yy = smooth(y)
gpuarrayYY = smooth(gpuarrayY)
yy = smooth(y,span)
yy = smooth(y,
yy = smooth(y,span,
yy = smooth(y,'sgolay',degree)
yy = smooth(y,span,'sgolay',degree)
yy = smooth(x,y,...)
yy = smooth(y) smooths the data in the column vector
y using a moving average filter. Results are returned in the column vector
yy. The default span for the moving average is
The first few elements of
yy are given by
yy(1) = y(1)
yy(2) = (y(1) + y(2) + y(3))/3
yy(3) = (y(1) + y(2) + y(3) + y(4) + y(5))/5
yy(4) = (y(2) + y(3) + y(4) + y(5) + y(6))/5
Because of the way endpoints are handled, the result differs from the result returned by the
gpuarrayYY = smooth(gpuarrayY) performs the operation on a GPU. The input
gpuarrayY is a gpuArray column vector. The output
gpuarrayYY is a gpuArray column vector. This syntax requires the Parallel Computing Toolbox™.
Note: You can use gpuArray x and y inputs with the smooth function, but this is only recommended with the default
'method', 'moving'. Using GPU data with other methods does not offer any performance advantage.
yy = smooth(y,span) sets the span of the moving average to
span must be odd.
yy = smooth(y, smooths the data in
y using the method
method and the default span. Supported values for
method are listed in the table below.
Moving average (default). A lowpass filter with filter coefficients equal to the reciprocal of the span.
Local regression using weighted linear least squares and a 1st degree polynomial model
Local regression using weighted linear least squares and a 2nd degree polynomial model
Savitzky-Golay filter. A generalized moving average with filter coefficients determined by an unweighted linear least-squares regression and a polynomial model of specified degree (default is 2). The method can accept nonuniform predictor data.
A robust version of
'lowess' that assigns lower weight to outliers in the regression. The method assigns zero weight to data outside six mean absolute deviations.
A robust version of
'loess' that assigns lower weight to outliers in the regression. The method assigns zero weight to data outside six mean absolute deviations.
yy = smooth(y,span, sets the span of
span. For the
span is a percentage of the total number of data points, less than or equal to 1. For the moving average and Savitzky-Golay methods,
span must be odd (an even
span is automatically reduced by
yy = smooth(y,'sgolay',degree) uses the Savitzky-Golay method with polynomial degree specified by
yy = smooth(y,span,'sgolay',degree) uses the number of data points specified by
span in the Savitzky-Golay calculation.
span must be odd and
degree must be less than
yy = smooth(x,y,...) additionally specifies
x data. If
x is not provided, methods that require
x data assume
x = 1:length(y). You should specify
x data when it is not uniformly spaced or sorted. If
x is not uniform and you do not specify
lowess is used. If the smoothing method requires
x to be sorted, the sorting occurs automatically.