OpenVDB 11.0.0

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Public Types | Public Member Functions | Static Public Member Functions | Protected Types | Protected Attributes | List of all members
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
__hostdev__ | Stats ()
__hostdev__ Stats & | add (const ValueT &val)
| Add a single sample.
__hostdev__ Stats & | add (const ValueT &val, uint64_t n)
| Add n samples with constant value val.
__hostdev__ Stats & | add (const Stats &other)
| Add the samples from the other Stats instance.
__hostdev__ size_t | size () const
__hostdev__ double | avg () const
| Return the arithmetic mean, i.e. average, value.
__hostdev__ double | mean () const
__hostdev__ double | var () const
| Return the population variance.
__hostdev__ double | variance () const
__hostdev__ double | std () const
| Return the standard deviation (=Sqrt(variance)) as defined from the (biased) population variance.
__hostdev__ double | stdDev () const
template<typename NodeT >
__hostdev__ void | setStats (NodeT &node) const

## Static Public Member Functions
static __hostdev__ constexpr bool | hasMinMax ()
static __hostdev__ constexpr bool | hasAverage ()
static __hostdev__ constexpr bool | hasStdDeviation ()
static __hostdev__ constexpr bool | hasStats ()

## 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 >

__hostdev__ Stats | ( |  | ) |
| inline

## Member Function Documentation

## ◆ add() [1/3]

template<typename ValueT >

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

Add the samples from the other Stats instance.

## ◆ add() [2/3]

template<typename ValueT >

__hostdev__ Stats & add | ( | const ValueT & | val | ) |
| inline

Add a single sample.

## ◆ add() [3/3]

template<typename ValueT >

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

Add n samples with constant value val.

## ◆ avg()

template<typename ValueT >

__hostdev__ double avg | ( |  | ) | const
| inline

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

## ◆ hasAverage()

template<typename ValueT >

static __hostdev__ constexpr bool hasAverage | ( |  | ) |
| inline static constexpr

## ◆ hasMinMax()

template<typename ValueT >

static __hostdev__ constexpr bool hasMinMax | ( |  | ) |
| inline static constexpr

## ◆ hasStats()

template<typename ValueT >

static __hostdev__ constexpr bool hasStats | ( |  | ) |
| inline static constexpr

## ◆ hasStdDeviation()

template<typename ValueT >

static __hostdev__ constexpr bool hasStdDeviation | ( |  | ) |
| inline static constexpr

## ◆ mean()

template<typename ValueT >

__hostdev__ double mean | ( |  | ) | const
| inline

## ◆ setStats()

template<typename ValueT >
template<typename NodeT >

__hostdev__ void setStats | ( | NodeT & | node | ) | const
| inline

## ◆ size()

template<typename ValueT >

__hostdev__ size_t size | ( |  | ) | const
| inline

## ◆ std()

template<typename ValueT >

__hostdev__ double std | ( |  | ) | const
| inline

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

## ◆ stdDev()

template<typename ValueT >

__hostdev__ double stdDev | ( |  | ) | const
| inline

## ◆ var()

template<typename ValueT >

__hostdev__ double var | ( |  | ) | const
| inline

Return the population variance.

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

## ◆ variance()

template<typename ValueT >

__hostdev__ 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