public float Predict(
	Mat sample,
	Mat missing = null,
	Nullable<Range> slice = null,
	bool rawMode = false,
	bool returnSum = false
)

Public Function Predict ( 
	sample As Mat,
	Optional missing As Mat = Nothing,
	Optional slice As Nullable(Of Range) = Nothing,
	Optional rawMode As Boolean = false,
	Optional returnSum As Boolean = false
) As Single

public:
float Predict(
	Mat^ sample, 
	Mat^ missing = nullptr, 
	Nullable<Range> slice = nullptr, 
	bool rawMode = false, 
	bool returnSum = false
)

member Predict : 
        sample : Mat * 
        ?missing : Mat * 
        ?slice : Nullable<Range> * 
        ?rawMode : bool * 
        ?returnSum : bool 
(* Defaults:
        let _missing = defaultArg missing null
        let _slice = defaultArg slice null
        let _rawMode = defaultArg rawMode false
        let _returnSum = defaultArg returnSum false
*)
-> float32 

Parameters

sample

Type: OpenCvSharp.CPlusPlusMat
The input sample.

missing (Optional)

Type: OpenCvSharp.CPlusPlusMat
The optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits.

slice (Optional)

Type: SystemNullableRange
The continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used.

rawMode (Optional)

Type: SystemBoolean
The last parameter is 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.

returnSum (Optional)

Type: SystemBoolean

[Missing <param name="returnSum"/> documentation for "M:OpenCvSharp.CPlusPlus.CvBoost.Predict(OpenCvSharp.CPlusPlus.Mat,OpenCvSharp.CPlusPlus.Mat,System.Nullable{OpenCvSharp.CPlusPlus.Range},System.Boolean,System.Boolean)"]

Return Value

Reference