236 lines
6.7 KiB
PHP
236 lines
6.7 KiB
PHP
<?php
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// Pandora FMS - http://pandorafms.com
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// ==================================================
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// Copyright (c) 2005-2010 Artica Soluciones Tecnologicas
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// Please see http://pandorafms.org 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($module_id, $period = 5184000, $prediction_period, $max_value = false, $min_value = false, $csv = false){
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global $config;
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$module_data=grafico_modulo_sparse ($module_id, $period, 0,
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300, 300 , '', null,
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false, 0, false,
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0, '', 0, 1, false,
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true, '', 1, true);
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if (empty($module_data)){
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return array();
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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 = 300;
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$cont = 1;
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$data = array();
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$table->data = array();
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// Creates data for calculation
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foreach ($module_data as $utimestamp => $row) {
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if ($utimestamp == '') {
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continue;
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}
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$data[0] = '';
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$data[1] = $cont;
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$data[2] = date('d M Y H:i:s', $utimestamp);
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$data[3] = $utimestamp;
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$data[4] = $row['sum'];
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$data[5] = $utimestamp * $row['sum'];
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$data[6] = $utimestamp * $utimestamp;
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$data[7] = $row['sum'] * $row['sum'];
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if ($cont == 1){
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$data[8] = 0;
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}
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else {
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$data[8] = $utimestamp - $last_timestamp;
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}
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$sum_obs = $sum_obs + $cont;
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$sum_xi = $sum_xi + $utimestamp;
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$sum_yi = $sum_yi + $row['sum'];
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$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 = $utimestamp;
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$cont++;
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array_push($table->data, $data);
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}
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$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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$avg_x = $cont/$sum_xi;
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if ($sum_yi != 0)
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$avg_y = $cont/$sum_yi;
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else
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$avg_y = 0;
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$covariance = $sum_xi_yi/$cont;
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$dev_x = sqrt(($sum_xi2/$cont) - ($avg_x*$avg_x));
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$dev_y = sqrt(($sum_yi2/$cont) - ($avg_y*$avg_y));
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// Prevents division by zero
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if ($dev_x != 0 and $dev_y != 0){
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$linear_coef = $covariance / ($dev_x * $dev_y);
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}
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// Agent interval could be zero, 300 is the predefined
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($sum_obs == 0)? $agent_interval = 300 : $agent_interval = $sum_diff_dates / $sum_obs;
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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 * $sum_xi));
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$b = $b_num / $b_den;
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$a_num = ($sum_yi) - ($b * $sum_xi);
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$a = $a_num / $cont;
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// Data inicialization
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$output_data = array();
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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 = $prediction_period;
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if ($temp_range <= 21600) {
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$time_format = 'H:i:s';
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}
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elseif ($temp_range < 86400) {
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$time_format = 'H:i';
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}
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elseif ($temp_range < 1296000) {
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$time_format = 'M d';
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$time_format_2 = 'H\h';
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}
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elseif ($temp_range <= 2592000) {
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$time_format = 'M d';
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$time_format_2 = 'H\h';
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}
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else {
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$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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$output_data = array();
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$idx = 0;
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// Create data in graph format like
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while ($in_range){
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$timestamp_f = date($time_format, $current_ts);
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//$timestamp_f = date($time_format, $current_ts);
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$timestamp_f = graph_get_formatted_date($current_ts, $time_format, $time_format_2);
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if ($csv){
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$output_data[$idx]['date'] = $current_ts;
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$output_data[$idx]['data'] = ($a + ($b * $current_ts));
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}
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else{
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$output_data[$timestamp_f] = ($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 4 years
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if ($current_ts - $last_timestamp >= 126144000){
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return false;
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}
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//html_debug_print(" Date " . $timestamp_f . " data: " . $output_data[$timestamp_f]);
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// Found it
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if ($max_value >= $output_data[$timestamp_f] and $min_value <= $output_data[$timestamp_f]){
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return $current_ts;
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}
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}else if ($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 $output_data;
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}
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/**
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* Return a date when the date interval is reached
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*
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* @param int Module id.
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* @param int Given data period to make the prediction
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* @param int Max value in the interval.
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* @param int Min value in the interval.
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*
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* @return mixed timestamp with the prediction date or false
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*/
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function forecast_prediction_date ($module_id, $period = 5184000, $max_value = 0, $min_value = 0){
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// Checks interval
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if ($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);
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}
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