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2388 jpm 1
<?php
2
/**
3
 * PHPExcel
4
 *
5
 * Copyright (c) 2006 - 2013 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 - 2013 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
/**
30
 * PHPExcel_Best_Fit
31
 *
32
 * @category   PHPExcel
33
 * @package    PHPExcel_Shared_Trend
34
 * @copyright  Copyright (c) 2006 - 2013 PHPExcel (http://www.codeplex.com/PHPExcel)
35
 */
36
class PHPExcel_Best_Fit
37
{
38
	/**
39
	 * Indicator flag for a calculation error
40
	 *
41
	 * @var	boolean
42
	 **/
43
	protected $_error				= False;
44
 
45
	/**
46
	 * Algorithm type to use for best-fit
47
	 *
48
	 * @var	string
49
	 **/
50
	protected $_bestFitType			= 'undetermined';
51
 
52
	/**
53
	 * Number of entries in the sets of x- and y-value arrays
54
	 *
55
	 * @var	int
56
	 **/
57
	protected $_valueCount			= 0;
58
 
59
	/**
60
	 * X-value dataseries of values
61
	 *
62
	 * @var	float[]
63
	 **/
64
	protected $_xValues				= array();
65
 
66
	/**
67
	 * Y-value dataseries of values
68
	 *
69
	 * @var	float[]
70
	 **/
71
	protected $_yValues				= array();
72
 
73
	/**
74
	 * Flag indicating whether values should be adjusted to Y=0
75
	 *
76
	 * @var	boolean
77
	 **/
78
	protected $_adjustToZero		= False;
79
 
80
	/**
81
	 * Y-value series of best-fit values
82
	 *
83
	 * @var	float[]
84
	 **/
85
	protected $_yBestFitValues		= array();
86
 
87
	protected $_goodnessOfFit 		= 1;
88
 
89
	protected $_stdevOfResiduals	= 0;
90
 
91
	protected $_covariance			= 0;
92
 
93
	protected $_correlation			= 0;
94
 
95
	protected $_SSRegression		= 0;
96
 
97
	protected $_SSResiduals			= 0;
98
 
99
	protected $_DFResiduals			= 0;
100
 
101
	protected $_F					= 0;
102
 
103
	protected $_slope				= 0;
104
 
105
	protected $_slopeSE				= 0;
106
 
107
	protected $_intersect			= 0;
108
 
109
	protected $_intersectSE			= 0;
110
 
111
	protected $_Xoffset				= 0;
112
 
113
	protected $_Yoffset				= 0;
114
 
115
 
116
	public function getError() {
117
		return $this->_error;
118
	}	//	function getBestFitType()
119
 
120
 
121
	public function getBestFitType() {
122
		return $this->_bestFitType;
123
	}	//	function getBestFitType()
124
 
125
 
126
	/**
127
	 * Return the Y-Value for a specified value of X
128
	 *
129
	 * @param	 float		$xValue			X-Value
130
	 * @return	 float						Y-Value
131
	 */
132
	public function getValueOfYForX($xValue) {
133
		return False;
134
	}	//	function getValueOfYForX()
135
 
136
 
137
	/**
138
	 * Return the X-Value for a specified value of Y
139
	 *
140
	 * @param	 float		$yValue			Y-Value
141
	 * @return	 float						X-Value
142
	 */
143
	public function getValueOfXForY($yValue) {
144
		return False;
145
	}	//	function getValueOfXForY()
146
 
147
 
148
	/**
149
	 * Return the original set of X-Values
150
	 *
151
	 * @return	 float[]				X-Values
152
	 */
153
	public function getXValues() {
154
		return $this->_xValues;
155
	}	//	function getValueOfXForY()
156
 
157
 
158
	/**
159
	 * Return the Equation of the best-fit line
160
	 *
161
	 * @param	 int		$dp		Number of places of decimal precision to display
162
	 * @return	 string
163
	 */
164
	public function getEquation($dp=0) {
165
		return False;
166
	}	//	function getEquation()
167
 
168
 
169
	/**
170
	 * Return the Slope of the line
171
	 *
172
	 * @param	 int		$dp		Number of places of decimal precision to display
173
	 * @return	 string
174
	 */
175
	public function getSlope($dp=0) {
176
		if ($dp != 0) {
177
			return round($this->_slope,$dp);
178
		}
179
		return $this->_slope;
180
	}	//	function getSlope()
181
 
182
 
183
	/**
184
	 * Return the standard error of the Slope
185
	 *
186
	 * @param	 int		$dp		Number of places of decimal precision to display
187
	 * @return	 string
188
	 */
189
	public function getSlopeSE($dp=0) {
190
		if ($dp != 0) {
191
			return round($this->_slopeSE,$dp);
192
		}
193
		return $this->_slopeSE;
194
	}	//	function getSlopeSE()
195
 
196
 
197
	/**
198
	 * Return the Value of X where it intersects Y = 0
199
	 *
200
	 * @param	 int		$dp		Number of places of decimal precision to display
201
	 * @return	 string
202
	 */
203
	public function getIntersect($dp=0) {
204
		if ($dp != 0) {
205
			return round($this->_intersect,$dp);
206
		}
207
		return $this->_intersect;
208
	}	//	function getIntersect()
209
 
210
 
211
	/**
212
	 * Return the standard error of the Intersect
213
	 *
214
	 * @param	 int		$dp		Number of places of decimal precision to display
215
	 * @return	 string
216
	 */
217
	public function getIntersectSE($dp=0) {
218
		if ($dp != 0) {
219
			return round($this->_intersectSE,$dp);
220
		}
221
		return $this->_intersectSE;
222
	}	//	function getIntersectSE()
223
 
224
 
225
	/**
226
	 * Return the goodness of fit for this regression
227
	 *
228
	 * @param	 int		$dp		Number of places of decimal precision to return
229
	 * @return	 float
230
	 */
231
	public function getGoodnessOfFit($dp=0) {
232
		if ($dp != 0) {
233
			return round($this->_goodnessOfFit,$dp);
234
		}
235
		return $this->_goodnessOfFit;
236
	}	//	function getGoodnessOfFit()
237
 
238
 
239
	public function getGoodnessOfFitPercent($dp=0) {
240
		if ($dp != 0) {
241
			return round($this->_goodnessOfFit * 100,$dp);
242
		}
243
		return $this->_goodnessOfFit * 100;
244
	}	//	function getGoodnessOfFitPercent()
245
 
246
 
247
	/**
248
	 * Return the standard deviation of the residuals for this regression
249
	 *
250
	 * @param	 int		$dp		Number of places of decimal precision to return
251
	 * @return	 float
252
	 */
253
	public function getStdevOfResiduals($dp=0) {
254
		if ($dp != 0) {
255
			return round($this->_stdevOfResiduals,$dp);
256
		}
257
		return $this->_stdevOfResiduals;
258
	}	//	function getStdevOfResiduals()
259
 
260
 
261
	public function getSSRegression($dp=0) {
262
		if ($dp != 0) {
263
			return round($this->_SSRegression,$dp);
264
		}
265
		return $this->_SSRegression;
266
	}	//	function getSSRegression()
267
 
268
 
269
	public function getSSResiduals($dp=0) {
270
		if ($dp != 0) {
271
			return round($this->_SSResiduals,$dp);
272
		}
273
		return $this->_SSResiduals;
274
	}	//	function getSSResiduals()
275
 
276
 
277
	public function getDFResiduals($dp=0) {
278
		if ($dp != 0) {
279
			return round($this->_DFResiduals,$dp);
280
		}
281
		return $this->_DFResiduals;
282
	}	//	function getDFResiduals()
283
 
284
 
285
	public function getF($dp=0) {
286
		if ($dp != 0) {
287
			return round($this->_F,$dp);
288
		}
289
		return $this->_F;
290
	}	//	function getF()
291
 
292
 
293
	public function getCovariance($dp=0) {
294
		if ($dp != 0) {
295
			return round($this->_covariance,$dp);
296
		}
297
		return $this->_covariance;
298
	}	//	function getCovariance()
299
 
300
 
301
	public function getCorrelation($dp=0) {
302
		if ($dp != 0) {
303
			return round($this->_correlation,$dp);
304
		}
305
		return $this->_correlation;
306
	}	//	function getCorrelation()
307
 
308
 
309
	public function getYBestFitValues() {
310
		return $this->_yBestFitValues;
311
	}	//	function getYBestFitValues()
312
 
313
 
314
	protected function _calculateGoodnessOfFit($sumX,$sumY,$sumX2,$sumY2,$sumXY,$meanX,$meanY, $const) {
315
		$SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;
316
		foreach($this->_xValues as $xKey => $xValue) {
317
			$bestFitY = $this->_yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
318
 
319
			$SSres += ($this->_yValues[$xKey] - $bestFitY) * ($this->_yValues[$xKey] - $bestFitY);
320
			if ($const) {
321
				$SStot += ($this->_yValues[$xKey] - $meanY) * ($this->_yValues[$xKey] - $meanY);
322
			} else {
323
				$SStot += $this->_yValues[$xKey] * $this->_yValues[$xKey];
324
			}
325
			$SScov += ($this->_xValues[$xKey] - $meanX) * ($this->_yValues[$xKey] - $meanY);
326
			if ($const) {
327
				$SSsex += ($this->_xValues[$xKey] - $meanX) * ($this->_xValues[$xKey] - $meanX);
328
			} else {
329
				$SSsex += $this->_xValues[$xKey] * $this->_xValues[$xKey];
330
			}
331
		}
332
 
333
		$this->_SSResiduals = $SSres;
334
		$this->_DFResiduals = $this->_valueCount - 1 - $const;
335
 
336
		if ($this->_DFResiduals == 0.0) {
337
			$this->_stdevOfResiduals = 0.0;
338
		} else {
339
			$this->_stdevOfResiduals = sqrt($SSres / $this->_DFResiduals);
340
		}
341
		if (($SStot == 0.0) || ($SSres == $SStot)) {
342
			$this->_goodnessOfFit = 1;
343
		} else {
344
			$this->_goodnessOfFit = 1 - ($SSres / $SStot);
345
		}
346
 
347
		$this->_SSRegression = $this->_goodnessOfFit * $SStot;
348
		$this->_covariance = $SScov / $this->_valueCount;
349
		$this->_correlation = ($this->_valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->_valueCount * $sumX2 - pow($sumX,2)) * ($this->_valueCount * $sumY2 - pow($sumY,2)));
350
		$this->_slopeSE = $this->_stdevOfResiduals / sqrt($SSsex);
351
		$this->_intersectSE = $this->_stdevOfResiduals * sqrt(1 / ($this->_valueCount - ($sumX * $sumX) / $sumX2));
352
		if ($this->_SSResiduals != 0.0) {
353
			if ($this->_DFResiduals == 0.0) {
354
				$this->_F = 0.0;
355
			} else {
356
				$this->_F = $this->_SSRegression / ($this->_SSResiduals / $this->_DFResiduals);
357
			}
358
		} else {
359
			if ($this->_DFResiduals == 0.0) {
360
				$this->_F = 0.0;
361
			} else {
362
				$this->_F = $this->_SSRegression / $this->_DFResiduals;
363
			}
364
		}
365
	}	//	function _calculateGoodnessOfFit()
366
 
367
 
368
	protected function _leastSquareFit($yValues, $xValues, $const) {
369
		// calculate sums
370
		$x_sum = array_sum($xValues);
371
		$y_sum = array_sum($yValues);
372
		$meanX = $x_sum / $this->_valueCount;
373
		$meanY = $y_sum / $this->_valueCount;
374
		$mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
375
		for($i = 0; $i < $this->_valueCount; ++$i) {
376
			$xy_sum += $xValues[$i] * $yValues[$i];
377
			$xx_sum += $xValues[$i] * $xValues[$i];
378
			$yy_sum += $yValues[$i] * $yValues[$i];
379
 
380
			if ($const) {
381
				$mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
382
				$mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
383
			} else {
384
				$mBase += $xValues[$i] * $yValues[$i];
385
				$mDivisor += $xValues[$i] * $xValues[$i];
386
			}
387
		}
388
 
389
		// calculate slope
390
//		$this->_slope = (($this->_valueCount * $xy_sum) - ($x_sum * $y_sum)) / (($this->_valueCount * $xx_sum) - ($x_sum * $x_sum));
391
		$this->_slope = $mBase / $mDivisor;
392
 
393
		// calculate intersect
394
//		$this->_intersect = ($y_sum - ($this->_slope * $x_sum)) / $this->_valueCount;
395
		if ($const) {
396
			$this->_intersect = $meanY - ($this->_slope * $meanX);
397
		} else {
398
			$this->_intersect = 0;
399
		}
400
 
401
		$this->_calculateGoodnessOfFit($x_sum,$y_sum,$xx_sum,$yy_sum,$xy_sum,$meanX,$meanY,$const);
402
	}	//	function _leastSquareFit()
403
 
404
 
405
	/**
406
	 * Define the regression
407
	 *
408
	 * @param	float[]		$yValues	The set of Y-values for this regression
409
	 * @param	float[]		$xValues	The set of X-values for this regression
410
	 * @param	boolean		$const
411
	 */
412
	function __construct($yValues, $xValues=array(), $const=True) {
413
		//	Calculate number of points
414
		$nY = count($yValues);
415
		$nX = count($xValues);
416
 
417
		//	Define X Values if necessary
418
		if ($nX == 0) {
419
			$xValues = range(1,$nY);
420
			$nX = $nY;
421
		} elseif ($nY != $nX) {
422
			//	Ensure both arrays of points are the same size
423
			$this->_error = True;
424
			return False;
425
		}
426
 
427
		$this->_valueCount = $nY;
428
		$this->_xValues = $xValues;
429
		$this->_yValues = $yValues;
430
	}	//	function __construct()
431
 
432
}	//	class bestFit