trainingSetFileName, string $modelFileName): string
{
return sprintf(
@ -276,9 +234,6 @@ class SupportVectorMachine
);
}
/**
* @param string $path
*/
private function ensureDirectorySeparator(string &$path)
{
if (substr($path, -1) !== DIRECTORY_SEPARATOR) {
@ -286,11 +241,6 @@ class SupportVectorMachine
}
}
/**
* @param string $path
*
* @throws InvalidArgumentException
*/
private function verifyBinPath(string $path)
{
if (!is_dir($path)) {

7
src/Phpml/Tokenization/Tokenizer.php
View File

@ -6,10 +6,5 @@ namespace Phpml\Tokenization;
interface Tokenizer
{
/**
* @param string $text
*
* @return array
*/
public function tokenize(string $text): array;
public function tokenize(string $text) : array;
}

7
src/Phpml/Tokenization/WhitespaceTokenizer.php
View File

@ -6,12 +6,7 @@ namespace Phpml\Tokenization;
class WhitespaceTokenizer implements Tokenizer
{
/**
* @param string $text
*
* @return array
*/
public function tokenize(string $text): array
public function tokenize(string $text) : array
{
return preg_split('/[\pZ\pC]+/u', $text, -1, PREG_SPLIT_NO_EMPTY);
}

7
src/Phpml/Tokenization/WordTokenizer.php
View File

@ -6,12 +6,7 @@ namespace Phpml\Tokenization;
class WordTokenizer implements Tokenizer
{
/**
* @param string $text
*
* @return array
*/
public function tokenize(string $text): array
public function tokenize(string $text) : array
{
$tokens = [];
preg_match_all('/\w\w+/u', $text, $tokens);

2
tests/Phpml/Classification/Ensemble/BaggingTest.php
View File

@ -4,8 +4,8 @@ declare(strict_types=1);
namespace tests\Phpml\Classification\Ensemble;
use Phpml\Classification\Ensemble\Bagging;
use Phpml\Classification\DecisionTree;
use Phpml\Classification\Ensemble\Bagging;
use Phpml\Classification\NaiveBayes;
use Phpml\ModelManager;
use PHPUnit\Framework\TestCase;

2
tests/Phpml/Classification/Ensemble/RandomForestTest.php
View File

@ -4,8 +4,8 @@ declare(strict_types=1);
namespace tests\Phpml\Classification\Ensemble;
use Phpml\Classification\Ensemble\RandomForest;
use Phpml\Classification\DecisionTree;
use Phpml\Classification\Ensemble\RandomForest;
use Phpml\Classification\NaiveBayes;
class RandomForestTest extends BaggingTest

4
tests/Phpml/Classification/MLPClassifierTest.php
View File

@ -5,8 +5,8 @@ declare(strict_types=1);
namespace tests\Phpml\Classification;
use Phpml\Classification\MLPClassifier;
use Phpml\NeuralNetwork\Node\Neuron;
use Phpml\ModelManager;
use Phpml\NeuralNetwork\Node\Neuron;
use PHPUnit\Framework\TestCase;
class MLPClassifierTest extends TestCase
@ -194,7 +194,7 @@ class MLPClassifierTest extends TestCase
*
* @return array
*/
private function getSynapsesNodes(array $synapses): array
private function getSynapsesNodes(array $synapses) : array
{
$nodes = [];
foreach ($synapses as $synapse) {

2
tests/Phpml/Classification/SVCTest.php
View File

@ -5,8 +5,8 @@ declare(strict_types=1);
namespace tests\Phpml\Classification;
use Phpml\Classification\SVC;
use Phpml\SupportVectorMachine\Kernel;
use Phpml\ModelManager;
use Phpml\SupportVectorMachine\Kernel;
use PHPUnit\Framework\TestCase;
class SVCTest extends TestCase

Some files were not shown because too many files have changed in this diff Show More