نرم کردن نمودار در متلب ( smoothing plot )

حسین اتحادی دوشنبه 1 شهریور 1395 11:03 ب.ظ نظرات ()

smooth

Smooth response data

Syntax

yy = smooth(y) gpuarrayYY = smooth(gpuarrayY) yy = smooth(y,span) yy = smooth(y,method) yy = smooth(y,span,method) yy = smooth(y,'sgolay',degree) yy = smooth(y,span,'sgolay',degree) yy = smooth(x,y,...)

Description

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 5.

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 filter function.

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. span must be odd.

yy = smooth(y,method) smooths the data in y using the method method and the default span. Supported values for method are listed in the table below.

`method`	Description
`'moving'`	Moving average (default). A lowpass filter with filter coefficients equal to the reciprocal of the span.
`'lowess'`	Local regression using weighted linear least squares and a 1st degree polynomial model
`'loess'`	Local regression using weighted linear least squares and a 2nd degree polynomial model
`'sgolay'`	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.
`'rlowess'`	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.
`'rloess'`	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,method) sets the span of method to span. For the loess and lowess methods, 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 1).

yy = smooth(y,'sgolay',degree) uses the Savitzky-Golay method with polynomial degree specified by degree.

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 span.

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 method, lowess is used. If the smoothing method requires x to be sorted, the sorting occurs automatically.

Examples

read more..

ادامه مطلب