Class to calculate simple (first and second moments) averages. More...

#include <yat/statistics/Averager.h>

Inheritance diagram for theplu::yat::statistics::Averager:

Public Member Functions
	Averager (void)

	Averager (double x, double xx, long n)

	Averager (const Averager &a)

const Averager &	operator= (const Averager &)
	The assignment operator.

template<class Derived >
const Averager &	operator+= (const averager_base2< Derived > &other)
	plus assignment operator More...

double	cv (void) const
	Coeffient of variation. More...

double	standard_error (void) const

double	std (void) const
	The standard deviation is defined as the square root of the variance. More...

double	std (double m) const
	The standard deviation is defined as the square root of the variance. More...

double	sum_xx (void) const

double	sum_xx_centered (void) const

double	variance (double m) const
	The variance with known mean. More...

double	variance (void) const
	The estimated variance. More...

double	variance_unbiased (void) const

void	add (double x, long n=1)
	add a data point More...

double	mean (void) const
	mean More...

long	n (void) const
	number of data points More...

void	rescale (double a)
	Rescales the object. More...

void	reset (void)
	Reset object. More...

double	sum_x (void) const

Protected Member Functions
void	add2 (double mean, double cm2, long int n)

void	add2 (double delta)

double	rescale2 (double x)

void	add1 (double x, long int n)

void	add1 (double delta)

double	rescale1 (double x)

Protected Attributes
double	cm2_

long int	n_

double	mean_
	mean

Friends
class	averager_base< Averager >

Related Functions
(Note that these are not member functions.)
template<typename InputIterator >
void	add (Averager &a, InputIterator first, InputIterator last)
	adding a range of values to Averager a More...

Detailed Description

Class to calculate simple (first and second moments) averages.

See Also: AveragerWeighted AveragerPair AveragerPairWeighted

Constructor & Destructor Documentation

theplu::yat::statistics::Averager::Averager ( void )

Default constructor

theplu::yat::statistics::Averager::Averager	(	double	x,
		double	xx,
		long	n
	)

Constructor taking sum of x, sum of squared x, xx, and number of samples n.

theplu::yat::statistics::Averager::Averager ( const Averager & a )

Copy constructor

Member Function Documentation

void theplu::yat::statistics::averager_base< Averager >::add	(	double	x,
		long	n = `1`
	)

inlineinherited

add a data point

Adding n number of data point(s) with value x.

void theplu::yat::statistics::averager_base< Averager >::add1	(	double	x,
		long int	n
	)

protectedinherited

add n data points with value x

void theplu::yat::statistics::averager_base< Averager >::add1 ( double delta )

protectedinherited

add one data point with value delta + mean()

void theplu::yat::statistics::averager_base2< Averager >::add2	(	double	mean,
		double	cm2,
		long int	n
	)

protectedinherited

add a set of n data points with mean mean and centralized squaed sum cm

void theplu::yat::statistics::averager_base2< Averager >::add2 ( double delta )

protectedinherited

add one data point with value delta + mean()

double theplu::yat::statistics::averager_base2< Averager >::cv ( void ) const

inlineinherited

Coeffient of variation.

Coeffient of variation (cv) is defined as ratio between the standard deviation and the mean: $\frac{\sigma}{\mu}$ .

Returns: standard deviation divided by mean.

double theplu::yat::statistics::averager_base< Averager >::mean ( void ) const

inlineinherited

mean

Returns: Mean of presented data, $\frac{1}{n}\sum x_i$

long theplu::yat::statistics::averager_base< Averager >::n ( void ) const

inlineinherited

number of data points

Returns: Number of data points

template<class Derived >

const Averager & theplu::yat::statistics::Averager::operator+= ( const averager_base2< Derived > & other )

plus assignment operator

Add another Averager

void theplu::yat::statistics::averager_base< Averager >::rescale ( double a )

inlineinherited

Rescales the object.

$\forall x_i \rightarrow a*x_i$ ,

double theplu::yat::statistics::averager_base< Averager >::rescale1 ( double x )

protectedinherited

rescale as $mean \rightarrow x mean$

double theplu::yat::statistics::averager_base2< Averager >::rescale2 ( double x )

protectedinherited

rescales $cm2 \rightarrow x^2 cm2$ and calles rescale1(double)

void theplu::yat::statistics::averager_base< Averager >::reset ( void )

inlineinherited

Reset object.

Restore Averager as if data were never added

double theplu::yat::statistics::averager_base2< Averager >::standard_error ( void ) const

inlineinherited

Returns: Standard error, i.e. standard deviation of the mean $\sqrt{variance()/n}$

double theplu::yat::statistics::averager_base2< Averager >::std ( void ) const

inlineinherited

The standard deviation is defined as the square root of the variance.

Returns: The standard deviation, root of the variance().

double theplu::yat::statistics::averager_base2< Averager >::std ( double m ) const

inlineinherited

The standard deviation is defined as the square root of the variance.

Returns: Standard deviation around m, root of the variance(m).

double theplu::yat::statistics::averager_base< Averager >::sum_x ( void ) const

inlineinherited

Returns: The sum of data values

double theplu::yat::statistics::averager_base2< Averager >::sum_xx ( void ) const

inlineinherited

Returns: The sum of squares

double theplu::yat::statistics::averager_base2< Averager >::sum_xx_centered ( void ) const

inlineinherited

Returns: $\sum_i (x_i-m)^2$

double theplu::yat::statistics::averager_base2< Averager >::variance ( double m ) const

inlineinherited

The variance with known mean.

The variance is calculated as $\frac{1}{n}\sum (x_i-m)^2$ .

Returns: Variance when the mean is known to be m.

double theplu::yat::statistics::averager_base2< Averager >::variance ( void ) const

inlineinherited

The estimated variance.

The variance is calculated as $\frac{1}{N}\sum_i (x_i-m)^2$ , where $ m $ is the mean.

Returns: Estimation of variance

double theplu::yat::statistics::averager_base2< Averager >::variance_unbiased ( void ) const

inlineinherited

The variance is calculated using the $ (n-1) $ correction, which means it is the best unbiased estimator of the variance $\frac{1}{N-1}\sum_i (x_i-m)^2$ , where $ m $ is the mean.