Computes K-Means in the distributed processing mode on the master node.
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◆ DistributedStep2Master() [1/2]
Constructs the K-Means algorithm by copying input objects and parameters of another K-Means algorithm
- Parameters
-
context | Context to manage algorithm |
other | An algorithm to be used as the source to initialize the input objects and parameters of the algorithm |
◆ DistributedStep2Master() [2/2]
Constructs the K-Means algorithm
- Parameters
-
context | Context to manage algorithm |
cls | Data type to use in intermediate computations for the algorithm, Double.class or Float.class |
method | Computation method of the algorithm, Method |
nClusters | Number of clusters for the algorithm |
◆ clone()
Returns the newly allocated K-Means algorithm with a copy of input objects and parameters of this K-Means algorithm
- Parameters
-
context | Context to manage algorithm |
- Returns
- The newly allocated algorithm
◆ compute()
Runs the K-Means algorithm
- Returns
- Partial results of the K-Means algorithm
◆ finalizeCompute()
Computes the results of the K-Means algorithm
- Returns
- Results of the K-Means algorithm
◆ setPartialResult()
Registers user-allocated memory to store partial results of the K-Means algorithm
- Parameters
-
partialResult | Structure to store partial results of the K-Means algorithm |
◆ setResult()
void setResult |
( |
Result |
result | ) |
|
Registers user-allocated memory to store the results of the K-Means algorithm
- Parameters
-
result | Structure to store the results of the K-Means algorithm |
◆ input
◆ method
Computation method for the algorithm
The documentation for this class was generated from the following file:
- kmeans/DistributedStep2Master.java