QuadraticDiscriminantFunction.Evaluate Method

public double Evaluate(
	Vector<double> features
)

Public Function Evaluate ( 
	features As Vector(Of Double)
) As Double

public:
double Evaluate(
	Vector<double>^ features
)

member Evaluate : 
        features : Vector<float> -> float 

Parameters

features  Vector<Double>

The point at which to evaluate the function.

Return Value

This method computes the value of the quadratic discriminant function for a given feature vector. It transforms the input features using the inverse square root of the covariance matrix and the centroid, then calculates the norm squared of the transformed features, adjusts for the log determinant of the covariance matrix, and adds the log prior probability.

Use this method when you need to evaluate the discriminant function for a single observation.

public Vector<double> Evaluate(
	Matrix<double> features,
	Vector<double>? scores = null
)

Public Function Evaluate ( 
	features As Matrix(Of Double),
	Optional scores As Vector(Of Double) = Nothing
) As Vector(Of Double)

public:
Vector<double>^ Evaluate(
	Matrix<double>^ features, 
	Vector<double>^ scores = nullptr
)

member Evaluate : 
        features : Matrix<float> * 
        ?scores : Vector<float> 
(* Defaults:
        let _scores = defaultArg scores null
*)
-> Vector<float> 

Parameters

features  Matrix<Double>

The point at which to evaluate the function.

scores  Vector<Double>  (Optional)

Optional. The vector to store the result in.

Return Value

This method computes the value of the quadratic discriminant function for each row in a matrix of feature vectors. It transforms the input features using the inverse square root of the covariance matrix and the centroid, then calculates the norm squared of the transformed features for each row, adjusts for the log determinant of the covariance matrix, and adds the log prior probability.

Use this method when you need to evaluate the discriminant function for multiple observations at once.

Reference