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>

Inheritance diagram for Stats< ValueT, 1 >:

Public Types
using	ValueType = ValueT

Public Member Functions
	Stats ()
Stats &	add (const ValueT &val)
	Add a single sample.
Stats &	add (const ValueT &val, uint64_t n)
	Add n samples with constant value val.
Stats &	add (const Stats &other)
	Add the samples from the other Stats instance.
size_t	size () const
double	avg () const
	Return the arithmetic mean, i.e. average, value.
double	mean () const
double	var () const
	Return the population variance.
double	variance () const
double	std () const
	Return the standard deviation (=Sqrt(variance)) as defined from the (biased) population variance.
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

template<typename ValueT>

using BaseT = Extrema<ValueT, 1>

protected

RealT

template<typename ValueT>

using RealT = double

protected

ValueType

template<typename ValueT>

using ValueType = ValueT

Constructor & Destructor Documentation

Stats()

template<typename ValueT>

Stats ( )

inline

Member Function Documentation

add() [1/3]

template<typename ValueT>

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

inline

Add the samples from the other Stats instance.

add() [2/3]

template<typename ValueT>

Stats & add ( const ValueT & val )

inline

Add a single sample.

add() [3/3]

template<typename ValueT>

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

inline

Add n samples with constant value val.

avg()

template<typename ValueT>

double avg ( ) const

inline

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

hasAverage()

template<typename ValueT>

constexpr bool hasAverage ( )

inlinestaticconstexpr

hasMinMax()

template<typename ValueT>

constexpr bool hasMinMax ( )

inlinestaticconstexpr

hasStdDeviation()

template<typename ValueT>

constexpr bool hasStdDeviation ( )

inlinestaticconstexpr

mean()

template<typename ValueT>

double mean ( ) const

inline

size()

template<typename ValueT>

size_t size ( ) const

inline

std()

template<typename ValueT>

double std ( ) const

inline

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

stdDev()

template<typename ValueT>

double stdDev ( ) const

inline

var()

template<typename ValueT>

double var ( ) const

inline

Return the population variance.

Note

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

variance()

template<typename ValueT>

double variance ( ) const

inline

Member Data Documentation

mAux

template<typename ValueT>

double mAux

protected

mAvg

template<typename ValueT>

double mAvg

protected

mSize

template<typename ValueT>

size_t mSize

protected