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authorAnton Luka Šijanec <anton@sijanec.eu>2022-01-11 12:35:47 +0100
committerAnton Luka Šijanec <anton@sijanec.eu>2022-01-11 12:35:47 +0100
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+<?php
+/**
+ * PHPExcel
+ *
+ * Copyright (c) 2006 - 2012 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 - 2012 PHPExcel (http://www.codeplex.com/PHPExcel)
+ * @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
+ * @version 1.7.8, 2012-10-12
+ */
+
+
+require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
+
+
+/**
+ * PHPExcel_Logarithmic_Best_Fit
+ *
+ * @category PHPExcel
+ * @package PHPExcel_Shared_Trend
+ * @copyright Copyright (c) 2006 - 2012 PHPExcel (http://www.codeplex.com/PHPExcel)
+ */
+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);
+ } // 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 exp(($yValue - $this->getIntersect()) / $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.' * log(X)';
+ } // function getEquation()
+
+
+ /**
+ * 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 $const
+ */
+ private function _logarithmic_regression($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);
+ } // function _logarithmic_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) {
+ $this->_logarithmic_regression($yValues, $xValues, $const);
+ }
+ } // function __construct()
+
+} // class logarithmicBestFit \ No newline at end of file