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<?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/>.
/**
* Abstract linear indicator.
*
* @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\local\indicator;
defined('MOODLE_INTERNAL') || die();
/**
* Abstract linear indicator.
*
* @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
*/
abstract class linear extends base {
/**
* Set to false to avoid context features to be added as dataset features.
*
* @return bool
*/
protected static function include_averages() {
return true;
}
/**
* get_feature_headers
*
* @return array
*/
public static function get_feature_headers() {
$fullclassname = '\\' . get_called_class();
if (static::include_averages()) {
// The calculated value + context indicators.
$headers = array($fullclassname, $fullclassname . '/mean');
} else {
$headers = array($fullclassname);
}
return $headers;
}
/**
* Show only the main feature.
*
* @param float $value
* @param string $subtype
* @return bool
*/
public function should_be_displayed($value, $subtype) {
if ($subtype != false) {
return false;
}
return true;
}
/**
* get_display_value
*
* @param float $value
* @param string $subtype
* @return string
*/
public function get_display_value($value, $subtype = false) {
$diff = static::get_max_value() - static::get_min_value();
return round(100 * ($value - static::get_min_value()) / $diff) . '%';
}
/**
* get_calculation_outcome
*
* @param float $value
* @param string $subtype
* @return int
*/
public function get_calculation_outcome($value, $subtype = false) {
if ($value < 0) {
return self::OUTCOME_NEGATIVE;
} else {
return self::OUTCOME_OK;
}
}
/**
* Converts the calculated values to a list of features for the dataset.
*
* @param array $calculatedvalues
* @return array
*/
protected function to_features($calculatedvalues) {
// Null mean if all calculated values are null.
$nullmean = true;
foreach ($calculatedvalues as $value) {
if (!is_null($value)) {
// Early break, we don't want to spend a lot of time here.
$nullmean = false;
break;
}
}
if ($nullmean) {
$mean = null;
} else {
$mean = round(array_sum($calculatedvalues) / count($calculatedvalues), 2);
}
foreach ($calculatedvalues as $sampleid => $calculatedvalue) {
if (!is_null($calculatedvalue)) {
$calculatedvalue = round($calculatedvalue, 2);
}
if (static::include_averages()) {
$calculatedvalues[$sampleid] = array($calculatedvalue, $mean);
} else {
// Basically just convert the scalar to an array of scalars with a single value.
$calculatedvalues[$sampleid] = array($calculatedvalue);
}
}
// Returns each sample as an array of values, appending the mean to the calculated value.
return $calculatedvalues;
}
}