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NumClustMinVarianceC::NumClustMinVarianceC(const NumMagnitudeC &) NumClustCriterionC::Body(void) NumClustCriterionC::Body(void) const NumClustCriterionC::Copy(void) const NumClustCriterionC::operator=(const NumClustCriterionC &) NumClustCriterionC::Criterion(const DListC &,const NumClassifyC &) const NumClustCriterionC::GetInfo(void) const NumClustCriterionC::GetName(void) const NumClustCriterionC::Save(ostream &) const RCHandleAC::Abstract(void) RCHandleAC::IsHandleType(const DT &) const RCHandleAC::CheckHandleType(const DT &) const RCHandleAC::Save(ostream &) const RCHandleAC::Copy(void) const RCHandleC::IsValid(void) const RCHandleC::IsValidObject(void) const RCHandleC::operator=(const RCHandleC &) RCHandleC::operator==(const RCHandleC &) const RCHandleC::operator!=(const RCHandleC &) const RCHandleC::Hash(void) const RCHandleC::IsConst(void) const RCHandleC::IsNotConst(void) const RCHandleC::SetConst(void) const RCHandleC::Invalidate(void) RCHandleC::Body(void) RCHandleC::Body(void) const

NumClustMinVarianceC
Related Minimum Variance clustering criteria.
include	"amma/Num/NumClustMinVariance.hh"
User Level:	Default
Library:	NumPerformance
Example:	testNumPerformance.cc
Section:	Pattern Recognition.Performance Evaluation.Implementation
In Scope:	std

Comments:
Class for measuring clustering performance. Determines the average squared distance between samples in a cluster. The distance can be anything measured using an object derived from NumMagnitudeC.

public NumClustCriterionC

NumClustMinVarianceC(const NumMagnitudeC & distance)

Contructor

distance	to measure distance between samples in clusters

NumClustCriterionBC & Body()

Access body.

const NumClustCriterionBC & Body() const

Access body.

NumClustCriterionC Copy() const

Makes a deep copy

NumClustCriterionC & operator=(const NumClustCriterionC & oth)

Assignment operator

RealT Criterion(const DListC<VectorC> & data,const NumClassifyC & classifier) const

Determines clustering criterion function

data	data set containing feature vectors
classifier	classifier to test

The classifier is assumed to have been trained already and is used to predict the label for each feature vector. A clustering criterion is then used to judge the quality of the clusters. This function does all the work so must be overloaded in derived classes.

const StringC GetInfo() const

Gets string describing object

const StringC GetName() const

Gets type name of the object

BooleanT Save(ostream & out) const

Writes object to stream, cna be loaded using constructor

RCAbstractC Abstract()

Create an abstract handle to this object.

BooleanT IsHandleType(const DT &) const

Is handle of given type ?

void CheckHandleType(const DT & dummy) const

Check handle type. Throw an expception if not.

BooleanT Save(ostream & out) const

Save to ostream.

RCHandleAC<NumClustCriterionBC> Copy() const

Creat a deep copy of this object.
protected:

BooleanT IsValid() const

Check its a valid handle.

BooleanT IsValidObject() const

Check its a valid handle, and that the object is pointed to is also valid.

RCHandleC<NumClustCriterionBC> & operator=(const RCHandleC<NumClustCriterionBC> & oth)

Assignment.

BooleanT operator==(const RCHandleC<NumClustCriterionBC> & oth) const

Comparison operator.

BooleanT operator!=(const RCHandleC<NumClustCriterionBC> & oth) const

Comparison operator.

UIntT Hash() const

Hash function.x

BooleanT IsConst() const

Is object constant ?

BooleanT IsNotConst() const

Is object not constant ?

void SetConst(void) const

Lock the object.
This is const as a convience though it actual modified the object, often object you wish to ensure are constant already have const set. protected:

void Invalidate()

Turn this into an invalid handle.

NumClustCriterionBC & Body()

Direct access to body.

const NumClustCriterionBC & Body() const

Constant access to body.