F_VN_CreateBoostClassifierExp
Create a Boost classifier. The initial reference count is set to one if a new model is created and kept, otherwise. The Boost classifier is only applicable to binary classification problems. It learns to distinguish between samples labelled with two user-defined class labels by incrementally adding weak classifiers to improve the classification results. Models of this type neither support on-line training (sample by sample) nor retraining. (expert function)
Syntax
Definition:
FUNCTION F_VN_CreateBoostClassifierExp : HRESULT
VAR_INPUT
ipMlModel : Reference To ITcVnMlModel;
eType : ETcVnBoostClassifierType;
nMaxDepth : UDINT;
nMinSamples : UDINT;
nWeakClassifiers : UDINT;
fWeightTrimRate : LREAL;
hrPrev : HRESULT;
END_VAR
Inputs
|
Name |
Type |
Description |
|---|---|---|
|
ipMlModel |
Reference To ITcVnMlModel |
Returns the created model (Non-zero interface pointers are reused.) |
|
eType |
Learning algorithm type (default: TCVN_BCT_REAL) | |
|
nMaxDepth |
UDINT |
Maximum tree depth (default: 1) |
|
nMinSamples |
UDINT |
Minimum number of samples within a node required for splitting (default: 10) |
|
nWeakClassifiers |
UDINT |
Number of weak classifiers (default: 100) |
|
fWeightTrimRate |
LREAL |
Weight threshold used during training (off: 0; default: 0.95). |
|
hrPrev |
HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.) |
Return valueFurther information
The function F_VN_CreateBoostClassifierExp is an expert variant of F_VN_CreateBoostClassifier. It contains additional parameters.
Parameter
Model
The created model is returned in the interface pointer ipMlModel.
Learning technique
The learning algorithm can be selected via eType. Supported are: Discrete AdaBoost, Real AdaBoost, LogitBoost and Gentle AdaBoost.
Maximum tree depth
nMaxDepth indicates the maximum number of decision levels in a tree. Decision trees with a tree depth of 1 are usually used for boost classifiers. However, higher values are possible.
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.
Number of decision trees
nWeakClassifiers indicates the number of decision trees (weak learners) that are combined for the classifier by boosting.
Runtime optimization
With fWeightTrimRate, the number of samples used for the individual learning steps can be limited using their automatically determined weightings. Values in the interval from 0 to 1 are permitted, whereby only samples with weightings > 1 - fWeightTrimRate are taken into account in the following learning step. The value 0 disables this optimization.
Application
A boost classifier model can be created like this, for example:
hr := F_VN_CreateBoostClassifierExp(
ipMlModel := ipMlModel,
eType := TCVN_BCT_REAL,
nMaxDepth := 1,
nMinSamples := 10,
nWeakClassifiers := 100,
fWeightTrimRate := 0.95,
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 |