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evaluate_class_mlpT_evaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp (Operator)

Name

evaluate_class_mlpT_evaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp — Calculate the evaluation of a feature vector by a multilayer perceptron.

Signature

evaluate_class_mlp( : : MLPHandle, Features : Result)

Herror T_evaluate_class_mlp(const Htuple MLPHandle, const Htuple Features, Htuple* Result)

Herror evaluate_class_mlp(const HTuple& MLPHandle, const HTuple& Features, HTuple* Result)

HTuple HClassMlp::EvaluateClassMlp(const HTuple& Features) const

void EvaluateClassMlp(const HTuple& MLPHandle, const HTuple& Features, HTuple* Result)

HTuple HClassMlp::EvaluateClassMlp(const HTuple& Features) const

void HOperatorSetX.EvaluateClassMlp(
[in] VARIANT MLPHandle, [in] VARIANT Features, [out] VARIANT* Result)

VARIANT HClassMlpX.EvaluateClassMlp([in] VARIANT Features)

static void HOperatorSet.EvaluateClassMlp(HTuple MLPHandle, HTuple features, out HTuple result)

HTuple HClassMlp.EvaluateClassMlp(HTuple features)

Description

evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp computes the result ResultResultResultResultResultresult of evaluating the feature vector FeaturesFeaturesFeaturesFeaturesFeaturesfeatures with the multilayer perceptron (MLP) MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle. The formulas used for the evaluation are described with create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp. Before calling evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp, the MLP must be trained with train_class_mlptrain_class_mlpTrainClassMlptrain_class_mlpTrainClassMlpTrainClassMlp.

If the MLP is used for regression (function approximation), i.e., if (OutputFunction = 'linear'"linear""linear""linear""linear""linear"), ResultResultResultResultResultresult is the value of the function at the coordinate FeaturesFeaturesFeaturesFeaturesFeaturesfeatures. For OutputFunction = 'logistic'"logistic""logistic""logistic""logistic""logistic" and 'softmax'"softmax""softmax""softmax""softmax""softmax", the values in ResultResultResultResultResultresult can be interpreted as probabilities. Hence, for OutputFunction = 'logistic'"logistic""logistic""logistic""logistic""logistic" the elements of ResultResultResultResultResultresult represent the probabilities of the presence of the respective independent attributes. Typically, a threshold of 0.5 is used to decide whether the attribute is present or not. Depending on the application, other thresholds may be used as well. For OutputFunction = 'softmax'"softmax""softmax""softmax""softmax""softmax" usually the position of the maximum value of ResultResultResultResultResultresult is interpreted as the class of the feature vector, and the corresponding value as the probability of the class. In this case, classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp should be used instead of evaluate_class_mlpevaluate_class_mlpEvaluateClassMlpevaluate_class_mlpEvaluateClassMlpEvaluateClassMlp because classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp directly returns the class and corresponding probability.

Parallelization

Parameters

MLPHandleMLPHandleMLPHandleMLPHandleMLPHandleMLPHandle (input_control)  class_mlp HClassMlp, HTupleHTupleHClassMlp, HTupleHClassMlpX, VARIANTHtuple (integer) (IntPtr) (Hlong) (Hlong) (Hlong) (Hlong)

MLP handle.

FeaturesFeaturesFeaturesFeaturesFeaturesfeatures (input_control)  real-array HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Feature vector.

ResultResultResultResultResultresult (output_control)  real-array HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Result of evaluating the feature vector with the MLP.

Result

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

Possible Predecessors

train_class_mlptrain_class_mlpTrainClassMlptrain_class_mlpTrainClassMlpTrainClassMlp, read_class_mlpread_class_mlpReadClassMlpread_class_mlpReadClassMlpReadClassMlp

Alternatives

classify_class_mlpclassify_class_mlpClassifyClassMlpclassify_class_mlpClassifyClassMlpClassifyClassMlp

See also

create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp

References

Christopher M. Bishop: “Neural Networks for Pattern Recognition”; Oxford University Press, Oxford; 1995.
Andrew Webb: “Statistical Pattern Recognition”; Arnold, London; 1999.

Module

Foundation


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