# Apriori Associator

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

### Constructor Parameters

* $support - minimum threshold of [support](https://en.wikipedia.org/wiki/Association_rule_learning#Support), i.e. the ratio of samples which contain both X and Y for a rule "if X then Y"
* $confidence - minimum threshold of [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Confidence), i.e. the ratio of samples containing both X and Y to those containing X

```
use Phpml\Association\Apriori;

$associator = new Apriori($support = 0.5, $confidence = 0.5);
```

### Train

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

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

use Phpml\Association\Apriori;

$associator = new Apriori($support = 0.5, $confidence = 0.5);
$associator->train($samples, $labels);
```

You can train the associator using multiple data sets, predictions will be based on all the training data.

### Predict

To predict sample label use the `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

To get generated association rules, simply use the `rules` method.

```
$associator->getRules();
// return [['antecedent' => ['alpha', 'theta'], 'consequent' => ['beta'], 'support' => 1.0, 'confidence' => 1.0], ... ]
```

### Frequent item sets

To generate k-length frequent item sets, simply use the `apriori` method.

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