no data point lies on the perimeter and put the figure in the hold on mode found by: a) Computing 100 equally spaced x values (call the vectorx1 over the range [0, 4피, b) Recompute yl = polyval(coef, xl ) and plot x1 vs y1. Use Matlab polyval function in its interpolation mode with the best fitted curve coef the polyfit curve fits the data. Replot the initial data for x and y with the points as o'. Specify a degree of two for the x terms and degree of three for the y terms. Once the best fitted curve is found delete the figure. The vectors x, y, and z contain data generated from Franke's bivariate test function, with added noise and scaling. When the fit is good the latter will pass through all data points. You will then use the Matlab code fragment yl-polyval(coef, x) to get the y values based on each fit Then, plot(x, yl) superimposed on the original data. As an example, let's start with some random data: some 3d points data mvnrnd ( 0 0 0, 1 -0.5 0.8 -0.5 1.1 0 0.8 0 1, 50) As BasSwinckels showed, by constructing the desired design matrix. There is a solution page by MathWorks describing the process. Assign the return values from the polyfit function to the variable cocf. To fit a curve onto a set of points, we can use ordinary least-squares regression. Use Matlab polyfit function (through trials and errors) to find the best fitted curve to the data. Put the Matlab figure in the hold on mode so you can superpose other plots. Use Matlab linspace function to compute 10 values for x from [0,4자 Compute the values ofy = cos(x) and plot x vs y with the letter o as in ‘o', so the graph shows data points Use Matlab axis function to scale the x and y axes so that there are no points on the 2. The function polyfit requires trials and errors to get the proper polynomial order to match a given data set. Will use Matlab's polyfit and polyval functions.
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