Name
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp — Clear the training data of a multilayer perceptron.
Herror clear_samples_class_mlp(const Hlong MLPHandle)
Herror T_clear_samples_class_mlp(const Htuple MLPHandle)
Herror clear_samples_class_mlp(const HTuple& MLPHandle)
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp clears all training samples that
have been added to the multilayer perceptron (MLP)
MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle with add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp or
read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp. clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp
should only be used if the MLP is trained in the same process that
uses the MLP for evaluation with evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp or for
classification with classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp. In this case, the
memory required for the training samples can be freed with
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp, and hence memory can be saved. In
the normal usage, in which the MLP is trained offline and written to
a file with write_class_mlpwrite_class_mlpWriteClassMlpwrite_class_mlpWriteClassMlpWriteClassMlp, it is typically unnecessary to
call clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp because write_class_mlpwrite_class_mlpWriteClassMlpwrite_class_mlpWriteClassMlpWriteClassMlp
does not save the training samples, and hence the online process,
which reads the MLP with read_class_mlpread_class_mlpReadClassMlpread_class_mlpReadClassMlpReadClassMlp, requires no memory
for the training samples.
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
This operator modifies the state of the following input parameter:
The value of this parameter may not be shared across multiple threads without external synchronization.
If the parameters are valid, the operator
clear_samples_class_mlpclear_samples_class_mlpClearSamplesClassMlpclear_samples_class_mlpClearSamplesClassMlpClearSamplesClassMlp returns the value 2 (H_MSG_TRUE). If
necessary an exception is raised.
train_class_mlptrain_class_mlpTrainClassMlptrain_class_mlpTrainClassMlpTrainClassMlp,
write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlp
create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp,
clear_class_mlpclear_class_mlpClearClassMlpclear_class_mlpClearClassMlpClearClassMlp,
add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp,
read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp
Foundation