OpenVDB  10.0.1
Public Types | Public Member Functions | Static Public Member Functions | Protected Types | Protected Attributes | List of all members
Stats< ValueT, 0 > Class Template Reference

This class computes statistics (minimum value, maximum value, mean, variance and standard deviation) of a population of floating-point values. More...

#include <nanovdb/util/GridStats.h>

Inherits Extrema< ValueT, 0 >.

Public Types

using ValueType = ValueT
 

Public Member Functions

 Stats ()
 
 Stats (const ValueT &val)
 
Statsadd (const ValueT &val)
 Add a single sample. More...
 
Statsadd (const ValueT &val, uint64_t n)
 Add n samples with constant value val. More...
 
Statsadd (const Stats &other)
 Add the samples from the other Stats instance. More...
 
size_t size () const
 
Extremamin (const ValueT &v)
 
const ValueT & min () const
 
Extremamax (const ValueT &v)
 
const ValueT & max () const
 
Extremaadd (const Extrema &other)
 
 operator bool () const
 
double avg () const
 Return the arithmetic mean, i.e. average, value. More...
 
double mean () const
 Return the arithmetic mean, i.e. average, value. More...
 
double var () const
 Return the population variance. More...
 
double variance () const
 Return the population variance. More...
 
double std () const
 Return the standard deviation (=Sqrt(variance)) as defined from the (biased) population variance. More...
 
double stdDev () const
 Return the standard deviation (=Sqrt(variance)) as defined from the (biased) population variance. More...
 

Static Public Member Functions

static constexpr bool hasMinMax ()
 
static constexpr bool hasAverage ()
 
static constexpr bool hasStdDeviation ()
 
static constexpr size_t size ()
 

Protected Types

using BaseT = Extrema< ValueT, 0 >
 
using RealT = double
 

Protected Attributes

size_t mSize
 
double mAvg
 
double mAux
 
ValueT mMin
 
ValueT mMax
 

Detailed Description

template<typename ValueT>
class nanovdb::Stats< ValueT, 0 >

This class computes statistics (minimum value, maximum value, mean, variance and standard deviation) of a population of floating-point values.

variance = Mean[ (X-Mean[X])^2 ] = Mean[X^2] - Mean[X]^2, standard deviation = sqrt(variance)

Note
This class employs incremental computation and double precision.

Member Typedef Documentation

using BaseT = Extrema<ValueT, 0>
protected
using RealT = double
protected
using ValueType = ValueT

Constructor & Destructor Documentation

Stats ( )
inline
Stats ( const ValueT &  val)
inline

Member Function Documentation

Extrema& add ( const Extrema< ValueT, 0 > &  other)
inlineinherited
Stats& add ( const ValueT &  val)
inline

Add a single sample.

Stats& add ( const ValueT &  val,
uint64_t  n 
)
inline

Add n samples with constant value val.

Stats& add ( const Stats< ValueT, 0 > &  other)
inline

Add the samples from the other Stats instance.

double avg ( ) const
inline

Return the arithmetic mean, i.e. average, value.

static constexpr bool hasAverage ( )
inlinestatic
static constexpr bool hasMinMax ( )
inlinestatic
static constexpr bool hasStdDeviation ( )
inlinestatic
Extrema& max ( const ValueT &  v)
inlineinherited
const ValueT& max ( ) const
inlineinherited
double mean ( ) const
inline

Return the arithmetic mean, i.e. average, value.

Extrema& min ( const ValueT &  v)
inlineinherited
const ValueT& min ( ) const
inlineinherited
operator bool ( ) const
inlineinherited
static constexpr size_t size ( )
inlinestaticinherited
size_t size ( ) const
inline
double std ( ) const
inline

Return the standard deviation (=Sqrt(variance)) as defined from the (biased) population variance.

double stdDev ( ) const
inline

Return the standard deviation (=Sqrt(variance)) as defined from the (biased) population variance.

double var ( ) const
inline

Return the population variance.

Note
The unbiased sample variance = population variance * num/(num-1)
double variance ( ) const
inline

Return the population variance.

Note
The unbiased sample variance = population variance * num/(num-1)

Member Data Documentation

double mAux
protected
double mAvg
protected
ValueT mMax
protectedinherited
ValueT mMin
protectedinherited
size_t mSize
protected