Emgu CV Library Documentation
CvDTreePredict Method (model, sample, missingDataMask, rawMode)
NamespacesEmgu.CV.MLMlInvokeCvDTreePredict(IntPtr, IntPtr, IntPtr, Boolean)

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The method takes the feature vector and the optional missing measurement mask on input, traverses the decision tree and returns the reached leaf node on output. The prediction result, either the class label or the estimated function value, may be retrieved as value field of the CvDTreeNode structure
Declaration Syntax
C#Visual BasicVisual C++
public static IntPtr CvDTreePredict(
	IntPtr model,
	IntPtr sample,
	IntPtr missingDataMask,
	bool rawMode
)
Public Shared Function CvDTreePredict ( _
	model As IntPtr, _
	sample As IntPtr, _
	missingDataMask As IntPtr, _
	rawMode As Boolean _
) As IntPtr
public:
static IntPtr CvDTreePredict(
	IntPtr model, 
	IntPtr sample, 
	IntPtr missingDataMask, 
	bool rawMode
)
Parameters
model (IntPtr)
The decision tree model
sample (IntPtr)
The sample to be predicted
missingDataMask (IntPtr)
Can be IntPtr.Zero if not needed. When specified, it is an 8-bit matrix of the same size as trainData, is used to mark the missed values (non-zero elements of the mask)
rawMode (Boolean)
Normally set to false that implies a regular input. If it is true, the method assumes that all the values of the discrete input variables have been already normalized to 0..num_of_categoriesi-1 ranges. (as the decision tree uses such normalized representation internally). It is useful for faster prediction with tree ensembles. For ordered input variables the flag is not used.
Return Value
Pointer to the reached leaf node on output. The prediction result, either the class label or the estimated function value, may be retrieved as value field of the CvDTreeNode structure

Assembly: Emgu.CV.ML (Module: Emgu.CV.ML) Version: 1.0.0.0 (1.0.0.0)