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								<?php
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								/**
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								 * PHPExcel
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								 *
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								 * Copyright (c) 2006 - 2014 PHPExcel
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								 *
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								 * This library is free software; you can redistribute it and/or
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								 * modify it under the terms of the GNU Lesser General Public
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								 * License as published by the Free Software Foundation; either
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								 * version 2.1 of the License, or (at your option) any later version.
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								 *
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								 * This library 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 GNU
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								 * Lesser General Public License for more details.
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								 *
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								 * You should have received a copy of the GNU Lesser General Public
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								 * License along with this library; if not, write to the Free Software
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								 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
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								 *
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								 * @category   PHPExcel
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								 * @package    PHPExcel_Shared_Trend
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								 * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
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								 * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt	LGPL
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								 * @version    ##VERSION##, ##DATE##
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								 */
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								require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
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								/**
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								 * PHPExcel_Logarithmic_Best_Fit
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								 *
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								 * @category   PHPExcel
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								 * @package    PHPExcel_Shared_Trend
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								 * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
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								 */
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								class PHPExcel_Logarithmic_Best_Fit extends PHPExcel_Best_Fit
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								{
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									/**
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									 * Algorithm type to use for best-fit
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									 * (Name of this trend class)
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									 *
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									 * @var	string
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									 **/
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									protected $_bestFitType		= 'logarithmic';
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									/**
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									 * Return the Y-Value for a specified value of X
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									 *
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									 * @param	 float		$xValue			X-Value
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									 * @return	 float						Y-Value
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									 **/
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									public function getValueOfYForX($xValue) {
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										return $this->getIntersect() + $this->getSlope() * log($xValue - $this->_Xoffset);
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									}	//	function getValueOfYForX()
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									/**
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									 * Return the X-Value for a specified value of Y
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									 *
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									 * @param	 float		$yValue			Y-Value
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									 * @return	 float						X-Value
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									 **/
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									public function getValueOfXForY($yValue) {
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										return exp(($yValue - $this->getIntersect()) / $this->getSlope());
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									}	//	function getValueOfXForY()
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									/**
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									 * Return the Equation of the best-fit line
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									 *
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									 * @param	 int		$dp		Number of places of decimal precision to display
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									 * @return	 string
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									 **/
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									public function getEquation($dp=0) {
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										$slope = $this->getSlope($dp);
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										$intersect = $this->getIntersect($dp);
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										return 'Y = '.$intersect.' + '.$slope.' * log(X)';
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									}	//	function getEquation()
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									/**
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									 * Execute the regression and calculate the goodness of fit for a set of X and Y data values
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									 *
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									 * @param	 float[]	$yValues	The set of Y-values for this regression
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									 * @param	 float[]	$xValues	The set of X-values for this regression
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									 * @param	 boolean	$const
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									 */
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									private function _logarithmic_regression($yValues, $xValues, $const) {
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										foreach($xValues as &$value) {
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											if ($value < 0.0) {
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												$value = 0 - log(abs($value));
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											} elseif ($value > 0.0) {
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												$value = log($value);
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											}
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										}
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										unset($value);
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										$this->_leastSquareFit($yValues, $xValues, $const);
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									}	//	function _logarithmic_regression()
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									/**
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									 * Define the regression and calculate the goodness of fit for a set of X and Y data values
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									 *
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									 * @param	float[]		$yValues	The set of Y-values for this regression
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									 * @param	float[]		$xValues	The set of X-values for this regression
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									 * @param	boolean		$const
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									 */
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									function __construct($yValues, $xValues=array(), $const=True) {
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										if (parent::__construct($yValues, $xValues) !== False) {
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											$this->_logarithmic_regression($yValues, $xValues, $const);
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										}
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									}	//	function __construct()
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								}	//	class logarithmicBestFit
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