◆ aggregateKMeans() [1/4]
| AggregateKMeansResponse gpudb::GPUdb::aggregateKMeans | ( | const AggregateKMeansRequest & | request_ | ) | 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.
| [in] | request_ | Request object containing the parameters for the operation. |
◆ aggregateKMeans() [2/4]
| AggregateKMeansResponse & gpudb::GPUdb::aggregateKMeans | ( | const AggregateKMeansRequest & | request_, |
| AggregateKMeansResponse & | response_ ) 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.
◆ aggregateKMeans() [3/4]
| AggregateKMeansResponse gpudb::GPUdb::aggregateKMeans | ( | 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 ) 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.
| [in] | tableName | 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. |
| [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.
|
◆ aggregateKMeans() [4/4]
| AggregateKMeansResponse & gpudb::GPUdb::aggregateKMeans | ( | 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, | ||
| AggregateKMeansResponse & | response_ ) 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.
| [in] | tableName | 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. |
| [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.
|
| [out] | response_ | Response object containing the results of the operation. |