How do you approximate a function for interpolated points? I use the natural cubic spline to interpolate points as follows for n = 500 points:
t=[0; 3; 6; 9] z=[0; 6; 6; 0] plot(t,z,'ro') ti=linspace(0,9,500) zn = natcubicspline(t,z,ti) yn = line(ti,zn)
Is there any way approximate a function for these n-many points (for large n)? Or are there ways to treat interpolated points as if they are a function, i.e. find the gradient of the zn vector? Because zn is a vector of constants, this wouldn't necessarily be helpful.
Update: In particular, my data appears to form a 2nd degree polynomial, so I went ahead and used the following Matlab function to fit my data:
p = polyfit(transpose(ti),zn,2)
Which yields coefficient estimates for a 2nd degree polynomial. It does fit the data, but with high error values, and I have to multiply this coefficient vector by a vector [1 z z^2] to get the correct polynomial. Is there any way to streamline this?