NormalRBFuncBC -

Developer Documentation

Centre for Vision, Speech & Signal Processing

Parent Classes:

NumFuncBC

Variables:

SArray1dC _params;: Parameters of distribution
VectorC _weights;: Mixing coefficients of distributions
SArray1dC _invCov;: We pre-compute inverse of covariance matrix
SArray1dC _det;: We pre-compute the determinant of cov
BooleanT init;: Flag which determines whether we should initialise model before running an algorithm
BooleanT fullCov;: Flag which determines whether full covariances should computed
RealT konst;: The constant used in pdf. it does not get set until dimensionality is known Internal routines

Methods:

NormalRBFuncBC(const NormalRBFuncBC & oth)

Copy Constructor

NormalRBFuncBC(UIntT mixes)

Construct from a set number of mixes

NormalRBFuncBC(const SArray1dC<VecMatC> & params,const VectorC & weights)

Constructor

params	array of distribution statistics. Each VecMatC contains the mean and covariance for the class corresponding to its index value in the array.

NormalRBFuncBC(const Array1dC<VecMatC> & params,const VectorC & weights)

Constructor

params	array of distribution statistics. Each VecMatC contains the mean and covariance for the class corresponding to its index value in the array.

NormalRBFuncBC(const VectorC & vec,const MatrixC & cov)

Constructs a single component mixture model. Obviously weight set to 1.0

NormalRBFuncBC(istream & in)

Constructs from stream and provides class name

BodyRefCounterVC & Copy() const

Makes a deep copy and is virtual

VectorC Evaluate(const VectorC & X) const

Evaluate Y=f(X). This function returns the class conditional

BooleanT Save(ostream & out) const

Writes object to stream, can be loaded using constructor

void Save(FilenameC & out) const

Writes object to stream, can be loaded using constructor

void Join(const NormalRBFuncC & rbf)

Joins two models together. It will also normalise the weights

BooleanT InitKmeans(const DListC<VectorC> & data)

Initialise the centres using the kmeans alg

BooleanT InitRandom(const DListC<VectorC> & data)

Initialise the centres from random points in the list

RealT NegLogLikelihood(const DListC<VectorC> & data) const

calculate the likelihood of the data

BooleanT ExpectationMaximization(const DListC<VectorC> & data)

Use the Expectation Maximization algorithm to estimate the paramters from the unlabelled data.

BooleanT EM(const DListC<VectorC> & data)

perform one iterarion of EM alg on data

BooleanT ProbPcaEM(const DListC<VectorC> & data,UIntT dimen)

Use the Probabilistic PCA version of EM alg

BooleanT Update(const DListC<VectorC> & data,RealT alpha,BooleanT freqSen)

Perform on-line version of EM algorithm on data

UIntT Size() const

Return the number of components in mixture

UIntT Dim() const

Return the dimensionality of the model

VectorC Weights() const

Access to the mixing coefficients

void Weights(const VectorC & w)

Set the mixing coefficients

SArray1dC<VecMatC> Parameters() const

Access to distribution parameters held by object

void Parameters(const SArray1dC<VecMatC> & p)

Change parameters of object

SArray1dC<MatrixC> getInvCov() const

Access to pre-computed inverses

void setInvCov(const SArray1dC<MatrixC> & i)

Change pre-computed inverses

SArray1dC<RealT> getDet() const

Access to pre-computed determinants

void setDet(const SArray1dC<RealT> & d)

Change pre-computed determinants

Array1dC<VecMatC> Array() const

Array access to distribution parameters held by object

RealT Posterior(const VectorC & X,UIntT index)

Find the normalised posterior prob of vector X for mixture index

VectorC PosteriorProb(const VectorC & X)

Find the normalised posterior prob for elements of X

void PreCompute()

Precompute the determinant and inverse matrix

BooleanT Init() const

Returns status of initialisation flag

void Init(BooleanT flag)

Set initialisation flag

BooleanT FullCovMatrix() const

Get status of full covariance matrix flag

void FullCovMatrix(BooleanT mode)

Toggle whether full covariance matrices or diagonal matrices are computed

void Normalise()

This routine normalises the weights to equal 1

RealT L2norm(const NormalRBFuncBC & model) const

This algorithm computes the L2 norm between this and func (i.e. distance metric)

DListC<NormalRBFuncC> Group() const

Group the components

DListC<NormalRBFuncC> Group(const SArray1dC<VecMatC> & compStats) const

Group the components

DListC<NormalRBFuncC> GroupOR(const SArray1dC<VecMatC> & compStats) const

Group the components

DListC<NormalRBFuncC> GroupEuclid(const SArray1dC<VectorC> & groupsMean) const

Group the components

DListC<NormalRBFuncC> GroupMahal(const SArray1dC<VecMatC> & groupsParams) const

Group the components

DListC<NormalRBFuncC> GroupBattachar(const SArray1dC<VecMatC> & groupsParams) const

Group the components

NumLabelC GroupLabel(const DListC<NormalRBFuncC> & group,const VectorC & X) const

Labeling of the vector X using groups of NormalRBFunc

DListC<NumLabelC> GroupLabel(const DListC<NormalRBFuncC> & group,const DListC<VectorC> & L) const

Labeling of the list using groups of NormalRBFunc

RealT PredictedProb(const WindowT & win) const

Calculate the predicted probability in a given window for a diagonal model

RealT PredictedProb(const WindowT & win,IntT ncalls,IntT itmx) const

Calculate the predicted probability in a given window for a model with a full covariance matrix

void InsertComponent(const VectorC & mn,const MatrixC & cov,const RealT wt)

This routine inserts a component into the structure Friends -------

ostream & operator<<(ostream & s,const NormalRBFuncBC & out)

output stream operator

istream & operator>>(istream & s,NormalRBFuncBC & in)

input stream operator

void initmodel(const DListC<VectorC> & data)

initialise model e.t.c

MatrixC peaks() const

Groups using by analysing the peaks between centre

MatrixC stats(const SArray1dC<VecMatC> & compStats) const

Groups using component stats procedure

DListC<NormalRBFuncC> group(const MatrixC & merge) const

Turns merge matrix into group

IntT gradient(const SArray1dC<RealT> & histo) const

This routine determines whether two clusters centres should be merged

#include "amma/Num/NumFuncB.hh"

BodyRefCounterVC & Copy() const: Makes a deep copy and is virtual
As the copy constructor but is virtual so can be called from a base class reference.
void SetSizeX(UIntT X): Sets size of input vector
void SetSizeY(UIntT Y): Sets size of output vector
VectorC Apply(const VectorC & X): Evaluate Y=f(X) as a process
VectorC Evaluate(const VectorC & X) const: Evaluate Y=f(X)
This is the main function that does all the work and must be overloaded in derived classes.
DListC<VectorC> Evaluate(const DListC<VectorC> & listX) const: Evaluate Y=f(X) for a list of X
This is achieved by iterating through the list of X and using the Evaluate member function that takes a single X. For sophisticated applications where speed is important, this function can be overloaded to do batch processing.
VectorC operator()(const VectorC & X) const: Evaluate Y=f(X)
MatrixC Jacobian(const VectorC & X) const: Calculate Jacobian matrix at X
Performs numerical estimation of the Jacobian using differences. This function has and should be overloaded for all cases where the Jacobian can be calculated analytically.
const StringC GetInfo() const: Derived class information
const StringC & GetName() const: Derived class type
UIntT SizeX() const: Size of input vectors
UIntT SizeY() const: Size of output vectors
BooleanT Save(ostream & out) const: Writes object to stream, can be loaded using constructor

#include "amma/DP/Process.hh"

VectorC Apply(const VectorC &): Apply operation.
IntT ApplyArray(const SArray1dC<VectorC> & in,SArray1dC<VectorC> & out): Apply operation to an array of elements.
returns the number of elements processed.
BooleanT Save(ostream & out) const: Save to ostream.
const type_info & InputType() const: Get input type.
const type_info & OutputType() const: Get input type.
BooleanT Save(ostream & out) const: Save to ostream.
const type_info & InputType() const: Get input type.
const type_info & OutputType() const: Get input type.
ProcTypeT OpType() const: Operation type lossy/lossless.
BooleanT IsStateless() const: Is operation stateless ?

#include "amma/DP/Entity.hh"

BooleanT Save(ostream & out) const: Save to ostream.
BodyRefCounterVC & Copy() const: Creat a copy of this object.

#include "amma/BRefCntV.hh"

BodyRefCounterVC & Copy() const: Creat a copy of this object.
BooleanT operator==(const BodyRefCounterVC & oth) const: Compair identitys.
BooleanT operator!=(const BodyRefCounterVC & oth) const: Compair identitys.
BooleanT Save(ostream & out) const: Save to ostream.

#include "amma/BRefCnt.hh"

void AddReference(): Another reference to the object has been created.
void RemoveReference(): One reference to the object was deleted.
Locking scheme -------------- The object is possible to lock (to make constant). The scheme assumes that the object is created, it can be locked, after that it can only be destroyed. The locked object cannot be unlocked. This locking scheme is very useful during debugging, using assert() function, when it is necassary to check that object is treated as constant and the constancy is not violated by any casting or passing through the copy constructor of shared objects.
void SetConst(void) const: This locks the object.
Often objects you wish to lock are already const.
void SetConst(void): This locks the object.
BooleanT IsConst(void) const: Returns TRUE if the object is locked, ie. it is assumed to be constant.
BooleanT IsNotConst(void) const: Returns TRUE if the object is unlocked.
ie. there is no special information if the object is constant or not. Counter state information -------------------------
BooleanT ToBeDeleted() const: Returns TRUE if there is only one reference to the object and the whole object or its reference counting part can be deleted.
BooleanT ToBeDeletedRemoveIgnoreNoRemove(): Decrement refrence by 1 return true if this leaves no refrences to the object.
BooleanT ToBeDeletedRemove(): Decrement refrence by 1 return true if this leaves no refrences to the object. This also checks the NoRemove flag.
BooleanT BodyMightBeDeleted() const: Returns TRUE if the reference counted part of the object can be deleted, ie. flag NOREMOVE is false .
BooleanT IsCountZero() const: Are there any refrences left ?
BooleanT BRCPtrCanDeleteObject() const: Used by BRRCPtrC to establish if an object has ZERO refrences and can be deleted.
IntT Count() const: Returns the current state of the counter, ie. how many references to this object exist.
const BodyRefCounterC & operator=(const BodyRefCounterC & b): It has not meaning to assign object 'b' to this object because it would destroy a history of the object which is counted. So this is a dummy function.
BooleanT IsValidObject() const: Test if object is valid.
When amma check is disabled this always returns true.
BooleanT UserBitTest(IntT x) const: Test user flag.
void UserBitSet(IntT x,BooleanT setit = TRUE): Test user flag.
void UserBitZero(IntT x): Set bit to zero.
void ReportBRCError(char * Msg): Report error, used in BRCPtrC.
UIntT Hash() const: Hash on address of object.
void SetUndeletable(): Make object undeletable.
Usefull to prevent recursive deleting in graph structures. Only hackers need this function.
void ReportInvalidObject(char * Msg = 0) const: Tell user about validation failure.

#include "amma/RefCBase.hh"

LabelT Label() const: Returns the label of this reference counter.
The member function is useful mainly to recognize objects during debugging. The value of the label is uniquely defined pointer.

Programmer:Kieron Messer, Documentation by CxxDoc: Tue Mar 20 10:49:27 2001

NormalRBFuncBC

include	"amma/Num/NormalRBFuncB.hh"
User Level:	Default
Library:	NumRBF
Example:	testNumPerformance.cc
Section:	Pattern Recognition.Radial Basis Functions
In Scope:	std