Apply comments / coding styles

* Remove user-specific gitignore
* Add return type hints
* Avoid global namespace in docs
* Rename rules -> getRules
* Split up rule generation

Todo:
* Move set theory out to math
* Extract rule generation

.gitignore

@@ -1,4 +1,3 @@
-/.idea/
 /vendor/
 humbuglog.*
 /bin/phpunit

docs/machine-learning/association/apriori.md

@@ -8,7 +8,9 @@ Association rule learning based on [Apriori algorithm](https://en.wikipedia.org/
 * $confidence - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Confidence), minimum relative amount of item set in frequent item sets
 
 ```
-$associator = new \Phpml\Association\Apriori($support = 0.5, $confidence = 0.5);
+use Phpml\Association\Apriori;
+
+$associator = new Apriori($support = 0.5, $confidence = 0.5);
 ```
 
 ### Train
@@ -19,7 +21,9 @@ To train a associator simply provide train samples and labels (as `array`). Exam
 $samples = [['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta'], ['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta']];
 $labels  = [];
 
-$associator = new \Phpml\Association\Apriori(0.5, 0.5);
+use Phpml\Association\Apriori;
+
+$associator = new Apriori($support = 0.5, $confidence = 0.5);
 $associator->train($samples, $labels);
 ```
 
@@ -37,10 +41,10 @@ $associator->predict([['alpha','epsilon'],['beta','theta']]);
 
 ### Associating
 
-Generating association rules simply use `rules` method.
+Get generated association rules simply use `rules` method.
  
 ```
-$associator->rules();
+$associator->getRules();
 // return [['antecedent' => ['alpha', 'theta'], 'consequent' => ['beta], 'support' => 1.0, 'confidence' => 1.0], ... ]
 ```
 

src/Phpml/Association/Apriori.php

@@ -53,18 +53,18 @@ class Apriori implements Associator
      * @param float $support
      * @param float $confidence
      */
-    public function __construct($support = 0.0, $confidence = 0.0)
+    public function __construct(float $support = 0.0, float $confidence = 0.0)
     {
         $this->support = $support;
         $this->confidence = $confidence;
     }
 
     /**
-     * Generates apriori association rules.
+     * Get all association rules which are generated for every k-length frequent item set.
      *
      * @return mixed[][]
      */
-    public function rules()
+    public function getRules() : array
     {
         if (!$this->large) {
             $this->large = $this->apriori();
@@ -76,31 +76,17 @@ class Apriori implements Associator
 
         $this->rules = [];
 
-        for ($k = 2; !empty($this->large[$k]); ++$k) {
+        $this->generateAllRules();
-            foreach ($this->large[$k] as $frequent) {
-                foreach ($this->antecedents($frequent) as $antecedent) {
-                    if ($this->confidence <= ($confidence = $this->confidence($frequent, $antecedent))) {
-                        $consequent    = array_values(array_diff($frequent, $antecedent));
-                        $this->rules[] = [
-                            self::ARRAY_KEY_ANTECEDENT => $antecedent,
-                            self::ARRAY_KEY_CONSEQUENT => $consequent,
-                            self::ARRAY_KEY_SUPPORT    => $this->support($consequent),
-                            self::ARRAY_KEY_CONFIDENCE => $confidence,
-                        ];
-                    }
-                }
-            }
-        }
 
         return $this->rules;
     }
 
     /**
-     * Generates frequent item sets
+     * Generates frequent item sets.
      *
      * @return mixed[][][]
      */
-    public function apriori()
+    public function apriori() : array
     {
         $L = [];
         $L[1] = $this->items();
@@ -119,13 +105,47 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    protected function predictSample(array $sample)
+    protected function predictSample(array $sample) : array
     {
-        $predicts = array_values(array_filter($this->rules(), function($rule) use ($sample) {
+        $predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample) {
             return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample);
         }));
 
-        return array_map(function($rule) { return $rule[self::ARRAY_KEY_CONSEQUENT]; }, $predicts);
+        return array_map(function ($rule) {
+            return $rule[self::ARRAY_KEY_CONSEQUENT];
+        }, $predicts);
+    }
+
+    /**
+     * Generate rules for each k-length frequent item set.
+     */
+    private function generateAllRules()
+    {
+        for ($k = 2; !empty($this->large[$k]); ++$k) {
+            foreach ($this->large[$k] as $frequent) {
+                $this->generateRules($frequent);
+            }
+        }
+    }
+
+    /**
+     * Generate confident rules for frequent item set.
+     *
+     * @param mixed[] $frequent
+     */
+    private function generateRules(array $frequent)
+    {
+        foreach ($this->antecedents($frequent) as $antecedent) {
+            if ($this->confidence <= ($confidence = $this->confidence($frequent, $antecedent))) {
+                $consequent = array_values(array_diff($frequent, $antecedent));
+                $this->rules[] = [
+                    self::ARRAY_KEY_ANTECEDENT => $antecedent,
+                    self::ARRAY_KEY_CONSEQUENT => $consequent,
+                    self::ARRAY_KEY_SUPPORT => $this->support($consequent),
+                    self::ARRAY_KEY_CONFIDENCE => $confidence,
+                ];
+            }
+        }
     }
 
     /**
@@ -135,7 +155,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function powerSet(array $sample)
+    private function powerSet(array $sample) : array
     {
         $results = [[]];
         foreach ($sample as $item) {
@@ -154,7 +174,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function antecedents(array $sample)
+    private function antecedents(array $sample) : array
     {
         $cardinality = count($sample);
         $antecedents = $this->powerSet($sample);
@@ -169,7 +189,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function items()
+    private function items() : array
     {
         $items = [];
 
@@ -193,7 +213,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function frequent(array $samples)
+    private function frequent(array $samples) : array
     {
         return array_filter($samples, function ($entry) {
             return $this->support($entry) >= $this->support;
@@ -207,7 +227,7 @@ class Apriori implements Associator
      *
      * @return mixed[][]
      */
-    private function candidates(array $samples)
+    private function candidates(array $samples) : array
     {
         $candidates = [];
 
@@ -244,7 +264,7 @@ class Apriori implements Associator
      *
      * @return float
      */
-    private function confidence(array $set, array $subset)
+    private function confidence(array $set, array $subset) : float
     {
         return $this->support($set) / $this->support($subset);
     }
@@ -259,7 +279,7 @@ class Apriori implements Associator
      *
      * @return float
      */
-    private function support(array $sample)
+    private function support(array $sample) : float
     {
         return $this->frequency($sample) / count($this->samples);
     }
@@ -273,7 +293,7 @@ class Apriori implements Associator
      *
      * @return int
      */
-    private function frequency(array $sample)
+    private function frequency(array $sample) : int
     {
         return count(array_filter($this->samples, function ($entry) use ($sample) {
             return $this->subset($entry, $sample);
@@ -290,7 +310,7 @@ class Apriori implements Associator
      *
      * @return bool
      */
-    private function contains(array $system, array $set)
+    private function contains(array $system, array $set) : bool
     {
         return (bool) array_filter($system, function ($entry) use ($set) {
             return $this->equals($entry, $set);
@@ -305,7 +325,7 @@ class Apriori implements Associator
      *
      * @return bool
      */
-    private function subset(array $set, array $subset)
+    private function subset(array $set, array $subset) : bool
     {
         return !array_diff($subset, array_intersect($subset, $set));
     }
@@ -318,7 +338,7 @@ class Apriori implements Associator
      *
      * @return bool
      */
-    private function equals(array $set1, array $set2)
+    private function equals(array $set1, array $set2) : bool
     {
         return array_diff($set1, $set2) == array_diff($set2, $set1);
     }

tests/Phpml/Association/AprioriTest.php

@@ -71,12 +71,12 @@ class AprioriTest extends \PHPUnit_Framework_TestCase
         $this->assertTrue($this->invoke($apriori, 'contains', [$L[2], [3, 4]]));
     }
 
-    public function testRules()
+    public function testGetRules()
     {
         $apriori = new Apriori(0.4, 0.8);
         $apriori->train($this->sampleChars, []);
 
-        $this->assertCount(19, $apriori->rules());
+        $this->assertCount(19, $apriori->getRules());
     }
 
     public function testAntecedents()