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: require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/JAMA/Matrix.php';
31:
32:
33: /**
34: * PHPExcel_Polynomial_Best_Fit
35: *
36: * @category PHPExcel
37: * @package PHPExcel_Shared_Trend
38: * @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
39: */
40: class PHPExcel_Polynomial_Best_Fit extends PHPExcel_Best_Fit
41: {
42: /**
43: * Algorithm type to use for best-fit
44: * (Name of this trend class)
45: *
46: * @var string
47: **/
48: protected $_bestFitType = 'polynomial';
49:
50: /**
51: * Polynomial order
52: *
53: * @protected
54: * @var int
55: **/
56: protected $_order = 0;
57:
58:
59: /**
60: * Return the order of this polynomial
61: *
62: * @return int
63: **/
64: public function getOrder() {
65: return $this->_order;
66: } // function getOrder()
67:
68:
69: /**
70: * Return the Y-Value for a specified value of X
71: *
72: * @param float $xValue X-Value
73: * @return float Y-Value
74: **/
75: public function getValueOfYForX($xValue) {
76: $retVal = $this->getIntersect();
77: $slope = $this->getSlope();
78: foreach($slope as $key => $value) {
79: if ($value != 0.0) {
80: $retVal += $value * pow($xValue, $key + 1);
81: }
82: }
83: return $retVal;
84: } // function getValueOfYForX()
85:
86:
87: /**
88: * Return the X-Value for a specified value of Y
89: *
90: * @param float $yValue Y-Value
91: * @return float X-Value
92: **/
93: public function getValueOfXForY($yValue) {
94: return ($yValue - $this->getIntersect()) / $this->getSlope();
95: } // function getValueOfXForY()
96:
97:
98: /**
99: * Return the Equation of the best-fit line
100: *
101: * @param int $dp Number of places of decimal precision to display
102: * @return string
103: **/
104: public function getEquation($dp=0) {
105: $slope = $this->getSlope($dp);
106: $intersect = $this->getIntersect($dp);
107:
108: $equation = 'Y = '.$intersect;
109: foreach($slope as $key => $value) {
110: if ($value != 0.0) {
111: $equation .= ' + '.$value.' * X';
112: if ($key > 0) {
113: $equation .= '^'.($key + 1);
114: }
115: }
116: }
117: return $equation;
118: } // function getEquation()
119:
120:
121: /**
122: * Return the Slope of the line
123: *
124: * @param int $dp Number of places of decimal precision to display
125: * @return string
126: **/
127: public function getSlope($dp=0) {
128: if ($dp != 0) {
129: $coefficients = array();
130: foreach($this->_slope as $coefficient) {
131: $coefficients[] = round($coefficient,$dp);
132: }
133: return $coefficients;
134: }
135: return $this->_slope;
136: } // function getSlope()
137:
138:
139: public function getCoefficients($dp=0) {
140: return array_merge(array($this->getIntersect($dp)),$this->getSlope($dp));
141: } // function getCoefficients()
142:
143:
144: /**
145: * Execute the regression and calculate the goodness of fit for a set of X and Y data values
146: *
147: * @param int $order Order of Polynomial for this regression
148: * @param float[] $yValues The set of Y-values for this regression
149: * @param float[] $xValues The set of X-values for this regression
150: * @param boolean $const
151: */
152: private function _polynomial_regression($order, $yValues, $xValues, $const) {
153: // calculate sums
154: $x_sum = array_sum($xValues);
155: $y_sum = array_sum($yValues);
156: $xx_sum = $xy_sum = 0;
157: for($i = 0; $i < $this->_valueCount; ++$i) {
158: $xy_sum += $xValues[$i] * $yValues[$i];
159: $xx_sum += $xValues[$i] * $xValues[$i];
160: $yy_sum += $yValues[$i] * $yValues[$i];
161: }
162: /*
163: * This routine uses logic from the PHP port of polyfit version 0.1
164: * written by Michael Bommarito and Paul Meagher
165: *
166: * The function fits a polynomial function of order $order through
167: * a series of x-y data points using least squares.
168: *
169: */
170: for ($i = 0; $i < $this->_valueCount; ++$i) {
171: for ($j = 0; $j <= $order; ++$j) {
172: $A[$i][$j] = pow($xValues[$i], $j);
173: }
174: }
175: for ($i=0; $i < $this->_valueCount; ++$i) {
176: $B[$i] = array($yValues[$i]);
177: }
178: $matrixA = new Matrix($A);
179: $matrixB = new Matrix($B);
180: $C = $matrixA->solve($matrixB);
181:
182: $coefficients = array();
183: for($i = 0; $i < $C->m; ++$i) {
184: $r = $C->get($i, 0);
185: if (abs($r) <= pow(10, -9)) {
186: $r = 0;
187: }
188: $coefficients[] = $r;
189: }
190:
191: $this->_intersect = array_shift($coefficients);
192: $this->_slope = $coefficients;
193:
194: $this->_calculateGoodnessOfFit($x_sum,$y_sum,$xx_sum,$yy_sum,$xy_sum);
195: foreach($this->_xValues as $xKey => $xValue) {
196: $this->_yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
197: }
198: } // function _polynomial_regression()
199:
200:
201: /**
202: * Define the regression and calculate the goodness of fit for a set of X and Y data values
203: *
204: * @param int $order Order of Polynomial for this regression
205: * @param float[] $yValues The set of Y-values for this regression
206: * @param float[] $xValues The set of X-values for this regression
207: * @param boolean $const
208: */
209: function __construct($order, $yValues, $xValues=array(), $const=True) {
210: if (parent::__construct($yValues, $xValues) !== False) {
211: if ($order < $this->_valueCount) {
212: $this->_bestFitType .= '_'.$order;
213: $this->_order = $order;
214: $this->_polynomial_regression($order, $yValues, $xValues, $const);
215: if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) {
216: $this->_error = True;
217: }
218: } else {
219: $this->_error = True;
220: }
221: }
222: } // function __construct()
223:
224: } // class polynomialBestFit