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kinetica.AggregateKMeansRequest Class Reference

A set of parameters for Kinetica.aggregateKMeans(string,IList{string},int,double,IDictionary{string, string}). More...

+ Inheritance diagram for kinetica.AggregateKMeansRequest:
+ Collaboration diagram for kinetica.AggregateKMeansRequest:

Classes

struct  Options
 Optional parameters. More...
 

Public Member Functions

 AggregateKMeansRequest ()
 Constructs an AggregateKMeansRequest object with default parameters. More...
 
 AggregateKMeansRequest (string table_name, IList< string > column_names, int k, double tolerance, IDictionary< string, string > options=null)
 Constructs an AggregateKMeansRequest object with the specified parameters. More...
 
- Public Member Functions inherited from kinetica.KineticaData
 KineticaData (KineticaType type)
 Constructor from Kinetica Type More...
 
 KineticaData (System.Type type=null)
 Default constructor, with optional System.Type More...
 
object Get (int fieldPos)
 Retrieve a specific property from this object More...
 
void Put (int fieldPos, object fieldValue)
 Write a specific property to this object More...
 

Properties

string table_name [get, set]
 Name of the table on which the operation will be performed. More...
 
IList< string > column_names [get, set]
 List of column names on which the operation would be performed. More...
 
int k = new List<string>() [get, set]
 The number of mean points to be determined by the algorithm. More...
 
double tolerance [get, set]
 Stop iterating when the distances between successive points is less than the given tolerance. More...
 
IDictionary< string, string > options [get, set]
 Optional parameters. More...
 
- Properties inherited from kinetica.KineticaData
Schema Schema [get]
 Avro Schema for this class More...
 

Additional Inherited Members

- Static Public Member Functions inherited from kinetica.KineticaData
static RecordSchema SchemaFromType (System.Type t, KineticaType ktype=null)
 Create an Avro Schema from a System.Type and a KineticaType. More...
 

Detailed Description

A set of parameters for Kinetica.aggregateKMeans(string,IList{string},int,double,IDictionary{string, string}).


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 36 of file AggregateKMeans.cs.

Constructor & Destructor Documentation

kinetica.AggregateKMeansRequest.AggregateKMeansRequest ( )
inline

Constructs an AggregateKMeansRequest object with default parameters.

Definition at line 318 of file AggregateKMeans.cs.

kinetica.AggregateKMeansRequest.AggregateKMeansRequest ( string  table_name,
IList< string >  column_names,
int  k,
double  tolerance,
IDictionary< string, string >  options = null 
)
inline

Constructs an AggregateKMeansRequest object with the specified parameters.

Parameters
table_nameName of the table on which the operation will be performed. Must be an existing table, in [schema_name.]table_name format, using standard name resolution rules.
column_namesList 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.
kThe number of mean points to be determined by the algorithm.
toleranceStop iterating when the distances between successive points is less than the given tolerance.
optionsOptional parameters.
  • WHITEN: When set to 1 each of the columns is first normalized by its stdv - default is not to whiten.
  • MAX_ITERS: Number of times to try to hit the tolerance limit before giving up - default is 10.
  • 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.
  • CREATE_TEMP_TABLE: If true, a unique temporary table name will be generated in the sys_temp schema and used in place of result_table. If result_table_persist is false (or unspecified), then this is always allowed even if the caller does not have permission to create tables. The generated name is returned in qualified_result_table_name. Supported values: The default value is FALSE.
  • RESULT_TABLE: The name of a table used to store the results, in [schema_name.]table_name format, using standard name resolution rules and meeting table naming criteria. If this option is specified, the results are not returned in the response.
  • RESULT_TABLE_PERSIST: If true, then the result table specified in result_table will be persisted and will not expire unless a ttl is specified. If false, then the result table will be an in-memory table and will expire unless a ttl is specified otherwise. Supported values: The default value is FALSE.
  • TTL: Sets the TTL of the table specified in result_table.
The default value is an empty Dictionary.

Definition at line 424 of file AggregateKMeans.cs.

Property Documentation

IList<string> kinetica.AggregateKMeansRequest.column_names
getset

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.

Definition at line 216 of file AggregateKMeans.cs.

int kinetica.AggregateKMeansRequest.k = new List<string>()
getset

The number of mean points to be determined by the algorithm.

Definition at line 220 of file AggregateKMeans.cs.

IDictionary<string, string> kinetica.AggregateKMeansRequest.options
getset

Optional parameters.

  • WHITEN: When set to 1 each of the columns is first normalized by its stdv - default is not to whiten.
  • MAX_ITERS: Number of times to try to hit the tolerance limit before giving up - default is 10.
  • 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.
  • CREATE_TEMP_TABLE: If true, a unique temporary table name will be generated in the sys_temp schema and used in place of result_table. If result_table_persist is false (or unspecified), then this is always allowed even if the caller does not have permission to create tables. The generated name is returned in qualified_result_table_name. Supported values: The default value is FALSE.
  • RESULT_TABLE: The name of a table used to store the results, in [schema_name.]table_name format, using standard name resolution rules and meeting table naming criteria. If this option is specified, the results are not returned in the response.
  • RESULT_TABLE_PERSIST: If true, then the result table specified in result_table will be persisted and will not expire unless a ttl is specified. If false, then the result table will be an in-memory table and will expire unless a ttl is specified otherwise. Supported values: The default value is FALSE.
  • TTL: Sets the TTL of the table specified in result_table.

The default value is an empty Dictionary.

Definition at line 313 of file AggregateKMeans.cs.

string kinetica.AggregateKMeansRequest.table_name
getset

Name of the table on which the operation will be performed.

Must be an existing table, in [schema_name.]table_name format, using standard name resolution rules.

Definition at line 210 of file AggregateKMeans.cs.

double kinetica.AggregateKMeansRequest.tolerance
getset

Stop iterating when the distances between successive points is less than the given tolerance.

Definition at line 224 of file AggregateKMeans.cs.

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