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David Monllaó c1b1a5d6ac Support for multiple training datasets (#38 )

* Multiple training data sets allowed

* Tests with multiple training data sets

* Updating docs according to #38

Documenting all models which predictions will be based on all
training data provided.

Some models already supported multiple training data sets.

2017-02-01 19:06:38 +01:00

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Apriori Associator

Association rule learning based on Apriori algorithm for frequent item set mining.

Constructor Parameters

$support - confidence, minimum relative amount of frequent item set in train sample
$confidence - confidence, minimum relative amount of item set in frequent item sets

use Phpml\Association\Apriori;

$associator = new Apriori($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  = [];

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 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

Get generated association rules simply use rules method.

$associator->getRules();
// 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 => [...], ...]

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