F_VN_CreateSvmModel

Create an SVM 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.

Syntax

Definition:

FUNCTION F_VN_CreateSvmModel : HRESULT
VAR_INPUT
    ipMlModel   : Reference To  ITcVnMlModel;
    eSvmType    : ETcVnSvm;
    fC          : LREAL;
    fNu         : LREAL;
    fP          : LREAL;
    eKernelType : ETcVnSvmKernelType;
    fGamma      : LREAL;
    fCoef0      : LREAL;
    fDegree     : LREAL;
    hrPrev      : HRESULT;
END_VAR

Inputs

Name	Type	Description
ipMlModel	Reference To ITcVnMlModel	Returns the created model (Non-zero interface pointers are reused.)
eSvmType	ETcVnSvm	SVM model type
fC	LREAL	Parameter C (required for TCVN_SVM_C_CLASSIFIER, TCVN_SVM_EPS_REGRESSOR, and TCVN_SVM_NU_REGRESSOR; ignored otherwise)
fNu	LREAL	Parameter nu (required for TCVN_SVM_NU_CLASSIFIER, TCVN_SVM_NOVELTY_DETECTOR, and TCVN_SVM_NU_REGRESSOR; ignored otherwise)
fP	LREAL	Parameter p (required for TCVN_SVM_EPS_REGRESSOR; ignored otherwise)
eKernelType	ETcVnSvmKernelType	Kernel type
fGamma	LREAL	Parameter gamma (used by polynomial, RBF, sigmoid, and chi-squared kernels; ignored otherwise)
fCoef0	LREAL	parameter coef0 (used by polynomial and sigmoid kernels; ignored otherwise)
fDegree	LREAL	Degree (used by polynomial kernels; ignored otherwise)
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_CreateSvmModel creates a Support Vector Machine (SVM) model.

Support Vector Machine models

Support Vector Machines can also be used for sophisticated non-linear problems. The prediction is based exclusively on the so-called support vectors, which is why SVM models are comparatively memory-efficient. All samples are required for training at the same time (batch training) and post-training is not possible.

Parameter

Model

The created model is returned in the interface pointer ipMlModel.

Model type

eSvmType specifies whether the SVM model is used for classification, regression or anomaly detection:

TCVN_SVM_C_CLASSIFIER
TCVN_SVM_NU_CLASSIFIER
TCVN_SVM_NOVELTY_DETECTOR
TCVN_SVM_EPS_REGRESSOR
TCVN_SVM_NU_REGRESSOR

Model parameters

The use and meaning of the parameters fC, fNu and fP depends on the selected eSvmType.

Kernel type

The kernel type used for the calculation is defined with eKernelType. The kernel type depends on the task / data distribution and must be adapted accordingly.

Kernel parameters

The use and meaning of the parameters fGamma, fCoef0 and fDegree depends on the selected eKernelType.

Expert parameters

The expert variants F_VN_CreateSvmModelExp and F_VN_CreateSvmModelExp2 contain additional parameters.

Application

For example, an SVM model for classification can be created like this:

hr := F_VN_CreateSvmModel(
    ipMlModel   := ipMlModel,
    eSvmType    := TCVN_SVM_NU_CLASSIFIER,
    fC          := 0,
    fNu         := 0.1,
    fP          := 0,
    eKernelType := TCVN_SKT_RBF,
    fGamma      := 1,
    fCoef0      := 0,
    fDegree     := 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