learn_sampset_boxlearn_sampset_boxlearn_sampset_boxLearnSampsetBoxLearnSampsetBox (Operator)

Name

learn_sampset_boxlearn_sampset_boxlearn_sampset_boxLearnSampsetBoxLearnSampsetBox — Train the classifier with one data set.

Signature

learn_sampset_box( : : ClassifHandle, SampKey, Outfile, NSamples, StopError, ErrorN : )

Description

learn_sampset_boxlearn_sampset_boxlearn_sampset_boxLearnSampsetBoxLearnSampsetBox trains the classifier with data for the key SampKeySampKeySampKeySampKeysampKey (see read_sampsetread_sampsetread_sampsetReadSampsetReadSampset). The training sequence is terminated at least after NSamplesNSamplesNSamplesNSamplesNSamples examples. If NSamplesNSamplesNSamplesNSamplesNSamples is bigger than the number of examples in SampKeySampKeySampKeySampKeysampKey, then a cyclic start at the beginning occurs. If the error underpasses the value StopErrorStopErrorStopErrorStopErrorstopError, then the training sequence is prematurely terminated. StopErrorStopErrorStopErrorStopErrorstopError is calculated with N / ErrorN. Whereby N significates the number of examples which were wrong classified during the last ErrorNErrorNErrorNErrorNerrorN training examples. Typically ErrorNErrorNErrorNErrorNerrorN is the number of examples in SampKeySampKeySampKeySampKeysampKey and NSamplesNSamplesNSamplesNSamplesNSamples is a multiple of it. If you want a data set with 100 examples to run 5 times at most and if you want it to terminate with an error lower than 5%, then the corresponding values are NSamplesNSamplesNSamplesNSamplesNSamples = 500, ErrorNErrorNErrorNErrorNerrorN = 100 and StopErrorStopErrorStopErrorStopErrorstopError = 0.05. A protocol of the training activity is going to be written in file OutfileOutfileOutfileOutfileoutfile.

Parallelization

Multithreading type: exclusive (runs in parallel only with independent operators).
Multithreading scope: local (may only be called from the same thread in which the window, model, or tool instance was created).
Processed without parallelization.

Parameters

ClassifHandleClassifHandleClassifHandleClassifHandleclassifHandle (input_control) class_box → (integer)

Handle of the classifier.

SampKeySampKeySampKeySampKeysampKey (input_control) feature_set → (integer)

Number of the data set to train.

OutfileOutfileOutfileOutfileoutfile (input_control) filename.write → (string)

Name of the protocol file.

Default value: 'training_prot' "training_prot" "training_prot" "training_prot" "training_prot"

NSamplesNSamplesNSamplesNSamplesNSamples (input_control) integer → (integer)

Number of arrays of attributes to learn.

Default value: 500

StopErrorStopErrorStopErrorStopErrorstopError (input_control) real → (real)

Classification error for termination.

Default value: 0.05

ErrorNErrorNErrorNErrorNerrorN (input_control) integer → (integer)

Error during the assignment.

Default value: 100

learn_sampset_boxlearn_sampset_boxlearn_sampset_boxLearnSampsetBoxLearnSampsetBox returns 2 (H_MSG_TRUE). An exception is raised if key SampKeySampKeySampKeySampKeysampKey does not exist or there are problems while opening the file.