npl::Classifier Class Reference

Base class for all ND classifiers. More...

#include <statistics.h>

Inherited by npl::ExpMax, and npl::KMeans.

Public Member Functions
	Classifier (size_t rank)
	Initializes the classifier. More...
virtual Eigen::VectorXi	classify (const Ref< const MatrixXd > samples)=0
	Given a matrix of samples (Samples x Dims, sample on each row), apply the classifier to each sample and return a vector of the classes. More...
virtual size_t	classify (const Ref< const MatrixXd > samples, Ref< VectorXi > oclass)=0
	Given a matrix of samples (Samples x Dims, sample on each row), apply the classifier to each sample and return a vector of the classes. More...
virtual int	update (const Ref< const MatrixXd > samples, bool reinit=false)=0
	Updates the classifier with new samples, if reinit is true then no prior information will be used. If reinit is false then any existing information will be left intact. In Kmeans that would mean that the means will be left at their previous state. More...
void	compute (const Ref< const MatrixXd > samples)
	Alias for updateClasses with reinit = true. This will perform a classification scheme on all the input samples. More...

Public Attributes
const int	ndim
	Number of dimensions, must be set at construction. This is the number of columns in input samples. More...
int	maxit
	Maximum number of iterations. Set below 0 for infinite. More...

Protected Attributes
bool	m_valid
	Whether the classifier has been initialized yet. More...

Detailed Description

Base class for all ND classifiers.

Definition at line 656 of file statistics.h.

Constructor & Destructor Documentation

npl::Classifier::Classifier ( size_t  rank )

inline

Initializes the classifier.

Parameters

rank	Number of dimensions of samples

Definition at line 664 of file statistics.h.

Member Function Documentation

virtual Eigen::VectorXi npl::Classifier::classify ( const Ref< const MatrixXd >  samples )

pure virtual

Given a matrix of samples (Samples x Dims, sample on each row), apply the classifier to each sample and return a vector of the classes.

Parameters

samples

Set of samples, 1 per row

Returns

Vector of classes, rows match up with input sample rows

Implemented in npl::ExpMax, and npl::KMeans.

virtual size_t npl::Classifier::classify ( const Ref< const MatrixXd >  samples, Ref< VectorXi >  oclass  )

pure virtual

Given a matrix of samples (Samples x Dims, sample on each row), apply the classifier to each sample and return a vector of the classes.

Parameters

samples	Set of samples, 1 per row
oclass	Output classes. This vector will be resized to have the same number of rows as samples matrix.

Returns

the number of changed classifications

Implemented in npl::ExpMax, and npl::KMeans.

void npl::Classifier::compute ( const Ref< const MatrixXd >  samples )

inline

Alias for updateClasses with reinit = true. This will perform a classification scheme on all the input samples.

Parameters

samples

Samples, S x D matrix with S is the number of samples and D is the dimensionality. This must match the internal dimension count.

Definition at line 713 of file statistics.h.

virtual int npl::Classifier::update ( const Ref< const MatrixXd >  samples, bool  reinit = false  )

pure virtual

Updates the classifier with new samples, if reinit is true then no prior information will be used. If reinit is false then any existing information will be left intact. In Kmeans that would mean that the means will be left at their previous state.

Parameters

samples	Samples, S x D matrix with S is the number of samples and D is the dimensionality. This must match the internal dimension count.
reinit	whether to reinitialize the classifier before updating

return -1 if maximum number of iterations hit, 0 otherwise (converged)

Implemented in npl::ExpMax, and npl::KMeans.

Member Data Documentation

bool npl::Classifier::m_valid

protected

Whether the classifier has been initialized yet.

Definition at line 732 of file statistics.h.

int npl::Classifier::maxit

Maximum number of iterations. Set below 0 for infinite.

Definition at line 727 of file statistics.h.

const int npl::Classifier::ndim

Number of dimensions, must be set at construction. This is the number of columns in input samples.

Definition at line 716 of file statistics.h.

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

• /home/micahc/coding/npl/lib/statistics.h