276 lines
8.1 KiB
PHP
276 lines
8.1 KiB
PHP
<?php
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// Pandora FMS - https://pandorafms.com
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// ==================================================
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// Copyright (c) 2005-2023 Pandora FMS
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// Please see https://pandorafms.com/community/ for full contribution list
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// This program is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public License
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// as published by the Free Software Foundation; version 2
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// This program 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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* @package Include
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* @subpackage Forecast
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*/
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/**
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* Create a prediction based on module data with least square method (linear regression)
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*
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* @param int Module id.
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* @param int Period of the module data.
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* @param int Period of the prediction or false to use it in prediction_date function (see below).
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* @param int Maximun value using this function for prediction_date.
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* @param int Minimun value using this function for prediction_date.
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* @param bool Result data for CSV file exportation.
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*
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* @return array Void array or prediction of the module data.
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*/
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function forecast_projection_graph(
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$module_id,
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$period=SECONDS_2MONTHS,
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$prediction_period=false,
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$max_value=false,
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$min_value=false,
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$csv=false,
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$server_name=''
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) {
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global $config;
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$max_exec_time = ini_get('max_execution_time');
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if ($max_exec_time !== false) {
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$max_exec_time = (int) $max_exec_time;
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}
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$begin_time = time();
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$params = [
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'agent_module_id' => $module_id,
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'period' => $period,
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'return_data' => 1,
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'projection' => true,
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];
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if (is_metaconsole()) {
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$id_meta = metaconsole_get_id_server($server_name);
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$server = metaconsole_get_connection_by_id($id_meta);
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metaconsole_connect($server);
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}
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$module_data = grafico_modulo_sparse($params);
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if (is_metaconsole()) {
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metaconsole_restore_db();
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}
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if (empty($module_data)) {
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return [];
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}
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// Prevents bad behaviour over image error
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else if (!is_array($module_data) and preg_match('/^<img(.)*$/', $module_data)) {
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return;
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}
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// Data initialization
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$sum_obs = 0;
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$sum_xi = 0;
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$sum_yi = 0;
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$sum_xi_yi = 0;
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$sum_xi2 = 0;
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$sum_yi2 = 0;
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$sum_diff_dates = 0;
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$last_timestamp = get_system_time();
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$agent_interval = SECONDS_5MINUTES;
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$cont = 1;
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$data = [];
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// $table->data = array();
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// Creates data for calculation
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if (is_array($module_data) || is_object($module_data)) {
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foreach ($module_data['sum1']['data'] as $key => $row) {
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if ($row[0] == '') {
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continue;
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}
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$row[0] = ($row[0] / 1000);
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$data[0] = '';
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$data[1] = $cont;
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$data[2] = date($config['date_format'], $row[0]);
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$data[3] = $row[0];
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$data[4] = $row[1];
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$data[5] = ($row[0] * $row[1]);
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$data[6] = ($row[0] * $row[0]);
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$data[7] = ($row[1] * $row[1]);
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if ($cont == 1) {
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$data[8] = 0;
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} else {
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$data[8] = ($row[0] - $last_timestamp);
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}
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$sum_obs = ($sum_obs + $cont);
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$sum_xi = ($sum_xi + $row[0]);
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$sum_yi = ($sum_yi + $row[1]);
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$sum_xi_yi = ($sum_xi_yi + $data[5]);
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$sum_xi2 = ($sum_xi2 + $data[6]);
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$sum_yi2 = ($sum_yi2 + $data[7]);
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$sum_diff_dates = ($sum_diff_dates + $data[8]);
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$last_timestamp = $row[0];
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$cont++;
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}
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}
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$cont--;
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// Calculation over data above:
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// 1. Calculation of linear correlation coefficient...
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// 1.1 Average for X: Sum(Xi)/Obs
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// 1.2 Average for Y: Sum(Yi)/Obs
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// 2. Covariance between vars
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// 3.1 Standard deviation for X: sqrt((Sum(Xi²)/Obs) - (avg X)²)
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// 3.2 Standard deviation for Y: sqrt((Sum(Yi²)/Obs) - (avg Y)²)
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// Linear correlation coefficient:
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// Agent interval could be zero, 300 is the predefined
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if ($sum_obs == 0) {
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$agent_interval = SECONDS_5MINUTES;
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} else {
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$agent_interval = ($sum_diff_dates / $sum_obs);
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}
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// Could be a inverse correlation coefficient
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// if $linear_coef < 0.0
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// if $linear_coef >= -1.0 and $linear_coef <= -0.8999
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// Function variables have an inverse linear relathionship!
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// else
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// Function variables don't have an inverse linear relathionship!
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// Could be a direct correlation coefficient
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// else
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// if ($linear_coef >= 0.8999 and $linear_coef <= 1.0) {
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// Function variables have a direct linear relathionship!
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// else
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// Function variables don't have a direct linear relathionship!
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// 2. Calculation of linear regresion...
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$b_num = (($cont * $sum_xi_yi) - ($sum_xi * $sum_yi));
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$b_den = (($cont * $sum_xi2) - ($sum_xi * $sum_xi));
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if ($b_den == 0) {
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return;
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}
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$b = ($b_num / $b_den);
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$a_num = (($sum_yi) - ($b * $sum_xi));
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if ($cont != 0) {
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$a = ($a_num / $cont);
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} else {
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$a = 0;
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}
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// Data inicialization
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$output_data = [];
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if ($prediction_period != false) {
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$limit_timestamp = ($last_timestamp + $prediction_period);
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}
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$current_ts = $last_timestamp;
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$in_range = true;
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$time_format_2 = '';
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$temp_range = $period;
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if ($period < $prediction_period) {
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$temp_range = $prediction_period;
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}
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if ($temp_range <= SECONDS_6HOURS) {
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$time_format = 'H:i:s';
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} else if ($temp_range < SECONDS_1DAY) {
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$time_format = 'H:i';
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} else if ($temp_range < SECONDS_15DAYS) {
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$time_format = 'M d';
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$time_format_2 = 'H\h';
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} else if ($temp_range <= SECONDS_1MONTH) {
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$time_format = 'M d';
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$time_format_2 = 'H\h';
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} else {
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$time_format = 'M d';
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}
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// Aplying linear regression to module data in order to do the prediction
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$idx = 0;
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// Create data in graph format like
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while ($in_range) {
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$now = time();
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// Check that exec time is not greater than half max exec server time
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if ($max_exec_time != false) {
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if (($begin_time + ($max_exec_time / 2)) < $now) {
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return false;
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}
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}
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$timestamp_f = ($current_ts * 1000);
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if ($csv) {
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$output_data[$idx]['date'] = $current_ts;
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$output_data[$idx]['data'] = ($a + ($b * $current_ts));
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} else {
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$output_data[$idx][0] = $timestamp_f;
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$output_data[$idx][1] = ($a + ($b * $current_ts));
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}
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// Using this function for prediction_date
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if ($prediction_period == false) {
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// These statements stop the prediction when interval is greater than 2 years
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if (($current_ts - $last_timestamp) >= 94608000
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|| $max_value == $min_value
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) {
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return false;
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}
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// Found it
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if (($max_value >= $output_data[$idx][1])
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&& ($min_value <= $output_data[$idx][0])
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) {
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return ($current_ts + ($sum_diff_dates * $agent_interval));
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}
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} else if ($current_ts > $limit_timestamp) {
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$in_range = false;
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}
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$current_ts = ($current_ts + $agent_interval);
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$idx++;
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}
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return $output_data;
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}
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/**
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* Return a date when the date interval is reached
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*
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* @param int Module id.
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* @param int Given data period to make the prediction
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* @param int Max value in the interval.
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* @param int Min value in the interval.
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*
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* @return mixed timestamp with the prediction date or false
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*/
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function forecast_prediction_date(
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$module_id,
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$period=SECONDS_2MONTHS,
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$max_value=0,
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$min_value=0,
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$server_name=''
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) {
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// Checks interval
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if ($min_value > $max_value) {
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return false;
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}
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return forecast_projection_graph($module_id, $period, false, $max_value, $min_value, false, $server_name);
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}
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