Stats< ValueT, 1 > 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, 1 >.

Public Types
using	ValueType = ValueT

Public Member Functions
	Stats ()
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
double	avg () const
	Return the arithmetic mean, i.e. average, value. More...
double	mean () const
double	var () const
	Return the population variance. More...
double	variance () const
double	std () const
	Return the standard deviation (=Sqrt(variance)) as defined from the (biased) population variance. More...
double	stdDev () const

Static Public Member Functions
static constexpr bool	hasMinMax ()
static constexpr bool	hasAverage ()
static constexpr bool	hasStdDeviation ()

Protected Types
using	BaseT = Extrema< ValueT, 1 >
using	RealT = double

Protected Attributes
size_t	mSize
double	mAvg
double	mAux

Detailed Description

template<typename ValueT>
class nanovdb::Stats< ValueT, 1 >

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

BaseT

using BaseT = Extrema<ValueT, 1>

protected

RealT

using RealT = double

protected

ValueType

using ValueType = ValueT

Constructor & Destructor Documentation

Stats()

Stats ( )

inline

Member Function Documentation

add() [1/3]

Stats& add ( const Stats< ValueT, 1 > &  other )

inline

Add the samples from the other Stats instance.

add() [2/3]

Stats& add ( const ValueT &  val )

inline

Add a single sample.

add() [3/3]

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

inline

Add n samples with constant value val.

avg()

double avg ( ) const

inline

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

hasAverage()

static constexpr bool hasAverage ( )

inlinestaticconstexpr

hasMinMax()

static constexpr bool hasMinMax ( )

inlinestaticconstexpr

hasStdDeviation()

static constexpr bool hasStdDeviation ( )

inlinestaticconstexpr

mean()

double mean ( ) const

inline

size()

size_t size ( ) const

inline

std()

double std ( ) const

inline

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

stdDev()

double stdDev ( ) const

inline

var()

double var ( ) const

inline

Return the population variance.

Note

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

variance()

double variance ( ) const

inline

Member Data Documentation

mAux

double mAux

protected

mAvg

double mAvg

protected

mSize

size_t mSize

protected