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get_sample_class_mlpGetSampleClassMlpGetSampleClassMlpget_sample_class_mlpT_get_sample_class_mlp🔗

Short description🔗

get_sample_class_mlpGetSampleClassMlpGetSampleClassMlpget_sample_class_mlpT_get_sample_class_mlp — Return a training sample from the training data of a multilayer perceptron.

Signature🔗

get_sample_class_mlp( class_mlp MLPHandle, integer IndexSample, out real Features, out real Target )void GetSampleClassMlp( const HTuple& MLPHandle, const HTuple& IndexSample, HTuple* Features, HTuple* Target )static void HOperatorSet.GetSampleClassMlp( HTuple MLPHandle, HTuple indexSample, out HTuple features, out HTuple target )def get_sample_class_mlp( mlphandle: HHandle, index_sample: int ) -> Tuple[Sequence[float], Sequence[float]]

Herror T_get_sample_class_mlp( const Htuple MLPHandle, const Htuple IndexSample, Htuple* Features, Htuple* Target )

HTuple HClassMlp::GetSampleClassMlp( Hlong IndexSample, HTuple* Target ) const

HTuple HClassMlp.GetSampleClassMlp( int indexSample, out HTuple target )

Description🔗

get_sample_class_mlpGetSampleClassMlp reads out a training sample from the multilayer perceptron (MLP) given by MLPHandleMLPHandlemlphandle that was added with add_sample_class_mlpAddSampleClassMlp or read_samples_class_mlpReadSamplesClassMlp. The index of the sample is specified with IndexSampleindexSampleindex_sample. The index is counted from 0, i.e., IndexSampleindexSampleindex_sample must be a number between 0 and NumSamplesnumSamplesnum_samples \(-\) 1, where NumSamplesnumSamplesnum_samples can be determined with get_sample_num_class_mlpGetSampleNumClassMlp. The training sample is returned in Featuresfeaturesfeatures and Targettargettarget. Featuresfeaturesfeatures is a feature vector of length NumInputnumInputnum_input, while Targettargettarget is a target vector of length NumOutputnumOutputnum_output (see add_sample_class_mlpAddSampleClassMlp and create_class_mlpCreateClassMlp).

get_sample_class_mlpGetSampleClassMlp can, for example, be used to reclassify the training data with classify_class_mlpClassifyClassMlp in order to determine which training samples, if any, are classified incorrectly.

Execution information🔗

Execution information

  • Multithreading type: reentrant (runs in parallel with non-exclusive operators).

  • Multithreading scope: global (may be called from any thread).

  • Processed without parallelization.

Parameters🔗

MLPHandleMLPHandlemlphandle (input_control) class_mlp → (handle)HTuple (HHandle)HClassMlp, HTuple (IntPtr)HHandleHtuple (handle)

MLP handle.

IndexSampleindexSampleindex_sample (input_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Number of stored training sample.

Featuresfeaturesfeatures (output_control) real-array → (real)HTuple (double)HTuple (double)Sequence[float]Htuple (double)

Feature vector of the training sample.

Targettargettarget (output_control) real-array → (real)HTuple (double)HTuple (double)Sequence[float]Htuple (double)

Target vector of the training sample.

Example🔗

(HDevelop)

* Train an MLP
create_class_mlp (NumIn, NumHidden, NumOut, 'softmax', \
                  'canonical_variates', NumComp, 42, MLPHandle)
read_samples_class_mlp (MLPHandle, 'samples.mtf')
train_class_mlp (MLPHandle, 100, 1, 0.01, Error, ErrorLog)
* Reclassify the training samples
get_sample_num_class_mlp (MLPHandle, NumSamples)
for I := 0 to NumSamples-1 by 1
    get_sample_class_mlp (MLPHandle, I, Data, Target)
    classify_class_mlp (MLPHandle, Data, 1, Class, Confidence)
    Result := gen_tuple_const(NumOut,0)
    Result[Class] := 1
    Diffs := Target-Result
    if (sum(fabs(Diffs)) > 0)
        * Sample has been classified incorrectly
    endif
endfor

Result🔗

If the parameters are valid, the operator get_sample_class_mlpGetSampleClassMlp returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

Combinations with other operators🔗

Combinations

Possible predecessors

add_sample_class_mlpAddSampleClassMlp, read_samples_class_mlpReadSamplesClassMlp, get_sample_num_class_mlpGetSampleNumClassMlp

Possible successors

classify_class_mlpClassifyClassMlp, evaluate_class_mlpEvaluateClassMlp

See also

create_class_mlpCreateClassMlp

Module🔗

Foundation