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<?php
/**
* PHPExcel
*
* Copyright (c) 2006 - 2014 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 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
* @version 1.8.0, 2014-03-02
*/
require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
/**
* PHPExcel_Logarithmic_Best_Fit
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
*/
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);
} // 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 exp(($yValue - $this->getIntersect()) / $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 = '.$intersect.' + '.$slope.' * log(X)';
} // function getEquation()
/**
* 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 $const
*/
private function _logarithmic_regression($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);
$this->_leastSquareFit($yValues, $xValues, $const);
} // function _logarithmic_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) {
$this->_logarithmic_regression($yValues, $xValues, $const);
}
} // function __construct()
} // class logarithmicBestFit
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