clear_samples_class_gmmT_clear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm (Operator)
Name
clear_samples_class_gmmT_clear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm — Clear the training data of a Gaussian Mixture Model.
Signature
Herror T_clear_samples_class_gmm(const Htuple GMMHandle)
def clear_samples_class_gmm(gmmhandle: MaybeSequence[HHandle]) -> None
Description
clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm clears all training samples that
have been stored in the Gaussian Mixture Model (GMM)
GMMHandleGMMHandleGMMHandleGMMHandleGMMHandlegmmhandle. clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm should only be
used if the GMM is trained in the same process that uses the GMM for
evaluation with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm or for classification
with classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmClassifyClassGmmclassify_class_gmm. In this case, the memory required
for the training samples can be freed with
clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm, and hence memory can be saved. In
the normal usage, in which the GMM is trained offline and written to
a file with write_class_gmmwrite_class_gmmWriteClassGmmWriteClassGmmWriteClassGmmwrite_class_gmm, it is typically unnecessary to
call clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm because write_class_gmmwrite_class_gmmWriteClassGmmWriteClassGmmWriteClassGmmwrite_class_gmm
does not save the training samples, and hence the online process,
which reads the GMM with read_class_gmmread_class_gmmReadClassGmmReadClassGmmReadClassGmmread_class_gmm, requires no memory
for the training samples.
Execution Information
- 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:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
Parameters
GMMHandleGMMHandleGMMHandleGMMHandleGMMHandlegmmhandle (input_control, state is modified) class_gmm(-array) → HClassGmm, HTupleMaybeSequence[HHandle]HTupleHtuple (handle) (IntPtr) (HHandle) (handle)
GMM handle.
Result
If the parameters are valid, the operator
clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm returns the value TRUE. If
necessary an exception is raised.
Possible Predecessors
train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmTrainClassGmmtrain_class_gmm,
write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm
See also
create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmmcreate_class_gmm,
clear_class_gmmclear_class_gmmClearClassGmmClearClassGmmClearClassGmmclear_class_gmm,
add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm,
read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmReadSamplesClassGmmread_samples_class_gmm
Module
Foundation