moodle/analytics/classes/predictor.php
David Monllao 413f19bc49 MDL-59211 analytics: Make cibot happy
Part of MDL-57791 epic.
2017-07-24 08:36:49 +02:00

77 lines
2.1 KiB
PHP

<?php
// This file is part of Moodle - http://moodle.org/
//
// Moodle is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// Moodle is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Moodle. If not, see <http://www.gnu.org/licenses/>.
/**
* Predictions processor interface.
*
* @package core_analytics
* @copyright 2017 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
namespace core_analytics;
defined('MOODLE_INTERNAL') || die();
/**
* Predictors interface.
*
* @package core_analytics
* @copyright 2016 David Monllao {@link http://www.davidmonllao.com}
* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
*/
interface predictor {
/**
* Is it ready to predict?
*
* @return bool
*/
public function is_ready();
/**
* Train the provided dataset.
*
* @param int $modelid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function train($modelid, \stored_file $dataset, $outputdir);
/**
* Predict the provided dataset samples.
*
* @param int $modelid
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function predict($modelid, \stored_file $dataset, $outputdir);
/**
* evaluate
*
* @param int $modelid
* @param float $maxdeviation
* @param int $niterations
* @param \stored_file $dataset
* @param string $outputdir
* @return \stdClass
*/
public function evaluate($modelid, $maxdeviation, $niterations, \stored_file $dataset, $outputdir);
}