Algorithm class for training naive Bayes model on the second step in the distributed processing mode.
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◆ TrainingDistributedStep2Master() [1/2]
Constructs multinomial naive Bayes training algorithm by copying input objects and parameters of another multinomial naive Bayes training algorithm
- Parameters
-
context | Context to manage the multinomial naive Bayes training |
other | An algorithm to be used as the source to initialize the input objects and parameters of the algorithm |
◆ TrainingDistributedStep2Master() [2/2]
Constructs multinomial naive Bayes training algorithm
- Parameters
-
context | Context to manage the multinomial naive Bayes training |
cls | Data type to use in intermediate computations of the multinomial naive Bayes training on the second step in the distributed processing mode, Double.class or Float.class |
method | Multinomial naive Bayes training method on the second step in the distributed processing mode, TrainingMethod |
nClasses | Number of classes |
◆ clone()
Returns the newly allocated multinomial naive Bayes training algorithm with a copy of input objects and parameters of this multinomial naive Bayes training algorithm
- Parameters
-
context | Context to manage the multinomial naive Bayes training |
- Returns
- The newly allocated algorithm
◆ compute()
Computes naive Bayes training results on the second step in the distributed processing mode
- Returns
- Naive Bayes training results on the second step in the distributed processing mode
◆ finalizeCompute()
Computes naive Bayes training results on the second step in the distributed processing mode
- Returns
- Naive Bayes training results on the second step in the distributed processing mode
◆ setPartialResult()
Registers user-allocated memory to store naive Bayes training partial results
- Parameters
-
result | Structure to store naive Bayes training partial results |
◆ setResult()
Registers user-allocated memory to store naive Bayes training results
- Parameters
-
result | Structure to store naive Bayes training results |
◆ input
Input objects for the algorithm
◆ method
Training method for the algorithm
◆ parameter
Parameters of the algorithm
The documentation for this class was generated from the following file:
- multinomial_naive_bayes/training/TrainingDistributedStep2Master.java