GCC Code Coverage Report

Directory: ./
File: openvdb/openvdb/math/Stats.h
Date: 2022-07-25 17:40:05
Exec Total Coverage
Lines: 80 84 95.2%
Functions: 8 9 88.9%
Branches: 227 429 52.9%
Line Branch Exec Source
1 // Copyright Contributors to the OpenVDB Project
2 // SPDX-License-Identifier: MPL-2.0
3 //
4 /// @file Stats.h
5 ///
6 /// @author Ken Museth
7 ///
8 /// @brief Classes to compute statistics and histograms
9
10 #ifndef OPENVDB_MATH_STATS_HAS_BEEN_INCLUDED
11 #define OPENVDB_MATH_STATS_HAS_BEEN_INCLUDED
12
13 #include <iosfwd> // for ostringstream
14 #include <openvdb/version.h>
15 #include <openvdb/Exceptions.h>
16 #include <iostream>
17 #include <iomanip>
18 #include <sstream>
19 #include <vector>
20 #include <functional>// for std::less
21 #include "Math.h"
22
23 namespace openvdb {
24 OPENVDB_USE_VERSION_NAMESPACE
25 namespace OPENVDB_VERSION_NAME {
26 namespace math {
27
28 /// @brief Templated class to compute the minimum and maximum values.
29 template <typename ValueType, typename Less = std::less<ValueType> >
30 class MinMax
31 {
32 using Limits = std::numeric_limits<ValueType>;
33 public:
34
35 /// @brief Empty constructor
36 ///
37 /// @warning Only use this constructor with POD types
38 4 MinMax() : mMin(Limits::max()), mMax(Limits::lowest())
39 {
40 static_assert(std::numeric_limits<ValueType>::is_specialized,
41 "openvdb::math::MinMax default constructor requires a std::numeric_limits specialization");
42 }
43
44 /// @brief Constructor
45
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 55 MinMax(const ValueType &min, const ValueType &max) : mMin(min), mMax(max)
46 {
47 }
48
49 /// @brief Default copy constructor
50 MinMax(const MinMax &other) = default;
51
52 /// Add a single sample.
53
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 3 inline void add(const ValueType &val, const Less &less = Less())
54 {
55
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 11 if (less(val, mMin)) mMin = val;
56
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 10 if (less(mMax, val)) mMax = val;
57 3 }
58
59 /// Return the minimum value.
60 inline const ValueType& min() const { return mMin; }
61
62 /// Return the maximum value.
63 inline const ValueType& max() const { return mMax; }
64
65 /// Add the samples from the other Stats instance.
66 inline void add(const MinMax& other, const Less &less = Less())
67 {
68 if (less(other.mMin, mMin)) mMin = other.mMin;
69 if (less(mMax, other.mMax)) mMax = other.mMax;
70 }
71
72 /// @brief Print MinMax to the specified output stream.
73 void print(const std::string &name= "", std::ostream &strm=std::cout, int precision=3) const
74 {
75 // Write to a temporary string stream so as not to affect the state
76 // (precision, field width, etc.) of the output stream.
77 std::ostringstream os;
78 os << std::setprecision(precision) << std::setiosflags(std::ios::fixed);
79 os << "MinMax ";
80 if (!name.empty()) os << "for \"" << name << "\" ";
81 os << " Min=" << mMin << ", Max=" << mMax << std::endl;
82 strm << os.str();
83 }
84
85 protected:
86
87 ValueType mMin, mMax;
88 };//end MinMax
89
90 /// @brief This class computes the minimum and maximum values of a population
91 /// of floating-point values.
92 class Extrema
93 {
94 public:
95
96 /// @brief Constructor
97 /// @warning The min/max values are initiated to extreme values
98 Extrema()
99 9 : mSize(0)
100 , mMin(std::numeric_limits<double>::max())
101
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 20 , mMax(-mMin)
102 {
103 }
104
105 /// Add a single sample.
106 void add(double val)
107 {
108
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 1127223 ++mSize;
109
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 12494935 mMin = std::min<double>(val, mMin);
110
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 66565901 mMax = std::max<double>(val, mMax);
111 }
112
113 /// Add @a n samples with constant value @a val.
114 void add(double val, uint64_t n)
115 {
116 174350 mSize += n;
117
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 115507 mMin = std::min<double>(val, mMin);
118
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 174724 mMax = std::max<double>(val, mMax);
119 }
120
121 /// Return the size of the population, i.e., the total number of samples.
122 22 inline uint64_t size() const { return mSize; }
123
124 /// Return the minimum value.
125
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 53 inline double min() const { return mMin; }
126
127 /// Return the maximum value.
128
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 53 inline double max() const { return mMax; }
129
130 /// Return the range defined as the maximum value minus the minimum value.
131 4 inline double range() const { return mMax - mMin; }
132
133 /// Add the samples from the other Stats instance.
134 void add(const Extrema& other)
135 {
136
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 50 if (other.mSize > 0) this->join(other);
137 }
138
139 /// @brief Print extrema to the specified output stream.
140 void print(const std::string &name= "", std::ostream &strm=std::cout, int precision=3) const
141 {
142 // Write to a temporary string stream so as not to affect the state
143 // (precision, field width, etc.) of the output stream.
144 std::ostringstream os;
145 os << std::setprecision(precision) << std::setiosflags(std::ios::fixed);
146 os << "Extrema ";
147 if (!name.empty()) os << "for \"" << name << "\" ";
148 if (mSize>0) {
149 os << "with " << mSize << " samples:\n"
150 << " Min=" << mMin
151 << ", Max=" << mMax
152 << ", Range="<< this->range() << std::endl;
153 } else {
154 os << ": no samples were added." << std::endl;
155 }
156 strm << os.str();
157 }
158
159 protected:
160
161 110 inline void join(const Extrema& other)
162 {
163
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 110 assert(other.mSize > 0);
164 110 mSize += other.mSize;
165
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 110 mMin = std::min<double>(mMin, other.mMin);
166
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 110 mMax = std::max<double>(mMax, other.mMax);
167 110 }
168
169 uint64_t mSize;
170 double mMin, mMax;
171 };//end Extrema
172
173
174 /// @brief This class computes statistics (minimum value, maximum
175 /// value, mean, variance and standard deviation) of a population
176 /// of floating-point values.
177 ///
178 /// @details variance = Mean[ (X-Mean[X])^2 ] = Mean[X^2] - Mean[X]^2,
179 /// standard deviation = sqrt(variance)
180 ///
181 /// @note This class employs incremental computation and double precision.
182 class Stats : public Extrema
183 {
184 public:
185 Stats()
186 37 : Extrema()
187 , mAvg(0.0)
188
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 23 , mAux(0.0)
189 {
190 }
191
192 /// Add a single sample.
193
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 43657178 void add(double val)
194 {
195 43657178 Extrema::add(val);
196 43657178 const double delta = val - mAvg;
197 43657178 mAvg += delta/double(mSize);
198 43657178 mAux += delta*(val - mAvg);
199 43657178 }
200
201 /// Add @a n samples with constant value @a val.
202 58841 void add(double val, uint64_t n)
203 {
204 58841 const double denom = 1.0/double(mSize + n);
205 58841 const double delta = val - mAvg;
206 58841 mAvg += denom * delta * double(n);
207
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 58841 mAux += denom * delta * delta * double(mSize) * double(n);
208 58841 Extrema::add(val, n);
209 58841 }
210
211 /// Add the samples from the other Stats instance.
212 62 void add(const Stats& other)
213 {
214
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 62 if (other.mSize > 0) {
215 61 const double denom = 1.0/double(mSize + other.mSize);
216 61 const double delta = other.mAvg - mAvg;
217 61 mAvg += denom * delta * double(other.mSize);
218 61 mAux += other.mAux + denom * delta * delta * double(mSize) * double(other.mSize);
219 61 Extrema::join(other);
220 }
221 62 }
222
223 //@{
224 /// Return the arithmetic mean, i.e. average, value.
225
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 1 inline double avg() const { return mAvg; }
226
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 26 inline double mean() const { return mAvg; }
227 //@}
228
229 //@{
230 /// @brief Return the population variance.
231 /// @note The unbiased sample variance = population variance *
232 //num/(num-1)
233
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 34 inline double var() const { return mSize<2 ? 0.0 : mAux/double(mSize); }
234 inline double variance() const { return this->var(); }
235 //@}
236
237 //@{
238 /// @brief Return the standard deviation (=Sqrt(variance)) as
239 /// defined from the (biased) population variance.
240 8 inline double std() const { return sqrt(this->var()); }
241 inline double stdDev() const { return this->std(); }
242 //@}
243
244 /// @brief Print statistics to the specified output stream.
245 void print(const std::string &name= "", std::ostream &strm=std::cout, int precision=3) const
246 {
247 // Write to a temporary string stream so as not to affect the state
248 // (precision, field width, etc.) of the output stream.
249 std::ostringstream os;
250 os << std::setprecision(precision) << std::setiosflags(std::ios::fixed);
251 os << "Statistics ";
252 if (!name.empty()) os << "for \"" << name << "\" ";
253 if (mSize>0) {
254 os << "with " << mSize << " samples:\n"
255 << " Min=" << mMin
256 << ", Max=" << mMax
257 << ", Ave=" << mAvg
258 << ", Std=" << this->stdDev()
259 << ", Var=" << this->variance() << std::endl;
260 } else {
261 os << ": no samples were added." << std::endl;
262 }
263 strm << os.str();
264 }
265
266 protected:
267 using Extrema::mSize;
268 using Extrema::mMin;
269 using Extrema::mMax;
270 double mAvg, mAux;
271 }; // end Stats
272
273
274 ////////////////////////////////////////
275
276
277 /// @brief This class computes a histogram, with a fixed interval width,
278 /// of a population of floating-point values.
279
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 26 class Histogram
280 {
281 public:
282 /// Construct with given minimum and maximum values and the given bin count.
283 5 Histogram(double min, double max, size_t numBins = 10)
284 5 : mSize(0), mMin(min), mMax(max + 1e-10),
285 5 mDelta(double(numBins)/(max-min)), mBins(numBins)
286 {
287
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 5 if ( mMax <= mMin ) {
288 ✗ OPENVDB_THROW(ValueError, "Histogram: expected min < max");
289
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 5 } else if ( numBins == 0 ) {
290 ✗ OPENVDB_THROW(ValueError, "Histogram: expected at least one bin");
291 }
292
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 51 for (size_t i=0; i<numBins; ++i) mBins[i]=0;
293 5 }
294
295 /// @brief Construct with the given bin count and with minimum and maximum values
296 /// taken from a Stats object.
297 2 Histogram(const Stats& s, size_t numBins = 10):
298 2 mSize(0), mMin(s.min()), mMax(s.max()+1e-10),
299 2 mDelta(double(numBins)/(mMax-mMin)), mBins(numBins)
300 {
301
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 2 if ( mMax <= mMin ) {
302 ✗ OPENVDB_THROW(ValueError, "Histogram: expected min < max");
303
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 2 } else if ( numBins == 0 ) {
304 ✗ OPENVDB_THROW(ValueError, "Histogram: expected at least one bin");
305 }
306
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 37 for (size_t i=0; i<numBins; ++i) mBins[i]=0;
307 2 }
308
309 /// @brief Add @a n samples with constant value @a val, provided that the
310 /// @a val falls within this histogram's value range.
311 /// @return @c true if the sample value falls within this histogram's value range.
312 inline bool add(double val, uint64_t n = 1)
313 {
314
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 1878902 if (val<mMin || val>mMax) return false;
315 1878902 mBins[size_t(mDelta*(val-mMin))] += n;
316 1878902 mSize += n;
317 1878902 return true;
318 }
319
320 /// @brief Add all the contributions from the other histogram, provided that
321 /// it has the same configuration as this histogram.
322
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 16 bool add(const Histogram& other)
323 {
324
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 16 if (!isApproxEqual(mMin, other.mMin) || !isApproxEqual(mMax, other.mMax) ||
325 mBins.size() != other.mBins.size()) return false;
326
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 160 for (size_t i=0, e=mBins.size(); i!=e; ++i) mBins[i] += other.mBins[i];
327 16 mSize += other.mSize;
328 16 return true;
329 }
330
331 /// Return the number of bins in this histogram.
332 inline size_t numBins() const { return mBins.size(); }
333 /// Return the lower bound of this histogram's value range.
334 inline double min() const { return mMin; }
335 /// Return the upper bound of this histogram's value range.
336 inline double max() const { return mMax; }
337 /// Return the minimum value in the <i>n</i>th bin.
338
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 60 inline double min(int n) const { return mMin+n/mDelta; }
339 /// Return the maximum value in the <i>n</i>th bin.
340
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 34 inline double max(int n) const { return mMin+(n+1)/mDelta; }
341 /// Return the number of samples in the <i>n</i>th bin.
342
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 51 inline uint64_t count(int n) const { return mBins[n]; }
343 /// Return the population size, i.e., the total number of samples.
344
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 4 inline uint64_t size() const { return mSize; }
345
346 /// Print the histogram to the specified output stream.
347 void print(const std::string& name = "", std::ostream& strm = std::cout) const
348 {
349 // Write to a temporary string stream so as not to affect the state
350 // (precision, field width, etc.) of the output stream.
351 std::ostringstream os;
352 os << std::setprecision(6) << std::setiosflags(std::ios::fixed) << std::endl;
353 os << "Histogram ";
354 if (!name.empty()) os << "for \"" << name << "\" ";
355 if (mSize > 0) {
356 os << "with " << mSize << " samples:\n";
357 os << "==============================================================\n";
358 os << "|| # | Min | Max | Frequency | % ||\n";
359 os << "==============================================================\n";
360 for (int i = 0, e = int(mBins.size()); i != e; ++i) {
361 os << "|| " << std::setw(4) << i << " | " << std::setw(14) << this->min(i) << " | "
362 << std::setw(14) << this->max(i) << " | " << std::setw(9) << mBins[i] << " | "
363 << std::setw(3) << (100*mBins[i]/mSize) << " ||\n";
364 }
365 os << "==============================================================\n";
366 } else {
367 os << ": no samples were added." << std::endl;
368 }
369 strm << os.str();
370 }
371
372 private:
373 uint64_t mSize;
374 double mMin, mMax, mDelta;
375 std::vector<uint64_t> mBins;
376 };// end Histogram
377
378 } // namespace math
379 } // namespace OPENVDB_VERSION_NAME
380 } // namespace openvdb
381
382 #endif // OPENVDB_MATH_STATS_HAS_BEEN_INCLUDED
383