gpudb::AggregateKMeansRequest Struct Reference

A set of input parameters for const. More...

#include <gpudb/protocol/aggregate_k_means.h>

Public Member Functions
	AggregateKMeansRequest ()
	Constructs an AggregateKMeansRequest object with default parameter values. More...
	AggregateKMeansRequest (const std::string &tableName_, const std::vector< std::string > &columnNames_, const int32_t k_, const double tolerance_, const std::map< std::string, std::string > &options_)
	Constructs an AggregateKMeansRequest object with the specified parameters. More...

Public Attributes
std::string	tableName
std::vector< std::string >	columnNames
int32_t	k
double	tolerance
std::map< std::string, std::string >	options

Detailed Description

A set of input parameters for const.

This endpoint runs the k-means algorithm - a heuristic algorithm that attempts to do k-means clustering. An ideal k-means clustering algorithm selects k points such that the sum of the mean squared distances of each member of the set to the nearest of the k points is minimized. The k-means algorithm however does not necessarily produce such an ideal cluster. It begins with a randomly selected set of k points and then refines the location of the points iteratively and settles to a local minimum. Various parameters and options are provided to control the heuristic search.

NOTE: The Kinetica instance being accessed must be running a CUDA (GPU-based) build to service this request.

Definition at line 29 of file aggregate_k_means.h.

Constructor & Destructor Documentation

gpudb::AggregateKMeansRequest::AggregateKMeansRequest ( )

inline

Constructs an AggregateKMeansRequest object with default parameter values.

Definition at line 36 of file aggregate_k_means.h.

gpudb::AggregateKMeansRequest::AggregateKMeansRequest ( const std::string &  tableName_, const std::vector< std::string > &  columnNames_, const int32_t  k_, const double  tolerance_, const std::map< std::string, std::string > &  options_  )

inline

Constructs an AggregateKMeansRequest object with the specified parameters.

Parameters

[in]	tableName_	Name of the table on which the operation will be performed. Must be an existing table or collection.
[in]	columnNames_	List of column names on which the operation would be performed. If n columns are provided then each of the k result points will have n dimensions corresponding to the n columns.
[in]	k_	The number of mean points to be determined by the algorithm.
[in]	tolerance_	Stop iterating when the distances between successive points is less than the given tolerance.
[in]	options_	Optional parameters. gpudb::aggregate_k_means_whiten: When set to 1 each of the columns is first normalized by its stdv - default is not to whiten. gpudb::aggregate_k_means_max_iters: Number of times to try to hit the tolerance limit before giving up - default is 10. gpudb::aggregate_k_means_num_tries: Number of times to run the k-means algorithm with a different randomly selected starting points - helps avoid local minimum. Default is 1.

Definition at line 80 of file aggregate_k_means.h.

Member Data Documentation

std::vector<std::string> gpudb::AggregateKMeansRequest::columnNames

Definition at line 90 of file aggregate_k_means.h.

int32_t gpudb::AggregateKMeansRequest::k

Definition at line 91 of file aggregate_k_means.h.

std::map<std::string, std::string> gpudb::AggregateKMeansRequest::options

Definition at line 93 of file aggregate_k_means.h.

std::string gpudb::AggregateKMeansRequest::tableName

Definition at line 89 of file aggregate_k_means.h.

double gpudb::AggregateKMeansRequest::tolerance

Definition at line 92 of file aggregate_k_means.h.

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

• gpudb/protocol/aggregate_k_means.h