14#ifndef MLPACK_METHODS_KMEANS_REFINED_START_HPP
15#define MLPACK_METHODS_KMEANS_REFINED_START_HPP
48 const double percentage = 0.02) :
49 samplings(samplings), percentage(percentage) { }
61 template<
typename MatType>
63 const size_t clusters,
64 arma::mat& centroids)
const;
77 template<
typename MatType>
79 const size_t clusters,
80 arma::Row<size_t>& assignments)
const;
93 template<
typename Archive>
96 ar & BOOST_SERIALIZATION_NVP(samplings);
97 ar & BOOST_SERIALIZATION_NVP(percentage);
111#include "refined_start_impl.hpp"
A refined approach for choosing initial points for k-means clustering.
double & Percentage()
Modify the percentage of the data used by each subsampling.
RefinedStart(const size_t samplings=100, const double percentage=0.02)
Create the RefinedStart object, optionally specifying parameters for the number of samplings to perfo...
size_t & Samplings()
Modify the number of samplings that will be performed.
void Cluster(const MatType &data, const size_t clusters, arma::Row< size_t > &assignments) const
Partition the given dataset into the given number of clusters according to the random sampling scheme...
void Cluster(const MatType &data, const size_t clusters, arma::mat ¢roids) const
Partition the given dataset into the given number of clusters according to the random sampling scheme...
size_t Samplings() const
Get the number of samplings that will be performed.
double Percentage() const
Get the percentage of the data used by each subsampling.
void serialize(Archive &ar, const unsigned int)
Serialize the object.
Linear algebra utility functions, generally performed on matrices or vectors.
The core includes that mlpack expects; standard C++ includes and Armadillo.