Computes initial clusters for the K-Means algorithm in the batch processing mode.
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◆ InitBatch() [1/2]
Constructs an algorithm for computing initial clusters for the K-Means algorithm in the batch processing mode by copying input objects and parameters of another algorithm
- Parameters
-
context | Context to manage initial clusters for the K-Means algorithm |
other | An algorithm to be used as the source to initialize the input objects and parameters of the algorithm |
◆ InitBatch() [2/2]
Constructs an algorithm for computing initial clusters for the K-Means algorithm in the batch processing mode
- Parameters
-
context | Context to manage initial clusters for the K-Means algorithm |
cls | Data type to use in intermediate computations of initial clusters for the K-Means algorithm, Double.class or Float.class |
method | Method of computing initial clusters for the algorithm, InitMethod |
nClusters | Number of initial clusters for the K-Means algorithm |
◆ clone()
Returns the newly allocated algorithm for computing initial clusters for the K-Means algorithm in the batch processing mode with a copy of input objects and parameters of this algorithm
- Parameters
-
context | Context to manage initial clusters for the K-Means algorithm |
- Returns
- The newly allocated algorithm
◆ compute()
Computes initial clusters for the K-Means algorithm
- Returns
- Results of computing initial clusters for the K-Means algorithm
◆ setResult()
Registers user-allocated memory to store the results of computing initial clusters for the K-Means algorithm
- Parameters
-
result | Structure to store the results of computing initial clusters for the K-Means algorithm |
◆ input
◆ method
Method for computing initial clusters
◆ parameter
Parameters for computing initial clusters
The documentation for this class was generated from the following file:
- kmeans/init/InitBatch.java