Added EasyCodingStandard + lots of code fixes (#156)

* travis: move coveralls here, decouple from package

* composer: use PSR4

* phpunit: simpler config

* travis: add ecs run

* composer: add ecs dev

* use standard vendor/bin directory for dependency bins, confuses with local bins and require gitignore handling

* ecs: add PSR2

* [cs] PSR2 spacing fixes

* [cs] PSR2 class name fix

* [cs] PHP7 fixes - return semicolon spaces, old rand functions, typehints

* [cs] fix less strict typehints

* fix typehints to make tests pass

* ecs: ignore typehint-less elements

* [cs] standardize arrays

* [cs] standardize docblock, remove unused comments

* [cs] use self where possible

* [cs] sort class elements, from public to private

* [cs] do not use yoda (found less yoda-cases, than non-yoda)

* space

* [cs] do not assign in condition

* [cs] use namespace imports if possible

* [cs] use ::class over strings

* [cs] fix defaults for arrays properties, properties and constants single spacing

* cleanup ecs comments

* [cs] use item per line in multi-items array

* missing line

* misc

* rebase

.gitignore

@@ -1,8 +1,4 @@
 /vendor/
 humbuglog.*
-/bin/phpunit
-.coverage
 .php_cs.cache
-/bin/php-cs-fixer
-/bin/coveralls
 /build

.travis.yml

@@ -6,7 +6,7 @@ matrix:
   include:
     - os: linux
       php: '7.1'
-      env: DISABLE_XDEBUG="true"
+      env: DISABLE_XDEBUG="true" STATIC_ANALYSIS="true"
 
     - os: linux
       php: '7.2'
@@ -21,7 +21,7 @@ matrix:
 
 before_install:
   - if [[ "${TRAVIS_OS_NAME}" == "osx" ]]; then /usr/bin/env bash bin/prepare_osx_env.sh ; fi
-  - if [[ DISABLE_XDEBUG == "true" ]]; then phpenv config-rm xdebug.ini; fi
+  - if [[ $DISABLE_XDEBUG == "true" ]]; then phpenv config-rm xdebug.ini; fi
 
 install:
   - if [[ "${TRAVIS_OS_NAME}" == "osx" ]]; then /usr/bin/env bash bin/handle_brew_pkg.sh "${_PHP}" ; fi
@@ -29,10 +29,12 @@ install:
   - php composer.phar install --dev --no-interaction --ignore-platform-reqs
 
 script:
-  - bin/phpunit $PHPUNIT_FLAGS
+  - vendor/bin/phpunit $PHPUNIT_FLAGS
+  - if [[ $STATIC_ANALYSIS != "" ]]; then vendor/bin/ecs check src tests; fi
 
 after_success:
   - |
     if [[ $PHPUNIT_FLAGS != "" ]]; then
-      php bin/coveralls -v
+      wget https://github.com/satooshi/php-coveralls/releases/download/v1.0.1/coveralls.phar;
+      php coveralls.phar --verbose;
     fi

composer.json

@@ -12,8 +12,8 @@
         }
     ],
     "autoload": {
-        "psr-0": {
-            "Phpml": "src/"
+        "psr-4": {
+            "Phpml\\": "src/Phpml"
         }
     },
     "require": {
@@ -22,9 +22,8 @@
     "require-dev": {
         "phpunit/phpunit": "^6.0",
         "friendsofphp/php-cs-fixer": "^2.4",
-        "php-coveralls/php-coveralls": "^1.0"
-    },
-    "config": {
-        "bin-dir": "bin"
+        "symplify/easy-coding-standard": "dev-master as 2.5",
+        "symplify/coding-standard": "dev-master as 2.5",
+        "symplify/package-builder": "dev-master#3604bea as 2.5"
     }
 }

easy-coding-standard.neon

@@ -0,0 +1,39 @@
+includes:
+    - vendor/symplify/easy-coding-standard/config/psr2.neon
+    - vendor/symplify/easy-coding-standard/config/php70.neon
+    - vendor/symplify/easy-coding-standard/config/clean-code.neon
+    - vendor/symplify/easy-coding-standard/config/common/array.neon
+    - vendor/symplify/easy-coding-standard/config/common/docblock.neon
+    - vendor/symplify/easy-coding-standard/config/common/namespaces.neon
+    - vendor/symplify/easy-coding-standard/config/common/control-structures.neon
+
+    # many errors, need help
+    #- vendor/symplify/easy-coding-standard/config/common/strict.neon
+
+checkers:
+    - Symplify\CodingStandard\Fixer\Import\ImportNamespacedNameFixer
+    - Symplify\CodingStandard\Fixer\Php\ClassStringToClassConstantFixer
+    - Symplify\CodingStandard\Fixer\Property\ArrayPropertyDefaultValueFixer
+    - Symplify\CodingStandard\Fixer\ClassNotation\PropertyAndConstantSeparationFixer
+    - Symplify\CodingStandard\Fixer\ArrayNotation\StandaloneLineInMultilineArrayFixer
+
+parameters:
+    exclude_checkers:
+        # from strict.neon
+        - PhpCsFixer\Fixer\PhpUnit\PhpUnitStrictFixer
+    skip:
+        PhpCsFixer\Fixer\Alias\RandomApiMigrationFixer:
+            # random_int() breaks code
+            - src/Phpml/CrossValidation/RandomSplit.php
+        SlevomatCodingStandard\Sniffs\Classes\UnusedPrivateElementsSniff:
+            # magic calls
+            - src/Phpml/Preprocessing/Normalizer.php
+
+    skip_codes:
+        # missing typehints
+        - SlevomatCodingStandard\Sniffs\TypeHints\TypeHintDeclarationSniff.MissingParameterTypeHint
+        - SlevomatCodingStandard\Sniffs\TypeHints\TypeHintDeclarationSniff.MissingTraversableParameterTypeHintSpecification
+        - SlevomatCodingStandard\Sniffs\TypeHints\TypeHintDeclarationSniff.MissingReturnTypeHint
+        - SlevomatCodingStandard\Sniffs\TypeHints\TypeHintDeclarationSniff.MissingTraversableReturnTypeHintSpecification
+        - SlevomatCodingStandard\Sniffs\TypeHints\TypeHintDeclarationSniff.MissingPropertyTypeHint
+        - SlevomatCodingStandard\Sniffs\TypeHints\TypeHintDeclarationSniff.MissingTraversablePropertyTypeHintSpecification

phpunit.xml

@@ -6,11 +6,9 @@
         beStrictAboutTestSize="true"
         beStrictAboutChangesToGlobalState="true"
 >
-    <testsuites>
-        <testsuite name="PHP-ML Test Suite">
-            <directory>tests/*</directory>
-        </testsuite>
-    </testsuites>​
+    <testsuite name="PHP-ML Test Suite">
+        <directory>tests/*</directory>
+    </testsuite>
 
     <filter>
         <whitelist processUncoveredFilesFromWhitelist="true">

src/Phpml/Association/Apriori.php

@@ -31,7 +31,7 @@ class Apriori implements Associator
      *
      * @var mixed[][][]
      */
-    private $large;
+    private $large = [];
 
     /**
      * Minimum relative frequency of transactions.
@@ -45,7 +45,7 @@ class Apriori implements Associator
      *
      * @var mixed[][]
      */
-    private $rules;
+    private $rules = [];
 
     /**
      * Apriori constructor.
@@ -61,7 +61,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    public function getRules() : array
+    public function getRules(): array
     {
         if (!$this->large) {
             $this->large = $this->apriori();
@@ -83,7 +83,7 @@ class Apriori implements Associator
      *
      * @return mixed[][][]
      */
-    public function apriori() : array
+    public function apriori(): array
     {
         $L = [];
         $L[1] = $this->items();
@@ -102,7 +102,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    protected function predictSample(array $sample) : array
+    protected function predictSample(array $sample): array
     {
         $predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample) {
             return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample);
@@ -133,7 +133,8 @@ class Apriori implements Associator
     private function generateRules(array $frequent): void
     {
         foreach ($this->antecedents($frequent) as $antecedent) {
-            if ($this->confidence <= ($confidence = $this->confidence($frequent, $antecedent))) {
+            $confidence = $this->confidence($frequent, $antecedent);
+            if ($this->confidence <= $confidence) {
                 $consequent = array_values(array_diff($frequent, $antecedent));
                 $this->rules[] = [
                     self::ARRAY_KEY_ANTECEDENT => $antecedent,
@@ -152,7 +153,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function powerSet(array $sample) : array
+    private function powerSet(array $sample): array
     {
         $results = [[]];
         foreach ($sample as $item) {
@@ -171,7 +172,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function antecedents(array $sample) : array
+    private function antecedents(array $sample): array
     {
         $cardinality = count($sample);
         $antecedents = $this->powerSet($sample);
@@ -186,7 +187,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function items() : array
+    private function items(): array
     {
         $items = [];
 
@@ -210,7 +211,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function frequent(array $samples) : array
+    private function frequent(array $samples): array
     {
         return array_filter($samples, function ($entry) {
             return $this->support($entry) >= $this->support;
@@ -224,7 +225,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function candidates(array $samples) : array
+    private function candidates(array $samples): array
     {
         $candidates = [];
 
@@ -259,7 +260,7 @@ class Apriori implements Associator
      * @param mixed[] $set
      * @param mixed[] $subset
      */
-    private function confidence(array $set, array $subset) : float
+    private function confidence(array $set, array $subset): float
     {
         return $this->support($set) / $this->support($subset);
     }
@@ -272,7 +273,7 @@ class Apriori implements Associator
      *
      * @param mixed[] $sample
      */
-    private function support(array $sample) : float
+    private function support(array $sample): float
     {
         return $this->frequency($sample) / count($this->samples);
     }
@@ -284,7 +285,7 @@ class Apriori implements Associator
      *
      * @param mixed[] $sample
      */
-    private function frequency(array $sample) : int
+    private function frequency(array $sample): int
     {
         return count(array_filter($this->samples, function ($entry) use ($sample) {
             return $this->subset($entry, $sample);
@@ -299,7 +300,7 @@ class Apriori implements Associator
      * @param mixed[][] $system
      * @param mixed[]   $set
      */
-    private function contains(array $system, array $set) : bool
+    private function contains(array $system, array $set): bool
     {
         return (bool) array_filter($system, function ($entry) use ($set) {
             return $this->equals($entry, $set);
@@ -312,7 +313,7 @@ class Apriori implements Associator
      * @param mixed[] $set
      * @param mixed[] $subset
      */
-    private function subset(array $set, array $subset) : bool
+    private function subset(array $set, array $subset): bool
     {
         return !array_diff($subset, array_intersect($subset, $set));
     }
@@ -323,7 +324,7 @@ class Apriori implements Associator
      * @param mixed[] $set1
      * @param mixed[] $set2
      */
-    private function equals(array $set1, array $set2) : bool
+    private function equals(array $set1, array $set2): bool
     {
         return array_diff($set1, $set2) == array_diff($set2, $set1);
     }

src/Phpml/Classification/DecisionTree.php

@@ -15,22 +15,18 @@ class DecisionTree implements Classifier
     use Trainable, Predictable;
 
     public const CONTINUOUS = 1;
+
     public const NOMINAL = 2;
 
-    /**
-     * @var array
-     */
-    protected $columnTypes;
-
-    /**
-     * @var array
-     */
-    private $labels = [];
-
     /**
      * @var int
      */
-    private $featureCount = 0;
+    public $actualDepth = 0;
+
+    /**
+     * @var array
+     */
+    protected $columnTypes = [];
 
     /**
      * @var DecisionTreeLeaf
@@ -42,10 +38,15 @@ class DecisionTree implements Classifier
      */
     protected $maxDepth;
 
+    /**
+     * @var array
+     */
+    private $labels = [];
+
     /**
      * @var int
      */
-    public $actualDepth = 0;
+    private $featureCount = 0;
 
     /**
      * @var int
@@ -55,7 +56,7 @@ class DecisionTree implements Classifier
     /**
      * @var array
      */
-    private $selectedFeatures;
+    private $selectedFeatures = [];
 
     /**
      * @var array
@@ -100,7 +101,7 @@ class DecisionTree implements Classifier
         }
     }
 
-    public static function getColumnTypes(array $samples) : array
+    public static function getColumnTypes(array $samples): array
     {
         $types = [];
         $featureCount = count($samples[0]);
@@ -113,7 +114,122 @@ class DecisionTree implements Classifier
         return $types;
     }
 
-    protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf
+    /**
+     * @param mixed $baseValue
+     */
+    public function getGiniIndex($baseValue, array $colValues, array $targets): float
+    {
+        $countMatrix = [];
+        foreach ($this->labels as $label) {
+            $countMatrix[$label] = [0, 0];
+        }
+
+        foreach ($colValues as $index => $value) {
+            $label = $targets[$index];
+            $rowIndex = $value === $baseValue ? 0 : 1;
+            ++$countMatrix[$label][$rowIndex];
+        }
+
+        $giniParts = [0, 0];
+        for ($i = 0; $i <= 1; ++$i) {
+            $part = 0;
+            $sum = array_sum(array_column($countMatrix, $i));
+            if ($sum > 0) {
+                foreach ($this->labels as $label) {
+                    $part += pow($countMatrix[$label][$i] / (float) $sum, 2);
+                }
+            }
+
+            $giniParts[$i] = (1 - $part) * $sum;
+        }
+
+        return array_sum($giniParts) / count($colValues);
+    }
+
+    /**
+     * This method is used to set number of columns to be used
+     * when deciding a split at an internal node of the tree.  <br>
+     * If the value is given 0, then all features are used (default behaviour),
+     * otherwise the given value will be used as a maximum for number of columns
+     * randomly selected for each split operation.
+     *
+     * @return $this
+     *
+     * @throws InvalidArgumentException
+     */
+    public function setNumFeatures(int $numFeatures)
+    {
+        if ($numFeatures < 0) {
+            throw new InvalidArgumentException('Selected column count should be greater or equal to zero');
+        }
+
+        $this->numUsableFeatures = $numFeatures;
+
+        return $this;
+    }
+
+    /**
+     * A string array to represent columns. Useful when HTML output or
+     * column importances are desired to be inspected.
+     *
+     * @return $this
+     *
+     * @throws InvalidArgumentException
+     */
+    public function setColumnNames(array $names)
+    {
+        if ($this->featureCount !== 0 && count($names) !== $this->featureCount) {
+            throw new InvalidArgumentException(sprintf('Length of the given array should be equal to feature count %s', $this->featureCount));
+        }
+
+        $this->columnNames = $names;
+
+        return $this;
+    }
+
+    public function getHtml(): string
+    {
+        return $this->tree->getHTML($this->columnNames);
+    }
+
+    /**
+     * 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/>
+     */
+    public function getFeatureImportances(): array
+    {
+        if ($this->featureImportances !== null) {
+            return $this->featureImportances;
+        }
+
+        $sampleCount = count($this->samples);
+        $this->featureImportances = [];
+        foreach ($this->columnNames as $column => $columnName) {
+            $nodes = $this->getSplitNodesByColumn($column, $this->tree);
+
+            $importance = 0;
+            foreach ($nodes as $node) {
+                $importance += $node->getNodeImpurityDecrease($sampleCount);
+            }
+
+            $this->featureImportances[$columnName] = $importance;
+        }
+
+        // Normalize & sort the importances
+        $total = array_sum($this->featureImportances);
+        if ($total > 0) {
+            foreach ($this->featureImportances as &$importance) {
+                $importance /= $total;
+            }
+
+            arsort($this->featureImportances);
+        }
+
+        return $this->featureImportances;
+    }
+
+    protected function getSplitLeaf(array $records, int $depth = 0): DecisionTreeLeaf
     {
         $split = $this->getBestSplit($records);
         $split->level = $depth;
@@ -136,6 +252,7 @@ class DecisionTree implements Classifier
             if ($prevRecord && $prevRecord != $record) {
                 $allSame = false;
             }
+
             $prevRecord = $record;
 
             // According to the split criteron, this record will
@@ -163,6 +280,7 @@ class DecisionTree implements Classifier
             if ($leftRecords) {
                 $split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1);
             }
+
             if ($rightRecords) {
                 $split->rightLeaf = $this->getSplitLeaf($rightRecords, $depth + 1);
             }
@@ -171,7 +289,7 @@ class DecisionTree implements Classifier
         return $split;
     }
 
-    protected function getBestSplit(array $records) : DecisionTreeLeaf
+    protected function getBestSplit(array $records): DecisionTreeLeaf
     {
         $targets = array_intersect_key($this->targets, array_flip($records));
         $samples = array_intersect_key($this->samples, array_flip($records));
@@ -184,6 +302,7 @@ class DecisionTree implements Classifier
             foreach ($samples as $index => $row) {
                 $colValues[$index] = $row[$i];
             }
+
             $counts = array_count_values($colValues);
             arsort($counts);
             $baseValue = key($counts);
@@ -227,7 +346,7 @@ class DecisionTree implements Classifier
      * If any of above methods were not called beforehand, then all features
      * are returned by default.
      */
-    protected function getSelectedFeatures() : array
+    protected function getSelectedFeatures(): array
     {
         $allFeatures = range(0, $this->featureCount - 1);
         if ($this->numUsableFeatures === 0 && !$this->selectedFeatures) {
@@ -242,6 +361,7 @@ class DecisionTree implements Classifier
         if ($numFeatures > $this->featureCount) {
             $numFeatures = $this->featureCount;
         }
+
         shuffle($allFeatures);
         $selectedFeatures = array_slice($allFeatures, 0, $numFeatures, false);
         sort($selectedFeatures);
@@ -249,39 +369,7 @@ class DecisionTree implements Classifier
         return $selectedFeatures;
     }
 
-    /**
-     * @param mixed $baseValue
-     */
-    public function getGiniIndex($baseValue, array $colValues, array $targets) : float
-    {
-        $countMatrix = [];
-        foreach ($this->labels as $label) {
-            $countMatrix[$label] = [0, 0];
-        }
-
-        foreach ($colValues as $index => $value) {
-            $label = $targets[$index];
-            $rowIndex = $value === $baseValue ? 0 : 1;
-            ++$countMatrix[$label][$rowIndex];
-        }
-
-        $giniParts = [0, 0];
-        for ($i = 0; $i <= 1; ++$i) {
-            $part = 0;
-            $sum = array_sum(array_column($countMatrix, $i));
-            if ($sum > 0) {
-                foreach ($this->labels as $label) {
-                    $part += pow($countMatrix[$label][$i] / (float) $sum, 2);
-                }
-            }
-
-            $giniParts[$i] = (1 - $part) * $sum;
-        }
-
-        return array_sum($giniParts) / count($colValues);
-    }
-
-    protected function preprocess(array $samples) : array
+    protected function preprocess(array $samples): array
     {
         // Detect and convert continuous data column values into
         // discrete values by using the median as a threshold value
@@ -298,14 +386,16 @@ class DecisionTree implements Classifier
                     }
                 }
             }
+
             $columns[] = $values;
         }
+
         // Below method is a strange yet very simple & efficient method
         // to get the transpose of a 2D array
         return array_map(null, ...$columns);
     }
 
-    protected static function isCategoricalColumn(array $columnValues) : bool
+    protected static function isCategoricalColumn(array $columnValues): bool
     {
         $count = count($columnValues);
 
@@ -329,28 +419,6 @@ class DecisionTree implements Classifier
         return count($distinctValues) <= $count / 5;
     }
 
-    /**
-     * This method is used to set number of columns to be used
-     * when deciding a split at an internal node of the tree.  <br>
-     * If the value is given 0, then all features are used (default behaviour),
-     * otherwise the given value will be used as a maximum for number of columns
-     * randomly selected for each split operation.
-     *
-     * @return $this
-     *
-     * @throws InvalidArgumentException
-     */
-    public function setNumFeatures(int $numFeatures)
-    {
-        if ($numFeatures < 0) {
-            throw new InvalidArgumentException('Selected column count should be greater or equal to zero');
-        }
-
-        $this->numUsableFeatures = $numFeatures;
-
-        return $this;
-    }
-
     /**
      * Used to set predefined features to consider while deciding which column to use for a split
      */
@@ -359,71 +427,11 @@ class DecisionTree implements Classifier
         $this->selectedFeatures = $selectedFeatures;
     }
 
-    /**
-     * A string array to represent columns. Useful when HTML output or
-     * column importances are desired to be inspected.
-     *
-     * @return $this
-     *
-     * @throws InvalidArgumentException
-     */
-    public function setColumnNames(array $names)
-    {
-        if ($this->featureCount !== 0 && count($names) !== $this->featureCount) {
-            throw new InvalidArgumentException(sprintf('Length of the given array should be equal to feature count %s', $this->featureCount));
-        }
-
-        $this->columnNames = $names;
-
-        return $this;
-    }
-
-    public function getHtml() : string
-    {
-        return $this->tree->getHTML($this->columnNames);
-    }
-
-    /**
-     * 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/>
-     */
-    public function getFeatureImportances() : array
-    {
-        if ($this->featureImportances !== null) {
-            return $this->featureImportances;
-        }
-
-        $sampleCount = count($this->samples);
-        $this->featureImportances = [];
-        foreach ($this->columnNames as $column => $columnName) {
-            $nodes = $this->getSplitNodesByColumn($column, $this->tree);
-
-            $importance = 0;
-            foreach ($nodes as $node) {
-                $importance += $node->getNodeImpurityDecrease($sampleCount);
-            }
-
-            $this->featureImportances[$columnName] = $importance;
-        }
-
-        // Normalize & sort the importances
-        $total = array_sum($this->featureImportances);
-        if ($total > 0) {
-            foreach ($this->featureImportances as &$importance) {
-                $importance /= $total;
-            }
-            arsort($this->featureImportances);
-        }
-
-        return $this->featureImportances;
-    }
-
     /**
      * Collects and returns an array of internal nodes that use the given
      * column as a split criterion
      */
-    protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) : array
+    protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node): array
     {
         if (!$node || $node->isTerminal) {
             return [];

src/Phpml/Classification/DecisionTree/DecisionTreeLeaf.php

@@ -71,7 +71,15 @@ class DecisionTreeLeaf
      */
     public $level = 0;
 
-    public function evaluate(array $record) : bool
+    /**
+     * HTML representation of the tree without column names
+     */
+    public function __toString(): string
+    {
+        return $this->getHTML();
+    }
+
+    public function evaluate(array $record): bool
     {
         $recordField = $record[$this->columnIndex];
 
@@ -86,7 +94,7 @@ class DecisionTreeLeaf
      * Returns Mean Decrease Impurity (MDI) in the node.
      * For terminal nodes, this value is equal to 0
      */
-    public function getNodeImpurityDecrease(int $parentRecordCount) : float
+    public function getNodeImpurityDecrease(int $parentRecordCount): float
     {
         if ($this->isTerminal) {
             return 0.0;
@@ -111,7 +119,7 @@ class DecisionTreeLeaf
     /**
      * Returns HTML representation of the node including children nodes
      */
-    public function getHTML($columnNames = null) : string
+    public function getHTML($columnNames = null): string
     {
         if ($this->isTerminal) {
             $value = "<b>$this->classValue</b>";
@@ -154,12 +162,4 @@ class DecisionTreeLeaf
 
         return $str;
     }
-
-    /**
-     * HTML representation of the tree without column names
-     */
-    public function __toString() : string
-    {
-        return $this->getHTML();
-    }
 }

src/Phpml/Classification/Ensemble/AdaBoost.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\Classification\Ensemble;
 
+use Exception;
 use Phpml\Classification\Classifier;
 use Phpml\Classification\Linear\DecisionStump;
 use Phpml\Classification\WeightedClassifier;
@@ -11,6 +12,7 @@ use Phpml\Helper\Predictable;
 use Phpml\Helper\Trainable;
 use Phpml\Math\Statistic\Mean;
 use Phpml\Math\Statistic\StandardDeviation;
+use ReflectionClass;
 
 class AdaBoost implements Classifier
 {
@@ -98,11 +100,14 @@ class AdaBoost implements Classifier
         // Initialize usual variables
         $this->labels = array_keys(array_count_values($targets));
         if (count($this->labels) != 2) {
-            throw new \Exception('AdaBoost is a binary classifier and can classify between two classes only');
+            throw new Exception('AdaBoost is a binary classifier and can classify between two classes only');
         }
 
         // Set all target values to either -1 or 1
-        $this->labels = [1 => $this->labels[0], -1 => $this->labels[1]];
+        $this->labels = [
+            1 => $this->labels[0],
+            -1 => $this->labels[1],
+        ];
         foreach ($targets as $target) {
             $this->targets[] = $target == $this->labels[1] ? 1 : -1;
         }
@@ -132,13 +137,27 @@ class AdaBoost implements Classifier
         }
     }
 
+    /**
+     * @return mixed
+     */
+    public function predictSample(array $sample)
+    {
+        $sum = 0;
+        foreach ($this->alpha as $index => $alpha) {
+            $h = $this->classifiers[$index]->predict($sample);
+            $sum += $h * $alpha;
+        }
+
+        return $this->labels[$sum > 0 ? 1 : -1];
+    }
+
     /**
      * Returns the classifier with the lowest error rate with the
      * consideration of current sample weights
      */
-    protected function getBestClassifier() : Classifier
+    protected function getBestClassifier(): Classifier
     {
-        $ref = new \ReflectionClass($this->baseClassifier);
+        $ref = new ReflectionClass($this->baseClassifier);
         if ($this->classifierOptions) {
             $classifier = $ref->newInstanceArgs($this->classifierOptions);
         } else {
@@ -160,7 +179,7 @@ class AdaBoost implements Classifier
      * Resamples the dataset in accordance with the weights and
      * returns the new dataset
      */
-    protected function resample() : array
+    protected function resample(): array
     {
         $weights = $this->weights;
         $std = StandardDeviation::population($weights);
@@ -173,9 +192,10 @@ class AdaBoost implements Classifier
         foreach ($weights as $index => $weight) {
             $z = (int) round(($weight - $mean) / $std) - $minZ + 1;
             for ($i = 0; $i < $z; ++$i) {
-                if (rand(0, 1) == 0) {
+                if (random_int(0, 1) == 0) {
                     continue;
                 }
+
                 $samples[] = $this->samples[$index];
                 $targets[] = $this->targets[$index];
             }
@@ -187,7 +207,7 @@ class AdaBoost implements Classifier
     /**
      * Evaluates the classifier and returns the classification error rate
      */
-    protected function evaluateClassifier(Classifier $classifier) : float
+    protected function evaluateClassifier(Classifier $classifier): float
     {
         $total = (float) array_sum($this->weights);
         $wrong = 0;
@@ -204,7 +224,7 @@ class AdaBoost implements Classifier
     /**
      * Calculates alpha of a classifier
      */
-    protected function calculateAlpha(float $errorRate) : float
+    protected function calculateAlpha(float $errorRate): float
     {
         if ($errorRate == 0) {
             $errorRate = 1e-10;
@@ -231,18 +251,4 @@ class AdaBoost implements Classifier
 
         $this->weights = $weightsT1;
     }
-
-    /**
-     * @return mixed
-     */
-    public function predictSample(array $sample)
-    {
-        $sum = 0;
-        foreach ($this->alpha as $index => $alpha) {
-            $h = $this->classifiers[$index]->predict($sample);
-            $sum += $h * $alpha;
-        }
-
-        return $this->labels[$sum > 0 ? 1 : -1];
-    }
 }

src/Phpml/Classification/Ensemble/Bagging.php

@@ -4,10 +4,12 @@ declare(strict_types=1);
 
 namespace Phpml\Classification\Ensemble;
 
+use Exception;
 use Phpml\Classification\Classifier;
 use Phpml\Classification\DecisionTree;
 use Phpml\Helper\Predictable;
 use Phpml\Helper\Trainable;
+use ReflectionClass;
 
 class Bagging implements Classifier
 {
@@ -18,11 +20,6 @@ class Bagging implements Classifier
      */
     protected $numSamples;
 
-    /**
-     * @var array
-     */
-    private $targets = [];
-
     /**
      * @var int
      */
@@ -46,13 +43,18 @@ class Bagging implements Classifier
     /**
      * @var array
      */
-    protected $classifiers;
+    protected $classifiers = [];
 
     /**
      * @var float
      */
     protected $subsetRatio = 0.7;
 
+    /**
+     * @var array
+     */
+    private $targets = [];
+
     /**
      * @var array
      */
@@ -80,7 +82,7 @@ class Bagging implements Classifier
     public function setSubsetRatio(float $ratio)
     {
         if ($ratio < 0.1 || $ratio > 1.0) {
-            throw new \Exception('Subset ratio should be between 0.1 and 1.0');
+            throw new Exception('Subset ratio should be between 0.1 and 1.0');
         }
 
         $this->subsetRatio = $ratio;
@@ -123,14 +125,14 @@ class Bagging implements Classifier
         }
     }
 
-    protected function getRandomSubset(int $index) : array
+    protected function getRandomSubset(int $index): array
     {
         $samples = [];
         $targets = [];
         srand($index);
         $bootstrapSize = $this->subsetRatio * $this->numSamples;
         for ($i = 0; $i < $bootstrapSize; ++$i) {
-            $rand = rand(0, $this->numSamples - 1);
+            $rand = random_int(0, $this->numSamples - 1);
             $samples[] = $this->samples[$rand];
             $targets[] = $this->targets[$rand];
         }
@@ -138,11 +140,11 @@ class Bagging implements Classifier
         return [$samples, $targets];
     }
 
-    protected function initClassifiers() : array
+    protected function initClassifiers(): array
     {
         $classifiers = [];
         for ($i = 0; $i < $this->numClassifier; ++$i) {
-            $ref = new \ReflectionClass($this->classifier);
+            $ref = new ReflectionClass($this->classifier);
             if ($this->classifierOptions) {
                 $obj = $ref->newInstanceArgs($this->classifierOptions);
             } else {
@@ -155,12 +157,7 @@ class Bagging implements Classifier
         return $classifiers;
     }
 
-    /**
-     * @param Classifier $classifier
-     *
-     * @return Classifier
-     */
-    protected function initSingleClassifier($classifier)
+    protected function initSingleClassifier(Classifier $classifier): Classifier
     {
         return $classifier;
     }

src/Phpml/Classification/Ensemble/RandomForest.php

@@ -4,6 +4,8 @@ declare(strict_types=1);
 
 namespace Phpml\Classification\Ensemble;
 
+use Exception;
+use Phpml\Classification\Classifier;
 use Phpml\Classification\DecisionTree;
 
 class RandomForest extends Bagging
@@ -48,11 +50,11 @@ class RandomForest extends Bagging
     public function setFeatureSubsetRatio($ratio)
     {
         if (is_float($ratio) && ($ratio < 0.1 || $ratio > 1.0)) {
-            throw new \Exception('When a float given, feature subset ratio should be between 0.1 and 1.0');
+            throw new Exception('When a float given, feature subset ratio should be between 0.1 and 1.0');
         }
 
         if (is_string($ratio) && $ratio != 'sqrt' && $ratio != 'log') {
-            throw new \Exception("When a string given, feature subset ratio can only be 'sqrt' or 'log' ");
+            throw new Exception("When a string given, feature subset ratio can only be 'sqrt' or 'log' ");
         }
 
         $this->featureSubsetRatio = $ratio;
@@ -70,7 +72,7 @@ class RandomForest extends Bagging
     public function setClassifer(string $classifier, array $classifierOptions = [])
     {
         if ($classifier != DecisionTree::class) {
-            throw new \Exception('RandomForest can only use DecisionTree as base classifier');
+            throw new Exception('RandomForest can only use DecisionTree as base classifier');
         }
 
         return parent::setClassifer($classifier, $classifierOptions);
@@ -81,7 +83,7 @@ class RandomForest extends Bagging
      * each column in the given dataset. Importance values for a column
      * is the average importance of that column in all trees in the forest
      */
-    public function getFeatureImportances() : array
+    public function getFeatureImportances(): array
     {
         // Traverse each tree and sum importance of the columns
         $sum = [];
@@ -127,7 +129,7 @@ class RandomForest extends Bagging
      *
      * @return DecisionTree
      */
-    protected function initSingleClassifier($classifier)
+    protected function initSingleClassifier(Classifier $classifier): Classifier
     {
         if (is_float($this->featureSubsetRatio)) {
             $featureCount = (int) ($this->featureSubsetRatio * $this->featureCount);

src/Phpml/Classification/KNearestNeighbors.php

@@ -28,7 +28,7 @@ class KNearestNeighbors implements Classifier
      */
     public function __construct(int $k = 3, ?Distance $distanceMetric = null)
     {
-        if (null === $distanceMetric) {
+        if ($distanceMetric === null) {
             $distanceMetric = new Euclidean();
         }
 
@@ -60,7 +60,7 @@ class KNearestNeighbors implements Classifier
     /**
      * @throws \Phpml\Exception\InvalidArgumentException
      */
-    private function kNeighborsDistances(array $sample) : array
+    private function kNeighborsDistances(array $sample): array
     {
         $distances = [];
 

src/Phpml/Classification/Linear/Adaline.php

@@ -4,6 +4,8 @@ declare(strict_types=1);
 
 namespace Phpml\Classification\Linear;
 
+use Exception;
+
 class Adaline extends Perceptron
 {
     /**
@@ -41,7 +43,7 @@ class Adaline extends Perceptron
         int $trainingType = self::BATCH_TRAINING
     ) {
         if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) {
-            throw new \Exception('Adaline can only be trained with batch and online/stochastic gradient descent algorithm');
+            throw new Exception('Adaline can only be trained with batch and online/stochastic gradient descent algorithm');
         }
 
         $this->trainingType = $trainingType;

src/Phpml/Classification/Linear/DecisionStump.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\Classification\Linear;
 
+use Exception;
 use Phpml\Classification\DecisionTree;
 use Phpml\Classification\WeightedClassifier;
 use Phpml\Helper\OneVsRest;
@@ -24,7 +25,7 @@ class DecisionStump extends WeightedClassifier
     /**
      * @var array
      */
-    protected $binaryLabels;
+    protected $binaryLabels = [];
 
     /**
      * Lowest error rate obtained while training/optimizing the model
@@ -51,7 +52,7 @@ class DecisionStump extends WeightedClassifier
     /**
      * @var array
      */
-    protected $columnTypes;
+    protected $columnTypes = [];
 
     /**
      * @var int
@@ -68,7 +69,7 @@ class DecisionStump extends WeightedClassifier
      *
      * @var array
      */
-    protected $prob;
+    protected $prob = [];
 
     /**
      * A DecisionStump classifier is a one-level deep DecisionTree. It is generally
@@ -83,6 +84,25 @@ class DecisionStump extends WeightedClassifier
         $this->givenColumnIndex = $columnIndex;
     }
 
+    public function __toString(): string
+    {
+        return "IF $this->column $this->operator $this->value ".
+            'THEN '.$this->binaryLabels[0].' '.
+            'ELSE '.$this->binaryLabels[1];
+    }
+
+    /**
+     * While finding best split point for a numerical valued column,
+     * DecisionStump looks for equally distanced values between minimum and maximum
+     * values in the column. Given <i>$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)
+     */
+    public function setNumericalSplitCount(float $count): void
+    {
+        $this->numSplitCount = $count;
+    }
+
     /**
      * @throws \Exception
      */
@@ -101,7 +121,7 @@ class DecisionStump extends WeightedClassifier
         if ($this->weights) {
             $numWeights = count($this->weights);
             if ($numWeights != count($samples)) {
-                throw new \Exception('Number of sample weights does not match with number of samples');
+                throw new Exception('Number of sample weights does not match with number of samples');
             }
         } else {
             $this->weights = array_fill(0, count($samples), 1);
@@ -118,9 +138,12 @@ class DecisionStump extends WeightedClassifier
         }
 
         $bestSplit = [
-            'value' => 0, 'operator' => '',
-            'prob' => [], 'column' => 0,
-            'trainingErrorRate' => 1.0];
+            'value' => 0,
+            'operator' => '',
+            'prob' => [],
+            'column' => 0,
+            'trainingErrorRate' => 1.0,
+        ];
         foreach ($columns as $col) {
             if ($this->columnTypes[$col] == DecisionTree::CONTINUOUS) {
                 $split = $this->getBestNumericalSplit($samples, $targets, $col);
@@ -139,22 +162,10 @@ class DecisionStump extends WeightedClassifier
         }
     }
 
-    /**
-     * While finding best split point for a numerical valued column,
-     * DecisionStump looks for equally distanced values between minimum and maximum
-     * 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)
-     */
-    public function setNumericalSplitCount(float $count): void
-    {
-        $this->numSplitCount = $count;
-    }
-
     /**
      * Determines best split point for the given column
      */
-    protected function getBestNumericalSplit(array $samples, array $targets, int $col) : array
+    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:
@@ -173,9 +184,13 @@ class DecisionStump extends WeightedClassifier
             $threshold = array_sum($values) / (float) count($values);
             [$errorRate, $prob] = $this->calculateErrorRate($targets, $threshold, $operator, $values);
             if ($split == null || $errorRate < $split['trainingErrorRate']) {
-                $split = ['value' => $threshold, 'operator' => $operator,
-                        'prob' => $prob, 'column' => $col,
-                        'trainingErrorRate' => $errorRate];
+                $split = [
+                    'value' => $threshold,
+                    'operator' => $operator,
+                    'prob' => $prob,
+                    'column' => $col,
+                    'trainingErrorRate' => $errorRate,
+                ];
             }
 
             // Try other possible points one by one
@@ -183,9 +198,13 @@ class DecisionStump extends WeightedClassifier
                 $threshold = (float) $step;
                 [$errorRate, $prob] = $this->calculateErrorRate($targets, $threshold, $operator, $values);
                 if ($errorRate < $split['trainingErrorRate']) {
-                    $split = ['value' => $threshold, 'operator' => $operator,
-                        'prob' => $prob, 'column' => $col,
-                        'trainingErrorRate' => $errorRate];
+                    $split = [
+                        'value' => $threshold,
+                        'operator' => $operator,
+                        'prob' => $prob,
+                        'column' => $col,
+                        'trainingErrorRate' => $errorRate,
+                    ];
                 }
             }// for
         }
@@ -193,7 +212,7 @@ class DecisionStump extends WeightedClassifier
         return $split;
     }
 
-    protected function getBestNominalSplit(array $samples, array $targets, int $col) : array
+    protected function getBestNominalSplit(array $samples, array $targets, int $col): array
     {
         $values = array_column($samples, $col);
         $valueCounts = array_count_values($values);
@@ -206,9 +225,13 @@ class DecisionStump extends WeightedClassifier
                 [$errorRate, $prob] = $this->calculateErrorRate($targets, $val, $operator, $values);
 
                 if ($split == null || $split['trainingErrorRate'] < $errorRate) {
-                    $split = ['value' => $val, 'operator' => $operator,
-                        'prob' => $prob, 'column' => $col,
-                        'trainingErrorRate' => $errorRate];
+                    $split = [
+                        'value' => $val,
+                        'operator' => $operator,
+                        'prob' => $prob,
+                        'column' => $col,
+                        'trainingErrorRate' => $errorRate,
+                    ];
                 }
             }
         }
@@ -220,7 +243,7 @@ class DecisionStump extends WeightedClassifier
      * Calculates the ratio of wrong predictions based on the new threshold
      * value given as the parameter
      */
-    protected function calculateErrorRate(array $targets, float $threshold, string $operator, array $values) : array
+    protected function calculateErrorRate(array $targets, float $threshold, string $operator, array $values): array
     {
         $wrong = 0.0;
         $prob = [];
@@ -242,6 +265,7 @@ class DecisionStump extends WeightedClassifier
             if (!isset($prob[$predicted][$target])) {
                 $prob[$predicted][$target] = 0;
             }
+
             ++$prob[$predicted][$target];
         }
 
@@ -267,7 +291,7 @@ class DecisionStump extends WeightedClassifier
      *
      * @param mixed $label
      */
-    protected function predictProbability(array $sample, $label) : float
+    protected function predictProbability(array $sample, $label): float
     {
         $predicted = $this->predictSampleBinary($sample);
         if ((string) $predicted == (string) $label) {
@@ -292,11 +316,4 @@ class DecisionStump extends WeightedClassifier
     protected function resetBinary(): void
     {
     }
-
-    public function __toString() : string
-    {
-        return "IF $this->column $this->operator $this->value ".
-            'THEN '.$this->binaryLabels[0].' '.
-            'ELSE '.$this->binaryLabels[1];
-    }
 }

src/Phpml/Classification/Linear/LogisticRegression.php

@@ -4,6 +4,8 @@ declare(strict_types=1);
 
 namespace Phpml\Classification\Linear;
 
+use Closure;
+use Exception;
 use Phpml\Helper\Optimizer\ConjugateGradient;
 
 class LogisticRegression extends Adaline
@@ -70,18 +72,18 @@ class LogisticRegression extends Adaline
     ) {
         $trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
         if (!in_array($trainingType, $trainingTypes)) {
-            throw new \Exception('Logistic regression can only be trained with '.
+            throw new Exception('Logistic regression can only be trained with '.
                 'batch (gradient descent), online (stochastic gradient descent) '.
                 'or conjugate batch (conjugate gradients) algorithms');
         }
 
         if (!in_array($cost, ['log', 'sse'])) {
-            throw new \Exception("Logistic regression cost function can be one of the following: \n".
+            throw new Exception("Logistic regression cost function can be one of the following: \n".
                 "'log' for log-likelihood and 'sse' for sum of squared errors");
         }
 
         if ($penalty != '' && strtoupper($penalty) !== 'L2') {
-            throw new \Exception("Logistic regression supports only 'L2' regularization");
+            throw new Exception("Logistic regression supports only 'L2' regularization");
         }
 
         $this->learningRate = 0.001;
@@ -132,14 +134,14 @@ class LogisticRegression extends Adaline
                 return $this->runConjugateGradient($samples, $targets, $callback);
 
             default:
-                throw new \Exception('Logistic regression has invalid training type: %s.', $this->trainingType);
+                throw new Exception('Logistic regression has invalid training type: %s.', $this->trainingType);
         }
     }
 
     /**
      * Executes Conjugate Gradient method to optimize the weights of the LogReg model
      */
-    protected function runConjugateGradient(array $samples, array $targets, \Closure $gradientFunc): void
+    protected function runConjugateGradient(array $samples, array $targets, Closure $gradientFunc): void
     {
         if (empty($this->optimizer)) {
             $this->optimizer = (new ConjugateGradient($this->featureCount))
@@ -155,7 +157,7 @@ class LogisticRegression extends Adaline
      *
      * @throws \Exception
      */
-    protected function getCostFunction() : \Closure
+    protected function getCostFunction(): Closure
     {
         $penalty = 0;
         if ($this->penalty == 'L2') {
@@ -183,9 +185,11 @@ class LogisticRegression extends Adaline
                     if ($hX == 1) {
                         $hX = 1 - 1e-10;
                     }
+
                     if ($hX == 0) {
                         $hX = 1e-10;
                     }
+
                     $error = -$y * log($hX) - (1 - $y) * log(1 - $hX);
                     $gradient = $hX - $y;
 
@@ -218,16 +222,14 @@ class LogisticRegression extends Adaline
                 return $callback;
 
             default:
-                throw new \Exception(sprintf('Logistic regression has invalid cost function: %s.', $this->costFunction));
+                throw new Exception(sprintf('Logistic regression has invalid cost function: %s.', $this->costFunction));
         }
     }
 
     /**
      * Returns the output of the network, a float value between 0.0 and 1.0
-     *
-     * @return float
      */
-    protected function output(array $sample)
+    protected function output(array $sample): float
     {
         $sum = parent::output($sample);
 
@@ -237,7 +239,7 @@ class LogisticRegression extends Adaline
     /**
      * Returns the class value (either -1 or 1) for the given input
      */
-    protected function outputClass(array $sample) : int
+    protected function outputClass(array $sample): int
     {
         $output = $this->output($sample);
 
@@ -253,10 +255,10 @@ class LogisticRegression extends Adaline
      *
      * The probability is simply taken as the distance of the sample
      * to the decision plane.
-
+     *
      * @param mixed $label
      */
-    protected function predictProbability(array $sample, $label) : float
+    protected function predictProbability(array $sample, $label): float
     {
         $predicted = $this->predictSampleBinary($sample);
 

src/Phpml/Classification/Linear/Perceptron.php

@@ -4,6 +4,8 @@ declare(strict_types=1);
 
 namespace Phpml\Classification\Linear;
 
+use Closure;
+use Exception;
 use Phpml\Classification\Classifier;
 use Phpml\Helper\OneVsRest;
 use Phpml\Helper\Optimizer\GD;
@@ -34,7 +36,7 @@ class Perceptron implements Classifier, IncrementalEstimator
     /**
      * @var array
      */
-    protected $weights;
+    protected $weights = [];
 
     /**
      * @var float
@@ -73,11 +75,11 @@ class Perceptron implements Classifier, IncrementalEstimator
     public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true)
     {
         if ($learningRate <= 0.0 || $learningRate > 1.0) {
-            throw new \Exception('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
+            throw new Exception('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
         }
 
         if ($maxIterations <= 0) {
-            throw new \Exception('Maximum number of iterations must be an integer greater than 0');
+            throw new Exception('Maximum number of iterations must be an integer greater than 0');
         }
 
         if ($normalizeInputs) {
@@ -100,7 +102,10 @@ class Perceptron implements Classifier, IncrementalEstimator
         }
 
         // Set all target values to either -1 or 1
-        $this->labels = [1 => $labels[0], -1 => $labels[1]];
+        $this->labels = [
+            1 => $labels[0],
+            -1 => $labels[1],
+        ];
         foreach ($targets as $key => $target) {
             $targets[$key] = (string) $target == (string) $this->labels[1] ? 1 : -1;
         }
@@ -111,15 +116,6 @@ class Perceptron implements Classifier, IncrementalEstimator
         $this->runTraining($samples, $targets);
     }
 
-    protected function resetBinary(): void
-    {
-        $this->labels = [];
-        $this->optimizer = null;
-        $this->featureCount = 0;
-        $this->weights = null;
-        $this->costValues = [];
-    }
-
     /**
      * Normally enabling early stopping for the optimization procedure may
      * help saving processing time while in some cases it may result in
@@ -140,16 +136,23 @@ class Perceptron implements Classifier, IncrementalEstimator
     /**
      * Returns the cost values obtained during the training.
      */
-    public function getCostValues() : array
+    public function getCostValues(): array
     {
         return $this->costValues;
     }
 
+    protected function resetBinary(): void
+    {
+        $this->labels = [];
+        $this->optimizer = null;
+        $this->featureCount = 0;
+        $this->weights = null;
+        $this->costValues = [];
+    }
+
     /**
      * Trains the perceptron model with Stochastic Gradient Descent optimization
      * to get the correct set of weights
-     *
-     * @return void|mixed
      */
     protected function runTraining(array $samples, array $targets)
     {
@@ -171,7 +174,7 @@ class Perceptron implements Classifier, IncrementalEstimator
      * Executes a Gradient Descent algorithm for
      * the given cost function
      */
-    protected function runGradientDescent(array $samples, array $targets, \Closure $gradientFunc, bool $isBatch = false): void
+    protected function runGradientDescent(array $samples, array $targets, Closure $gradientFunc, bool $isBatch = false): void
     {
         $class = $isBatch ? GD::class : StochasticGD::class;
 
@@ -191,7 +194,7 @@ class Perceptron implements Classifier, IncrementalEstimator
      * Checks if the sample should be normalized and if so, returns the
      * normalized sample
      */
-    protected function checkNormalizedSample(array $sample) : array
+    protected function checkNormalizedSample(array $sample): array
     {
         if ($this->normalizer) {
             $samples = [$sample];
@@ -205,7 +208,7 @@ class Perceptron implements Classifier, IncrementalEstimator
     /**
      * Calculates net output of the network as a float value for the given input
      *
-     * @return int
+     * @return int|float
      */
     protected function output(array $sample)
     {
@@ -224,7 +227,7 @@ class Perceptron implements Classifier, IncrementalEstimator
     /**
      * Returns the class value (either -1 or 1) for the given input
      */
-    protected function outputClass(array $sample) : int
+    protected function outputClass(array $sample): int
     {
         return $this->output($sample) > 0 ? 1 : -1;
     }
@@ -237,7 +240,7 @@ class Perceptron implements Classifier, IncrementalEstimator
      *
      * @param mixed $label
      */
-    protected function predictProbability(array $sample, $label) : float
+    protected function predictProbability(array $sample, $label): float
     {
         $predicted = $this->predictSampleBinary($sample);
 

src/Phpml/Classification/MLPClassifier.php

@@ -14,7 +14,7 @@ class MLPClassifier extends MultilayerPerceptron implements Classifier
      *
      * @throws InvalidArgumentException
      */
-    public function getTargetClass($target) : int
+    public function getTargetClass($target): int
     {
         if (!in_array($target, $this->classes)) {
             throw InvalidArgumentException::invalidTarget($target);

src/Phpml/Classification/NaiveBayes.php

@@ -14,7 +14,9 @@ class NaiveBayes implements Classifier
     use Trainable, Predictable;
 
     public const CONTINUOS = 1;
+
     public const NOMINAL = 2;
+
     public const EPSILON = 1e-10;
 
     /**
@@ -73,6 +75,31 @@ class NaiveBayes implements Classifier
         }
     }
 
+    /**
+     * @return mixed
+     */
+    protected function predictSample(array $sample)
+    {
+        // Use NaiveBayes assumption for each label using:
+        //	P(label|features) = P(label) * P(feature0|label) * P(feature1|label) .... P(featureN|label)
+        // Then compare probability for each class to determine which label is most likely
+        $predictions = [];
+        foreach ($this->labels as $label) {
+            $p = $this->p[$label];
+            for ($i = 0; $i < $this->featureCount; ++$i) {
+                $Plf = $this->sampleProbability($sample, $i, $label);
+                $p += $Plf;
+            }
+
+            $predictions[$label] = $p;
+        }
+
+        arsort($predictions, SORT_NUMERIC);
+        reset($predictions);
+
+        return key($predictions);
+    }
+
     /**
      * Calculates vital statistics for each label & feature. Stores these
      * values in private array in order to avoid repeated calculation
@@ -108,7 +135,7 @@ class NaiveBayes implements Classifier
     /**
      * Calculates the probability P(label|sample_n)
      */
-    private function sampleProbability(array $sample, int $feature, string $label) : float
+    private function sampleProbability(array $sample, int $feature, string $label): float
     {
         $value = $sample[$feature];
         if ($this->dataType[$label][$feature] == self::NOMINAL) {
@@ -119,6 +146,7 @@ class NaiveBayes implements Classifier
 
             return $this->discreteProb[$label][$feature][$value];
         }
+
         $std = $this->std[$label][$feature] ;
         $mean = $this->mean[$label][$feature];
         // Calculate the probability density by use of normal/Gaussian distribution
@@ -137,7 +165,7 @@ class NaiveBayes implements Classifier
     /**
      * Return samples belonging to specific label
      */
-    private function getSamplesByLabel(string $label) : array
+    private function getSamplesByLabel(string $label): array
     {
         $samples = [];
         for ($i = 0; $i < $this->sampleCount; ++$i) {
@@ -148,28 +176,4 @@ class NaiveBayes implements Classifier
 
         return $samples;
     }
-
-    /**
-     * @return mixed
-     */
-    protected function predictSample(array $sample)
-    {
-        // Use NaiveBayes assumption for each label using:
-        //	P(label|features) = P(label) * P(feature0|label) * P(feature1|label) .... P(featureN|label)
-        // Then compare probability for each class to determine which label is most likely
-        $predictions = [];
-        foreach ($this->labels as $label) {
-            $p = $this->p[$label];
-            for ($i = 0; $i < $this->featureCount; ++$i) {
-                $Plf = $this->sampleProbability($sample, $i, $label);
-                $p += $Plf;
-            }
-            $predictions[$label] = $p;
-        }
-
-        arsort($predictions, SORT_NUMERIC);
-        reset($predictions);
-
-        return key($predictions);
-    }
 }

src/Phpml/Classification/WeightedClassifier.php

@@ -9,7 +9,7 @@ abstract class WeightedClassifier implements Classifier
     /**
      * @var array
      */
-    protected $weights;
+    protected $weights = [];
 
     /**
      * Sets the array including a weight for each sample

src/Phpml/Clustering/Clusterer.php

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

src/Phpml/Clustering/DBSCAN.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\Clustering;
 
+use array_merge;
 use Phpml\Math\Distance;
 use Phpml\Math\Distance\Euclidean;
 
@@ -26,7 +27,7 @@ class DBSCAN implements Clusterer
 
     public function __construct(float $epsilon = 0.5, int $minSamples = 3, ?Distance $distanceMetric = null)
     {
-        if (null === $distanceMetric) {
+        if ($distanceMetric === null) {
             $distanceMetric = new Euclidean();
         }
 
@@ -35,7 +36,7 @@ class DBSCAN implements Clusterer
         $this->distanceMetric = $distanceMetric;
     }
 
-    public function cluster(array $samples) : array
+    public function cluster(array $samples): array
     {
         $clusters = [];
         $visited = [];
@@ -44,6 +45,7 @@ class DBSCAN implements Clusterer
             if (isset($visited[$index])) {
                 continue;
             }
+
             $visited[$index] = true;
 
             $regionSamples = $this->getSamplesInRegion($sample, $samples);
@@ -55,7 +57,7 @@ class DBSCAN implements Clusterer
         return $clusters;
     }
 
-    private function getSamplesInRegion(array $localSample, array $samples) : array
+    private function getSamplesInRegion(array $localSample, array $samples): array
     {
         $region = [];
 
@@ -68,7 +70,7 @@ class DBSCAN implements Clusterer
         return $region;
     }
 
-    private function expandCluster(array $samples, array &$visited) : array
+    private function expandCluster(array $samples, array &$visited): array
     {
         $cluster = [];
 
@@ -84,7 +86,8 @@ class DBSCAN implements Clusterer
 
             $cluster[$index] = $sample;
         }
-        $cluster = \array_merge($cluster, ...$clusterMerge);
+
+        $cluster = array_merge($cluster, ...$clusterMerge);
 
         return $cluster;
     }

src/Phpml/Clustering/FuzzyCMeans.php

@@ -30,7 +30,7 @@ class FuzzyCMeans implements Clusterer
     /**
      * @var array|float[][]
      */
-    private $membership;
+    private $membership = [];
 
     /**
      * @var float
@@ -55,7 +55,7 @@ class FuzzyCMeans implements Clusterer
     /**
      * @var array
      */
-    private $samples;
+    private $samples = [];
 
     /**
      * @throws InvalidArgumentException
@@ -65,12 +65,63 @@ class FuzzyCMeans implements Clusterer
         if ($clustersNumber <= 0) {
             throw InvalidArgumentException::invalidClustersNumber();
         }
+
         $this->clustersNumber = $clustersNumber;
         $this->fuzziness = $fuzziness;
         $this->epsilon = $epsilon;
         $this->maxIterations = $maxIterations;
     }
 
+    public function getMembershipMatrix(): array
+    {
+        return $this->membership;
+    }
+
+    /**
+     * @param array|Point[] $samples
+     */
+    public function cluster(array $samples): array
+    {
+        // Initialize variables, clusters and membership matrix
+        $this->sampleCount = count($samples);
+        $this->samples = &$samples;
+        $this->space = new Space(count($samples[0]));
+        $this->initClusters();
+
+        // Our goal is minimizing the objective value while
+        // executing the clustering steps at a maximum number of iterations
+        $lastObjective = 0.0;
+        $iterations = 0;
+        do {
+            // Update the membership matrix and cluster centers, respectively
+            $this->updateMembershipMatrix();
+            $this->updateClusters();
+
+            // Calculate the new value of the objective function
+            $objectiveVal = $this->getObjective();
+            $difference = abs($lastObjective - $objectiveVal);
+            $lastObjective = $objectiveVal;
+        } while ($difference > $this->epsilon && $iterations++ <= $this->maxIterations);
+
+        // Attach (hard cluster) each data point to the nearest cluster
+        for ($k = 0; $k < $this->sampleCount; ++$k) {
+            $column = array_column($this->membership, $k);
+            arsort($column);
+            reset($column);
+            $i = key($column);
+            $cluster = $this->clusters[$i];
+            $cluster->attach(new Point($this->samples[$k]));
+        }
+
+        // Return grouped samples
+        $grouped = [];
+        foreach ($this->clusters as $cluster) {
+            $grouped[] = $cluster->getPoints();
+        }
+
+        return $grouped;
+    }
+
     protected function initClusters(): void
     {
         // Membership array is a matrix of cluster number by sample counts
@@ -87,7 +138,7 @@ class FuzzyCMeans implements Clusterer
             $row = [];
             $total = 0.0;
             for ($k = 0; $k < $cols; ++$k) {
-                $val = rand(1, 5) / 10.0;
+                $val = random_int(1, 5) / 10.0;
                 $row[] = $val;
                 $total += $val;
             }
@@ -146,13 +197,13 @@ class FuzzyCMeans implements Clusterer
         }
     }
 
-    protected function getDistanceCalc(int $row, int $col) : float
+    protected function getDistanceCalc(int $row, int $col): float
     {
         $sum = 0.0;
         $distance = new Euclidean();
         $dist1 = $distance->distance(
-                $this->clusters[$row]->getCoordinates(),
-                $this->samples[$col]
+            $this->clusters[$row]->getCoordinates(),
+            $this->samples[$col]
         );
 
         for ($j = 0; $j < $this->clustersNumber; ++$j) {
@@ -187,54 +238,4 @@ class FuzzyCMeans implements Clusterer
 
         return $sum;
     }
-
-    public function getMembershipMatrix() : array
-    {
-        return $this->membership;
-    }
-
-    /**
-     * @param array|Point[] $samples
-     */
-    public function cluster(array $samples) : array
-    {
-        // Initialize variables, clusters and membership matrix
-        $this->sampleCount = count($samples);
-        $this->samples = &$samples;
-        $this->space = new Space(count($samples[0]));
-        $this->initClusters();
-
-        // Our goal is minimizing the objective value while
-        // executing the clustering steps at a maximum number of iterations
-        $lastObjective = 0.0;
-        $iterations = 0;
-        do {
-            // Update the membership matrix and cluster centers, respectively
-            $this->updateMembershipMatrix();
-            $this->updateClusters();
-
-            // Calculate the new value of the objective function
-            $objectiveVal = $this->getObjective();
-            $difference = abs($lastObjective - $objectiveVal);
-            $lastObjective = $objectiveVal;
-        } while ($difference > $this->epsilon && $iterations++ <= $this->maxIterations);
-
-        // Attach (hard cluster) each data point to the nearest cluster
-        for ($k = 0; $k < $this->sampleCount; ++$k) {
-            $column = array_column($this->membership, $k);
-            arsort($column);
-            reset($column);
-            $i = key($column);
-            $cluster = $this->clusters[$i];
-            $cluster->attach(new Point($this->samples[$k]));
-        }
-
-        // Return grouped samples
-        $grouped = [];
-        foreach ($this->clusters as $cluster) {
-            $grouped[] = $cluster->getPoints();
-        }
-
-        return $grouped;
-    }
 }

src/Phpml/Clustering/KMeans.php

@@ -10,6 +10,7 @@ use Phpml\Exception\InvalidArgumentException;
 class KMeans implements Clusterer
 {
     public const INIT_RANDOM = 1;
+
     public const INIT_KMEANS_PLUS_PLUS = 2;
 
     /**
@@ -32,7 +33,7 @@ class KMeans implements Clusterer
         $this->initialization = $initialization;
     }
 
-    public function cluster(array $samples) : array
+    public function cluster(array $samples): array
     {
         $space = new Space(count($samples[0]));
         foreach ($samples as $sample) {

src/Phpml/Clustering/KMeans/Cluster.php

@@ -28,7 +28,7 @@ class Cluster extends Point implements IteratorAggregate, Countable
         $this->points = new SplObjectStorage();
     }
 
-    public function getPoints() : array
+    public function getPoints(): array
     {
         $points = [];
         foreach ($this->points as $point) {
@@ -38,7 +38,7 @@ class Cluster extends Point implements IteratorAggregate, Countable
         return $points;
     }
 
-    public function toArray() : array
+    public function toArray(): array
     {
         return [
             'centroid' => parent::toArray(),
@@ -46,7 +46,7 @@ class Cluster extends Point implements IteratorAggregate, Countable
         ];
     }
 
-    public function attach(Point $point) : Point
+    public function attach(Point $point): Point
     {
         if ($point instanceof self) {
             throw new LogicException('cannot attach a cluster to another');
@@ -57,7 +57,7 @@ class Cluster extends Point implements IteratorAggregate, Countable
         return $point;
     }
 
-    public function detach(Point $point) : Point
+    public function detach(Point $point): Point
     {
         $this->points->detach($point);
 
@@ -76,7 +76,8 @@ class Cluster extends Point implements IteratorAggregate, Countable
 
     public function updateCentroid(): void
     {
-        if (!$count = count($this->points)) {
+        $count = count($this->points);
+        if (!$count) {
             return;
         }
 

src/Phpml/Clustering/KMeans/Point.php

@@ -16,7 +16,7 @@ class Point implements ArrayAccess
     /**
      * @var array
      */
-    protected $coordinates;
+    protected $coordinates = [];
 
     public function __construct(array $coordinates)
     {
@@ -24,7 +24,7 @@ class Point implements ArrayAccess
         $this->coordinates = $coordinates;
     }
 
-    public function toArray() : array
+    public function toArray(): array
     {
         return $this->coordinates;
     }
@@ -66,7 +66,7 @@ class Point implements ArrayAccess
         return $minPoint;
     }
 
-    public function getCoordinates() : array
+    public function getCoordinates(): array
     {
         return $this->coordinates;
     }

src/Phpml/Clustering/KMeans/Space.php

@@ -25,7 +25,7 @@ class Space extends SplObjectStorage
         $this->dimension = $dimension;
     }
 
-    public function toArray() : array
+    public function toArray(): array
     {
         $points = [];
         foreach ($this as $point) {
@@ -35,7 +35,7 @@ class Space extends SplObjectStorage
         return ['points' => $points];
     }
 
-    public function newPoint(array $coordinates) : Point
+    public function newPoint(array $coordinates): Point
     {
         if (count($coordinates) != $this->dimension) {
             throw new LogicException('('.implode(',', $coordinates).') is not a point of this space');
@@ -65,7 +65,7 @@ class Space extends SplObjectStorage
         parent::attach($point, $data);
     }
 
-    public function getDimension() : int
+    public function getDimension(): int
     {
         return $this->dimension;
     }
@@ -92,7 +92,7 @@ class Space extends SplObjectStorage
         return [$min, $max];
     }
 
-    public function getRandomPoint(Point $min, Point $max) : Point
+    public function getRandomPoint(Point $min, Point $max): Point
     {
         $point = $this->newPoint(array_fill(0, $this->dimension, null));
 
@@ -106,7 +106,7 @@ class Space extends SplObjectStorage
     /**
      * @return array|Cluster[]
      */
-    public function cluster(int $clustersNumber, int $initMethod = KMeans::INIT_RANDOM) : array
+    public function cluster(int $clustersNumber, int $initMethod = KMeans::INIT_RANDOM): array
     {
         $clusters = $this->initializeClusters($clustersNumber, $initMethod);
 
@@ -119,7 +119,7 @@ class Space extends SplObjectStorage
     /**
      * @return array|Cluster[]
      */
-    protected function initializeClusters(int $clustersNumber, int $initMethod) : array
+    protected function initializeClusters(int $clustersNumber, int $initMethod): array
     {
         switch ($initMethod) {
             case KMeans::INIT_RANDOM:
@@ -139,7 +139,7 @@ class Space extends SplObjectStorage
         return $clusters;
     }
 
-    protected function iterate($clusters) : bool
+    protected function iterate($clusters): bool
     {
         $convergence = true;
 
@@ -177,19 +177,7 @@ class Space extends SplObjectStorage
         return $convergence;
     }
 
-    private function initializeRandomClusters(int $clustersNumber) : array
-    {
-        $clusters = [];
-        [$min, $max] = $this->getBoundaries();
-
-        for ($n = 0; $n < $clustersNumber; ++$n) {
-            $clusters[] = new Cluster($this, $this->getRandomPoint($min, $max)->getCoordinates());
-        }
-
-        return $clusters;
-    }
-
-    protected function initializeKMPPClusters(int $clustersNumber) : array
+    protected function initializeKMPPClusters(int $clustersNumber): array
     {
         $clusters = [];
         $this->rewind();
@@ -218,4 +206,16 @@ class Space extends SplObjectStorage
 
         return $clusters;
     }
+
+    private function initializeRandomClusters(int $clustersNumber): array
+    {
+        $clusters = [];
+        [$min, $max] = $this->getBoundaries();
+
+        for ($n = 0; $n < $clustersNumber; ++$n) {
+            $clusters[] = new Cluster($this, $this->getRandomPoint($min, $max)->getCoordinates());
+        }
+
+        return $clusters;
+    }
 }

src/Phpml/CrossValidation/Split.php

@@ -31,39 +31,40 @@ abstract class Split
 
     public function __construct(Dataset $dataset, float $testSize = 0.3, ?int $seed = null)
     {
-        if (0 >= $testSize || 1 <= $testSize) {
+        if ($testSize <= 0 || $testSize >= 1) {
             throw InvalidArgumentException::percentNotInRange('testSize');
         }
+
         $this->seedGenerator($seed);
 
         $this->splitDataset($dataset, $testSize);
     }
 
-    abstract protected function splitDataset(Dataset $dataset, float $testSize);
-
-    public function getTrainSamples() : array
+    public function getTrainSamples(): array
     {
         return $this->trainSamples;
     }
 
-    public function getTestSamples() : array
+    public function getTestSamples(): array
     {
         return $this->testSamples;
     }
 
-    public function getTrainLabels() : array
+    public function getTrainLabels(): array
     {
         return $this->trainLabels;
     }
 
-    public function getTestLabels() : array
+    public function getTestLabels(): array
     {
         return $this->testLabels;
     }
 
+    abstract protected function splitDataset(Dataset $dataset, float $testSize);
+
     protected function seedGenerator(?int $seed = null): void
     {
-        if (null === $seed) {
+        if ($seed === null) {
             mt_srand();
         } else {
             mt_srand($seed);

src/Phpml/CrossValidation/StratifiedRandomSplit.php

@@ -21,7 +21,7 @@ class StratifiedRandomSplit extends RandomSplit
     /**
      * @return Dataset[]|array
      */
-    private function splitByTarget(Dataset $dataset) : array
+    private function splitByTarget(Dataset $dataset): array
     {
         $targets = $dataset->getTargets();
         $samples = $dataset->getSamples();
@@ -38,7 +38,7 @@ class StratifiedRandomSplit extends RandomSplit
         return $datasets;
     }
 
-    private function createDatasets(array $uniqueTargets, array $split) : array
+    private function createDatasets(array $uniqueTargets, array $split): array
     {
         $datasets = [];
         foreach ($uniqueTargets as $target) {

src/Phpml/Dataset/ArrayDataset.php

@@ -31,12 +31,12 @@ class ArrayDataset implements Dataset
         $this->targets = $targets;
     }
 
-    public function getSamples() : array
+    public function getSamples(): array
     {
         return $this->samples;
     }
 
-    public function getTargets() : array
+    public function getTargets(): array
     {
         return $this->targets;
     }

src/Phpml/Dataset/CsvDataset.php

@@ -11,7 +11,7 @@ class CsvDataset extends ArrayDataset
     /**
      * @var array
      */
-    protected $columnNames;
+    protected $columnNames = [];
 
     /**
      * @throws FileException
@@ -22,7 +22,8 @@ class CsvDataset extends ArrayDataset
             throw FileException::missingFile(basename($filepath));
         }
 
-        if (false === $handle = fopen($filepath, 'rb')) {
+        $handle = fopen($filepath, 'rb');
+        if ($handle === false) {
             throw FileException::cantOpenFile(basename($filepath));
         }
 
@@ -44,7 +45,7 @@ class CsvDataset extends ArrayDataset
         parent::__construct($samples, $targets);
     }
 
-    public function getColumnNames() : array
+    public function getColumnNames(): array
     {
         return $this->columnNames;
     }

src/Phpml/Dataset/Dataset.php

@@ -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;
 }

src/Phpml/DimensionReduction/EigenTransformerBase.php

@@ -84,7 +84,7 @@ abstract class EigenTransformerBase
     /**
      * Returns the reduced data
      */
-    protected function reduce(array $data) : array
+    protected function reduce(array $data): array
     {
         $m1 = new Matrix($data);
         $m2 = new Matrix($this->eigVectors);

src/Phpml/DimensionReduction/KernelPCA.php

@@ -4,6 +4,8 @@ declare(strict_types=1);
 
 namespace Phpml\DimensionReduction;
 
+use Closure;
+use Exception;
 use Phpml\Math\Distance\Euclidean;
 use Phpml\Math\Distance\Manhattan;
 use Phpml\Math\Matrix;
@@ -11,8 +13,11 @@ use Phpml\Math\Matrix;
 class KernelPCA extends PCA
 {
     public const KERNEL_RBF = 1;
+
     public const KERNEL_SIGMOID = 2;
+
     public const KERNEL_LAPLACIAN = 3;
+
     public const KERNEL_LINEAR = 4;
 
     /**
@@ -34,7 +39,7 @@ class KernelPCA extends PCA
      *
      * @var array
      */
-    protected $data;
+    protected $data = [];
 
     /**
      * Kernel principal component analysis (KernelPCA) is an extension of PCA using
@@ -54,7 +59,7 @@ class KernelPCA extends PCA
     {
         $availableKernels = [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN, self::KERNEL_LINEAR];
         if (!in_array($kernel, $availableKernels)) {
-            throw new \Exception('KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian');
+            throw new Exception('KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian');
         }
 
         parent::__construct($totalVariance, $numFeatures);
@@ -69,7 +74,7 @@ class KernelPCA extends PCA
      * $data is an n-by-m matrix and returned array is
      * n-by-k matrix where k <= m
      */
-    public function fit(array $data) : array
+    public function fit(array $data): array
     {
         $numRows = count($data);
         $this->data = $data;
@@ -88,11 +93,32 @@ class KernelPCA extends PCA
         return Matrix::transposeArray($this->eigVectors);
     }
 
+    /**
+     * Transforms the given sample to a lower dimensional vector by using
+     * the variables obtained during the last run of <code>fit</code>.
+     *
+     * @throws \Exception
+     */
+    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');
+        }
+
+        if (is_array($sample[0])) {
+            throw new Exception('KernelPCA::transform() accepts only one-dimensional arrays');
+        }
+
+        $pairs = $this->getDistancePairs($sample);
+
+        return $this->projectSample($pairs);
+    }
+
     /**
      * 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
      */
-    protected function calculateKernelMatrix(array $data, int $numRows) : array
+    protected function calculateKernelMatrix(array $data, int $numRows): array
     {
         $kernelFunc = $this->getKernel();
 
@@ -116,7 +142,7 @@ class KernelPCA extends PCA
      *
      * K′ = K − N.K −  K.N + N.K.N where N is n-by-n matrix filled with 1/n
      */
-    protected function centerMatrix(array $matrix, int $n) : array
+    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);
@@ -140,7 +166,7 @@ class KernelPCA extends PCA
      *
      * @throws \Exception
      */
-    protected function getKernel(): \Closure
+    protected function getKernel(): Closure
     {
         switch ($this->kernel) {
             case self::KERNEL_LINEAR:
@@ -173,11 +199,11 @@ class KernelPCA extends PCA
                 };
 
             default:
-                throw new \Exception(sprintf('KernelPCA initialized with invalid kernel: %d', $this->kernel));
+                throw new Exception(sprintf('KernelPCA initialized with invalid kernel: %d', $this->kernel));
         }
     }
 
-    protected function getDistancePairs(array $sample) : array
+    protected function getDistancePairs(array $sample): array
     {
         $kernel = $this->getKernel();
 
@@ -189,7 +215,7 @@ class KernelPCA extends PCA
         return $pairs;
     }
 
-    protected function projectSample(array $pairs) : array
+    protected function projectSample(array $pairs): array
     {
         // Normalize eigenvectors by eig = eigVectors / eigValues
         $func = function ($eigVal, $eigVect) {
@@ -203,25 +229,4 @@ class KernelPCA extends PCA
         // return k.dot(eig)
         return Matrix::dot($pairs, $eig);
     }
-
-    /**
-     * Transforms the given sample to a lower dimensional vector by using
-     * the variables obtained during the last run of <code>fit</code>.
-     *
-     * @throws \Exception
-     */
-    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');
-        }
-
-        if (is_array($sample[0])) {
-            throw new \Exception('KernelPCA::transform() accepts only one-dimensional arrays');
-        }
-
-        $pairs = $this->getDistancePairs($sample);
-
-        return $this->projectSample($pairs);
-    }
 }

src/Phpml/DimensionReduction/LDA.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\DimensionReduction;
 
+use Exception;
 use Phpml\Math\Matrix;
 
 class LDA extends EigenTransformerBase
@@ -16,22 +17,22 @@ class LDA extends EigenTransformerBase
     /**
      * @var array
      */
-    public $labels;
+    public $labels = [];
 
     /**
      * @var array
      */
-    public $means;
+    public $means = [];
 
     /**
      * @var array
      */
-    public $counts;
+    public $counts = [];
 
     /**
      * @var float[]
      */
-    public $overallMean;
+    public $overallMean = [];
 
     /**
      * Linear Discriminant Analysis (LDA) is used to reduce the dimensionality
@@ -50,18 +51,21 @@ class LDA extends EigenTransformerBase
     public function __construct(?float $totalVariance = null, ?int $numFeatures = null)
     {
         if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
-            throw new \Exception('Total variance can be a value between 0.1 and 0.99');
+            throw new Exception('Total variance can be a value between 0.1 and 0.99');
         }
+
         if ($numFeatures !== null && $numFeatures <= 0) {
-            throw new \Exception('Number of features to be preserved should be greater than 0');
+            throw new Exception('Number of features to be preserved should be greater than 0');
         }
+
         if ($totalVariance !== null && $numFeatures !== null) {
-            throw new \Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
+            throw new Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
         }
 
         if ($numFeatures !== null) {
             $this->numFeatures = $numFeatures;
         }
+
         if ($totalVariance !== null) {
             $this->totalVariance = $totalVariance;
         }
@@ -70,7 +74,7 @@ class LDA extends EigenTransformerBase
     /**
      * Trains the algorithm to transform the given data to a lower dimensional space.
      */
-    public function fit(array $data, array $classes) : array
+    public function fit(array $data, array $classes): array
     {
         $this->labels = $this->getLabels($classes);
         $this->means = $this->calculateMeans($data, $classes);
@@ -86,10 +90,29 @@ class LDA extends EigenTransformerBase
         return $this->reduce($data);
     }
 
+    /**
+     * Transforms the given sample to a lower dimensional vector by using
+     * the eigenVectors obtained in the last run of <code>fit</code>.
+     *
+     * @throws \Exception
+     */
+    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');
+        }
+
+        if (!is_array($sample[0])) {
+            $sample = [$sample];
+        }
+
+        return $this->reduce($sample);
+    }
+
     /**
      * Returns unique labels in the dataset
      */
-    protected function getLabels(array $classes) : array
+    protected function getLabels(array $classes): array
     {
         $counts = array_count_values($classes);
 
@@ -100,7 +123,7 @@ 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
      */
-    protected function calculateMeans(array $data, array $classes) : array
+    protected function calculateMeans(array $data, array $classes): array
     {
         $means = [];
         $counts = [];
@@ -113,6 +136,7 @@ class LDA extends EigenTransformerBase
                 if (!isset($means[$label][$col])) {
                     $means[$label][$col] = 0.0;
                 }
+
                 $means[$label][$col] += $val;
                 $overallMean[$col] += $val;
             }
@@ -146,7 +170,7 @@ class LDA extends EigenTransformerBase
      * is a n by m matrix where n is number of classes and
      * m is number of columns
      */
-    protected function calculateClassVar(array $data, array $classes) : Matrix
+    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));
@@ -169,7 +193,7 @@ class LDA extends EigenTransformerBase
      * is an n by m matrix where n is number of classes and
      * m is number of columns
      */
-    protected function calculateClassCov() : Matrix
+    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));
@@ -187,7 +211,7 @@ class LDA extends EigenTransformerBase
     /**
      * Returns the result of the calculation (x - m)T.(x - m)
      */
-    protected function calculateVar(array $row, array $means) : Matrix
+    protected function calculateVar(array $row, array $means): Matrix
     {
         $x = new Matrix($row, false);
         $m = new Matrix($means, false);
@@ -195,23 +219,4 @@ class LDA extends EigenTransformerBase
 
         return $diff->transpose()->multiply($diff);
     }
-
-    /**
-     * Transforms the given sample to a lower dimensional vector by using
-     * the eigenVectors obtained in the last run of <code>fit</code>.
-     *
-     * @throws \Exception
-     */
-    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');
-        }
-
-        if (!is_array($sample[0])) {
-            $sample = [$sample];
-        }
-
-        return $this->reduce($sample);
-    }
 }

src/Phpml/DimensionReduction/PCA.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\DimensionReduction;
 
+use Exception;
 use Phpml\Math\Statistic\Covariance;
 use Phpml\Math\Statistic\Mean;
 
@@ -35,18 +36,21 @@ class PCA extends EigenTransformerBase
     public function __construct(?float $totalVariance = null, ?int $numFeatures = null)
     {
         if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
-            throw new \Exception('Total variance can be a value between 0.1 and 0.99');
+            throw new Exception('Total variance can be a value between 0.1 and 0.99');
         }
+
         if ($numFeatures !== null && $numFeatures <= 0) {
-            throw new \Exception('Number of features to be preserved should be greater than 0');
+            throw new Exception('Number of features to be preserved should be greater than 0');
         }
+
         if ($totalVariance !== null && $numFeatures !== null) {
-            throw new \Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
+            throw new Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
         }
 
         if ($numFeatures !== null) {
             $this->numFeatures = $numFeatures;
         }
+
         if ($totalVariance !== null) {
             $this->totalVariance = $totalVariance;
         }
@@ -58,7 +62,7 @@ class PCA extends EigenTransformerBase
      * $data is an n-by-m matrix and returned array is
      * n-by-k matrix where k <= m
      */
-    public function fit(array $data) : array
+    public function fit(array $data): array
     {
         $n = count($data[0]);
 
@@ -73,6 +77,27 @@ class PCA extends EigenTransformerBase
         return $this->reduce($data);
     }
 
+    /**
+     * Transforms the given sample to a lower dimensional vector by using
+     * the eigenVectors obtained in the last run of <code>fit</code>.
+     *
+     * @throws \Exception
+     */
+    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');
+        }
+
+        if (!is_array($sample[0])) {
+            $sample = [$sample];
+        }
+
+        $sample = $this->normalize($sample, count($sample[0]));
+
+        return $this->reduce($sample);
+    }
+
     protected function calculateMeans(array $data, int $n): void
     {
         // Calculate means for each dimension
@@ -87,7 +112,7 @@ class PCA extends EigenTransformerBase
      * Normalization of the data includes subtracting mean from
      * each dimension therefore dimensions will be centered to zero
      */
-    protected function normalize(array $data, int $n) : array
+    protected function normalize(array $data, int $n): array
     {
         if (empty($this->means)) {
             $this->calculateMeans($data, $n);
@@ -102,25 +127,4 @@ class PCA extends EigenTransformerBase
 
         return $data;
     }
-
-    /**
-     * Transforms the given sample to a lower dimensional vector by using
-     * the eigenVectors obtained in the last run of <code>fit</code>.
-     *
-     * @throws \Exception
-     */
-    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');
-        }
-
-        if (!is_array($sample[0])) {
-            $sample = [$sample];
-        }
-
-        $sample = $this->normalize($sample, count($sample[0]));
-
-        return $this->reduce($sample);
-    }
 }

src/Phpml/Exception/DatasetException.php

@@ -4,9 +4,11 @@ declare(strict_types=1);
 
 namespace Phpml\Exception;
 
-class DatasetException extends \Exception
+use Exception;
+
+class DatasetException extends Exception
 {
-    public static function missingFolder(string $path) : DatasetException
+    public static function missingFolder(string $path): self
     {
         return new self(sprintf('Dataset root folder "%s" missing.', $path));
     }

src/Phpml/Exception/FileException.php

@@ -4,19 +4,21 @@ declare(strict_types=1);
 
 namespace Phpml\Exception;
 
-class FileException extends \Exception
+use Exception;
+
+class FileException extends Exception
 {
-    public static function missingFile(string $filepath) : FileException
+    public static function missingFile(string $filepath): self
     {
         return new self(sprintf('File "%s" missing.', $filepath));
     }
 
-    public static function cantOpenFile(string $filepath) : FileException
+    public static function cantOpenFile(string $filepath): self
     {
         return new self(sprintf('File "%s" can\'t be open.', $filepath));
     }
 
-    public static function cantSaveFile(string $filepath) : FileException
+    public static function cantSaveFile(string $filepath): self
     {
         return new self(sprintf('File "%s" can\'t be saved.', $filepath));
     }

src/Phpml/Exception/InvalidArgumentException.php

@@ -4,39 +4,41 @@ declare(strict_types=1);
 
 namespace Phpml\Exception;
 
-class InvalidArgumentException extends \Exception
+use Exception;
+
+class InvalidArgumentException extends Exception
 {
-    public static function arraySizeNotMatch() : InvalidArgumentException
+    public static function arraySizeNotMatch(): self
     {
         return new self('Size of given arrays does not match');
     }
 
-    public static function percentNotInRange($name) : InvalidArgumentException
+    public static function percentNotInRange($name): self
     {
         return new self(sprintf('%s must be between 0.0 and 1.0', $name));
     }
 
-    public static function arrayCantBeEmpty() : InvalidArgumentException
+    public static function arrayCantBeEmpty(): self
     {
         return new self('The array has zero elements');
     }
 
-    public static function arraySizeToSmall(int $minimumSize = 2) : InvalidArgumentException
+    public static function arraySizeToSmall(int $minimumSize = 2): self
     {
         return new self(sprintf('The array must have at least %d elements', $minimumSize));
     }
 
-    public static function matrixDimensionsDidNotMatch() : InvalidArgumentException
+    public static function matrixDimensionsDidNotMatch(): self
     {
         return new self('Matrix dimensions did not match');
     }
 
-    public static function inconsistentMatrixSupplied() : InvalidArgumentException
+    public static function inconsistentMatrixSupplied(): self
     {
         return new self('Inconsistent matrix supplied');
     }
 
-    public static function invalidClustersNumber() : InvalidArgumentException
+    public static function invalidClustersNumber(): self
     {
         return new self('Invalid clusters number');
     }
@@ -44,57 +46,57 @@ class InvalidArgumentException extends \Exception
     /**
      * @param mixed $target
      */
-    public static function invalidTarget($target) : InvalidArgumentException
+    public static function invalidTarget($target): self
     {
         return new self(sprintf('Target with value "%s" is not part of the accepted classes', $target));
     }
 
-    public static function invalidStopWordsLanguage(string $language) : InvalidArgumentException
+    public static function invalidStopWordsLanguage(string $language): self
     {
         return new self(sprintf('Can\'t find "%s" language for StopWords', $language));
     }
 
-    public static function invalidLayerNodeClass() : InvalidArgumentException
+    public static function invalidLayerNodeClass(): self
     {
         return new self('Layer node class must implement Node interface');
     }
 
-    public static function invalidLayersNumber() : InvalidArgumentException
+    public static function invalidLayersNumber(): self
     {
         return new self('Provide at least 1 hidden layer');
     }
 
-    public static function invalidClassesNumber() : InvalidArgumentException
+    public static function invalidClassesNumber(): self
     {
         return new self('Provide at least 2 different classes');
     }
 
-    public static function inconsistentClasses() : InvalidArgumentException
+    public static function inconsistentClasses(): self
     {
         return new self('The provided classes don\'t match the classes provided in the constructor');
     }
 
-    public static function fileNotFound(string $file) : InvalidArgumentException
+    public static function fileNotFound(string $file): self
     {
         return new self(sprintf('File "%s" not found', $file));
     }
 
-    public static function fileNotExecutable(string $file) : InvalidArgumentException
+    public static function fileNotExecutable(string $file): self
     {
         return new self(sprintf('File "%s" is not executable', $file));
     }
 
-    public static function pathNotFound(string $path) : InvalidArgumentException
+    public static function pathNotFound(string $path): self
     {
         return new self(sprintf('The specified path "%s" does not exist', $path));
     }
 
-    public static function pathNotWritable(string $path) : InvalidArgumentException
+    public static function pathNotWritable(string $path): self
     {
         return new self(sprintf('The specified path "%s" is not writable', $path));
     }
 
-    public static function invalidOperator(string $operator) : InvalidArgumentException
+    public static function invalidOperator(string $operator): self
     {
         return new self(sprintf('Invalid operator "%s" provided', $operator));
     }

src/Phpml/Exception/MatrixException.php

@@ -4,19 +4,21 @@ declare(strict_types=1);
 
 namespace Phpml\Exception;
 
-class MatrixException extends \Exception
+use Exception;
+
+class MatrixException extends Exception
 {
-    public static function notSquareMatrix() : MatrixException
+    public static function notSquareMatrix(): self
     {
         return new self('Matrix is not square matrix');
     }
 
-    public static function columnOutOfRange() : MatrixException
+    public static function columnOutOfRange(): self
     {
         return new self('Column out of range');
     }
 
-    public static function singularMatrix() : MatrixException
+    public static function singularMatrix(): self
     {
         return new self('Matrix is singular');
     }

src/Phpml/Exception/NormalizerException.php

@@ -4,9 +4,11 @@ declare(strict_types=1);
 
 namespace Phpml\Exception;
 
-class NormalizerException extends \Exception
+use Exception;
+
+class NormalizerException extends Exception
 {
-    public static function unknownNorm() : NormalizerException
+    public static function unknownNorm(): self
     {
         return new self('Unknown norm supplied.');
     }

src/Phpml/Exception/SerializeException.php

@@ -4,14 +4,16 @@ declare(strict_types=1);
 
 namespace Phpml\Exception;
 
-class SerializeException extends \Exception
+use Exception;
+
+class SerializeException extends Exception
 {
-    public static function cantUnserialize(string $filepath)  : SerializeException
+    public static function cantUnserialize(string $filepath): self
     {
         return new self(sprintf('"%s" can not be unserialized.', $filepath));
     }
 
-    public static function cantSerialize(string $classname)  : SerializeException
+    public static function cantSerialize(string $classname): self
     {
         return new self(sprintf('Class "%s" can not be serialized.', $classname));
     }

src/Phpml/FeatureExtraction/StopWords.php

@@ -11,19 +11,19 @@ class StopWords
     /**
      * @var array
      */
-    protected $stopWords;
+    protected $stopWords = [];
 
     public function __construct(array $stopWords)
     {
         $this->stopWords = array_fill_keys($stopWords, true);
     }
 
-    public function isStopWord(string $token) : bool
+    public function isStopWord(string $token): bool
     {
         return isset($this->stopWords[$token]);
     }
 
-    public static function factory(string $language = 'English') : StopWords
+    public static function factory(string $language = 'English'): self
     {
         $className = __NAMESPACE__."\\StopWords\\$language";
 

src/Phpml/FeatureExtraction/TfIdfTransformer.php

@@ -11,7 +11,7 @@ class TfIdfTransformer implements Transformer
     /**
      * @var array
      */
-    private $idf;
+    private $idf = [];
 
     public function __construct(?array $samples = null)
     {

src/Phpml/FeatureExtraction/TokenCountVectorizer.php

@@ -27,21 +27,18 @@ class TokenCountVectorizer implements Transformer
     /**
      * @var array
      */
-    private $vocabulary;
+    private $vocabulary = [];
 
     /**
      * @var array
      */
-    private $frequencies;
+    private $frequencies = [];
 
     public function __construct(Tokenizer $tokenizer, ?StopWords $stopWords = null, float $minDF = 0.0)
     {
         $this->tokenizer = $tokenizer;
         $this->stopWords = $stopWords;
         $this->minDF = $minDF;
-
-        $this->vocabulary = [];
-        $this->frequencies = [];
     }
 
     public function fit(array $samples): void
@@ -58,7 +55,7 @@ class TokenCountVectorizer implements Transformer
         $this->checkDocumentFrequency($samples);
     }
 
-    public function getVocabulary() : array
+    public function getVocabulary(): array
     {
         return array_flip($this->vocabulary);
     }
@@ -80,7 +77,7 @@ class TokenCountVectorizer implements Transformer
 
         foreach ($tokens as $token) {
             $index = $this->getTokenIndex($token);
-            if (false !== $index) {
+            if ($index !== false) {
                 $this->updateFrequency($token);
                 if (!isset($counts[$index])) {
                     $counts[$index] = 0;
@@ -155,7 +152,7 @@ class TokenCountVectorizer implements Transformer
         }
     }
 
-    private function getBeyondMinimumIndexes(int $samplesCount) : array
+    private function getBeyondMinimumIndexes(int $samplesCount): array
     {
         $indexes = [];
         foreach ($this->frequencies as $token => $frequency) {

src/Phpml/Helper/OneVsRest.php

@@ -36,6 +36,18 @@ trait OneVsRest
         $this->trainBylabel($samples, $targets);
     }
 
+    /**
+     * Resets the classifier and the vars internally used by OneVsRest to create multiple classifiers.
+     */
+    public function reset(): void
+    {
+        $this->classifiers = [];
+        $this->allLabels = [];
+        $this->costValues = [];
+
+        $this->resetBinary();
+    }
+
     protected function trainByLabel(array $samples, array $targets, array $allLabels = []): void
     {
         // Overwrites the current value if it exist. $allLabels must be provided for each partialTrain run.
@@ -44,6 +56,7 @@ trait OneVsRest
         } else {
             $this->allLabels = array_keys(array_count_values($targets));
         }
+
         sort($this->allLabels, SORT_STRING);
 
         // If there are only two targets, then there is no need to perform OvR
@@ -77,18 +90,6 @@ trait OneVsRest
         }
     }
 
-    /**
-     * Resets the classifier and the vars internally used by OneVsRest to create multiple classifiers.
-     */
-    public function reset(): void
-    {
-        $this->classifiers = [];
-        $this->allLabels = [];
-        $this->costValues = [];
-
-        $this->resetBinary();
-    }
-
     /**
      * Returns an instance of the current class after cleaning up OneVsRest stuff.
      *
@@ -105,29 +106,6 @@ trait OneVsRest
         return $classifier;
     }
 
-    /**
-     * Groups all targets into two groups: Targets equal to
-     * the given label and the others
-     *
-     * $targets is not passed by reference nor contains objects so this method
-     * changes will not affect the caller $targets array.
-     *
-     * @param mixed $label
-     *
-     * @return array Binarized targets and target's labels
-     */
-    private function binarizeTargets(array $targets, $label) : array
-    {
-        $notLabel = "not_$label";
-        foreach ($targets as $key => $target) {
-            $targets[$key] = $target == $label ? $label : $notLabel;
-        }
-
-        $labels = [$label, $notLabel];
-
-        return [$targets, $labels];
-    }
-
     /**
      * @return mixed
      */
@@ -155,8 +133,6 @@ trait OneVsRest
 
     /**
      * To be overwritten by OneVsRest classifiers.
-     *
-     * @return void
      */
     abstract protected function resetBinary(): void;
 
@@ -174,4 +150,27 @@ trait OneVsRest
      * @return mixed
      */
     abstract protected function predictSampleBinary(array $sample);
+
+    /**
+     * Groups all targets into two groups: Targets equal to
+     * the given label and the others
+     *
+     * $targets is not passed by reference nor contains objects so this method
+     * changes will not affect the caller $targets array.
+     *
+     * @param mixed $label
+     *
+     * @return array Binarized targets and target's labels
+     */
+    private function binarizeTargets(array $targets, $label): array
+    {
+        $notLabel = "not_$label";
+        foreach ($targets as $key => $target) {
+            $targets[$key] = $target == $label ? $label : $notLabel;
+        }
+
+        $labels = [$label, $notLabel];
+
+        return [$targets, $labels];
+    }
 }

src/Phpml/Helper/Optimizer/ConjugateGradient.php

@@ -4,6 +4,8 @@ declare(strict_types=1);
 
 namespace Phpml\Helper\Optimizer;
 
+use Closure;
+
 /**
  * Conjugate Gradient method to solve a non-linear f(x) with respect to unknown x
  * See https://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method)
@@ -17,7 +19,7 @@ namespace Phpml\Helper\Optimizer;
  */
 class ConjugateGradient extends GD
 {
-    public function runOptimization(array $samples, array $targets, \Closure $gradientCb) : array
+    public function runOptimization(array $samples, array $targets, Closure $gradientCb): array
     {
         $this->samples = $samples;
         $this->targets = $targets;
@@ -25,7 +27,7 @@ class ConjugateGradient extends GD
         $this->sampleCount = count($samples);
         $this->costValues = [];
 
-        $d = mp::muls($this->gradient($this->theta), -1);
+        $d = MP::muls($this->gradient($this->theta), -1);
 
         for ($i = 0; $i < $this->maxIterations; ++$i) {
             // Obtain α that minimizes f(θ + α.d)
@@ -59,7 +61,7 @@ class ConjugateGradient extends GD
      * Executes the callback function for the problem and returns
      * sum of the gradient for all samples & targets.
      */
-    protected function gradient(array $theta) : array
+    protected function gradient(array $theta): array
     {
         [, $gradient] = parent::gradient($theta);
 
@@ -69,7 +71,7 @@ class ConjugateGradient extends GD
     /**
      * Returns the value of f(x) for given solution
      */
-    protected function cost(array $theta) : float
+    protected function cost(array $theta): float
     {
         [$cost] = parent::gradient($theta);
 
@@ -90,14 +92,14 @@ class ConjugateGradient extends GD
      *		b-1) If cost function decreases, continue enlarging alpha
      *		b-2) If cost function increases, take the midpoint and try again
      */
-    protected function getAlpha(float $d) : float
+    protected function getAlpha(float $d): float
     {
         $small = 0.0001 * $d;
         $large = 0.01 * $d;
 
         // Obtain θ + α.d for two initial values, x0 and x1
-        $x0 = mp::adds($this->theta, $small);
-        $x1 = mp::adds($this->theta, $large);
+        $x0 = MP::adds($this->theta, $small);
+        $x1 = MP::adds($this->theta, $large);
 
         $epsilon = 0.0001;
         $iteration = 0;
@@ -113,9 +115,9 @@ class ConjugateGradient extends GD
 
             if ($fx1 < $fx0) {
                 $x0 = $x1;
-                $x1 = mp::adds($x1, 0.01); // Enlarge second
+                $x1 = MP::adds($x1, 0.01); // Enlarge second
             } else {
-                $x1 = mp::divs(mp::add($x1, $x0), 2.0);
+                $x1 = MP::divs(MP::add($x1, $x0), 2.0);
             } // Get to the midpoint
 
             $error = $fx1 / $this->dimensions;
@@ -135,7 +137,7 @@ class ConjugateGradient extends GD
      *
      * θ(k+1) = θ(k) + α.d
      */
-    protected function getNewTheta(float $alpha, array $d) : array
+    protected function getNewTheta(float $alpha, array $d): array
     {
         $theta = $this->theta;
 
@@ -164,7 +166,7 @@ class ConjugateGradient extends GD
      * See:
      *  R. Fletcher and C. M. Reeves, "Function minimization by conjugate gradients", Comput. J. 7 (1964), 149–154.
      */
-    protected function getBeta(array $newTheta) : float
+    protected function getBeta(array $newTheta): float
     {
         $dNew = array_sum($this->gradient($newTheta));
         $dOld = array_sum($this->gradient($this->theta)) + 1e-100;
@@ -177,11 +179,11 @@ class ConjugateGradient extends GD
      *
      * d(k+1) =–∇f(x(k+1)) + β(k).d(k)
      */
-    protected function getNewDirection(array $theta, float $beta, array $d) : array
+    protected function getNewDirection(array $theta, float $beta, array $d): array
     {
         $grad = $this->gradient($theta);
 
-        return mp::add(mp::muls($grad, -1), mp::muls($d, $beta));
+        return MP::add(MP::muls($grad, -1), MP::muls($d, $beta));
     }
 }
 
@@ -189,12 +191,12 @@ class ConjugateGradient extends GD
  * Handles element-wise vector operations between vector-vector
  * and vector-scalar variables
  */
-class mp
+class MP
 {
     /**
      * Element-wise <b>multiplication</b> of two vectors of the same size
      */
-    public static function mul(array $m1, array $m2) : array
+    public static function mul(array $m1, array $m2): array
     {
         $res = [];
         foreach ($m1 as $i => $val) {
@@ -207,7 +209,7 @@ class mp
     /**
      * Element-wise <b>division</b> of two vectors of the same size
      */
-    public static function div(array $m1, array $m2) : array
+    public static function div(array $m1, array $m2): array
     {
         $res = [];
         foreach ($m1 as $i => $val) {
@@ -220,7 +222,7 @@ class mp
     /**
      * Element-wise <b>addition</b> of two vectors of the same size
      */
-    public static function add(array $m1, array $m2, int $mag = 1) : array
+    public static function add(array $m1, array $m2, int $mag = 1): array
     {
         $res = [];
         foreach ($m1 as $i => $val) {
@@ -233,7 +235,7 @@ class mp
     /**
      * Element-wise <b>subtraction</b> of two vectors of the same size
      */
-    public static function sub(array $m1, array $m2) : array
+    public static function sub(array $m1, array $m2): array
     {
         return self::add($m1, $m2, -1);
     }
@@ -241,7 +243,7 @@ class mp
     /**
      * Element-wise <b>multiplication</b> of a vector with a scalar
      */
-    public static function muls(array $m1, float $m2) : array
+    public static function muls(array $m1, float $m2): array
     {
         $res = [];
         foreach ($m1 as $val) {
@@ -254,7 +256,7 @@ class mp
     /**
      * Element-wise <b>division</b> of a vector with a scalar
      */
-    public static function divs(array $m1, float $m2) : array
+    public static function divs(array $m1, float $m2): array
     {
         $res = [];
         foreach ($m1 as $val) {
@@ -267,7 +269,7 @@ class mp
     /**
      * Element-wise <b>addition</b> of a vector with a scalar
      */
-    public static function adds(array $m1, float $m2, int $mag = 1) : array
+    public static function adds(array $m1, float $m2, int $mag = 1): array
     {
         $res = [];
         foreach ($m1 as $val) {
@@ -280,7 +282,7 @@ class mp
     /**
      * Element-wise <b>subtraction</b> of a vector with a scalar
      */
-    public static function subs(array $m1, float $m2) : array
+    public static function subs(array $m1, float $m2): array
     {
         return self::adds($m1, $m2, -1);
     }

src/Phpml/Helper/Optimizer/GD.php

@@ -4,6 +4,8 @@ declare(strict_types=1);
 
 namespace Phpml\Helper\Optimizer;
 
+use Closure;
+
 /**
  * Batch version of Gradient Descent to optimize the weights
  * of a classifier given samples, targets and the objective function to minimize
@@ -17,7 +19,7 @@ class GD extends StochasticGD
      */
     protected $sampleCount = null;
 
-    public function runOptimization(array $samples, array $targets, \Closure $gradientCb) : array
+    public function runOptimization(array $samples, array $targets, Closure $gradientCb): array
     {
         $this->samples = $samples;
         $this->targets = $targets;
@@ -51,7 +53,7 @@ class GD extends StochasticGD
      * Calculates gradient, cost function and penalty term for each sample
      * then returns them as an array of values
      */
-    protected function gradient(array $theta) : array
+    protected function gradient(array $theta): array
     {
         $costs = [];
         $gradient = [];

src/Phpml/Helper/Optimizer/Optimizer.php

@@ -4,6 +4,9 @@ declare(strict_types=1);
 
 namespace Phpml\Helper\Optimizer;
 
+use Closure;
+use Exception;
+
 abstract class Optimizer
 {
     /**
@@ -11,7 +14,7 @@ abstract class Optimizer
      *
      * @var array
      */
-    protected $theta;
+    protected $theta = [];
 
     /**
      * Number of dimensions
@@ -30,7 +33,7 @@ abstract class Optimizer
         // Inits the weights randomly
         $this->theta = [];
         for ($i = 0; $i < $this->dimensions; ++$i) {
-            $this->theta[] = rand() / (float) getrandmax();
+            $this->theta[] = random_int(0, getrandmax()) / (float) getrandmax();
         }
     }
 
@@ -44,7 +47,7 @@ abstract class Optimizer
     public function setInitialTheta(array $theta)
     {
         if (count($theta) != $this->dimensions) {
-            throw new \Exception("Number of values in the weights array should be $this->dimensions");
+            throw new Exception("Number of values in the weights array should be $this->dimensions");
         }
 
         $this->theta = $theta;
@@ -56,5 +59,5 @@ abstract class Optimizer
      * Executes the optimization with the given samples & targets
      * and returns the weights
      */
-    abstract public function runOptimization(array $samples, array $targets, \Closure $gradientCb);
+    abstract public function runOptimization(array $samples, array $targets, Closure $gradientCb);
 }

src/Phpml/Helper/Optimizer/StochasticGD.php

@@ -4,6 +4,8 @@ declare(strict_types=1);
 
 namespace Phpml\Helper\Optimizer;
 
+use Closure;
+
 /**
  * Stochastic Gradient Descent optimization method
  * to find a solution for the equation A.ϴ = y where
@@ -66,6 +68,7 @@ class StochasticGD extends Optimizer
      * @var bool
      */
     protected $enableEarlyStop = true;
+
     /**
      * List of values obtained by evaluating the cost function at each iteration
      * of the algorithm
@@ -141,7 +144,7 @@ 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.
      */
-    public function runOptimization(array $samples, array $targets, \Closure $gradientCb) : array
+    public function runOptimization(array $samples, array $targets, Closure $gradientCb): array
     {
         $this->samples = $samples;
         $this->targets = $targets;
@@ -181,7 +184,16 @@ class StochasticGD extends Optimizer
         return $this->theta = $bestTheta;
     }
 
-    protected function updateTheta() : float
+    /**
+     * Returns the list of cost values for each iteration executed in
+     * last run of the optimization
+     */
+    public function getCostValues(): array
+    {
+        return $this->costValues;
+    }
+
+    protected function updateTheta(): float
     {
         $jValue = 0.0;
         $theta = $this->theta;
@@ -237,15 +249,6 @@ class StochasticGD extends Optimizer
         return false;
     }
 
-    /**
-     * Returns the list of cost values for each iteration executed in
-     * last run of the optimization
-     */
-    public function getCostValues() : array
-    {
-        return $this->costValues;
-    }
-
     /**
      * Clears the optimizer internal vars after the optimization process.
      */

src/Phpml/Math/Distance.php

@@ -10,5 +10,5 @@ interface Distance
      * @param array $a
      * @param array $b
      */
-    public function distance(array $a, array $b) : float;
+    public function distance(array $a, array $b): float;
 }

src/Phpml/Math/Distance/Chebyshev.php

@@ -12,7 +12,7 @@ class Chebyshev implements Distance
     /**
      * @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();

src/Phpml/Math/Distance/Euclidean.php

@@ -12,7 +12,7 @@ class Euclidean implements Distance
     /**
      * @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();
@@ -30,7 +30,7 @@ class Euclidean implements Distance
     /**
      * Square of Euclidean distance
      */
-    public function sqDistance(array $a, array $b) : float
+    public function sqDistance(array $a, array $b): float
     {
         return $this->distance($a, $b) ** 2;
     }

src/Phpml/Math/Distance/Manhattan.php

@@ -12,7 +12,7 @@ class Manhattan implements Distance
     /**
      * @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();

src/Phpml/Math/Distance/Minkowski.php

@@ -22,7 +22,7 @@ class Minkowski implements Distance
     /**
      * @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();

src/Phpml/Math/Kernel.php

@@ -7,10 +7,10 @@ namespace Phpml\Math;
 interface Kernel
 {
     /**
-     * @param float $a
-     * @param float $b
+     * @param float|array $a
+     * @param float|array $b
      *
-     * @return float
+     * @return float|array
      */
     public function compute($a, $b);
 }

src/Phpml/Math/Kernel/RBF.php

@@ -23,12 +23,11 @@ class RBF implements Kernel
      * @param array $a
      * @param array $b
      */
-    public function compute($a, $b)
+    public function compute($a, $b): float
     {
         $score = 2 * Product::scalar($a, $b);
         $squares = Product::scalar($a, $a) + Product::scalar($b, $b);
-        $result = exp(-$this->gamma * ($squares - $score));
 
-        return $result;
+        return exp(-$this->gamma * ($squares - $score));
     }
 }

src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php

@@ -1,6 +1,7 @@
 <?php
 
 declare(strict_types=1);
+
 /**
  *	Class to obtain eigenvalues and eigenvectors of a real matrix.
  *
@@ -54,6 +55,7 @@ class EigenvalueDecomposition
      *	@var array
      */
     private $d = [];
+
     private $e = [];
 
     /**
@@ -64,25 +66,26 @@ class EigenvalueDecomposition
     private $V = [];
 
     /**
-    *	Array for internal storage of nonsymmetric Hessenberg form.
-    *
-    *	@var array
-    */
+     *	Array for internal storage of nonsymmetric Hessenberg form.
+     *
+     *	@var array
+     */
     private $H = [];
 
     /**
-    *	Working storage for nonsymmetric algorithm.
-    *
-    *	@var array
-    */
-    private $ort;
+     *	Working storage for nonsymmetric algorithm.
+     *
+     *	@var array
+     */
+    private $ort = [];
 
     /**
-    *	Used for complex scalar division.
-    *
-    *	@var float
-    */
+     *	Used for complex scalar division.
+     *
+     *	@var float
+     */
     private $cdivr;
+
     private $cdivi;
 
     /**
@@ -116,6 +119,71 @@ class EigenvalueDecomposition
         }
     }
 
+    /**
+     * Return the eigenvector matrix
+     */
+    public function getEigenvectors(): array
+    {
+        $vectors = $this->V;
+
+        // Always return the eigenvectors of length 1.0
+        $vectors = new Matrix($vectors);
+        $vectors = array_map(function ($vect) {
+            $sum = 0;
+            for ($i = 0; $i < count($vect); ++$i) {
+                $sum += $vect[$i] ** 2;
+            }
+
+            $sum = sqrt($sum);
+            for ($i = 0; $i < count($vect); ++$i) {
+                $vect[$i] /= $sum;
+            }
+
+            return $vect;
+        }, $vectors->transpose()->toArray());
+
+        return $vectors;
+    }
+
+    /**
+     *	Return the real parts of the eigenvalues<br>
+     *  d = real(diag(D));
+     */
+    public function getRealEigenvalues(): array
+    {
+        return $this->d;
+    }
+
+    /**
+     *	Return the imaginary parts of the eigenvalues <br>
+     *  d = imag(diag(D))
+     */
+    public function getImagEigenvalues(): array
+    {
+        return $this->e;
+    }
+
+    /**
+     *	Return the block diagonal eigenvalue matrix
+     */
+    public function getDiagonalEigenvalues(): array
+    {
+        $D = [];
+
+        for ($i = 0; $i < $this->n; ++$i) {
+            $D[$i] = array_fill(0, $this->n, 0.0);
+            $D[$i][$i] = $this->d[$i];
+            if ($this->e[$i] == 0) {
+                continue;
+            }
+
+            $o = ($this->e[$i] > 0) ? $i + 1 : $i - 1;
+            $D[$i][$o] = $this->e[$i];
+        }
+
+        return $D;
+    }
+
     /**
      *	Symmetric Householder reduction to tridiagonal form.
      */
@@ -158,6 +226,7 @@ class EigenvalueDecomposition
                 for ($j = 0; $j < $i; ++$j) {
                     $this->e[$j] = 0.0;
                 }
+
                 // Apply similarity transformation to remaining columns.
                 for ($j = 0; $j < $i; ++$j) {
                     $f = $this->d[$j];
@@ -168,6 +237,7 @@ class EigenvalueDecomposition
                         $g += $this->V[$k][$j] * $this->d[$k];
                         $this->e[$k] += $this->V[$k][$j] * $f;
                     }
+
                     $this->e[$j] = $g;
                 }
 
@@ -185,16 +255,19 @@ class EigenvalueDecomposition
                 for ($j = 0; $j < $i; ++$j) {
                     $this->e[$j] -= $hh * $this->d[$j];
                 }
+
                 for ($j = 0; $j < $i; ++$j) {
                     $f = $this->d[$j];
                     $g = $this->e[$j];
                     for ($k = $j; $k <= $i_; ++$k) {
                         $this->V[$k][$j] -= ($f * $this->e[$k] + $g * $this->d[$k]);
                     }
+
                     $this->d[$j] = $this->V[$i - 1][$j];
                     $this->V[$i][$j] = 0.0;
                 }
             }
+
             $this->d[$i] = $h;
         }
 
@@ -207,16 +280,19 @@ class EigenvalueDecomposition
                 for ($k = 0; $k <= $i; ++$k) {
                     $this->d[$k] = $this->V[$k][$i + 1] / $h;
                 }
+
                 for ($j = 0; $j <= $i; ++$j) {
                     $g = 0.0;
                     for ($k = 0; $k <= $i; ++$k) {
                         $g += $this->V[$k][$i + 1] * $this->V[$k][$j];
                     }
+
                     for ($k = 0; $k <= $i; ++$k) {
                         $this->V[$k][$j] -= $g * $this->d[$k];
                     }
                 }
             }
+
             for ($k = 0; $k <= $i; ++$k) {
                 $this->V[$k][$i + 1] = 0.0;
             }
@@ -241,6 +317,7 @@ class EigenvalueDecomposition
         for ($i = 1; $i < $this->n; ++$i) {
             $this->e[$i - 1] = $this->e[$i];
         }
+
         $this->e[$this->n - 1] = 0.0;
         $f = 0.0;
         $tst1 = 0.0;
@@ -254,8 +331,10 @@ class EigenvalueDecomposition
                 if (abs($this->e[$m]) <= $eps * $tst1) {
                     break;
                 }
+
                 ++$m;
             }
+
             // If m == l, $this->d[l] is an eigenvalue,
             // otherwise, iterate.
             if ($m > $l) {
@@ -270,6 +349,7 @@ class EigenvalueDecomposition
                     if ($p < 0) {
                         $r *= -1;
                     }
+
                     $this->d[$l] = $this->e[$l] / ($p + $r);
                     $this->d[$l + 1] = $this->e[$l] * ($p + $r);
                     $dl1 = $this->d[$l + 1];
@@ -277,6 +357,7 @@ class EigenvalueDecomposition
                     for ($i = $l + 2; $i < $this->n; ++$i) {
                         $this->d[$i] -= $h;
                     }
+
                     $f += $h;
                     // Implicit QL transformation.
                     $p = $this->d[$m];
@@ -303,12 +384,14 @@ class EigenvalueDecomposition
                             $this->V[$k][$i] = $c * $this->V[$k][$i] - $s * $h;
                         }
                     }
+
                     $p = -$s * $s2 * $c3 * $el1 * $this->e[$l] / $dl1;
                     $this->e[$l] = $s * $p;
                     $this->d[$l] = $c * $p;
                     // Check for convergence.
                 } while (abs($this->e[$l]) > $eps * $tst1);
             }
+
             $this->d[$l] = $this->d[$l] + $f;
             $this->e[$l] = 0.0;
         }
@@ -323,6 +406,7 @@ class EigenvalueDecomposition
                     $p = $this->d[$j];
                 }
             }
+
             if ($k != $i) {
                 $this->d[$k] = $this->d[$i];
                 $this->d[$i] = $p;
@@ -354,6 +438,7 @@ class EigenvalueDecomposition
             for ($i = $m; $i <= $high; ++$i) {
                 $scale = $scale + abs($this->H[$i][$m - 1]);
             }
+
             if ($scale != 0.0) {
                 // Compute Householder transformation.
                 $h = 0.0;
@@ -361,10 +446,12 @@ class EigenvalueDecomposition
                     $this->ort[$i] = $this->H[$i][$m - 1] / $scale;
                     $h += $this->ort[$i] * $this->ort[$i];
                 }
+
                 $g = sqrt($h);
                 if ($this->ort[$m] > 0) {
                     $g *= -1;
                 }
+
                 $h -= $this->ort[$m] * $g;
                 $this->ort[$m] -= $g;
                 // Apply Householder similarity transformation
@@ -374,21 +461,25 @@ class EigenvalueDecomposition
                     for ($i = $high; $i >= $m; --$i) {
                         $f += $this->ort[$i] * $this->H[$i][$j];
                     }
+
                     $f /= $h;
                     for ($i = $m; $i <= $high; ++$i) {
                         $this->H[$i][$j] -= $f * $this->ort[$i];
                     }
                 }
+
                 for ($i = 0; $i <= $high; ++$i) {
                     $f = 0.0;
                     for ($j = $high; $j >= $m; --$j) {
                         $f += $this->ort[$j] * $this->H[$i][$j];
                     }
+
                     $f = $f / $h;
                     for ($j = $m; $j <= $high; ++$j) {
                         $this->H[$i][$j] -= $f * $this->ort[$j];
                     }
                 }
+
                 $this->ort[$m] = $scale * $this->ort[$m];
                 $this->H[$m][$m - 1] = $scale * $g;
             }
@@ -400,16 +491,19 @@ class EigenvalueDecomposition
                 $this->V[$i][$j] = ($i == $j ? 1.0 : 0.0);
             }
         }
+
         for ($m = $high - 1; $m >= $low + 1; --$m) {
             if ($this->H[$m][$m - 1] != 0.0) {
                 for ($i = $m + 1; $i <= $high; ++$i) {
                     $this->ort[$i] = $this->H[$i][$m - 1];
                 }
+
                 for ($j = $m; $j <= $high; ++$j) {
                     $g = 0.0;
                     for ($i = $m; $i <= $high; ++$i) {
                         $g += $this->ort[$i] * $this->V[$i][$j];
                     }
+
                     // Double division avoids possible underflow
                     $g = ($g / $this->ort[$m]) / $this->H[$m][$m - 1];
                     for ($i = $m; $i <= $high; ++$i) {
@@ -469,6 +563,7 @@ class EigenvalueDecomposition
                 $this->d[$i] = $this->H[$i][$i];
                 $this->e[$i] = 0.0;
             }
+
             for ($j = max($i - 1, 0); $j < $nn; ++$j) {
                 $norm = $norm + abs($this->H[$i][$j]);
             }
@@ -484,11 +579,14 @@ class EigenvalueDecomposition
                 if ($s == 0.0) {
                     $s = $norm;
                 }
+
                 if (abs($this->H[$l][$l - 1]) < $eps * $s) {
                     break;
                 }
+
                 --$l;
             }
+
             // Check for convergence
             // One root found
             if ($l == $n) {
@@ -513,11 +611,13 @@ class EigenvalueDecomposition
                     } else {
                         $z = $p - $z;
                     }
+
                     $this->d[$n - 1] = $x + $z;
                     $this->d[$n] = $this->d[$n - 1];
                     if ($z != 0.0) {
                         $this->d[$n] = $x - $w / $z;
                     }
+
                     $this->e[$n - 1] = 0.0;
                     $this->e[$n] = 0.0;
                     $x = $this->H[$n][$n - 1];
@@ -533,18 +633,21 @@ class EigenvalueDecomposition
                         $this->H[$n - 1][$j] = $q * $z + $p * $this->H[$n][$j];
                         $this->H[$n][$j] = $q * $this->H[$n][$j] - $p * $z;
                     }
+
                     // Column modification
                     for ($i = 0; $i <= $n; ++$i) {
                         $z = $this->H[$i][$n - 1];
                         $this->H[$i][$n - 1] = $q * $z + $p * $this->H[$i][$n];
                         $this->H[$i][$n] = $q * $this->H[$i][$n] - $p * $z;
                     }
+
                     // Accumulate transformations
                     for ($i = $low; $i <= $high; ++$i) {
                         $z = $this->V[$i][$n - 1];
                         $this->V[$i][$n - 1] = $q * $z + $p * $this->V[$i][$n];
                         $this->V[$i][$n] = $q * $this->V[$i][$n] - $p * $z;
                     }
+
                     // Complex pair
                 } else {
                     $this->d[$n - 1] = $x + $p;
@@ -552,6 +655,7 @@ class EigenvalueDecomposition
                     $this->e[$n - 1] = $z;
                     $this->e[$n] = -$z;
                 }
+
                 $n = $n - 2;
                 $iter = 0;
                 // No convergence yet
@@ -564,16 +668,19 @@ class EigenvalueDecomposition
                     $y = $this->H[$n - 1][$n - 1];
                     $w = $this->H[$n][$n - 1] * $this->H[$n - 1][$n];
                 }
+
                 // Wilkinson's original ad hoc shift
                 if ($iter == 10) {
                     $exshift += $x;
                     for ($i = $low; $i <= $n; ++$i) {
                         $this->H[$i][$i] -= $x;
                     }
+
                     $s = abs($this->H[$n][$n - 1]) + abs($this->H[$n - 1][$n - 2]);
                     $x = $y = 0.75 * $s;
                     $w = -0.4375 * $s * $s;
                 }
+
                 // MATLAB's new ad hoc shift
                 if ($iter == 30) {
                     $s = ($y - $x) / 2.0;
@@ -583,14 +690,17 @@ class EigenvalueDecomposition
                         if ($y < $x) {
                             $s = -$s;
                         }
+
                         $s = $x - $w / (($y - $x) / 2.0 + $s);
                         for ($i = $low; $i <= $n; ++$i) {
                             $this->H[$i][$i] -= $s;
                         }
+
                         $exshift += $s;
                         $x = $y = $w = 0.964;
                     }
                 }
+
                 // Could check iteration count here.
                 $iter = $iter + 1;
                 // Look for two consecutive small sub-diagonal elements
@@ -609,18 +719,22 @@ class EigenvalueDecomposition
                     if ($m == $l) {
                         break;
                     }
+
                     if (abs($this->H[$m][$m - 1]) * (abs($q) + abs($r)) <
                         $eps * (abs($p) * (abs($this->H[$m - 1][$m - 1]) + abs($z) + abs($this->H[$m + 1][$m + 1])))) {
                         break;
                     }
+
                     --$m;
                 }
+
                 for ($i = $m + 2; $i <= $n; ++$i) {
                     $this->H[$i][$i - 2] = 0.0;
                     if ($i > $m + 2) {
                         $this->H[$i][$i - 3] = 0.0;
                     }
                 }
+
                 // Double QR step involving rows l:n and columns m:n
                 for ($k = $m; $k <= $n - 1; ++$k) {
                     $notlast = ($k != $n - 1);
@@ -635,19 +749,23 @@ class EigenvalueDecomposition
                             $r = $r / $x;
                         }
                     }
+
                     if ($x == 0.0) {
                         break;
                     }
+
                     $s = sqrt($p * $p + $q * $q + $r * $r);
                     if ($p < 0) {
                         $s = -$s;
                     }
+
                     if ($s != 0) {
                         if ($k != $m) {
                             $this->H[$k][$k - 1] = -$s * $x;
                         } elseif ($l != $m) {
                             $this->H[$k][$k - 1] = -$this->H[$k][$k - 1];
                         }
+
                         $p = $p + $s;
                         $x = $p / $s;
                         $y = $q / $s;
@@ -661,9 +779,11 @@ class EigenvalueDecomposition
                                 $p = $p + $r * $this->H[$k + 2][$j];
                                 $this->H[$k + 2][$j] = $this->H[$k + 2][$j] - $p * $z;
                             }
+
                             $this->H[$k][$j] = $this->H[$k][$j] - $p * $x;
                             $this->H[$k + 1][$j] = $this->H[$k + 1][$j] - $p * $y;
                         }
+
                         // Column modification
                         for ($i = 0; $i <= min($n, $k + 3); ++$i) {
                             $p = $x * $this->H[$i][$k] + $y * $this->H[$i][$k + 1];
@@ -671,9 +791,11 @@ class EigenvalueDecomposition
                                 $p = $p + $z * $this->H[$i][$k + 2];
                                 $this->H[$i][$k + 2] = $this->H[$i][$k + 2] - $p * $r;
                             }
+
                             $this->H[$i][$k] = $this->H[$i][$k] - $p;
                             $this->H[$i][$k + 1] = $this->H[$i][$k + 1] - $p * $q;
                         }
+
                         // Accumulate transformations
                         for ($i = $low; $i <= $high; ++$i) {
                             $p = $x * $this->V[$i][$k] + $y * $this->V[$i][$k + 1];
@@ -681,6 +803,7 @@ class EigenvalueDecomposition
                                 $p = $p + $z * $this->V[$i][$k + 2];
                                 $this->V[$i][$k + 2] = $this->V[$i][$k + 2] - $p * $r;
                             }
+
                             $this->V[$i][$k] = $this->V[$i][$k] - $p;
                             $this->V[$i][$k + 1] = $this->V[$i][$k + 1] - $p * $q;
                         }
@@ -719,6 +842,7 @@ class EigenvalueDecomposition
                             } else {
                                 $this->H[$i][$n] = -$r / ($eps * $norm);
                             }
+
                             // Solve real equations
                         } else {
                             $x = $this->H[$i][$i + 1];
@@ -732,6 +856,7 @@ class EigenvalueDecomposition
                                 $this->H[$i + 1][$n] = (-$s - $y * $t) / $z;
                             }
                         }
+
                         // Overflow control
                         $t = abs($this->H[$i][$n]);
                         if (($eps * $t) * $t > 1) {
@@ -741,6 +866,7 @@ class EigenvalueDecomposition
                         }
                     }
                 }
+
                 // Complex vector
             } elseif ($q < 0) {
                 $l = $n - 1;
@@ -753,6 +879,7 @@ class EigenvalueDecomposition
                     $this->H[$n - 1][$n - 1] = $this->cdivr;
                     $this->H[$n - 1][$n] = $this->cdivi;
                 }
+
                 $this->H[$n][$n - 1] = 0.0;
                 $this->H[$n][$n] = 1.0;
                 for ($i = $n - 2; $i >= 0; --$i) {
@@ -763,6 +890,7 @@ class EigenvalueDecomposition
                         $ra = $ra + $this->H[$i][$j] * $this->H[$j][$n - 1];
                         $sa = $sa + $this->H[$i][$j] * $this->H[$j][$n];
                     }
+
                     $w = $this->H[$i][$i] - $p;
                     if ($this->e[$i] < 0.0) {
                         $z = $w;
@@ -783,6 +911,7 @@ class EigenvalueDecomposition
                             if ($vr == 0.0 & $vi == 0.0) {
                                 $vr = $eps * $norm * (abs($w) + abs($q) + abs($x) + abs($y) + abs($z));
                             }
+
                             $this->cdiv($x * $r - $z * $ra + $q * $sa, $x * $s - $z * $sa - $q * $ra, $vr, $vi);
                             $this->H[$i][$n - 1] = $this->cdivr;
                             $this->H[$i][$n] = $this->cdivi;
@@ -795,6 +924,7 @@ class EigenvalueDecomposition
                                 $this->H[$i + 1][$n] = $this->cdivi;
                             }
                         }
+
                         // Overflow control
                         $t = max(abs($this->H[$i][$n - 1]), abs($this->H[$i][$n]));
                         if (($eps * $t) * $t > 1) {
@@ -824,81 +954,9 @@ class EigenvalueDecomposition
                 for ($k = $low; $k <= min($j, $high); ++$k) {
                     $z = $z + $this->V[$i][$k] * $this->H[$k][$j];
                 }
+
                 $this->V[$i][$j] = $z;
             }
         }
     }
-
-    /**
-     * Return the eigenvector matrix
-     *
-     * @return array
-     */
-    public function getEigenvectors()
-    {
-        $vectors = $this->V;
-
-        // Always return the eigenvectors of length 1.0
-        $vectors = new Matrix($vectors);
-        $vectors = array_map(function ($vect) {
-            $sum = 0;
-            for ($i = 0; $i < count($vect); ++$i) {
-                $sum += $vect[$i] ** 2;
-            }
-
-            $sum = sqrt($sum);
-            for ($i = 0; $i < count($vect); ++$i) {
-                $vect[$i] /= $sum;
-            }
-
-            return $vect;
-        }, $vectors->transpose()->toArray());
-
-        return $vectors;
-    }
-
-    /**
-     *	Return the real parts of the eigenvalues<br>
-     *  d = real(diag(D));
-     *
-     *	@return array
-     */
-    public function getRealEigenvalues()
-    {
-        return $this->d;
-    }
-
-    /**
-     *	Return the imaginary parts of the eigenvalues <br>
-     *  d = imag(diag(D))
-     *
-     *	@return array
-     */
-    public function getImagEigenvalues()
-    {
-        return $this->e;
-    }
-
-    /**
-     *	Return the block diagonal eigenvalue matrix
-     *
-     *	@return array
-     */
-    public function getDiagonalEigenvalues()
-    {
-        $D = [];
-
-        for ($i = 0; $i < $this->n; ++$i) {
-            $D[$i] = array_fill(0, $this->n, 0.0);
-            $D[$i][$i] = $this->d[$i];
-            if ($this->e[$i] == 0) {
-                continue;
-            }
-
-            $o = ($this->e[$i] > 0) ? $i + 1 : $i - 1;
-            $D[$i][$o] = $this->e[$i];
-        }
-
-        return $D;
-    }
 }

src/Phpml/Math/LinearAlgebra/LUDecomposition.php

@@ -1,6 +1,7 @@
 <?php
 
 declare(strict_types=1);
+
 /**
  * @package JAMA
  *
@@ -90,6 +91,7 @@ class LUDecomposition
         for ($i = 0; $i < $this->m; ++$i) {
             $this->piv[$i] = $i;
         }
+
         $this->pivsign = 1;
         $LUcolj = [];
 
@@ -99,6 +101,7 @@ class LUDecomposition
             for ($i = 0; $i < $this->m; ++$i) {
                 $LUcolj[$i] = &$this->LU[$i][$j];
             }
+
             // Apply previous transformations.
             for ($i = 0; $i < $this->m; ++$i) {
                 $LUrowi = $this->LU[$i];
@@ -108,8 +111,10 @@ class LUDecomposition
                 for ($k = 0; $k < $kmax; ++$k) {
                     $s += $LUrowi[$k] * $LUcolj[$k];
                 }
+
                 $LUrowi[$j] = $LUcolj[$i] -= $s;
             }
+
             // Find pivot and exchange if necessary.
             $p = $j;
             for ($i = $j + 1; $i < $this->m; ++$i) {
@@ -117,17 +122,20 @@ class LUDecomposition
                     $p = $i;
                 }
             }
+
             if ($p != $j) {
                 for ($k = 0; $k < $this->n; ++$k) {
                     $t = $this->LU[$p][$k];
                     $this->LU[$p][$k] = $this->LU[$j][$k];
                     $this->LU[$j][$k] = $t;
                 }
+
                 $k = $this->piv[$p];
                 $this->piv[$p] = $this->piv[$j];
                 $this->piv[$j] = $k;
                 $this->pivsign = $this->pivsign * -1;
             }
+
             // Compute multipliers.
             if (($j < $this->m) && ($this->LU[$j][$j] != 0.0)) {
                 for ($i = $j + 1; $i < $this->m; ++$i) {
@@ -142,7 +150,7 @@ class LUDecomposition
      *
      * @return Matrix Lower triangular factor
      */
-    public function getL() : Matrix
+    public function getL(): Matrix
     {
         $L = [];
         for ($i = 0; $i < $this->m; ++$i) {
@@ -165,7 +173,7 @@ class LUDecomposition
      *
      * @return Matrix Upper triangular factor
      */
-    public function getU() : Matrix
+    public function getU(): Matrix
     {
         $U = [];
         for ($i = 0; $i < $this->n; ++$i) {
@@ -186,7 +194,7 @@ class LUDecomposition
      *
      * @return array Pivot vector
      */
-    public function getPivot() : array
+    public function getPivot(): array
     {
         return $this->piv;
     }
@@ -247,7 +255,7 @@ class LUDecomposition
      *
      * @throws MatrixException
      */
-    public function solve(Matrix $B) : array
+    public function solve(Matrix $B): array
     {
         if ($B->getRows() != $this->m) {
             throw MatrixException::notSquareMatrix();
@@ -268,11 +276,13 @@ class LUDecomposition
                 }
             }
         }
+
         // Solve U*X = Y;
         for ($k = $this->n - 1; $k >= 0; --$k) {
             for ($j = 0; $j < $nx; ++$j) {
                 $X[$k][$j] /= $this->LU[$k][$k];
             }
+
             for ($i = 0; $i < $k; ++$i) {
                 for ($j = 0; $j < $nx; ++$j) {
                     $X[$i][$j] -= $X[$k][$j] * $this->LU[$i][$k];
@@ -283,7 +293,7 @@ class LUDecomposition
         return $X;
     }
 
-    protected function getSubMatrix(array $matrix, array $RL, int $j0, int $jF) : array
+    protected function getSubMatrix(array $matrix, array $RL, int $j0, int $jF): array
     {
         $m = count($RL);
         $n = $jF - $j0;

src/Phpml/Math/Matrix.php

@@ -13,7 +13,7 @@ class Matrix
     /**
      * @var array
      */
-    private $matrix;
+    private $matrix = [];
 
     /**
      * @var int
@@ -56,7 +56,7 @@ class Matrix
         $this->matrix = $matrix;
     }
 
-    public static function fromFlatArray(array $array) : Matrix
+    public static function fromFlatArray(array $array): self
     {
         $matrix = [];
         foreach ($array as $value) {
@@ -66,12 +66,12 @@ class Matrix
         return new self($matrix);
     }
 
-    public function toArray() : array
+    public function toArray(): array
     {
         return $this->matrix;
     }
 
-    public function toScalar() : float
+    public function toScalar(): float
     {
         return $this->matrix[0][0];
     }
@@ -89,7 +89,7 @@ class Matrix
     /**
      * @throws MatrixException
      */
-    public function getColumnValues($column) : array
+    public function getColumnValues($column): array
     {
         if ($column >= $this->columns) {
             throw MatrixException::columnOutOfRange();
@@ -123,7 +123,7 @@ class Matrix
         return $this->columns === $this->rows;
     }
 
-    public function transpose() : Matrix
+    public function transpose(): self
     {
         if ($this->rows == 1) {
             $matrix = array_map(function ($el) {
@@ -136,7 +136,7 @@ class Matrix
         return new self($matrix, false);
     }
 
-    public function multiply(Matrix $matrix) : Matrix
+    public function multiply(self $matrix): self
     {
         if ($this->columns != $matrix->getRows()) {
             throw InvalidArgumentException::inconsistentMatrixSupplied();
@@ -157,7 +157,7 @@ class Matrix
         return new self($product, false);
     }
 
-    public function divideByScalar($value) : Matrix
+    public function divideByScalar($value): self
     {
         $newMatrix = [];
         for ($i = 0; $i < $this->rows; ++$i) {
@@ -169,7 +169,7 @@ class Matrix
         return new self($newMatrix, false);
     }
 
-    public function multiplyByScalar($value) : Matrix
+    public function multiplyByScalar($value): self
     {
         $newMatrix = [];
         for ($i = 0; $i < $this->rows; ++$i) {
@@ -184,7 +184,7 @@ class Matrix
     /**
      * Element-wise addition of the matrix with another one
      */
-    public function add(Matrix $other) : Matrix
+    public function add(self $other): self
     {
         return $this->_add($other);
     }
@@ -192,15 +192,74 @@ class Matrix
     /**
      * Element-wise subtracting of another matrix from this one
      */
-    public function subtract(Matrix $other) : Matrix
+    public function subtract(self $other): self
     {
         return $this->_add($other, -1);
     }
 
+    public function inverse(): self
+    {
+        if (!$this->isSquare()) {
+            throw MatrixException::notSquareMatrix();
+        }
+
+        $LU = new LUDecomposition($this);
+        $identity = $this->getIdentity();
+        $inverse = $LU->solve($identity);
+
+        return new self($inverse, false);
+    }
+
+    public function crossOut(int $row, int $column): self
+    {
+        $newMatrix = [];
+        $r = 0;
+        for ($i = 0; $i < $this->rows; ++$i) {
+            $c = 0;
+            if ($row != $i) {
+                for ($j = 0; $j < $this->columns; ++$j) {
+                    if ($column != $j) {
+                        $newMatrix[$r][$c] = $this->matrix[$i][$j];
+                        ++$c;
+                    }
+                }
+
+                ++$r;
+            }
+        }
+
+        return new self($newMatrix, false);
+    }
+
+    public function isSingular(): bool
+    {
+        return $this->getDeterminant() == 0;
+    }
+
+    /**
+     * Returns the transpose of given array
+     */
+    public static function transposeArray(array $array): array
+    {
+        return (new self($array, false))->transpose()->toArray();
+    }
+
+    /**
+     * Returns the dot product of two arrays<br>
+     * Matrix::dot(x, y) ==> x.y'
+     */
+    public static function dot(array $array1, array $array2): array
+    {
+        $m1 = new self($array1, false);
+        $m2 = new self($array2, false);
+
+        return $m1->multiply($m2->transpose())->toArray()[0];
+    }
+
     /**
      * Element-wise addition or substraction depending on the given sign parameter
      */
-    protected function _add(Matrix $other, int $sign = 1) : Matrix
+    protected function _add(self $other, int $sign = 1): self
     {
         $a1 = $this->toArray();
         $a2 = $other->toArray();
@@ -215,23 +274,10 @@ class Matrix
         return new self($newMatrix, false);
     }
 
-    public function inverse() : Matrix
-    {
-        if (!$this->isSquare()) {
-            throw MatrixException::notSquareMatrix();
-        }
-
-        $LU = new LUDecomposition($this);
-        $identity = $this->getIdentity();
-        $inverse = $LU->solve($identity);
-
-        return new self($inverse, false);
-    }
-
     /**
      * Returns diagonal identity matrix of the same size of this matrix
      */
-    protected function getIdentity() : Matrix
+    protected function getIdentity(): self
     {
         $array = array_fill(0, $this->rows, array_fill(0, $this->columns, 0));
         for ($i = 0; $i < $this->rows; ++$i) {
@@ -240,49 +286,4 @@ class Matrix
 
         return new self($array, false);
     }
-
-    public function crossOut(int $row, int $column) : Matrix
-    {
-        $newMatrix = [];
-        $r = 0;
-        for ($i = 0; $i < $this->rows; ++$i) {
-            $c = 0;
-            if ($row != $i) {
-                for ($j = 0; $j < $this->columns; ++$j) {
-                    if ($column != $j) {
-                        $newMatrix[$r][$c] = $this->matrix[$i][$j];
-                        ++$c;
-                    }
-                }
-                ++$r;
-            }
-        }
-
-        return new self($newMatrix, false);
-    }
-
-    public function isSingular() : bool
-    {
-        return 0 == $this->getDeterminant();
-    }
-
-    /**
-     * Returns the transpose of given array
-     */
-    public static function transposeArray(array $array) : array
-    {
-        return (new self($array, false))->transpose()->toArray();
-    }
-
-    /**
-     * Returns the dot product of two arrays<br>
-     * Matrix::dot(x, y) ==> x.y'
-     */
-    public static function dot(array $array1, array $array2) : array
-    {
-        $m1 = new self($array1, false);
-        $m2 = new self($array2, false);
-
-        return $m1->multiply($m2->transpose())->toArray()[0];
-    }
 }

src/Phpml/Math/Set.php

@@ -4,12 +4,15 @@ declare(strict_types=1);
 
 namespace Phpml\Math;
 
-class Set implements \IteratorAggregate
+use ArrayIterator;
+use IteratorAggregate;
+
+class Set implements IteratorAggregate
 {
     /**
      * @var string[]|int[]|float[]
      */
-    private $elements;
+    private $elements = [];
 
     /**
      * @param string[]|int[]|float[] $elements
@@ -22,7 +25,7 @@ class Set implements \IteratorAggregate
     /**
      * Creates the union of A and B.
      */
-    public static function union(Set $a, Set $b) : Set
+    public static function union(self $a, self $b): self
     {
         return new self(array_merge($a->toArray(), $b->toArray()));
     }
@@ -30,7 +33,7 @@ class Set implements \IteratorAggregate
     /**
      * Creates the intersection of A and B.
      */
-    public static function intersection(Set $a, Set $b) : Set
+    public static function intersection(self $a, self $b): self
     {
         return new self(array_intersect($a->toArray(), $b->toArray()));
     }
@@ -38,7 +41,7 @@ class Set implements \IteratorAggregate
     /**
      * Creates the difference of A and B.
      */
-    public static function difference(Set $a, Set $b) : Set
+    public static function difference(self $a, self $b): self
     {
         return new self(array_diff($a->toArray(), $b->toArray()));
     }
@@ -48,7 +51,7 @@ class Set implements \IteratorAggregate
      *
      * @return Set[]
      */
-    public static function cartesian(Set $a, Set $b) : array
+    public static function cartesian(self $a, self $b): array
     {
         $cartesian = [];
 
@@ -66,7 +69,7 @@ class Set implements \IteratorAggregate
      *
      * @return Set[]
      */
-    public static function power(Set $a) : array
+    public static function power(self $a): array
     {
         $power = [new self()];
 
@@ -79,24 +82,10 @@ class Set implements \IteratorAggregate
         return $power;
     }
 
-    /**
-     * Removes duplicates and rewrites index.
-     *
-     * @param string[]|int[]|float[] $elements
-     *
-     * @return string[]|int[]|float[]
-     */
-    private static function sanitize(array $elements) : array
-    {
-        sort($elements, SORT_ASC);
-
-        return array_values(array_unique($elements, SORT_ASC));
-    }
-
     /**
      * @param string|int|float $element
      */
-    public function add($element) : Set
+    public function add($element): self
     {
         return $this->addAll([$element]);
     }
@@ -104,7 +93,7 @@ class Set implements \IteratorAggregate
     /**
      * @param string[]|int[]|float[] $elements
      */
-    public function addAll(array $elements) : Set
+    public function addAll(array $elements): self
     {
         $this->elements = self::sanitize(array_merge($this->elements, $elements));
 
@@ -114,7 +103,7 @@ class Set implements \IteratorAggregate
     /**
      * @param string|int|float $element
      */
-    public function remove($element) : Set
+    public function remove($element): self
     {
         return $this->removeAll([$element]);
     }
@@ -122,7 +111,7 @@ class Set implements \IteratorAggregate
     /**
      * @param string[]|int[]|float[] $elements
      */
-    public function removeAll(array $elements) : Set
+    public function removeAll(array $elements): self
     {
         $this->elements = self::sanitize(array_diff($this->elements, $elements));
 
@@ -132,7 +121,7 @@ class Set implements \IteratorAggregate
     /**
      * @param string|int|float $element
      */
-    public function contains($element) : bool
+    public function contains($element): bool
     {
         return $this->containsAll([$element]);
     }
@@ -140,7 +129,7 @@ class Set implements \IteratorAggregate
     /**
      * @param string[]|int[]|float[] $elements
      */
-    public function containsAll(array $elements) : bool
+    public function containsAll(array $elements): bool
     {
         return !array_diff($elements, $this->elements);
     }
@@ -148,23 +137,37 @@ class Set implements \IteratorAggregate
     /**
      * @return string[]|int[]|float[]
      */
-    public function toArray() : array
+    public function toArray(): array
     {
         return $this->elements;
     }
 
-    public function getIterator() : \ArrayIterator
+    public function getIterator(): ArrayIterator
     {
-        return new \ArrayIterator($this->elements);
+        return new ArrayIterator($this->elements);
     }
 
-    public function isEmpty() : bool
+    public function isEmpty(): bool
     {
         return $this->cardinality() == 0;
     }
 
-    public function cardinality() : int
+    public function cardinality(): int
     {
         return count($this->elements);
     }
+
+    /**
+     * Removes duplicates and rewrites index.
+     *
+     * @param string[]|int[]|float[] $elements
+     *
+     * @return string[]|int[]|float[]
+     */
+    private static function sanitize(array $elements): array
+    {
+        sort($elements, SORT_ASC);
+
+        return array_values(array_unique($elements, SORT_ASC));
+    }
 }

src/Phpml/Math/Statistic/Correlation.php

@@ -14,7 +14,7 @@ class Correlation
      *
      * @throws InvalidArgumentException
      */
-    public static function pearson(array $x, array $y) : float
+    public static function pearson(array $x, array $y): float
     {
         if (count($x) !== count($y)) {
             throw InvalidArgumentException::arraySizeNotMatch();

src/Phpml/Math/Statistic/Covariance.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\Math\Statistic;
 
+use Exception;
 use Phpml\Exception\InvalidArgumentException;
 
 class Covariance
@@ -13,7 +14,7 @@ class Covariance
      *
      * @throws InvalidArgumentException
      */
-    public static function fromXYArrays(array $x, array $y, bool $sample = true, ?float $meanX = null, ?float $meanY = null) : float
+    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();
@@ -51,7 +52,7 @@ class Covariance
      * @throws InvalidArgumentException
      * @throws \Exception
      */
-    public static function fromDataset(array $data, int $i, int $k, bool $sample = true, ?float $meanX = null, ?float $meanY = null) : float
+    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();
@@ -63,7 +64,7 @@ class Covariance
         }
 
         if ($i < 0 || $k < 0 || $i >= $n || $k >= $n) {
-            throw new \Exception('Given indices i and k do not match with the dimensionality of data');
+            throw new Exception('Given indices i and k do not match with the dimensionality of data');
         }
 
         if ($meanX === null || $meanY === null) {
@@ -92,10 +93,12 @@ class Covariance
                     if ($index == $i) {
                         $val[0] = $col - $meanX;
                     }
+
                     if ($index == $k) {
                         $val[1] = $col - $meanY;
                     }
                 }
+
                 $sum += $val[0] * $val[1];
             }
         }
@@ -112,7 +115,7 @@ class Covariance
      *
      * @param array|null $means
      */
-    public static function covarianceMatrix(array $data, ?array $means = null) : array
+    public static function covarianceMatrix(array $data, ?array $means = null): array
     {
         $n = count($data[0]);
 

src/Phpml/Math/Statistic/Gaussian.php

@@ -41,7 +41,7 @@ class Gaussian
      * Returns probability density value of the given <i>$value</i> based on
      * given standard deviation and the mean
      */
-    public static function distributionPdf(float $mean, float $std, float $value) : float
+    public static function distributionPdf(float $mean, float $std, float $value): float
     {
         $normal = new self($mean, $std);
 

src/Phpml/Math/Statistic/Mean.php

@@ -11,7 +11,7 @@ class Mean
     /**
      * @throws InvalidArgumentException
      */
-    public static function arithmetic(array $numbers) : float
+    public static function arithmetic(array $numbers): float
     {
         self::checkArrayLength($numbers);
 
@@ -32,7 +32,7 @@ class Mean
         sort($numbers, SORT_NUMERIC);
         $median = $numbers[$middleIndex];
 
-        if (0 === $count % 2) {
+        if ($count % 2 === 0) {
             $median = ($median + $numbers[$middleIndex - 1]) / 2;
         }
 

src/Phpml/Math/Statistic/StandardDeviation.php

@@ -13,7 +13,7 @@ class StandardDeviation
      *
      * @throws InvalidArgumentException
      */
-    public static function population(array $a, bool $sample = true) : float
+    public static function population(array $a, bool $sample = true): float
     {
         if (empty($a)) {
             throw InvalidArgumentException::arrayCantBeEmpty();

src/Phpml/Metric/ClassificationReport.php

@@ -51,27 +51,27 @@ class ClassificationReport
         $this->computeAverage();
     }
 
-    public function getPrecision() : array
+    public function getPrecision(): array
     {
         return $this->precision;
     }
 
-    public function getRecall() : array
+    public function getRecall(): array
     {
         return $this->recall;
     }
 
-    public function getF1score() : array
+    public function getF1score(): array
     {
         return $this->f1score;
     }
 
-    public function getSupport() : array
+    public function getSupport(): array
     {
         return $this->support;
     }
 
-    public function getAverage() : array
+    public function getAverage(): array
     {
         return $this->average;
     }
@@ -93,6 +93,7 @@ class ClassificationReport
                 $this->average[$metric] = 0.0;
                 continue;
             }
+
             $this->average[$metric] = array_sum($values) / count($values);
         }
     }
@@ -102,7 +103,8 @@ class ClassificationReport
      */
     private function computePrecision(int $truePositive, int $falsePositive)
     {
-        if (0 == ($divider = $truePositive + $falsePositive)) {
+        $divider = $truePositive + $falsePositive;
+        if ($divider == 0) {
             return 0.0;
         }
 
@@ -114,23 +116,25 @@ class ClassificationReport
      */
     private function computeRecall(int $truePositive, int $falseNegative)
     {
-        if (0 == ($divider = $truePositive + $falseNegative)) {
+        $divider = $truePositive + $falseNegative;
+        if ($divider == 0) {
             return 0.0;
         }
 
         return $truePositive / $divider;
     }
 
-    private function computeF1Score(float $precision, float $recall) : float
+    private function computeF1Score(float $precision, float $recall): float
     {
-        if (0 == ($divider = $precision + $recall)) {
+        $divider = $precision + $recall;
+        if ($divider == 0) {
             return 0.0;
         }
 
         return 2.0 * (($precision * $recall) / $divider);
     }
 
-    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);

src/Phpml/Metric/ConfusionMatrix.php

@@ -6,7 +6,7 @@ namespace Phpml\Metric;
 
 class ConfusionMatrix
 {
-    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);
@@ -31,7 +31,7 @@ class ConfusionMatrix
         return $matrix;
     }
 
-    private static function generateMatrixWithZeros(array $labels) : array
+    private static function generateMatrixWithZeros(array $labels): array
     {
         $count = count($labels);
         $matrix = [];
@@ -43,7 +43,7 @@ class ConfusionMatrix
         return $matrix;
     }
 
-    private static function getUniqueLabels(array $labels) : array
+    private static function getUniqueLabels(array $labels): array
     {
         $labels = array_values(array_unique($labels));
         sort($labels);

src/Phpml/ModelManager.php

@@ -26,7 +26,7 @@ class ModelManager
         }
     }
 
-    public function restoreFromFile(string $filepath) : Estimator
+    public function restoreFromFile(string $filepath): Estimator
     {
         if (!file_exists($filepath) || !is_readable($filepath)) {
             throw FileException::cantOpenFile(basename($filepath));

src/Phpml/NeuralNetwork/ActivationFunction.php

@@ -9,5 +9,5 @@ interface ActivationFunction
     /**
      * @param float|int $value
      */
-    public function compute($value) : float;
+    public function compute($value): float;
 }

src/Phpml/NeuralNetwork/ActivationFunction/BinaryStep.php

@@ -11,7 +11,7 @@ class BinaryStep implements ActivationFunction
     /**
      * @param float|int $value
      */
-    public function compute($value) : float
+    public function compute($value): float
     {
         return $value >= 0 ? 1.0 : 0.0;
     }

src/Phpml/NeuralNetwork/ActivationFunction/Gaussian.php

@@ -11,7 +11,7 @@ class Gaussian implements ActivationFunction
     /**
      * @param float|int $value
      */
-    public function compute($value) : float
+    public function compute($value): float
     {
         return exp(-pow($value, 2));
     }

src/Phpml/NeuralNetwork/ActivationFunction/HyperbolicTangent.php

@@ -21,7 +21,7 @@ class HyperbolicTangent implements ActivationFunction
     /**
      * @param float|int $value
      */
-    public function compute($value) : float
+    public function compute($value): float
     {
         return tanh($this->beta * $value);
     }

src/Phpml/NeuralNetwork/ActivationFunction/PReLU.php

@@ -21,7 +21,7 @@ class PReLU implements ActivationFunction
     /**
      * @param float|int $value
      */
-    public function compute($value) : float
+    public function compute($value): float
     {
         return $value >= 0 ? $value : $this->beta * $value;
     }

src/Phpml/NeuralNetwork/ActivationFunction/Sigmoid.php

@@ -21,7 +21,7 @@ class Sigmoid implements ActivationFunction
     /**
      * @param float|int $value
      */
-    public function compute($value) : float
+    public function compute($value): float
     {
         return 1 / (1 + exp(-$this->beta * $value));
     }

src/Phpml/NeuralNetwork/ActivationFunction/ThresholdedReLU.php

@@ -21,7 +21,7 @@ class ThresholdedReLU implements ActivationFunction
     /**
      * @param float|int $value
      */
-    public function compute($value) : float
+    public function compute($value): float
     {
         return $value > $this->theta ? $value : 0.0;
     }

src/Phpml/NeuralNetwork/Layer.php

@@ -28,20 +28,6 @@ class Layer
         }
     }
 
-    /**
-     * @param ActivationFunction|null $activationFunction
-     *
-     * @return Neuron
-     */
-    private function createNode(string $nodeClass, ?ActivationFunction $activationFunction = null)
-    {
-        if (Neuron::class == $nodeClass) {
-            return new Neuron($activationFunction);
-        }
-
-        return new $nodeClass();
-    }
-
     public function addNode(Node $node): void
     {
         $this->nodes[] = $node;
@@ -50,8 +36,20 @@ class Layer
     /**
      * @return Node[]
      */
-    public function getNodes() : array
+    public function getNodes(): array
     {
         return $this->nodes;
     }
+
+    /**
+     * @return Neuron
+     */
+    private function createNode(string $nodeClass, ?ActivationFunction $activationFunction = null): Node
+    {
+        if ($nodeClass == Neuron::class) {
+            return new Neuron($activationFunction);
+        }
+
+        return new $nodeClass();
+    }
 }

src/Phpml/NeuralNetwork/Network.php

@@ -8,20 +8,15 @@ interface Network
 {
     /**
      * @param mixed $input
-     *
-     * @return self
      */
-    public function setInput($input);
+    public function setInput($input): self;
 
-    /**
-     * @return array
-     */
-    public function getOutput() : array;
+    public function getOutput(): array;
 
     public function addLayer(Layer $layer);
 
     /**
      * @return Layer[]
      */
-    public function getLayers() : array;
+    public function getLayers(): array;
 }

src/Phpml/NeuralNetwork/Network/LayeredNetwork.php

@@ -14,7 +14,7 @@ abstract class LayeredNetwork implements Network
     /**
      * @var Layer[]
      */
-    protected $layers;
+    protected $layers = [];
 
     public function addLayer(Layer $layer): void
     {
@@ -24,7 +24,7 @@ abstract class LayeredNetwork implements Network
     /**
      * @return Layer[]
      */
-    public function getLayers() : array
+    public function getLayers(): array
     {
         return $this->layers;
     }
@@ -39,7 +39,7 @@ abstract class LayeredNetwork implements Network
         return $this->layers[count($this->layers) - 1];
     }
 
-    public function getOutput() : array
+    public function getOutput(): array
     {
         $result = [];
         foreach ($this->getOutputLayer()->getNodes() as $neuron) {
@@ -54,7 +54,7 @@ abstract class LayeredNetwork implements Network
      *
      * @return $this
      */
-    public function setInput($input)
+    public function setInput($input): Network
     {
         $firstLayer = $this->layers[0];
 

src/Phpml/NeuralNetwork/Network/MultilayerPerceptron.php

@@ -20,41 +20,36 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
 {
     use Predictable;
 
-    /**
-     * @var int
-     */
-    private $inputLayerFeatures;
-
-    /**
-     * @var array
-     */
-    private $hiddenLayers;
-
     /**
      * @var array
      */
     protected $classes = [];
 
-    /**
-     * @var int
-     */
-    private $iterations;
-
     /**
      * @var ActivationFunction
      */
     protected $activationFunction;
 
-    /**
-     * @var float
-     */
-    private $learningRate;
-
     /**
      * @var Backpropagation
      */
     protected $backpropagation = null;
 
+    /**
+     * @var int
+     */
+    private $inputLayerFeatures;
+
+    /**
+     * @var array
+     */
+    private $hiddenLayers = [];
+
+    /**
+     * @var float
+     */
+    private $learningRate;
+
     /**
      * @throws InvalidArgumentException
      */
@@ -78,18 +73,6 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
         $this->initNetwork();
     }
 
-    private function initNetwork(): void
-    {
-        $this->addInputLayer($this->inputLayerFeatures);
-        $this->addNeuronLayers($this->hiddenLayers, $this->activationFunction);
-        $this->addNeuronLayers([count($this->classes)], $this->activationFunction);
-
-        $this->addBiasNodes();
-        $this->generateSynapses();
-
-        $this->backpropagation = new Backpropagation($this->learningRate);
-    }
-
     public function train(array $samples, array $targets): void
     {
         $this->reset();
@@ -127,6 +110,18 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
         $this->removeLayers();
     }
 
+    private function initNetwork(): void
+    {
+        $this->addInputLayer($this->inputLayerFeatures);
+        $this->addNeuronLayers($this->hiddenLayers, $this->activationFunction);
+        $this->addNeuronLayers([count($this->classes)], $this->activationFunction);
+
+        $this->addBiasNodes();
+        $this->generateSynapses();
+
+        $this->backpropagation = new Backpropagation($this->learningRate);
+    }
+
     private function addInputLayer(int $nodes): void
     {
         $this->addLayer(new Layer($nodes, Input::class));

src/Phpml/NeuralNetwork/Node.php

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

src/Phpml/NeuralNetwork/Node/Bias.php

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

src/Phpml/NeuralNetwork/Node/Input.php

@@ -18,7 +18,7 @@ class Input implements Node
         $this->input = $input;
     }
 
-    public function getOutput() : float
+    public function getOutput(): float
     {
         return $this->input;
     }

src/Phpml/NeuralNetwork/Node/Neuron.php

@@ -5,6 +5,7 @@ declare(strict_types=1);
 namespace Phpml\NeuralNetwork\Node;
 
 use Phpml\NeuralNetwork\ActivationFunction;
+use Phpml\NeuralNetwork\ActivationFunction\Sigmoid;
 use Phpml\NeuralNetwork\Node;
 use Phpml\NeuralNetwork\Node\Neuron\Synapse;
 
@@ -13,7 +14,7 @@ class Neuron implements Node
     /**
      * @var Synapse[]
      */
-    protected $synapses;
+    protected $synapses = [];
 
     /**
      * @var ActivationFunction
@@ -27,7 +28,7 @@ class Neuron implements Node
 
     public function __construct(?ActivationFunction $activationFunction = null)
     {
-        $this->activationFunction = $activationFunction ?: new ActivationFunction\Sigmoid();
+        $this->activationFunction = $activationFunction ?: new Sigmoid();
         $this->synapses = [];
         $this->output = 0;
     }
@@ -45,9 +46,9 @@ class Neuron implements Node
         return $this->synapses;
     }
 
-    public function getOutput() : float
+    public function getOutput(): float
     {
-        if (0 === $this->output) {
+        if ($this->output === 0) {
             $sum = 0;
             foreach ($this->synapses as $synapse) {
                 $sum += $synapse->getOutput();

src/Phpml/NeuralNetwork/Node/Neuron/Synapse.php

@@ -27,12 +27,7 @@ class Synapse
         $this->weight = $weight ?: $this->generateRandomWeight();
     }
 
-    protected function generateRandomWeight() : float
-    {
-        return 1 / random_int(5, 25) * (random_int(0, 1) ? -1 : 1);
-    }
-
-    public function getOutput() : float
+    public function getOutput(): float
     {
         return $this->weight * $this->node->getOutput();
     }
@@ -42,7 +37,7 @@ class Synapse
         $this->weight += $delta;
     }
 
-    public function getWeight() : float
+    public function getWeight(): float
     {
         return $this->weight;
     }
@@ -51,4 +46,9 @@ class Synapse
     {
         return $this->node;
     }
+
+    protected function generateRandomWeight(): float
+    {
+        return 1 / random_int(5, 25) * (random_int(0, 1) ? -1 : 1);
+    }
 }

src/Phpml/NeuralNetwork/Training/Backpropagation.php

@@ -47,6 +47,7 @@ class Backpropagation
                     }
                 }
             }
+
             $this->prevSigmas = $this->sigmas;
         }
 
@@ -55,7 +56,7 @@ class Backpropagation
         $this->prevSigmas = null;
     }
 
-    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 - $neuronOutput);
@@ -65,6 +66,7 @@ class Backpropagation
             if ($targetClass === $key) {
                 $value = 1;
             }
+
             $sigma *= ($value - $neuronOutput);
         } else {
             $sigma *= $this->getPrevSigma($neuron);
@@ -75,7 +77,7 @@ class Backpropagation
         return $sigma;
     }
 
-    private function getPrevSigma(Neuron $neuron) : float
+    private function getPrevSigma(Neuron $neuron): float
     {
         $sigma = 0.0;
 

src/Phpml/NeuralNetwork/Training/Backpropagation/Sigma.php

@@ -29,12 +29,12 @@ class Sigma
         return $this->neuron;
     }
 
-    public function getSigma() : float
+    public function getSigma(): float
     {
         return $this->sigma;
     }
 
-    public function getSigmaForNeuron(Neuron $neuron) : float
+    public function getSigmaForNeuron(Neuron $neuron): float
     {
         $sigma = 0.0;
 

src/Phpml/Pipeline.php

@@ -9,7 +9,7 @@ class Pipeline implements Estimator
     /**
      * @var array|Transformer[]
      */
-    private $transformers;
+    private $transformers = [];
 
     /**
      * @var Estimator
@@ -41,7 +41,7 @@ class Pipeline implements Estimator
     /**
      * @return array|Transformer[]
      */
-    public function getTransformers() : array
+    public function getTransformers(): array
     {
         return $this->transformers;
     }

src/Phpml/Preprocessing/Imputer.php

@@ -9,6 +9,7 @@ use Phpml\Preprocessing\Imputer\Strategy;
 class Imputer implements Preprocessor
 {
     public const AXIS_COLUMN = 0;
+
     public const AXIS_ROW = 1;
 
     /**
@@ -64,9 +65,9 @@ class Imputer implements Preprocessor
         }
     }
 
-    private function getAxis(int $column, array $currentSample) : array
+    private function getAxis(int $column, array $currentSample): array
     {
-        if (self::AXIS_ROW === $this->axis) {
+        if ($this->axis === self::AXIS_ROW) {
             return array_diff($currentSample, [$this->missingValue]);
         }
 

src/Phpml/Preprocessing/Imputer/Strategy/MeanStrategy.php

@@ -9,7 +9,7 @@ use Phpml\Preprocessing\Imputer\Strategy;
 
 class MeanStrategy implements Strategy
 {
-    public function replaceValue(array $currentAxis) : float
+    public function replaceValue(array $currentAxis): float
     {
         return Mean::arithmetic($currentAxis);
     }

src/Phpml/Preprocessing/Imputer/Strategy/MedianStrategy.php

@@ -9,7 +9,7 @@ use Phpml\Preprocessing\Imputer\Strategy;
 
 class MedianStrategy implements Strategy
 {
-    public function replaceValue(array $currentAxis) : float
+    public function replaceValue(array $currentAxis): float
     {
         return Mean::median($currentAxis);
     }

src/Phpml/Preprocessing/Normalizer.php

@@ -11,7 +11,9 @@ use Phpml\Math\Statistic\StandardDeviation;
 class Normalizer implements Preprocessor
 {
     public const NORM_L1 = 1;
+
     public const NORM_L2 = 2;
+
     public const NORM_STD = 3;
 
     /**
@@ -27,12 +29,12 @@ class Normalizer implements Preprocessor
     /**
      * @var array
      */
-    private $std;
+    private $std = [];
 
     /**
      * @var array
      */
-    private $mean;
+    private $mean = [];
 
     /**
      * @throws NormalizerException
@@ -69,7 +71,7 @@ class Normalizer implements Preprocessor
         $methods = [
             self::NORM_L1 => 'normalizeL1',
             self::NORM_L2 => 'normalizeL2',
-            self::NORM_STD => 'normalizeSTD'
+            self::NORM_STD => 'normalizeSTD',
         ];
         $method = $methods[$this->norm];
 
@@ -87,7 +89,7 @@ class Normalizer implements Preprocessor
             $norm1 += abs($feature);
         }
 
-        if (0 == $norm1) {
+        if ($norm1 == 0) {
             $count = count($sample);
             $sample = array_fill(0, $count, 1.0 / $count);
         } else {
@@ -103,9 +105,10 @@ class Normalizer implements Preprocessor
         foreach ($sample as $feature) {
             $norm2 += $feature * $feature;
         }
+
         $norm2 = sqrt((float) $norm2);
 
-        if (0 == $norm2) {
+        if ($norm2 == 0) {
             $sample = array_fill(0, count($sample), 1);
         } else {
             foreach ($sample as &$feature) {

src/Phpml/Regression/LeastSquares.php

@@ -28,7 +28,7 @@ class LeastSquares implements Regression
     /**
      * @var array
      */
-    private $coefficients;
+    private $coefficients = [];
 
     public function train(array $samples, array $targets): void
     {
@@ -51,12 +51,12 @@ class LeastSquares implements Regression
         return $result;
     }
 
-    public function getCoefficients() : array
+    public function getCoefficients(): array
     {
         return $this->coefficients;
     }
 
-    public function getIntercept() : float
+    public function getIntercept(): float
     {
         return $this->intercept;
     }
@@ -79,7 +79,7 @@ class LeastSquares implements Regression
     /**
      * Add one dimension for intercept calculation.
      */
-    private function getSamplesMatrix() : Matrix
+    private function getSamplesMatrix(): Matrix
     {
         $samples = [];
         foreach ($this->samples as $sample) {
@@ -90,7 +90,7 @@ class LeastSquares implements Regression
         return new Matrix($samples);
     }
 
-    private function getTargetsMatrix() : Matrix
+    private function getTargetsMatrix(): Matrix
     {
         if (is_array($this->targets[0])) {
             return new Matrix($this->targets);

src/Phpml/SupportVectorMachine/DataTransformer.php

@@ -34,7 +34,7 @@ class DataTransformer
         return $set;
     }
 
-    public static function predictions(string $rawPredictions, array $labels) : array
+    public static function predictions(string $rawPredictions, array $labels): array
     {
         $numericLabels = self::numericLabels($labels);
         $results = [];
@@ -47,7 +47,7 @@ class DataTransformer
         return $results;
     }
 
-    public static function numericLabels(array $labels) : array
+    public static function numericLabels(array $labels): array
     {
         $numericLabels = [];
         foreach ($labels as $label) {

src/Phpml/SupportVectorMachine/SupportVectorMachine.php

@@ -167,7 +167,7 @@ class SupportVectorMachine
     }
 
     /**
-     * @return array
+     * @return array|string
      */
     public function predict(array $samples)
     {

src/Phpml/Tokenization/Tokenizer.php

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

src/Phpml/Tokenization/WhitespaceTokenizer.php

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

src/Phpml/Tokenization/WordTokenizer.php

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

tests/Phpml/Association/AprioriTest.php

@@ -7,6 +7,7 @@ namespace tests\Phpml\Classification;
 use Phpml\Association\Apriori;
 use Phpml\ModelManager;
 use PHPUnit\Framework\TestCase;
+use ReflectionClass;
 
 class AprioriTest extends TestCase
 {
@@ -172,7 +173,6 @@ class AprioriTest extends TestCase
     /**
      * Invokes objects method. Private/protected will be set accessible.
      *
-     * @param object &$object Instantiated object to be called on
      * @param string $method  Method name to be called
      * @param array  $params  Array of params to be passed
      *
@@ -180,7 +180,7 @@ class AprioriTest extends TestCase
      */
     public function invoke(&$object, $method, array $params = [])
     {
-        $reflection = new \ReflectionClass(get_class($object));
+        $reflection = new ReflectionClass(get_class($object));
         $method = $reflection->getMethod($method);
         $method->setAccessible(true);
 
@@ -195,7 +195,7 @@ class AprioriTest extends TestCase
         $testSamples = [['alpha', 'epsilon'], ['beta', 'theta']];
         $predicted = $classifier->predict($testSamples);
 
-        $filename = 'apriori-test-'.rand(100, 999).'-'.uniqid();
+        $filename = 'apriori-test-'.random_int(100, 999).'-'.uniqid();
         $filepath = tempnam(sys_get_temp_dir(), $filename);
         $modelManager = new ModelManager();
         $modelManager->saveToFile($classifier, $filepath);

tests/Phpml/Classification/DecisionTreeTest.php

@@ -24,7 +24,7 @@ class DecisionTreeTest extends TestCase
         ['sunny',       75,    70,    'true',     'Play'],
         ['overcast',    72,    90,    'true',     'Play'],
         ['overcast',    81,    75,    'false',    'Play'],
-        ['rain',        71,    80,    'true',     'Dont_play']
+        ['rain',        71,    80,    'true',     'Dont_play'],
     ];
 
     private $extraData = [
@@ -32,16 +32,6 @@ class DecisionTreeTest extends TestCase
         ['scorching',  100,     93,     'true',    'Dont_play'],
     ];
 
-    private function getData($input)
-    {
-        $targets = array_column($input, 4);
-        array_walk($input, function (&$v): void {
-            array_splice($v, 4, 1);
-        });
-
-        return [$input, $targets];
-    }
-
     public function testPredictSingleSample()
     {
         [$data, $targets] = $this->getData($this->data);
@@ -68,7 +58,7 @@ class DecisionTreeTest extends TestCase
         $testSamples = [['sunny', 78, 72, 'false'], ['overcast', 60, 60, 'false']];
         $predicted = $classifier->predict($testSamples);
 
-        $filename = 'decision-tree-test-'.rand(100, 999).'-'.uniqid();
+        $filename = 'decision-tree-test-'.random_int(100, 999).'-'.uniqid();
         $filepath = tempnam(sys_get_temp_dir(), $filename);
         $modelManager = new ModelManager();
         $modelManager->saveToFile($classifier, $filepath);
@@ -83,6 +73,16 @@ class DecisionTreeTest extends TestCase
         [$data, $targets] = $this->getData($this->data);
         $classifier = new DecisionTree(5);
         $classifier->train($data, $targets);
-        $this->assertTrue(5 >= $classifier->actualDepth);
+        $this->assertTrue($classifier->actualDepth <= 5);
+    }
+
+    private function getData($input)
+    {
+        $targets = array_column($input, 4);
+        array_walk($input, function (&$v): void {
+            array_splice($v, 4, 1);
+        });
+
+        return [$input, $targets];
     }
 }