A set of parameters for Kinetica.aggregateKMeans. More…
Inheritance diagram for kinetica.AggregateKMeansRequest:
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Collaboration diagram for kinetica.AggregateKMeansRequest:
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Classes | |
| struct | Options |
| A set of string constants for the parameter AggregateKMeansRequest.options. More… | |
Public Member Functions | |
| AggregateKMeansRequest () | |
| Constructs an AggregateKMeansRequest object with default parameters. | |
| 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. | |
| AggregateKMeansRequest () | |
| Constructs an AggregateKMeansRequest object with default parameters. | |
| 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. | |
| Public Member Functions inherited from kinetica.KineticaData | |
| KineticaData (KineticaType type) | |
| Constructor from Kinetica Type. | |
| KineticaData (System.Type type=null) | |
| Default constructor, with optional System.Type. | |
| object | Get (int fieldPos) |
| Retrieve a specific property from this object. | |
| void | Put (int fieldPos, object fieldValue) |
| Write a specific property to this object. | |
| KineticaData (KineticaType type) | |
| Constructor from Kinetica Type. | |
| KineticaData (System.Type type=null) | |
| Default constructor, with optional System.Type. | |
| object | Get (int fieldPos) |
| Retrieve a specific property from this object. | |
| void | Put (int fieldPos, object fieldValue) |
| Write a specific property to this object. | |
Properties | |
| string | table_name [get, set] |
| Name of the table on which the operation will be performed. | |
| IList< string > | column_names = new List<string>() [get, set] |
| List of column names on which the operation would be performed. | |
| int | k [get, set] |
| The number of mean points to be determined by the algorithm. | |
| double | tolerance [get, set] |
| Stop iterating when the distances between successive points is less than the given tolerance. | |
| IDictionary< string, string > | options = new Dictionary<string, string>() [get, set] |
| Optional parameters. | |
| Properties inherited from kinetica.KineticaData | |
| Schema | Schema [get] |
| Avro Schema for this class. | |
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. | |
| static ? RecordSchema | SchemaFromType (System.Type t, KineticaType? ktype=null) |
| Create an Avro Schema from a System.Type and a KineticaType. | |
Detailed Description
A set of parameters for Kinetica.aggregateKMeans.
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 25 of file AggregateKMeans.cs.
Constructor & Destructor Documentation
◆ AggregateKMeansRequest() [1/4]
| inline |
Constructs an AggregateKMeansRequest object with default parameters.
Definition at line 273 of file AggregateKMeans.cs.
◆ AggregateKMeansRequest() [2/4]
| inline |
Constructs an AggregateKMeansRequest object with the specified parameters.
Parameters
| table_name | 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. |
| column_names | 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. |
| k | The number of mean points to be determined by the algorithm. |
| tolerance | Stop iterating when the distances between successive points is less than the given tolerance. |
| options | Optional parameters.
|
Definition at line 404 of file AggregateKMeans.cs.
◆ AggregateKMeansRequest() [3/4]
| inline |
Constructs an AggregateKMeansRequest object with default parameters.
Definition at line 273 of file AggregateKMeans.cs.
◆ AggregateKMeansRequest() [4/4]
| inline |
Constructs an AggregateKMeansRequest object with the specified parameters.
Parameters
| table_name | 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. |
| column_names | 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. |
| k | The number of mean points to be determined by the algorithm. |
| tolerance | Stop iterating when the distances between successive points is less than the given tolerance. |
| options | Optional parameters.
|
Definition at line 404 of file AggregateKMeans.cs.
Property Documentation
◆ 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 145 of file AggregateKMeans.cs.
◆ k
| getset |
The number of mean points to be determined by the algorithm.
Definition at line 149 of file AggregateKMeans.cs.
◆ 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 269 of file AggregateKMeans.cs.
◆ 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 138 of file AggregateKMeans.cs.
◆ tolerance
| getset |
Stop iterating when the distances between successive points is less than the given tolerance.
Definition at line 153 of file AggregateKMeans.cs.
The documentation for this class was generated from the following files:
- _build/public-os_ubuntu24.04-arch_amd64-cc_gcc_13.3.0/install/Kinetica/Protocol/AggregateKMeans.cs
- Kinetica/Protocol/AggregateKMeans.cs