get_sample_class_mlpT_get_sample_class_mlpGetSampleClassMlpget_sample_class_mlpGetSampleClassMlpGetSampleClassMlp (Operator)

Name

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

Signature

Description

get_sample_class_mlpget_sample_class_mlpGetSampleClassMlpget_sample_class_mlpGetSampleClassMlpGetSampleClassMlp reads out a training sample from the multilayer perceptron (MLP) given by MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle that was added with add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp or read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp. The index of the sample is specified with IndexSampleIndexSampleIndexSampleIndexSampleIndexSampleindexSample. The index is counted from 0, i.e., IndexSampleIndexSampleIndexSampleIndexSampleIndexSampleindexSample must be a number between 0 and IndexSamples - 1, where IndexSamples can be determined with get_sample_num_class_mlpget_sample_num_class_mlpGetSampleNumClassMlpget_sample_num_class_mlpGetSampleNumClassMlpGetSampleNumClassMlp. The training sample is returned in FeaturesFeaturesFeaturesFeaturesFeaturesfeatures and TargetTargetTargetTargetTargettarget. FeaturesFeaturesFeaturesFeaturesFeaturesfeatures is a feature vector of length NumInput, while TargetTargetTargetTargetTargettarget is a target vector of length NumOutput (see add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp and create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp).

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

Parallelization

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

Example (HDevelop)

* Train an MLP
create_class_mlp (NIn, NHidden, NOut, 'softmax', 'canonical_variates',\
                  NComp, 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(NOut,0)
    Result[Class] := 1
    Diffs := Target-Result
    if (sum(fabs(Diffs)) > 0)
        * Sample has been classified incorrectly
    endif
endfor
clear_class_mlp (MLPHandle)

Result

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

Possible Predecessors

add_sample_class_mlpadd_sample_class_mlpAddSampleClassMlpadd_sample_class_mlpAddSampleClassMlpAddSampleClassMlp, read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp, get_sample_num_class_mlpget_sample_num_class_mlpGetSampleNumClassMlpget_sample_num_class_mlpGetSampleNumClassMlpGetSampleNumClassMlp

Possible Successors

classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp, evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp

See also

create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp

Module

Foundation