35 #ifndef OPENVDB_MATH_STATS_HAS_BEEN_INCLUDED
36 #define OPENVDB_MATH_STATS_HAS_BEEN_INCLUDED
39 #include <openvdb/version.h>
62 Stats(): mSize(0), mAvg(0.0), mAux(0.0),
63 mMin(std::numeric_limits<double>::
max()), mMax(-mMin) {}
69 mMin = std::min<double>(val, mMin);
70 mMax = std::max<double>(val, mMax);
71 const double delta = val - mAvg;
72 mAvg += delta/double(mSize);
73 mAux += delta*(val - mAvg);
77 void add(
double val, uint64_t n)
79 mMin = std::min<double>(val, mMin);
80 mMax = std::max<double>(val, mMax);
81 const double denom = 1.0/double(mSize + n);
82 const double delta = val - mAvg;
83 mAvg += denom*delta*n;
84 mAux += denom*delta*delta*mSize*n;
91 if (other.mSize > 0) {
92 mMin = std::min<double>(mMin, other.mMin);
93 mMax = std::max<double>(mMax, other.mMax);
94 const double denom = 1.0/double(mSize + other.mSize);
95 const double delta = other.mAvg - mAvg;
96 mAvg += denom*delta*other.mSize;
97 mAux += other.mAux + denom*delta*delta*mSize*other.mSize;
103 inline uint64_t
size()
const {
return mSize; }
106 inline double min()
const {
return mMin; }
109 inline double max()
const {
return mMax; }
112 inline double avg()
const {
return mAvg; }
114 inline double mean()
const {
return mAvg; }
121 inline double var()
const {
return mSize<2 ? 0.0 : mAux/double(mSize); }
122 inline double variance()
const {
return this->var(); }
126 inline double std()
const {
return sqrt(this->var()); }
129 inline double stdDev()
const {
return this->std(); }
133 void print(
const std::string &name=
"", std::ostream &strm=std::cout,
int precision=3)
const
137 std::ostringstream os;
138 os << std::setprecision(precision) << std::setiosflags(std::ios::fixed);
140 if (!name.empty()) os <<
"for \"" << name <<
"\" ";
142 os <<
"with " << mSize <<
" samples:\n"
146 <<
", Std=" << this->stdDev()
147 <<
", Var=" << this->variance() << std::endl;
149 os <<
": no samples were added." << std::endl;
156 double mAvg, mAux, mMin, mMax;
170 : mSize(0), mMin(min), mMax(max+1e-10),
171 mDelta(double(numBins)/(max-min)), mBins(numBins)
174 assert(mMax-mMin > 1e-10);
175 for (
size_t i=0; i<numBins; ++i) mBins[i]=0;
181 mSize(0), mMin(s.
min()), mMax(s.
max()+1e-10),
182 mDelta(double(numBins)/(mMax-mMin)), mBins(numBins)
185 assert(mMax-mMin > 1e-10);
186 for (
size_t i=0; i<numBins; ++i) mBins[i]=0;
192 inline bool add(
double val, uint64_t n = 1)
194 if (val<mMin || val>mMax)
return false;
195 mBins[size_t(mDelta*(val-mMin))] += n;
205 mBins.size() != other.mBins.size())
return false;
206 for (
size_t i=0, e=mBins.size(); i!=e; ++i) mBins[i] += other.mBins[i];
207 mSize += other.mSize;
212 inline size_t numBins()
const {
return mBins.size(); }
214 inline double min()
const {
return mMin; }
216 inline double max()
const {
return mMax; }
218 inline double min(
int n)
const {
return mMin+n/mDelta; }
220 inline double max(
int n)
const {
return mMin+(n+1)/mDelta; }
222 inline uint64_t
count(
int n)
const {
return mBins[n]; }
224 inline uint64_t
size()
const {
return mSize; }
227 void print(
const std::string& name =
"", std::ostream& strm = std::cout)
const
231 std::ostringstream os;
232 os << std::setprecision(6) << std::setiosflags(std::ios::fixed) << std::endl;
234 if (!name.empty()) os <<
"for \"" << name <<
"\" ";
236 os <<
"with " << mSize <<
" samples:\n";
237 os <<
"==============================================================\n";
238 os <<
"|| # | Min | Max | Frequency | % ||\n";
239 os <<
"==============================================================\n";
240 for (
size_t i=0, e=mBins.size(); i!=e; ++i) {
241 os <<
"|| " << std::setw(4) << i <<
" | " << std::setw(14) << this->
min(i) <<
" | "
242 << std::setw(14) << this->
max(i) <<
" | " << std::setw(9) << mBins[i] <<
" | "
243 << std::setw(3) << (100*mBins[i]/mSize) <<
" ||\n";
245 os <<
"==============================================================\n";
247 os <<
": no samples were added." << std::endl;
254 double mMin, mMax, mDelta;
255 std::vector<uint64_t> mBins;
262 #endif // OPENVDB_MATH_STATS_HAS_BEEN_INCLUDED
void add(double val)
Add a single sample.
Definition: Stats.h:66
OPENVDB_API Hermite min(const Hermite &, const Hermite &)
min and max operations done directly on the compressed data.
bool add(const Histogram &other)
Add all the contributions from the other histogram, provided that it has the same configuration as th...
Definition: Stats.h:202
double max(int n) const
Return the maximum value in the nth bin.
Definition: Stats.h:220
double mean() const
Return the arithmetic mean, i.e. average, value.
Definition: Stats.h:114
General-purpose arithmetic and comparison routines, most of which accept arbitrary value types (or at...
double min() const
Return the minimum value.
Definition: Stats.h:106
uint64_t size() const
Return the size of the population, i.e., the total number of samples.
Definition: Stats.h:103
Stats()
Definition: Stats.h:62
This class computes statistics (minimum value, maximum value, mean, variance and standard deviation) ...
Definition: Stats.h:59
double min() const
Return the lower bound of this histogram's value range.
Definition: Stats.h:214
std::string str() const
String representation.
#define OPENVDB_VERSION_NAME
Definition: version.h:45
uint64_t size() const
Return the population size, i.e., the total number of samples.
Definition: Stats.h:224
bool add(double val, uint64_t n=1)
Add n samples with constant value val, provided that the val falls within this histogram's value rang...
Definition: Stats.h:192
void add(double val, uint64_t n)
Add n samples with constant value val.
Definition: Stats.h:77
OPENVDB_API Hermite max(const Hermite &, const Hermite &)
min and max operations done directly on the compressed data.
Histogram(double min, double max, size_t numBins=10)
Construct with given minimum and maximum values and the given bin count.
Definition: Stats.h:169
double max() const
Return the upper bound of this histogram's value range.
Definition: Stats.h:216
This class computes a histogram, with a fixed interval width, of a population of floating-point value...
Definition: Stats.h:165
double var() const
Return the population variance.
Definition: Stats.h:121
size_t numBins() const
Return the number of bins in this histogram.
Definition: Stats.h:212
uint64_t count(int n) const
Return the number of samples in the nth bin.
Definition: Stats.h:222
bool isApproxEqual(const Hermite &lhs, const Hermite &rhs)
Definition: Hermite.h:470
double stdDev() const
Return the standard deviation (=Sqrt(variance)) as defined from the (biased) population variance...
Definition: Stats.h:129
#define OPENVDB_USE_VERSION_NAMESPACE
Definition: version.h:67
void print(const std::string &name="", std::ostream &strm=std::cout) const
Print the histogram to the specified output stream.
Definition: Stats.h:227
void print(const std::string &name="", std::ostream &strm=std::cout, int precision=3) const
Print statistics to the specified output stream.
Definition: Stats.h:133
void add(const Stats &other)
Add the samples from the other Stats instance.
Definition: Stats.h:89
double min(int n) const
Return the minimum value in the nth bin.
Definition: Stats.h:218
double max() const
Return the maximum value.
Definition: Stats.h:109
Histogram(const Stats &s, size_t numBins=10)
Construct with the given bin count and with minimum and maximum values taken from a Stats object...
Definition: Stats.h:180
double variance() const
Return the population variance.
Definition: Stats.h:122