For each input vector (which are rows of the matrix samples) the method finds k <= get_max_k() nearest neighbor. In case of regression, the predicted result will be a mean value of the particular vector's neighbor responses. In case of classification the class is determined by voting.

Namespace:  Emgu.CV.ML
Assembly:  Emgu.CV.ML (in Emgu.CV.ML.dll) Version: 2.0.0.0 (2.0.0.0)

Syntax

C#
public float FindNearest(
	Matrix<float> samples,
	int k,
	Matrix<float> results,
	Matrix<float> kNearestNeighbors,
	Matrix<float> neighborResponses,
	Matrix<float> dist
)
Visual Basic (Declaration)
Public Function FindNearest ( _
	samples As Matrix(Of Single), _
	k As Integer, _
	results As Matrix(Of Single), _
	kNearestNeighbors As Matrix(Of Single), _
	neighborResponses As Matrix(Of Single), _
	dist As Matrix(Of Single) _
) As Single
Visual C++
public:
float FindNearest(
	Matrix<float>^ samples, 
	int k, 
	Matrix<float>^ results, 
	Matrix<float>^ kNearestNeighbors, 
	Matrix<float>^ neighborResponses, 
	Matrix<float>^ dist
)

Parameters

samples
Type: Emgu.CV..::.Matrix<(Of <(Single>)>)
The sample matrix where each row is a sample
k
Type: System..::.Int32
The number of nearest neighbor to find
results
Type: Emgu.CV..::.Matrix<(Of <(Single>)>)
Can be null if not needed. If regression, return a mean value of the particular vector's neighbor responses; If classification, return the class determined by voting.
kNearestNeighbors
Type: Emgu.CV..::.Matrix<(Of <(Single>)>)
Should be null if not needed. Setting it to non-null values incures a performance panalty. A matrix of (k * samples.Rows) rows and (samples.Cols) columns that will be filled the data of the K nearest-neighbor for each sample
neighborResponses
Type: Emgu.CV..::.Matrix<(Of <(Single>)>)
Should be null if not needed. The response of the neighbors. A vector of k*_samples->rows elements.
dist
Type: Emgu.CV..::.Matrix<(Of <(Single>)>)
Should be null if not needed. The distances from the input vectors to the neighbors. A vector of k*_samples->rows elements.

Return Value

In case of regression, the predicted result will be a mean value of the particular vector's neighbor responses. In case of classification the class is determined by voting

See Also