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📄 predictor.php(2.62 KB)
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Editing: classifier.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/>.

/**
 * Classifier 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();

/**
 * Classifier 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 classifier extends predictor {

/**
     * Train this processor classification model using the provided supervised learning dataset.
     *
     * @param string $uniqueid
     * @param \stored_file $dataset
     * @param string $outputdir
     * @return \stdClass
     */
    public function train_classification($uniqueid, \stored_file $dataset, $outputdir);

/**
     * Classifies the provided dataset samples.
     *
     * @param string $uniqueid
     * @param \stored_file $dataset
     * @param string $outputdir
     * @return \stdClass
     */
    public function classify($uniqueid, \stored_file $dataset, $outputdir);

/**
     * Evaluates this processor classification model using the provided supervised learning dataset.
     *
     * @param string $uniqueid
     * @param float $maxdeviation
     * @param int $niterations
     * @param \stored_file $dataset
     * @param string $outputdir
     * @param  string $trainedmodeldir
     * @return \stdClass
     */
    public function evaluate_classification($uniqueid, $maxdeviation, $niterations, \stored_file $dataset,
            $outputdir, $trainedmodeldir);
}

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