You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
111 lines
3.6 KiB
111 lines
3.6 KiB
2 years ago
|
<?php
|
||
|
|
|
||
|
|
require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
|
||
|
|
|
||
|
|
/**
|
||
|
|
* PHPExcel_Logarithmic_Best_Fit
|
||
|
|
*
|
||
|
|
* 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##
|
||
|
|
*/
|
||
|
|
class PHPExcel_Logarithmic_Best_Fit extends PHPExcel_Best_Fit
|
||
|
|
{
|
||
|
|
/**
|
||
|
|
* Algorithm type to use for best-fit
|
||
|
|
* (Name of this trend class)
|
||
|
|
*
|
||
|
|
* @var string
|
||
|
|
**/
|
||
|
|
protected $bestFitType = 'logarithmic';
|
||
|
|
|
||
|
|
/**
|
||
|
|
* 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() + $this->getSlope() * log($xValue - $this->xOffset);
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* Return the X-Value for a specified value of Y
|
||
|
|
*
|
||
|
|
* @param float $yValue Y-Value
|
||
|
|
* @return float X-Value
|
||
|
|
**/
|
||
|
|
public function getValueOfXForY($yValue)
|
||
|
|
{
|
||
|
|
return exp(($yValue - $this->getIntersect()) / $this->getSlope());
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* 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 = '.$intersect.' + '.$slope.' * log(X)';
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* 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 $const
|
||
|
|
*/
|
||
|
|
private function logarithmicRegression($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);
|
||
|
|
|
||
|
|
$this->leastSquareFit($yValues, $xValues, $const);
|
||
|
|
}
|
||
|
|
|
||
|
|
/**
|
||
|
|
* 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 $const
|
||
|
|
*/
|
||
|
|
public function __construct($yValues, $xValues = array(), $const = true)
|
||
|
|
{
|
||
|
|
if (parent::__construct($yValues, $xValues) !== false) {
|
||
|
|
$this->logarithmicRegression($yValues, $xValues, $const);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|