139 lines
		
	
	
		
			4.4 KiB
		
	
	
	
		
			PHP
		
	
	
	
			
		
		
	
	
			139 lines
		
	
	
		
			4.4 KiB
		
	
	
	
		
			PHP
		
	
	
	
<?php
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require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
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/**
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 * PHPExcel_Exponential_Best_Fit
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 *
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 * Copyright (c) 2006 - 2015 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 - 2015 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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class PHPExcel_Exponential_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        = 'exponential';
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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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    {
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        return $this->getIntersect() * pow($this->getSlope(), ($xValue - $this->xOffset));
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    }
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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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    {
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        return log(($yValue + $this->yOffset) / $this->getIntersect()) / log($this->getSlope());
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    }
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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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    {
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        $slope = $this->getSlope($dp);
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        $intersect = $this->getIntersect($dp);
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        return 'Y = ' . $intersect . ' * ' . $slope . '^X';
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    }
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    /**
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     * Return the Slope of the line
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     *
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     * @param     int        $dp        Number of places of decimal precision to display
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     * @return     string
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     **/
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    public function getSlope($dp = 0)
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    {
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        if ($dp != 0) {
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            return round(exp($this->_slope), $dp);
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        }
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        return exp($this->_slope);
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    }
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    /**
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     * Return the Value of X where it intersects Y = 0
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     *
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     * @param     int        $dp        Number of places of decimal precision to display
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     * @return     string
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     **/
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    public function getIntersect($dp = 0)
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    {
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        if ($dp != 0) {
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            return round(exp($this->intersect), $dp);
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        }
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        return exp($this->intersect);
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    }
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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    $const
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     */
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    private function exponentialRegression($yValues, $xValues, $const)
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    {
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        foreach ($yValues 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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    }
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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        $const
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     */
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    public function __construct($yValues, $xValues = array(), $const = true)
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    {
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        if (parent::__construct($yValues, $xValues) !== false) {
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            $this->exponentialRegression($yValues, $xValues, $const);
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        }
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    }
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}
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