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/application/libraries/PHPExcel/Classes/PHPExcel/Shared/trend/logarithmicBestFitClass.php

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PHP | 120 lines | 33 code | 17 blank | 70 comment | 2 complexity | 6d3caa27a75068be1b388cf802991313 MD5 | raw file
Possible License(s): GPL-3.0
  1. <?php
  2. /**
  3. * PHPExcel
  4. *
  5. * Copyright (c) 2006 - 2012 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 - 2012 PHPExcel (http://www.codeplex.com/PHPExcel)
  24. * @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
  25. * @version 1.7.7, 2012-05-19
  26. */
  27. require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
  28. /**
  29. * PHPExcel_Logarithmic_Best_Fit
  30. *
  31. * @category PHPExcel
  32. * @package PHPExcel_Shared_Trend
  33. * @copyright Copyright (c) 2006 - 2012 PHPExcel (http://www.codeplex.com/PHPExcel)
  34. */
  35. class PHPExcel_Logarithmic_Best_Fit extends PHPExcel_Best_Fit
  36. {
  37. /**
  38. * Algorithm type to use for best-fit
  39. * (Name of this trend class)
  40. *
  41. * @var string
  42. **/
  43. protected $_bestFitType = 'logarithmic';
  44. /**
  45. * Return the Y-Value for a specified value of X
  46. *
  47. * @param float $xValue X-Value
  48. * @return float Y-Value
  49. **/
  50. public function getValueOfYForX($xValue) {
  51. return $this->getIntersect() + $this->getSlope() * log($xValue - $this->_Xoffset);
  52. } // function getValueOfYForX()
  53. /**
  54. * Return the X-Value for a specified value of Y
  55. *
  56. * @param float $yValue Y-Value
  57. * @return float X-Value
  58. **/
  59. public function getValueOfXForY($yValue) {
  60. return exp(($yValue - $this->getIntersect()) / $this->getSlope());
  61. } // function getValueOfXForY()
  62. /**
  63. * Return the Equation of the best-fit line
  64. *
  65. * @param int $dp Number of places of decimal precision to display
  66. * @return string
  67. **/
  68. public function getEquation($dp=0) {
  69. $slope = $this->getSlope($dp);
  70. $intersect = $this->getIntersect($dp);
  71. return 'Y = '.$intersect.' + '.$slope.' * log(X)';
  72. } // function getEquation()
  73. /**
  74. * Execute the regression and calculate the goodness of fit for a set of X and Y data values
  75. *
  76. * @param float[] $yValues The set of Y-values for this regression
  77. * @param float[] $xValues The set of X-values for this regression
  78. * @param boolean $const
  79. */
  80. private function _logarithmic_regression($yValues, $xValues, $const) {
  81. foreach($xValues as &$value) {
  82. if ($value < 0.0) {
  83. $value = 0 - log(abs($value));
  84. } elseif ($value > 0.0) {
  85. $value = log($value);
  86. }
  87. }
  88. unset($value);
  89. $this->_leastSquareFit($yValues, $xValues, $const);
  90. } // function _logarithmic_regression()
  91. /**
  92. * Define the regression and calculate the goodness of fit for a set of X and Y data values
  93. *
  94. * @param float[] $yValues The set of Y-values for this regression
  95. * @param float[] $xValues The set of X-values for this regression
  96. * @param boolean $const
  97. */
  98. function __construct($yValues, $xValues=array(), $const=True) {
  99. if (parent::__construct($yValues, $xValues) !== False) {
  100. $this->_logarithmic_regression($yValues, $xValues, $const);
  101. }
  102. } // function __construct()
  103. } // class logarithmicBestFit