[SerializableAttribute] public class MovingWindowFilter : CorrelationFilter
<SerializableAttribute> Public Class MovingWindowFilter Inherits CorrelationFilter
[SerializableAttribute] public ref class MovingWindowFilter : public CorrelationFilter
[<SerializableAttribute>] type MovingWindowFilter = class inherit CorrelationFilter end
Thetype exposes the following members.
Constructs a MovingWindowFilter instance which implements a moving average. Number of points to the left and right both default to two.
|MovingWindowFilter(Int32, Int32, DoubleVector)|
Constructs a MovingWindowFilter instance with the specified parameters.
Gets the filter coefficients.
Gets the number of points left for the filter window.
Gets the number of filter coefficients. Effectively the filter width.(Inherited from CorrelationFilter.)
Gets the number of points right for the filter window.
Gets / Sets the current boundary option.
Gets the width of the moving window.
Does the correlation and takes care of internal correlation and work objects.(Inherited from CorrelationFilter.)
Returns a vector of exponentially weighted moving average (EWMA) coefficients of length n.
Filters(Overrides CorrelationFilterFilter(DoubleVector, DoubleVector).)
Applies the filter to the given data using the given boundary option. The given boundary option sets the current boundary option.
|Filter(DoubleVector, MovingWindowFilterBoundaryOption, DoubleVector)|
Applies the filter to the given data using the given boundary option and places the output in a given vector. The given boundary options sets the current boundary option.
Constructs the coefficient vector that implements a moving average filter when used with the MovingWindowFilter class.
Constructs the coefficient vector that implements a Savitzky-Golay smoothing filter when used with the MovingWindowFilter class. The algorithm is also known by the terms, least-squares, or DIgital Smoothing POlynomial (DISPO). The filter coefficients, c(n) are chosen so as to approximate the underlying function in the window [i - nL, i + nR] with a polynomial, typically quadratic or quartic, and replace the point f(i) with the value of the approximating polynomial at i. The polynomial is fit using a least squares algorithm.
Sets the parameters for this filter.
Filtering coefficients for correlation operation.(Inherited from CorrelationFilter.)
The number of points to the left.
The number of points to the right.
Provides a working vector for correlation results.(Inherited from CorrelationFilter.)