F_VN_PredictSampleScalarExp

F_VN_PredictSampleScalarExp 1:

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

Syntax

Definition:

FUNCTION F_VN_PredictSampleScalarExp : HRESULT
VAR_INPUT
    ipRegressor : ITcVnMlModel;
    ipSample    : ITcUnknown;
END_VAR
VAR_IN_OUT
    fPrediction : REAL;
END_VAR
VAR_INPUT
    fNovelty    : Reference To REAL;
    hrPrev      : HRESULT;
END_VAR

F_VN_PredictSampleScalarExp 2: Inputs

Name

Type

Description

ipRegressor

ITcVnMlModel

Regressor 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_PredictSampleScalarExp 3: In/Outputs

Name

Type

Description

fPrediction

REAL

Returns the predicted output

F_VN_PredictSampleScalarExp 4: Return value

HRESULT

Further information

The function F_VN_PredictSampleScalarExp is the expert variant of F_VN_PredictSampleScalar. It contains additional parameters.

Parameter

Regression model

The previously trained regression model must be transferred to ipRegressor.

Sample

The sample container is transferred as ipSample. The container type must be either ContainerType_Vector_REAL or ContainerType_Vector_LREAL.

Prediction

The calculated prediction value is returned via fPrediction.

Degree of novelty

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

Application

For example, the regression value of a sample can be calculated as follows:

hr := F_VN_PredictSampleScalarExp(
    ipRegressor := ipRegressor,
    ipSample    := ipSample,
    fPrediction := fPrediction,
    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