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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: 2.4.10.1935 (2.4.10.1935)

Syntax

C#
public static float CvBoostPredict(
	IntPtr model,
	IntPtr sample,
	IntPtr missing,
	IntPtr weakResponses,
	MCvSlice slice,
	bool rawMode
)
Visual Basic
Public Shared Function CvBoostPredict ( 
	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 CvBoostPredict(
	IntPtr model, 
	IntPtr sample, 
	IntPtr missing, 
	IntPtr weakResponses, 
	MCvSlice slice, 
	bool rawMode
)
F#
static member CvBoostPredict : 
        model : IntPtr * 
        sample : IntPtr * 
        missing : IntPtr * 
        weakResponses : IntPtr * 
        slice : MCvSlice * 
        rawMode : bool -> float32 

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

Type: Single
The output class label based on the weighted voting

See Also