vendor/phpoffice/phpexcel/Classes/PHPExcel/Shared/trend/linearBestFitClass.php

   1 <?php
   2 /**
   3  * PHPExcel
   4  *
   5  * Copyright (c) 2006 - 2014 PHPExcel
   6  *
   7  * This library is free software; you can redistribute it and/or
   8  * modify it under the terms of the GNU Lesser General Public
   9  * License as published by the Free Software Foundation; either
  10  * version 2.1 of the License, or (at your option) any later version.
  11  *
  12  * This library is distributed in the hope that it will be useful,
  13  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  15  * Lesser General Public License for more details.
  16  *
  17  * You should have received a copy of the GNU Lesser General Public
  18  * License along with this library; if not, write to the Free Software
  19  * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
  20  *
  21  * @category   PHPExcel
  22  * @package    PHPExcel_Shared_Trend
  23  * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
  24  * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt    LGPL
  25  * @version    ##VERSION##, ##DATE##
  26  */
  27
  28
  29 require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
  30
  31
  32 /**
  33  * PHPExcel_Linear_Best_Fit
  34  *
  35  * @category   PHPExcel
  36  * @package    PHPExcel_Shared_Trend
  37  * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
  38  */
  39 class PHPExcel_Linear_Best_Fit extends PHPExcel_Best_Fit
  40 {
  41         /**
  42          * Algorithm type to use for best-fit
  43          * (Name of this trend class)
  44          *
  45          * @var string
  46          **/
  47         protected $_bestFitType         = 'linear';
  48
  49
  50         /**
  51          * Return the Y-Value for a specified value of X
  52          *
  53          * @param        float          $xValue                 X-Value
  54          * @return       float                                          Y-Value
  55          **/
  56         public function getValueOfYForX($xValue) {
  57                 return $this->getIntersect() + $this->getSlope() * $xValue;
  58         }       //      function getValueOfYForX()
  59
  60
  61         /**
  62          * Return the X-Value for a specified value of Y
  63          *
  64          * @param        float          $yValue                 Y-Value
  65          * @return       float                                          X-Value
  66          **/
  67         public function getValueOfXForY($yValue) {
  68                 return ($yValue - $this->getIntersect()) / $this->getSlope();
  69         }       //      function getValueOfXForY()
  70
  71
  72         /**
  73          * Return the Equation of the best-fit line
  74          *
  75          * @param        int            $dp             Number of places of decimal precision to display
  76          * @return       string
  77          **/
  78         public function getEquation($dp=0) {
  79                 $slope = $this->getSlope($dp);
  80                 $intersect = $this->getIntersect($dp);
  81
  82                 return 'Y = '.$intersect.' + '.$slope.' * X';
  83         }       //      function getEquation()
  84
  85
  86         /**
  87          * Execute the regression and calculate the goodness of fit for a set of X and Y data values
  88          *
  89          * @param        float[]        $yValues        The set of Y-values for this regression
  90          * @param        float[]        $xValues        The set of X-values for this regression
  91          * @param        boolean        $const
  92          */
  93         private function _linear_regression($yValues, $xValues, $const) {
  94                 $this->_leastSquareFit($yValues, $xValues,$const);
  95         }       //      function _linear_regression()
  96
  97
  98         /**
  99          * Define the regression and calculate the goodness of fit for a set of X and Y data values
 100          *
 101          * @param       float[]         $yValues        The set of Y-values for this regression
 102          * @param       float[]         $xValues        The set of X-values for this regression
 103          * @param       boolean         $const
 104          */
 105         function __construct($yValues, $xValues=array(), $const=True) {
 106                 if (parent::__construct($yValues, $xValues) !== False) {
 107                         $this->_linear_regression($yValues, $xValues, $const);
 108                 }
 109         }       //      function __construct()
 110
 111 }       //      class linearBestFit