src/utils/Accumulator.hpp

/*
 * Copyright (c) 2012 The University of Notre Dame. All Rights Reserved.
 *
 * The University of Notre Dame grants you ("Licensee") a
 * non-exclusive, royalty free, license to use, modify and
 * redistribute this software in source and binary code form, provided
 * that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the
 *    distribution.
 *
 * This software is provided "AS IS," without a warranty of any
 * kind. All express or implied conditions, representations and
 * warranties, including any implied warranty of merchantability,
 * fitness for a particular purpose or non-infringement, are hereby
 * excluded.  The University of Notre Dame and its licensors shall not
 * be liable for any damages suffered by licensee as a result of
 * using, modifying or distributing the software or its
 * derivatives. In no event will the University of Notre Dame or its
 * licensors be liable for any lost revenue, profit or data, or for
 * direct, indirect, special, consequential, incidental or punitive
 * damages, however caused and regardless of the theory of liability,
 * arising out of the use of or inability to use software, even if the
 * University of Notre Dame has been advised of the possibility of
 * such damages.
 *
 * SUPPORT OPEN SCIENCE!  If you use OpenMD or its source code in your
 * research, please cite the appropriate papers when you publish your
 * work.  Good starting points are:
 *                                                                      
 * [1]  Meineke, et al., J. Comp. Chem. 26, 252-271 (2005).             
 * [2]  Fennell & Gezelter, J. Chem. Phys. 124, 234104 (2006).          
 * [3]  Sun, Lin & Gezelter, J. Chem. Phys. 128, 234107 (2008).          
 * [4]  Kuang & Gezelter,  J. Chem. Phys. 133, 164101 (2010).
 * [5]  Vardeman, Stocker & Gezelter, J. Chem. Theory Comput. 7, 834 (2011).
 */

#ifndef UTILS_ACCUMULATOR_HPP
#define UTILS_ACCUMULATOR_HPP

#include <cmath>
#include <cassert>
#include "math/Vector3.hpp"

namespace OpenMD {


  class BaseAccumulator {
  public:
    virtual void clear() = 0;
    /**
     * get the number of accumulated values
     */
    virtual size_t count()  {
      return Count_;
    }
  protected:
    size_t Count_;

  };   


  /** 
   * Basic Accumulator class for numbers. 
   */  
  class Accumulator : public BaseAccumulator {    

    typedef RealType ElementType;
    typedef RealType ResultType;

  public:
    
    Accumulator() : BaseAccumulator() {
      this->clear();
    }

    /**
     * Accumulate another value
     */
    virtual void add(ElementType const& val) {
      Count_++;
      Avg_  += (val       - Avg_ ) / Count_;
      Avg2_ += (val * val - Avg2_) / Count_;
      Val_   = val;
      if (Count_ <= 1) {
        Max_ = val;
        Min_ = val;
      } else {
        Max_ = val > Max_ ? val : Max_;
        Min_ = val < Min_ ? val : Min_;
      }
    }
    
    /**
     * reset the Accumulator to the empty state
     */
    void clear() {
      Count_ = 0;
      Avg_   = 0;
      Avg2_  = 0;
      Val_   = 0;
    }
    

    /**
     * return the most recently added value
     */
    void getLastValue(ElementType &ret)  {
      ret = Val_;
      return;
    }    

    /**
     * compute the Mean
     */
    void getAverage(ResultType &ret)  {
      assert(Count_ != 0);
      ret = Avg_;
      return;
    }

    /**
     * compute the Variance
     */
    void getVariance(ResultType &ret)  {
      assert(Count_ != 0);
      ret = (Avg2_ - Avg_  * Avg_);
      return;
    }
    
    /**
     * compute error of average value
     */
    void getStdDev(ResultType &ret)  {
      assert(Count_ != 0);
      RealType var;
      this->getVariance(var);
      ret = sqrt(var);
      return;
    }

    /**
     * return the largest value
     */
    void getMax(ElementType &ret)  {
      assert(Count_ != 0);
      ret = Max_;
      return;
    }

    /**
     * return the smallest value
     */
    void getMin(ElementType &ret)  {
      assert(Count_ != 0);
      ret = Max_;
      return;
    }

    /**
     * return the 95% confidence interval:
     *
     * That is returns c, such that we have 95% confidence that the
     * true mean is within 2c of the Average (x):
     *
     *   x - c <= true mean <= x + c
     *
     */
    void get95percentConfidenceInterval(ResultType &ret) {
      assert(Count_ != 0);
      RealType sd;
      this->getStdDev(sd);
      ret = 1.960 * sd / sqrt(RealType(Count_));
      return;
    }

  private:
    ElementType Val_;
    ResultType Avg_;
    ResultType Avg2_;
    ElementType Min_;
    ElementType Max_;
  };

  class VectorAccumulator : public BaseAccumulator {
    
    typedef Vector3d ElementType;
    typedef Vector3d ResultType;
    
  public:
    VectorAccumulator() : BaseAccumulator() {
      this->clear();
    }

    /**
     * Accumulate another value
     */
    void add(ElementType const& val) {
      Count_++;
      RealType len(0.0);
      for (unsigned int i =0; i < 3; i++) {
        Avg_[i]  += (val[i]       - Avg_[i] ) / Count_;
        Avg2_[i] += (val[i] * val[i] - Avg2_[i]) / Count_;
        Val_[i]   = val[i];
        len += val[i]*val[i];
      }
      len = sqrt(len);
      AvgLen_  += (len       - AvgLen_ ) / Count_;
      AvgLen2_ += (len * len - AvgLen2_) / Count_;

      if (Count_ <= 1) {
        Max_ = len;
        Min_ = len;
      } else {
        Max_ = len > Max_ ? len : Max_;
        Min_ = len < Min_ ? len : Min_;
      }
    }

    /**
     * reset the Accumulator to the empty state
     */
    void clear() {
      Count_ = 0;
      Avg_ = V3Zero;
      Avg2_ = V3Zero;
      Val_ = V3Zero;
      AvgLen_   = 0;
      AvgLen2_  = 0;
    }
    
    /**
     * return the most recently added value
     */
    void getLastValue(ElementType &ret) {
      ret = Val_;
      return;
    }
    
    /**
     * compute the Mean
     */
    void getAverage(ResultType &ret) {
      assert(Count_ != 0);
      ret = Avg_;
      return;
    }
    
    /**
     * compute the Variance
     */
    void getVariance(ResultType &ret) {
      assert(Count_ != 0);
      for (unsigned int i =0; i < 3; i++) {
        ret[i] = (Avg2_[i] - Avg_[i]  * Avg_[i]);
      }
      return;
    }
    
    /**
     * compute error of average value
     */
    void getStdDev(ResultType &ret) {
      assert(Count_ != 0);
      ResultType var;
      this->getVariance(var);
      ret[0] = sqrt(var[0]);
      ret[1] = sqrt(var[1]);
      ret[2] = sqrt(var[2]);
      return;
    }

    /**
     * return the 95% confidence interval:
     *
     * That is returns c, such that we have 95% confidence that the
     * true mean is within 2c of the Average (x):
     *
     *   x - c <= true mean <= x + c
     *
     */
    void get95percentConfidenceInterval(ResultType &ret) {
      assert(Count_ != 0);
      ResultType sd;
      this->getStdDev(sd);
      ret[0] = 1.960 * sd[0] / sqrt(RealType(Count_));
      ret[1] = 1.960 * sd[1] / sqrt(RealType(Count_));
      ret[2] = 1.960 * sd[2] / sqrt(RealType(Count_));
      return;
    }

    /**
     * return the largest length
     */
    void getMaxLength(RealType &ret) {
      assert(Count_ != 0);
      ret = Max_;
      return;
    }

    /**
     * return the smallest length
     */
    void getMinLength(RealType &ret) {
      assert(Count_ != 0);
      ret = Min_;
      return;
    }

    /**
     * return the largest length
     */
    void getAverageLength(RealType &ret) {
      assert(Count_ != 0);
      ret = AvgLen_;
      return;
    }

    /**
     * compute the Variance of the length
     */
    void getLengthVariance(RealType &ret) {
      assert(Count_ != 0);      
      ret= (AvgLen2_ - AvgLen_ * AvgLen_);
      return;
    }
    
    /**
     * compute error of average value
     */
    void getLengthStdDev(RealType &ret) {
      assert(Count_ != 0);
      RealType var;
      this->getLengthVariance(var);
      ret = sqrt(var);
      return;
    }

    /**
     * return the 95% confidence interval:
     *
     * That is returns c, such that we have 95% confidence that the
     * true mean is within 2c of the Average (x):
     *
     *   x - c <= true mean <= x + c
     *
     */
    void getLength95percentConfidenceInterval(ResultType &ret) {
      assert(Count_ != 0);
      RealType sd;
      this->getLengthStdDev(sd);
      ret = 1.960 * sd / sqrt(RealType(Count_));
      return;
    }


  private:
    ResultType Val_;
    ResultType Avg_;
    ResultType Avg2_;
    RealType AvgLen_;
    RealType AvgLen2_;
    RealType Min_;
    RealType Max_;

  };

  class MatrixAccumulator : public BaseAccumulator {
    
    typedef Mat3x3d ElementType;
    typedef Mat3x3d ResultType;
    
  public:
    MatrixAccumulator() : BaseAccumulator() {
      this->clear();
    }

    /**
     * Accumulate another value
     */
    void add(ElementType const& val) {
      Count_++;
      for (unsigned int i = 0; i < 3; i++) {
        for (unsigned int j = 0; j < 3; j++) {          
          Avg_(i,j)  += (val(i,j)       - Avg_(i,j) ) / Count_;
          Avg2_(i,j) += (val(i,j) * val(i,j) - Avg2_(i,j)) / Count_;
          Val_(i,j)   = val(i,j);
        }
      }
    }

    /**
     * reset the Accumulator to the empty state
     */
    void clear() {
      Count_ = 0;
      Avg_ *= 0.0;
      Avg2_ *= 0.0;
      Val_ *= 0.0;
    }
    
    /**
     * return the most recently added value
     */
    void getLastValue(ElementType &ret) {
      ret = Val_;
      return;
    }
    
    /**
     * compute the Mean
     */
    void getAverage(ResultType &ret) {
      assert(Count_ != 0);
      ret = Avg_;
      return;
    }

    /**
     * compute the Variance
     */
    void getVariance(ResultType &ret) {
      assert(Count_ != 0);
      for (unsigned int i = 0; i < 3; i++) {
        for (unsigned int j = 0; j < 3; j++) {          
          ret(i,j) = (Avg2_(i,j) - Avg_(i,j)  * Avg_(i,j));
        }
      }
      return;
    }
    
    /**
     * compute error of average value
     */
    void getStdDev(ResultType &ret) {
      assert(Count_ != 0);
      Mat3x3d var;
      this->getVariance(var);
      for (unsigned int i = 0; i < 3; i++) {
        for (unsigned int j = 0; j < 3; j++) {
          ret(i,j) = sqrt(var(i,j));  
        }
      }
      return;
    }
      
    /**
     * return the 95% confidence interval:
     *
     * That is returns c, such that we have 95% confidence that the
     * true mean is within 2c of the Average (x):
     *
     *   x - c <= true mean <= x + c
     *
     */
    void get95percentConfidenceInterval(ResultType &ret) {
      assert(Count_ != 0);
      Mat3x3d sd;
      this->getStdDev(sd);
      for (unsigned int i = 0; i < 3; i++) {
        for (unsigned int j = 0; j < 3; j++) {
          ret(i,j) = 1.960 * sd(i,j) / sqrt(RealType(Count_));
        }
      }
      return;
    }

  private:
    ElementType Val_;
    ResultType Avg_;
    ResultType Avg2_;
  };


} 

#endif
Revision:	1980
Committed:	Sat Apr 5 21:17:12 2014 UTC (11 years, 6 months ago) by gezelter
File size:	11276 byte(s)
Log Message:	Fixes for compilation with PGI compilers
#	User	Rev	Content
1	gezelter	1765	/*
2			* Copyright (c) 2012 The University of Notre Dame. All Rights Reserved.
3			*
4			* The University of Notre Dame grants you ("Licensee") a
5			* non-exclusive, royalty free, license to use, modify and
6			* redistribute this software in source and binary code form, provided
7			* that the following conditions are met:
8			*
9			* 1. Redistributions of source code must retain the above copyright
10			* notice, this list of conditions and the following disclaimer.
11			*
12			* 2. Redistributions in binary form must reproduce the above copyright
13			* notice, this list of conditions and the following disclaimer in the
14			* documentation and/or other materials provided with the
15			* distribution.
16			*
17			* This software is provided "AS IS," without a warranty of any
18			* kind. All express or implied conditions, representations and
19			* warranties, including any implied warranty of merchantability,
20			* fitness for a particular purpose or non-infringement, are hereby
21			* excluded. The University of Notre Dame and its licensors shall not
22			* be liable for any damages suffered by licensee as a result of
23			* using, modifying or distributing the software or its
24			* derivatives. In no event will the University of Notre Dame or its
25			* licensors be liable for any lost revenue, profit or data, or for
26			* direct, indirect, special, consequential, incidental or punitive
27			* damages, however caused and regardless of the theory of liability,
28			* arising out of the use of or inability to use software, even if the
29			* University of Notre Dame has been advised of the possibility of
30			* such damages.
31			*
32			* SUPPORT OPEN SCIENCE! If you use OpenMD or its source code in your
33			* research, please cite the appropriate papers when you publish your
34			* work. Good starting points are:
35			*
36			* [1] Meineke, et al., J. Comp. Chem. 26, 252-271 (2005).
37			* [2] Fennell & Gezelter, J. Chem. Phys. 124, 234104 (2006).
38	gezelter	1879	* [3] Sun, Lin & Gezelter, J. Chem. Phys. 128, 234107 (2008).
39	gezelter	1765	* [4] Kuang & Gezelter, J. Chem. Phys. 133, 164101 (2010).
40			* [5] Vardeman, Stocker & Gezelter, J. Chem. Theory Comput. 7, 834 (2011).
41			*/
42
43			#ifndef UTILS_ACCUMULATOR_HPP
44			#define UTILS_ACCUMULATOR_HPP
45
46			#include <cmath>
47			#include <cassert>
48			#include "math/Vector3.hpp"
49
50			namespace OpenMD {
51
52	gezelter	1791
53			class BaseAccumulator {
54			public:
55			virtual void clear() = 0;
56			/**
57			* get the number of accumulated values
58			*/
59			virtual size_t count() {
60			return Count_;
61			}
62			protected:
63			size_t Count_;
64
65			};
66
67
68
69	gezelter	1765	/**
70			* Basic Accumulator class for numbers.
71	gezelter	1791	*/
72			class Accumulator : public BaseAccumulator {
73	gezelter	1765
74			typedef RealType ElementType;
75			typedef RealType ResultType;
76
77			public:
78
79	gezelter	1791	Accumulator() : BaseAccumulator() {
80	gezelter	1765	this->clear();
81			}
82
83			/**
84			* Accumulate another value
85			*/
86			virtual void add(ElementType const& val) {
87			Count_++;
88			Avg_ += (val - Avg_ ) / Count_;
89			Avg2_ += (val * val - Avg2_) / Count_;
90			Val_ = val;
91			if (Count_ <= 1) {
92			Max_ = val;
93			Min_ = val;
94			} else {
95			Max_ = val > Max_ ? val : Max_;
96			Min_ = val < Min_ ? val : Min_;
97			}
98			}
99
100			/**
101			* reset the Accumulator to the empty state
102			*/
103			void clear() {
104			Count_ = 0;
105			Avg_ = 0;
106			Avg2_ = 0;
107			Val_ = 0;
108			}
109
110
111			/**
112			* return the most recently added value
113			*/
114			void getLastValue(ElementType &ret) {
115			ret = Val_;
116			return;
117			}
118
119			/**
120			* compute the Mean
121			*/
122			void getAverage(ResultType &ret) {
123			assert(Count_ != 0);
124			ret = Avg_;
125			return;
126			}
127
128			/**
129			* compute the Variance
130			*/
131			void getVariance(ResultType &ret) {
132			assert(Count_ != 0);
133			ret = (Avg2_ - Avg_ * Avg_);
134			return;
135			}
136
137			/**
138			* compute error of average value
139			*/
140			void getStdDev(ResultType &ret) {
141			assert(Count_ != 0);
142			RealType var;
143			this->getVariance(var);
144			ret = sqrt(var);
145			return;
146			}
147
148			/**
149			* return the largest value
150			*/
151			void getMax(ElementType &ret) {
152			assert(Count_ != 0);
153			ret = Max_;
154			return;
155			}
156
157			/**
158			* return the smallest value
159			*/
160			void getMin(ElementType &ret) {
161			assert(Count_ != 0);
162			ret = Max_;
163			return;
164			}
165
166	gezelter	1979	/**
167			* return the 95% confidence interval:
168			*
169			* That is returns c, such that we have 95% confidence that the
170			* true mean is within 2c of the Average (x):
171			*
172			* x - c <= true mean <= x + c
173			*
174			*/
175			void get95percentConfidenceInterval(ResultType &ret) {
176			assert(Count_ != 0);
177			RealType sd;
178			this->getStdDev(sd);
179	gezelter	1980	ret = 1.960 * sd / sqrt(RealType(Count_));
180	gezelter	1979	return;
181			}
182
183	gezelter	1765	private:
184			ElementType Val_;
185			ResultType Avg_;
186			ResultType Avg2_;
187			ElementType Min_;
188			ElementType Max_;
189			};
190
191	gezelter	1791	class VectorAccumulator : public BaseAccumulator {
192	gezelter	1765
193			typedef Vector3d ElementType;
194			typedef Vector3d ResultType;
195
196			public:
197	gezelter	1791	VectorAccumulator() : BaseAccumulator() {
198	gezelter	1765	this->clear();
199			}
200
201			/**
202			* Accumulate another value
203			*/
204			void add(ElementType const& val) {
205			Count_++;
206			RealType len(0.0);
207			for (unsigned int i =0; i < 3; i++) {
208			Avg_[i] += (val[i] - Avg_[i] ) / Count_;
209			Avg2_[i] += (val[i] * val[i] - Avg2_[i]) / Count_;
210			Val_[i] = val[i];
211			len += val[i]*val[i];
212			}
213			len = sqrt(len);
214			AvgLen_ += (len - AvgLen_ ) / Count_;
215			AvgLen2_ += (len * len - AvgLen2_) / Count_;
216
217			if (Count_ <= 1) {
218			Max_ = len;
219			Min_ = len;
220			} else {
221			Max_ = len > Max_ ? len : Max_;
222			Min_ = len < Min_ ? len : Min_;
223			}
224			}
225
226			/**
227			* reset the Accumulator to the empty state
228			*/
229			void clear() {
230			Count_ = 0;
231			Avg_ = V3Zero;
232			Avg2_ = V3Zero;
233			Val_ = V3Zero;
234			AvgLen_ = 0;
235			AvgLen2_ = 0;
236			}
237
238			/**
239			* return the most recently added value
240			*/
241			void getLastValue(ElementType &ret) {
242			ret = Val_;
243			return;
244			}
245
246			/**
247			* compute the Mean
248			*/
249			void getAverage(ResultType &ret) {
250			assert(Count_ != 0);
251			ret = Avg_;
252			return;
253			}
254
255			/**
256			* compute the Variance
257			*/
258			void getVariance(ResultType &ret) {
259			assert(Count_ != 0);
260			for (unsigned int i =0; i < 3; i++) {
261			ret[i] = (Avg2_[i] - Avg_[i] * Avg_[i]);
262			}
263			return;
264			}
265
266			/**
267			* compute error of average value
268			*/
269			void getStdDev(ResultType &ret) {
270			assert(Count_ != 0);
271			ResultType var;
272			this->getVariance(var);
273			ret[0] = sqrt(var[0]);
274			ret[1] = sqrt(var[1]);
275			ret[2] = sqrt(var[2]);
276			return;
277			}
278
279			/**
280	gezelter	1979	* return the 95% confidence interval:
281			*
282			* That is returns c, such that we have 95% confidence that the
283			* true mean is within 2c of the Average (x):
284			*
285			* x - c <= true mean <= x + c
286			*
287			*/
288			void get95percentConfidenceInterval(ResultType &ret) {
289			assert(Count_ != 0);
290			ResultType sd;
291			this->getStdDev(sd);
292	gezelter	1980	ret[0] = 1.960 * sd[0] / sqrt(RealType(Count_));
293			ret[1] = 1.960 * sd[1] / sqrt(RealType(Count_));
294			ret[2] = 1.960 * sd[2] / sqrt(RealType(Count_));
295	gezelter	1979	return;
296			}
297
298			/**
299	gezelter	1765	* return the largest length
300			*/
301			void getMaxLength(RealType &ret) {
302			assert(Count_ != 0);
303			ret = Max_;
304			return;
305			}
306
307			/**
308			* return the smallest length
309			*/
310			void getMinLength(RealType &ret) {
311			assert(Count_ != 0);
312			ret = Min_;
313			return;
314			}
315
316			/**
317			* return the largest length
318			*/
319			void getAverageLength(RealType &ret) {
320			assert(Count_ != 0);
321			ret = AvgLen_;
322			return;
323			}
324
325			/**
326			* compute the Variance of the length
327			*/
328			void getLengthVariance(RealType &ret) {
329			assert(Count_ != 0);
330			ret= (AvgLen2_ - AvgLen_ * AvgLen_);
331			return;
332			}
333
334			/**
335			* compute error of average value
336			*/
337			void getLengthStdDev(RealType &ret) {
338			assert(Count_ != 0);
339			RealType var;
340			this->getLengthVariance(var);
341			ret = sqrt(var);
342			return;
343			}
344	gezelter	1879
345	gezelter	1979	/**
346			* return the 95% confidence interval:
347			*
348			* That is returns c, such that we have 95% confidence that the
349			* true mean is within 2c of the Average (x):
350			*
351			* x - c <= true mean <= x + c
352			*
353			*/
354			void getLength95percentConfidenceInterval(ResultType &ret) {
355			assert(Count_ != 0);
356			RealType sd;
357			this->getLengthStdDev(sd);
358	gezelter	1980	ret = 1.960 * sd / sqrt(RealType(Count_));
359	gezelter	1979	return;
360			}
361
362
363	gezelter	1765	private:
364			ResultType Val_;
365			ResultType Avg_;
366			ResultType Avg2_;
367			RealType AvgLen_;
368			RealType AvgLen2_;
369			RealType Min_;
370			RealType Max_;
371
372			};
373
374	gezelter	1791	class MatrixAccumulator : public BaseAccumulator {
375	gezelter	1765
376			typedef Mat3x3d ElementType;
377			typedef Mat3x3d ResultType;
378
379			public:
380	gezelter	1791	MatrixAccumulator() : BaseAccumulator() {
381	gezelter	1765	this->clear();
382			}
383
384			/**
385			* Accumulate another value
386			*/
387			void add(ElementType const& val) {
388			Count_++;
389			for (unsigned int i = 0; i < 3; i++) {
390			for (unsigned int j = 0; j < 3; j++) {
391			Avg_(i,j) += (val(i,j) - Avg_(i,j) ) / Count_;
392			Avg2_(i,j) += (val(i,j) * val(i,j) - Avg2_(i,j)) / Count_;
393			Val_(i,j) = val(i,j);
394			}
395			}
396			}
397
398			/**
399			* reset the Accumulator to the empty state
400			*/
401			void clear() {
402			Count_ = 0;
403			Avg_ *= 0.0;
404			Avg2_ *= 0.0;
405			Val_ *= 0.0;
406			}
407
408			/**
409			* return the most recently added value
410			*/
411			void getLastValue(ElementType &ret) {
412			ret = Val_;
413			return;
414			}
415
416			/**
417			* compute the Mean
418			*/
419			void getAverage(ResultType &ret) {
420			assert(Count_ != 0);
421			ret = Avg_;
422			return;
423			}
424
425			/**
426			* compute the Variance
427			*/
428			void getVariance(ResultType &ret) {
429			assert(Count_ != 0);
430			for (unsigned int i = 0; i < 3; i++) {
431			for (unsigned int j = 0; j < 3; j++) {
432			ret(i,j) = (Avg2_(i,j) - Avg_(i,j) * Avg_(i,j));
433			}
434			}
435			return;
436			}
437
438			/**
439			* compute error of average value
440			*/
441			void getStdDev(ResultType &ret) {
442			assert(Count_ != 0);
443			Mat3x3d var;
444			this->getVariance(var);
445			for (unsigned int i = 0; i < 3; i++) {
446			for (unsigned int j = 0; j < 3; j++) {
447			ret(i,j) = sqrt(var(i,j));
448			}
449			}
450			return;
451			}
452	gezelter	1979
453			/**
454			* return the 95% confidence interval:
455			*
456			* That is returns c, such that we have 95% confidence that the
457			* true mean is within 2c of the Average (x):
458			*
459			* x - c <= true mean <= x + c
460			*
461			*/
462			void get95percentConfidenceInterval(ResultType &ret) {
463			assert(Count_ != 0);
464			Mat3x3d sd;
465			this->getStdDev(sd);
466			for (unsigned int i = 0; i < 3; i++) {
467			for (unsigned int j = 0; j < 3; j++) {
468	gezelter	1980	ret(i,j) = 1.960 * sd(i,j) / sqrt(RealType(Count_));
469	gezelter	1979	}
470			}
471			return;
472			}
473
474	gezelter	1765	private:
475			ElementType Val_;
476			ResultType Avg_;
477			ResultType Avg2_;
478			};
479
480
481			}
482
483			#endif