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)
Stats &	add (const ValueT &val)
	Add a single sample. More...
Stats &	add (const ValueT &val, uint64_t n)
	Add n samples with constant value val. More...
Stats &	add (const Stats &other)
	Add the samples from the other Stats instance. More...
size_t	size () const
Extrema &	min (const ValueT &v)
const ValueT &	min () const
Extrema &	max (const ValueT &v)
const ValueT &	max () const
Extrema &	add (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