F_VN_CreateRTreesModelExp2

Create a random trees model of the specified type. The initial reference count is set to one if a new model is created and kept, otherwise. Models of this type neither support on-line training (sample by sample) nor retraining. Predictions can only be scalar. (expert function)

Syntax

Definition:

FUNCTION F_VN_CreateRTreesModelExp2 : HRESULT
VAR_INPUT
    ipMlModel           : Reference To  ITcVnMlModel;
    eRTreesType         : ETcVnRTrees;
    nMaxDepth           : UDINT;
    nMinSamples         : UDINT;
    nActiveVariables    : UDINT;
    nMaxIterations      : UDINT;
    fEpsilon            : LREAL;
    fRegressionAccuracy : REAL;
    ipClassPriors       : ITcVnContainer;
    hrPrev              : HRESULT;
END_VAR

Inputs

Name	Type	Description
ipMlModel	Reference To ITcVnMlModel	Returns the created model (Non-zero interface pointers are reused.)
eRTreesType	ETcVnRTrees	Random trees model type
nMaxDepth	UDINT	Maximum tree depth (default: 5)
nMinSamples	UDINT	Minimum number of samples within a node required for splitting (default: 10)
nActiveVariables	UDINT	Number of variables considered for splitting (0 means sqrt(total number of variables); default: 0)
nMaxIterations	UDINT	Maximum number of iterations (disabled if it equals 0 and fEpsilon is different from 0.0; triggers the usage of the default value of 50 if nMaxIterations and fEpsilon equal 0)
fEpsilon	LREAL	Maximum allowed difference of the error between two successive iterations (disabled if it equals 0.0 and nMaxIterations is different from 0; triggers the usage of the default value of 0.1 if nMaxIterations and fEpsilon equal 0)
fRegressionAccuracy	REAL	Termination criterion for regressors (only for regressors; set to default if not allowed; default: 0.0)
ipClassPriors	ITcVnContainer	Class priors (ContainerType_Vector_REAL or ContainerType_Vector_LREAL; only for classifiers; optional, set to 0 if not required or not allowed; default: 0)
hrPrev	HRESULT	HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.)

Return value

HRESULT

Further information

The function F_VN_CreateRTReesModelExp2 is an expert variant of F_VN_CreateRTreesModel. It contains additional parameters.

Parameter

Model

The created model is returned in the interface pointer ipMlModel.

Model type

eRTreesType specifies whether the Random Forest model is used for classification (TCVN_RT_CLASSIFIER) or for regression (TCVN_RT_REGRESSOR).

Maximum tree depth

nMaxDepth indicates the maximum number of decision levels in a tree.

Minimum number of samples within a node

nMinSamples is the minimum number of samples that must remain in a node during tree formation. Splits that would fall below this number are not carried out.

Splitting features

nActiveVariables is the number of features used to split the tree. With 0, sqrt(N) features are used with the number N of all available features.

Maximum iterations

A maximum of as many iterations as specified in nMaxIterations are used for the optimization. If the value is 0, the respective default value is used.

Termination limit

The optimization is aborted as soon as the error between two iterations does not change more than specified in fEpsilon. If the value is 0, the respective default value is used.

Regression accuracy

fRegressionAccuracy is the abort criterion for regression optimization. For a classification, the value must be set to 0.

Class-Priors

ipClassPriors is a container that can be used to specify the a priori probabilities of the individual classes. For a regression, the value should be set to 0.

Application

For example, an RTrees model for regression can be created like this:

hr := F_VN_CreateRTreesExp2(
    ipMlModel           := ipMlModel,
    eRTreesType         := TCVN_RT_REGRESSOR,
    nMaxDepth           := 8,
    nMinSamples         := 4,
    nActiveVariables    := 0,
    nMaxIterations      := 0,
    fEpsilon            := 0,
    fRegressionAccuracy := 0.3,
    ipClassPriors       := 0,
    hrPrev              := hr);

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