<?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_Exponential_Best_Fit

 *

 * @category   PHPExcel

 * @package    PHPExcel_Shared_Trend

 * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)

 */

class PHPExcel_Exponential_Best_Fit extends PHPExcel_Best_Fit

{

	/**

	 * Algorithm type to use for best-fit

	 * (Name of this trend class)

	 *

	 * @var	string

	 **/

	protected $_bestFitType		= 'exponential';





	/**

	 * Return the Y-Value for a specified value of X

	 *

	 * @param	 float		$xValue			X-Value

	 * @return	 float						Y-Value

	 **/

	public function getValueOfYForX($xValue) {

		return $this->getIntersect() * pow($this->getSlope(),($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 log(($yValue + $this->_Yoffset) / $this->getIntersect()) / log($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.'^X';

	}	//	function getEquation()





	/**

	 * Return the Slope of the line

	 *

	 * @param	 int		$dp		Number of places of decimal precision to display

	 * @return	 string

	 **/

	public function getSlope($dp=0) {

		if ($dp != 0) {

			return round(exp($this->_slope),$dp);

		}

		return exp($this->_slope);

	}	//	function getSlope()





	/**

	 * 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 _exponential_regression($yValues, $xValues, $const) {

		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 _exponential_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) {

			$this->_exponential_regression($yValues, $xValues, $const);

		}

	}	//	function __construct()



}	//	class exponentialBestFit