The method takes the feature vector and the optional missing measurement mask on input, traverses the random tree and returns the cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates)

Namespace: Emgu.CV.ML
Assembly: Emgu.CV.ML (in Emgu.CV.ML.dll) Version: 2.2.0.1010 (2.2.0.1010)

Syntax

         
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public float Predict(
	Matrix<float> sample,
	Matrix<byte> missingDataMask,
	Matrix<float> weakResponses,
	MCvSlice slice,
	bool rawMode
)
Public Function Predict ( _
	sample As Matrix(Of Single), _
	missingDataMask As Matrix(Of Byte), _
	weakResponses As Matrix(Of Single), _
	slice As MCvSlice, _
	rawMode As Boolean _
) As Single
public:
float Predict(
	Matrix<float>^ sample, 
	Matrix<unsigned char>^ missingDataMask, 
	Matrix<float>^ weakResponses, 
	MCvSlice slice, 
	bool rawMode
)

Parameters

sample
Matrix<(Of <(<'Single>)>)>
The sample to be predicted
missingDataMask
Matrix<(Of <(<'Byte>)>)>
Can be null 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)
weakResponses
Matrix<(Of <(<'Single>)>)>
Can be null 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
MCvSlice
The continuous subset of the sequence of weak classifiers to be used for prediction
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

The cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates)

See Also