theplu::yat::statistics::SNRScore Class Reference

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

#include <yat/statistics/SNRScore.h>

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

theplu::yat::statistics::Score

List of all members.

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.

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.

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

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::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_ [protected, inherited]

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:

Generated on Tue Jan 18 02:21:18 2011 for yat by  doxygen 1.5.5