142 lines
		
	
	
		
			4.6 KiB
		
	
	
	
		
			PHP
		
	
	
	
	
	
			
		
		
	
	
			142 lines
		
	
	
		
			4.6 KiB
		
	
	
	
		
			PHP
		
	
	
	
	
	
| <?php
 | |
| /**
 | |
|  * PHPExcel
 | |
|  *
 | |
|  * Copyright (c) 2006 - 2015 PHPExcel
 | |
|  *
 | |
|  * This library is free software; you can redistribute it and/or
 | |
|  * modify it under the terms of the GNU Lesser General Public
 | |
|  * License as published by the Free Software Foundation; either
 | |
|  * version 2.1 of the License, or (at your option) any later version.
 | |
|  *
 | |
|  * This library 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
 | |
|  * Lesser General Public License for more details.
 | |
|  *
 | |
|  * You should have received a copy of the GNU Lesser General Public
 | |
|  * License along with this library; if not, write to the Free Software
 | |
|  * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
 | |
|  *
 | |
|  * @category   PHPExcel
 | |
|  * @package    PHPExcel_Shared_Trend
 | |
|  * @copyright  Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
 | |
|  * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt    LGPL
 | |
|  * @version    ##VERSION##, ##DATE##
 | |
|  */
 | |
| 
 | |
| 
 | |
| require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php';
 | |
| 
 | |
| 
 | |
| /**
 | |
|  * PHPExcel_Power_Best_Fit
 | |
|  *
 | |
|  * @category   PHPExcel
 | |
|  * @package    PHPExcel_Shared_Trend
 | |
|  * @copyright  Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
 | |
|  */
 | |
| class PHPExcel_Power_Best_Fit extends PHPExcel_Best_Fit
 | |
| {
 | |
|     /**
 | |
|      * Algorithm type to use for best-fit
 | |
|      * (Name of this trend class)
 | |
|      *
 | |
|      * @var    string
 | |
|      **/
 | |
|     protected $_bestFitType        = 'power';
 | |
| 
 | |
| 
 | |
|     /**
 | |
|      * Return the Y-Value for a specified value of X
 | |
|      *
 | |
|      * @param     float        $xValue            X-Value
 | |
|      * @return     float                        Y-Value
 | |
|      **/
 | |
|     public function getValueOfYForX($xValue) {
 | |
|         return $this->getIntersect() * pow(($xValue - $this->_Xoffset),$this->getSlope());
 | |
|     }    //    function getValueOfYForX()
 | |
| 
 | |
| 
 | |
|     /**
 | |
|      * Return the X-Value for a specified value of Y
 | |
|      *
 | |
|      * @param     float        $yValue            Y-Value
 | |
|      * @return     float                        X-Value
 | |
|      **/
 | |
|     public function getValueOfXForY($yValue) {
 | |
|         return pow((($yValue + $this->_Yoffset) / $this->getIntersect()),(1 / $this->getSlope()));
 | |
|     }    //    function getValueOfXForY()
 | |
| 
 | |
| 
 | |
|     /**
 | |
|      * Return the Equation of the best-fit line
 | |
|      *
 | |
|      * @param     int        $dp        Number of places of decimal precision to display
 | |
|      * @return     string
 | |
|      **/
 | |
|     public function getEquation($dp=0) {
 | |
|         $slope = $this->getSlope($dp);
 | |
|         $intersect = $this->getIntersect($dp);
 | |
| 
 | |
|         return 'Y = '.$intersect.' * X^'.$slope;
 | |
|     }    //    function getEquation()
 | |
| 
 | |
| 
 | |
|     /**
 | |
|      * Return the Value of X where it intersects Y = 0
 | |
|      *
 | |
|      * @param     int        $dp        Number of places of decimal precision to display
 | |
|      * @return     string
 | |
|      **/
 | |
|     public function getIntersect($dp=0) {
 | |
|         if ($dp != 0) {
 | |
|             return round(exp($this->_intersect),$dp);
 | |
|         }
 | |
|         return exp($this->_intersect);
 | |
|     }    //    function getIntersect()
 | |
| 
 | |
| 
 | |
|     /**
 | |
|      * Execute the regression and calculate the goodness of fit for a set of X and Y data values
 | |
|      *
 | |
|      * @param     float[]    $yValues    The set of Y-values for this regression
 | |
|      * @param     float[]    $xValues    The set of X-values for this regression
 | |
|      * @param     boolean    $const
 | |
|      */
 | |
|     private function _power_regression($yValues, $xValues, $const) {
 | |
|         foreach($xValues as &$value) {
 | |
|             if ($value < 0.0) {
 | |
|                 $value = 0 - log(abs($value));
 | |
|             } elseif ($value > 0.0) {
 | |
|                 $value = log($value);
 | |
|             }
 | |
|         }
 | |
|         unset($value);
 | |
|         foreach($yValues as &$value) {
 | |
|             if ($value < 0.0) {
 | |
|                 $value = 0 - log(abs($value));
 | |
|             } elseif ($value > 0.0) {
 | |
|                 $value = log($value);
 | |
|             }
 | |
|         }
 | |
|         unset($value);
 | |
| 
 | |
|         $this->_leastSquareFit($yValues, $xValues, $const);
 | |
|     }    //    function _power_regression()
 | |
| 
 | |
| 
 | |
|     /**
 | |
|      * Define the regression and calculate the goodness of fit for a set of X and Y data values
 | |
|      *
 | |
|      * @param     float[]    $yValues    The set of Y-values for this regression
 | |
|      * @param     float[]    $xValues    The set of X-values for this regression
 | |
|      * @param     boolean    $const
 | |
|      */
 | |
|     function __construct($yValues, $xValues=array(), $const=True) {
 | |
|         if (parent::__construct($yValues, $xValues) !== False) {
 | |
|             $this->_power_regression($yValues, $xValues, $const);
 | |
|         }
 | |
|     }    //    function __construct()
 | |
| 
 | |
| }    //    class powerBestFit
 | 
