<?php require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php'); /** * PHPExcel_Logarithmic_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_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); } /** * 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()); } /** * 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)'; } /** * 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 $const */ private function logarithmicRegression($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); } /** * 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) { $this->logarithmicRegression($yValues, $xValues, $const); } } }