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<?php
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// This file is part of Moodle - http://moodle.org/
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//
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// Moodle is free software: you can redistribute it and/or modify
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// it under the terms of the GNU General Public License as published by
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// the Free Software Foundation, either version 3 of the License, or
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// (at your option) any later version.
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//
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// Moodle is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with Moodle. If not, see <http://www.gnu.org/licenses/>.
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/**
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* Abstract linear indicator.
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*
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* @package core_analytics
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* @copyright 2017 David Monllao {@link http://www.davidmonllao.com}
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* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
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*/
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namespace core_analytics\local\indicator;
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defined('MOODLE_INTERNAL') || die();
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/**
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* Abstract linear indicator.
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*
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* @package core_analytics
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* @copyright 2017 David Monllao {@link http://www.davidmonllao.com}
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* @license http://www.gnu.org/copyleft/gpl.html GNU GPL v3 or later
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*/
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abstract class linear extends base {
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/**
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* Set to false to avoid context features to be added as dataset features.
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*
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* @return bool
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*/
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protected static function include_averages() {
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return true;
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}
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/**
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* get_feature_headers
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*
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* @return array
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*/
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public static function get_feature_headers() {
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$fullclassname = '\\' . get_called_class();
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if (static::include_averages()) {
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// The calculated value + context indicators.
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$headers = array($fullclassname, $fullclassname . '/mean');
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} else {
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$headers = array($fullclassname);
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}
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return $headers;
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}
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/**
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* Show only the main feature.
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*
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* @param float $value
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* @param string $subtype
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* @return bool
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*/
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public function should_be_displayed($value, $subtype) {
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if ($subtype != false) {
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return false;
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}
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return true;
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}
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/**
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* get_display_value
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*
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* @param float $value
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* @param string $subtype
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* @return string
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*/
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public function get_display_value($value, $subtype = false) {
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$diff = static::get_max_value() - static::get_min_value();
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return round(100 * ($value - static::get_min_value()) / $diff) . '%';
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}
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/**
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* get_calculation_outcome
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*
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* @param float $value
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* @param string $subtype
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* @return int
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*/
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public function get_calculation_outcome($value, $subtype = false) {
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if ($value < 0) {
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return self::OUTCOME_NEGATIVE;
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} else {
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return self::OUTCOME_OK;
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}
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}
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/**
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* Converts the calculated values to a list of features for the dataset.
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*
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* @param array $calculatedvalues
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* @return array
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*/
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protected function to_features($calculatedvalues) {
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// Null mean if all calculated values are null.
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$nullmean = true;
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foreach ($calculatedvalues as $value) {
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if (!is_null($value)) {
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// Early break, we don't want to spend a lot of time here.
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$nullmean = false;
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break;
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}
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}
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if ($nullmean) {
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$mean = null;
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} else {
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$mean = round(array_sum($calculatedvalues) / count($calculatedvalues), 2);
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}
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foreach ($calculatedvalues as $sampleid => $calculatedvalue) {
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if (!is_null($calculatedvalue)) {
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$calculatedvalue = round($calculatedvalue, 2);
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}
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if (static::include_averages()) {
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$calculatedvalues[$sampleid] = array($calculatedvalue, $mean);
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} else {
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// Basically just convert the scalar to an array of scalars with a single value.
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$calculatedvalues[$sampleid] = array($calculatedvalue);
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}
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}
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// Returns each sample as an array of values, appending the mean to the calculated value.
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return $calculatedvalues;
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}
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}
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