Smoothing.Loess Method

public static Vector<double> Loess(
	Vector<double> signal,
	double smoothingFactor,
	int robustSteps = 0,
	double delta = 0
)

Public Shared Function Loess ( 
	signal As Vector(Of Double),
	smoothingFactor As Double,
	Optional robustSteps As Integer = 0,
	Optional delta As Double = 0
) As Vector(Of Double)

public:
static Vector<double>^ Loess(
	Vector<double>^ signal, 
	double smoothingFactor, 
	int robustSteps = 0, 
	double delta = 0
)

static member Loess : 
        signal : Vector<float> * 
        smoothingFactor : float * 
        ?robustSteps : int * 
        ?delta : float 
(* Defaults:
        let _robustSteps = defaultArg robustSteps 0
        let _delta = defaultArg delta 0
*)
-> Vector<float> 

Parameters

signal  Vector<Double>

The signal to filter.

smoothingFactor  Double

The size of the neighbourhood that contributes to the smoothing at each point as a fraction of the total range of the input.

robustSteps  Int32  (Optional)

(Optional.) The number of robustness iterations. The default is 0.

delta  Double  (Optional)

(Optional.) Distance within which robust regression is not recomputed.

Return Value

When the number of data points is large, the computation time can be reduced by setting delta to a suitable value. After the initial robust regression weights are computed, the full regression is only performed for points that are delta apart. Interpolation is used for any intermediate points.

public static Vector<double> Loess(
	Vector<double> signal,
	int windowLength,
	int robustSteps = 0,
	double delta = 0
)

Public Shared Function Loess ( 
	signal As Vector(Of Double),
	windowLength As Integer,
	Optional robustSteps As Integer = 0,
	Optional delta As Double = 0
) As Vector(Of Double)

public:
static Vector<double>^ Loess(
	Vector<double>^ signal, 
	int windowLength, 
	int robustSteps = 0, 
	double delta = 0
)

static member Loess : 
        signal : Vector<float> * 
        windowLength : int * 
        ?robustSteps : int * 
        ?delta : float 
(* Defaults:
        let _robustSteps = defaultArg robustSteps 0
        let _delta = defaultArg delta 0
*)
-> Vector<float> 

Parameters

signal  Vector<Double>

The signal to filter.

windowLength  Int32

The length of the smoothing window.

robustSteps  Int32  (Optional)

(Optional.) The number of robustness iterations. The default is 0.

delta  Double  (Optional)

(Optional.) Distance within which robust regression is not recomputed.

Return Value

When the number of data points is large, the computation time can be reduced by setting delta to a suitable value. After the initial robust regression weights are computed, the full regression is only performed for points that are delta apart. Interpolation is used for any intermediate points.

public static Vector<double> Loess(
	Vector<double> x,
	Vector<double> signal,
	double smoothingFactor,
	int robustSteps = 0,
	double delta = 0
)

Public Shared Function Loess ( 
	x As Vector(Of Double),
	signal As Vector(Of Double),
	smoothingFactor As Double,
	Optional robustSteps As Integer = 0,
	Optional delta As Double = 0
) As Vector(Of Double)

public:
static Vector<double>^ Loess(
	Vector<double>^ x, 
	Vector<double>^ signal, 
	double smoothingFactor, 
	int robustSteps = 0, 
	double delta = 0
)

static member Loess : 
        x : Vector<float> * 
        signal : Vector<float> * 
        smoothingFactor : float * 
        ?robustSteps : int * 
        ?delta : float 
(* Defaults:
        let _robustSteps = defaultArg robustSteps 0
        let _delta = defaultArg delta 0
*)
-> Vector<float> 

Parameters

x  Vector<Double>

The x values of the signal.

signal  Vector<Double>

The signal to filter.

smoothingFactor  Double

The size of the neighbourhood that contributes to the smoothing at each point as a fraction of the total range of x.

robustSteps  Int32  (Optional)

(Optional.) The number of robustness iterations. The default is 0.

delta  Double  (Optional)

(Optional.) Distance within which robust regression is not recomputed.

Return Value

When the number of data points is large, the computation time can be reduced by setting delta to a suitable value. After the initial robust regression weights are computed, the full regression is only performed for points that are delta apart. Interpolation is used for any intermediate points.

public static Vector<double> Loess(
	Vector<double> x,
	Vector<double> signal,
	int windowLength,
	int robustSteps = 0,
	double delta = 0
)

Public Shared Function Loess ( 
	x As Vector(Of Double),
	signal As Vector(Of Double),
	windowLength As Integer,
	Optional robustSteps As Integer = 0,
	Optional delta As Double = 0
) As Vector(Of Double)

public:
static Vector<double>^ Loess(
	Vector<double>^ x, 
	Vector<double>^ signal, 
	int windowLength, 
	int robustSteps = 0, 
	double delta = 0
)

static member Loess : 
        x : Vector<float> * 
        signal : Vector<float> * 
        windowLength : int * 
        ?robustSteps : int * 
        ?delta : float 
(* Defaults:
        let _robustSteps = defaultArg robustSteps 0
        let _delta = defaultArg delta 0
*)
-> Vector<float> 

Parameters

x  Vector<Double>

The x values of the signal.

signal  Vector<Double>

The signal to filter.

windowLength  Int32

The length of the smoothing window.

robustSteps  Int32  (Optional)

(Optional.) The number of robustness iterations. The default is 0.

delta  Double  (Optional)

(Optional.) Distance within which robust regression is not recomputed.

Return Value

When the number of data points is large, the computation time can be reduced by setting delta to a suitable value. After the initial robust regression weights are computed, the full regression is only performed for points that are delta apart. Interpolation is used for any intermediate points.

Reference