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 29require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php'); 30 31 32/** 33 * PHPExcel_Exponential_Best_Fit 34 * 35 * @category PHPExcel 36 * @package PHPExcel_Shared_Trend 37 * @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel) 38 */ 39class PHPExcel_Exponential_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 = 'exponential'; 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 $this->getIntersect() * pow($this->getSlope(),($xValue - $this->_Xoffset)); 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 log(($yValue + $this->_Yoffset) / $this->getIntersect()) / log($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 * Return the Slope of the line 88 * 89 * @param int $dp Number of places of decimal precision to display 90 * @return string 91 **/ 92 public function getSlope($dp=0) { 93 if ($dp != 0) { 94 return round(exp($this->_slope),$dp); 95 } 96 return exp($this->_slope); 97 } // function getSlope() 98 99 100 /** 101 * Return the Value of X where it intersects Y = 0 102 * 103 * @param int $dp Number of places of decimal precision to display 104 * @return string 105 **/ 106 public function getIntersect($dp=0) { 107 if ($dp != 0) { 108 return round(exp($this->_intersect),$dp); 109 } 110 return exp($this->_intersect); 111 } // function getIntersect() 112 113 114 /** 115 * Execute the regression and calculate the goodness of fit for a set of X and Y data values 116 * 117 * @param float[] $yValues The set of Y-values for this regression 118 * @param float[] $xValues The set of X-values for this regression 119 * @param boolean $const 120 */ 121 private function _exponential_regression($yValues, $xValues, $const) { 122 foreach($yValues as &$value) { 123 if ($value < 0.0) { 124 $value = 0 - log(abs($value)); 125 } elseif ($value > 0.0) { 126 $value = log($value); 127 } 128 } 129 unset($value); 130 131 $this->_leastSquareFit($yValues, $xValues, $const); 132 } // function _exponential_regression() 133 134 135 /** 136 * Define the regression and calculate the goodness of fit for a set of X and Y data values 137 * 138 * @param float[] $yValues The set of Y-values for this regression 139 * @param float[] $xValues The set of X-values for this regression 140 * @param boolean $const 141 */ 142 function __construct($yValues, $xValues=array(), $const=True) { 143 if (parent::__construct($yValues, $xValues) !== False) { 144 $this->_exponential_regression($yValues, $xValues, $const); 145 } 146 } // function __construct() 147 148} // class exponentialBestFit