Class for score based on signal-to-noise ratio (SNRScore). More...

#include <yat/statistics/SNRScore.h>

Inheritance diagram for theplu::yat::statistics::SNRScore:

Public Member Functions
	SNRScore (bool absolute=true)
	Default Constructor.

virtual	~SNRScore (void)
	The destructor.

double	score (const classifier::Target &target, const utility::VectorBase &value) const

double	score (const classifier::Target &target, const classifier::DataLookupWeighted1D &value) const

double	score (const classifier::Target &target, const utility::VectorBase &value, const utility::VectorBase &weight) const

void	absolute (bool absolute)
	Function changing mode of Score.

virtual double	score (const classifier::Target &target, const classifier::DataLookup1D &value) const

double	score (const classifier::Target &target, const classifier::DataLookup1D &value, const classifier::DataLookup1D &weight) const

Protected Attributes
bool	absolute_

Detailed Description

Class for score based on signal-to-noise ratio (SNRScore).

Also sometimes referred to as Golub score. The score is the ratio between difference in mean and the sum of standard deviations for two groups: $\frac{ m_x-m_y}{ s_x + s_y}$ where $ s $ is standard deviation.

Member Function Documentation

double theplu::yat::statistics::SNRScore::score	(	const classifier::Target &	target,
		const utility::VectorBase &	value
	)		const

virtual

SNRScore is defined as $\frac{m_x-m_y}{s_x+s_y}$ where $ m $ and $ s $ are mean and standard deviation, respectively.

See Also: Averager

Returns: SNRScore score. If absolute=true absolute value of SNRScore is returned

Implements theplu::yat::statistics::Score.

virtual double theplu::yat::statistics::Score::score	(	const classifier::Target &	target,
		const classifier::DataLookup1D &	value
	)		const

virtualinherited

Function calculating the score. In absolute mode, also the score using negated class labels is calculated, and the largest of the two scores are calculated.

value is copied to a utility::vector and that operator is called. If speed is important this operator should be implemented in inherited class to avoid copying.

Returns: score

double theplu::yat::statistics::SNRScore::score	(	const classifier::Target &	target,
		const classifier::DataLookupWeighted1D &	value
	)		const

virtual

SNRScore is defined as $\frac{m_x-m_y}{s_x+s_y}$ where $ m $ and $ s $ are weighted versions of mean and standard deviation, respectively.

See Also: AveragerWeighted

Returns: SNRScore score. If absolute=true absolute value of SNRScore is returned

Reimplemented from theplu::yat::statistics::Score.

double theplu::yat::statistics::SNRScore::score	(	const classifier::Target &	target,
		const utility::VectorBase &	value,
		const utility::VectorBase &	weight
	)		const

virtual

SNRScore is defined as $\frac{m_x-m_y}{s_x+s_y}$ where $ m $ and $ s $ are weighted versions of mean and standard deviation, respectively.

See Also: AveragerWeighted

Returns: SNRScore score. If absolute=true absolute value of SNRScore is returned

Implements theplu::yat::statistics::Score.

double theplu::yat::statistics::Score::score	(	const classifier::Target &	target,
		const classifier::DataLookup1D &	value,
		const classifier::DataLookup1D &	weight
	)		const

inherited

Function calculating the weighted version of score. In absolute mode, also the score using negated class labels is calculated, and the largest of the two scores are calculated. Absolute mode should be used when two-tailed test is wanted.

value and weight are copied to utility::vector and the corresponding operator is called. If speed is important this operator should be implemented in inherited class to avoid copying.

Member Data Documentation

bool theplu::yat::statistics::Score::absolute_

protectedinherited

true if method is absolute, which means if score is below expected value (by chance) E, score returns E-score+E instead.

The documentation for this class was generated from the following file:

yat/statistics/SNRScore.h

Public Member Functions

Protected Attributes

Detailed Description

Member Function Documentation

Member Data Documentation