# Apriori Associator

Association rule learning based on [Apriori algorithm](https://en.wikipedia.org/wiki/Apriori_algorithm) for frequent item set mining.

### Constructor Parameters

* $support - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Support), minimum relative amount of frequent item set in train sample
* $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);
```

### Train

To train a associator simply provide train samples and labels (as `array`). Example:

```
$samples = [['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta'], ['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta']];
$labels  = [];

$associator = new \Phpml\Association\Apriori(0.5, 0.5);
$associator->train($samples, $labels);
```

### Predict

To predict sample label use `predict` method. You can provide one sample or array of samples:

```
$associator->predict(['alpha','theta']);
// return [[['beta']]]

$associator->predict([['alpha','epsilon'],['beta','theta']]);
// return [[['beta']], [['alpha']]]
```

### Associating

Generating association rules simply use `rules` method.
 
```
$associator->rules();
// return [['antecedent' => ['alpha', 'theta'], 'consequent' => ['beta], 'support' => 1.0, 'confidence' => 1.0], ... ]
```

### Frequent item sets

Generating k-length frequent item sets simply use `apriori` method.

```
$associator->apriori();
// return [ 1 => [['alpha'], ['beta'], ['theta'], ['epsilon']], 2 => [...], ...]
```