F_VN_PredictSampleClassExp

F_VN_PredictSampleClassExp 1:

Classify a single sample. (expert function)
Can use available TwinCAT Job Tasks for executing parallel code regions.

Syntax

Definition:

FUNCTION F_VN_PredictSampleClassExp : HRESULT
VAR_INPUT
    ipClassifier : ITcVnMlModel;
    ipSample     : ITcUnknown;
END_VAR
VAR_IN_OUT
    nClass       : DINT;
END_VAR
VAR_INPUT
    fNovelty     : Reference To REAL;
    hrPrev       : HRESULT;
END_VAR

F_VN_PredictSampleClassExp 2: Inputs

Name

Type

Description

ipClassifier

ITcVnMlModel

Classifier to be used

ipSample

ITcUnknown

Container holding a single input sample (ContainerType_Vector_REAL or ContainerType_Vector_LREAL)

fNovelty

Reference To REAL

Returns the degree of novelty (0.0 if a sample is completely known; > 0.0 otherwise) of the presented sample (optional, set to 0 if not required)

hrPrev

HRESULT

HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.)

F_VN_PredictSampleClassExp 3: In/Outputs

Name

Type

Description

nClass

DINT

Returns the classification result

F_VN_PredictSampleClassExp 4: Return value

HRESULT

Further information

The function F_VN_PredictSampleClassExp is the expert variant of F_VN_PredictSampleClass. It contains additional parameters.

Parameter

Classification model

The previously trained classification model must be transferred to ipClassifier.

Sample

The samples are transferred to ipSample in a container. The container type must be either ContainerType_Vector_REAL or ContainerType_Vector_LREAL.

Class

The class of the sample is returned as the classification result via nClass.

Degree of novelty

The degree of novelty of the sample is returned via fNovelty.

Application

For example, a sample can be classified as follows:

hr := F_VN_PredictSampleClassExp(
    ipClassifier    := ipMlModel,
    ipSample        := ipSample,
    nClass          := nClassResult,
    fNovelty        := fNovelty,
    hrPrev          := hr);

Related functions

Required License

TC3 Vision Machine Learning

System Requirements

Development environment

Target platform

PLC libraries to include

TwinCAT V3.1.4024.54 or later

PC or CX (x64) with PL50, e.g. Intel 4-core Atom CPU

Tc3_Vision