ml::Clustering::Model Class Reference

Abstract clustering model. More...

#include <Clustering.hpp>

Inheritance diagram for ml::Clustering::Model:

Public Member Functions
virtual	~Model ()
	Virtual destructor.
virtual bool	fit (Eigen::Ref< const Eigen::MatrixXd > data)=0
	Fits the model. More...
virtual unsigned int	number_clusters () const =0
	Returns the number of clusters. More...
virtual const std::vector< unsigned int > &	labels () const =0
	Returns a const reference to resulting cluster labels for each datapoint. Value make sense only if fitting converged successfully.
virtual const Eigen::MatrixXd &	centroids () const =0
	Returns a const reference to the matrix of cluster centroids (in columns). More...
virtual bool	converged () const =0
	Reports if the model converged.

Detailed Description

Abstract clustering model.

Member Function Documentation

fit()

virtual bool ml::Clustering::Model::fit ( Eigen::Ref< const Eigen::MatrixXd >  data )

pure virtual

Fits the model.

Parameters

[in]

data

Matrix (column-major order) with a data point in every column.

Returns

true if fitting converged successfully.

Exceptions

std::invalid_argument

If data has no rows, or if the sample size (number of columns in data) is too low.

Implemented in ml::EM, and ml::Clustering::KMeans.

number_clusters()

virtual unsigned int ml::Clustering::Model::number_clusters ( ) const

pure virtual

Returns the number of clusters.

Value make sense only if fitting converged successfully.

Implemented in ml::EM, and ml::Clustering::KMeans.

centroids()

virtual const Eigen::MatrixXd& ml::Clustering::Model::centroids ( ) const

pure virtual

Returns a const reference to the matrix of cluster centroids (in columns).

A centroid represent the central location of the cluster. It is e.g. a mean of all points in the cluster. Value make sense only if fitting converged successfully.

Implemented in ml::EM, and ml::Clustering::KMeans.

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

• ML/Clustering.hpp