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◆ aggregateKMeans() [1/2]

AggregateKMeansResponse kinetica.Kinetica.aggregateKMeans (AggregateKMeansRequestrequest_)
 inline

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.

Parameters
request_Request object containing the parameters for the operation.
Returns
Response object containing the result of the operation.

Definition at line 5359 of file KineticaFunctions.cs.

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◆ aggregateKMeans() [2/2]

AggregateKMeansResponse kinetica.Kinetica.aggregateKMeans (stringtable_name,
IList< string >column_names,
intk,
doubletolerance,
IDictionary< string, string >options = null )
 inline

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.

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.
Returns
Response object containing the result of the operation.

Definition at line 5534 of file KineticaFunctions.cs.

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◆ AggregateKMeansAsync() [1/2]

async System.Threading.Tasks.Task< AggregateKMeansResponse > kinetica.Kinetica.AggregateKMeansAsync (AggregateKMeansRequestrequest_,
System.Threading.CancellationTokencancellationToken = default )
 inline

This endpoint runs the k-means algorithm - a heuristic algorithm that attempts to do k-means clustering.

(async)

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.

Parameters
request_Request object containing the parameters for the operation.
cancellationTokenCancellation token to cancel the operation.
Returns
Task that returns the response object containing the result of the operation.

Definition at line 5385 of file KineticaFunctions.cs.

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◆ AggregateKMeansAsync() [2/2]

async System.Threading.Tasks.Task< AggregateKMeansResponse > kinetica.Kinetica.AggregateKMeansAsync (stringtable_name,
IList< string >column_names,
intk,
doubletolerance,
IDictionary< string, string >options = null,
System.Threading.CancellationTokencancellationToken = default )
 inline

This endpoint runs the k-means algorithm - a heuristic algorithm that attempts to do k-means clustering.

(async)

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.

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.
cancellationTokenCancellation token to cancel the operation.
Returns
Task that returns the response object containing the result of the operation.

Definition at line 5687 of file KineticaFunctions.cs.