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 1.8.0, 2014-03-02
26: */
27:
28:
29: require_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: */
39: class 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