Subversion Repositories eFlore/Applications.cel

<?php
/**
 * PHPExcel
 *
 * Copyright (c) 2006 - 2013 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 - 2013 PHPExcel (http://www.codeplex.com/PHPExcel)
 * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt    LGPL
 * @version    ##VERSION##, ##DATE##
 */


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


/**
 * PHPExcel_Logarithmic_Best_Fit
 *
 * @category   PHPExcel
 * @package    PHPExcel_Shared_Trend
 * @copyright  Copyright (c) 2006 - 2013 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