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Runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting

Namespace: Emgu.CV.ML
Assembly: Emgu.CV.ML (in Emgu.CV.ML.dll) Version: 2.4.2.1777 (2.4.2.1777)

Syntax

C#
public static float CvGBTreesPredict(
	IntPtr model,
	IntPtr sample,
	IntPtr missing,
	IntPtr weakResponses,
	MCvSlice slice,
	bool rawMode
)
Visual Basic
Public Shared Function CvGBTreesPredict ( _
	model As IntPtr, _
	sample As IntPtr, _
	missing As IntPtr, _
	weakResponses As IntPtr, _
	slice As MCvSlice, _
	rawMode As Boolean _
) As Single
Visual C++
public:
static float CvGBTreesPredict(
	IntPtr model, 
	IntPtr sample, 
	IntPtr missing, 
	IntPtr weakResponses, 
	MCvSlice slice, 
	bool rawMode
)

Parameters

model
Type: System..::..IntPtr
The Boost Tree model
sample
Type: System..::..IntPtr
The input sample
missing
Type: System..::..IntPtr
Can be IntPtr.Zero if not needed. The optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits
weakResponses
Type: System..::..IntPtr
Can be IntPtr.Zero if not needed. a floating-point vector, of responses from each individual weak classifier. The number of elements in the vector must be equal to the slice length.
slice
Type: Emgu.CV.Structure..::..MCvSlice
The continuous subset of the sequence of weak classifiers to be used for prediction
rawMode
Type: System..::..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

The output class label based on the weighted voting

See Also