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✏️ Editing: exponentialBestFitClass.php

<?php

require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');

/**
 * PHPExcel_Exponential_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_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));
    }

/**
     * 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());
    }

/**
     * 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';
    }

/**
     * 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);
    }

/**
     * 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);
    }

/**
     * 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    $const
     */
    private function exponentialRegression($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);
    }

/**
     * 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) {
            $this->exponentialRegression($yValues, $xValues, $const);
        }
    }
}

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