Locally weighted polynomial regression LWPR is a popular instance based al gorithm for learning continuous non linear mappings For more than two or three in puts and for more than a few thousand dat apoints the computational expense of pre dictions is daunting We discuss drawbacks with previous approaches to dealing with this problem
Polynomial fit functions
=== === === ===
RegressionObject.cls contains a class that provides an easy way to add polynomial regression functionality to any application. If you just want linear regression or a very high degree, no matter: this class has good performance and scales seamlessly with the complexity of your problem.
Finds the polynomial p10 of degree less than or equal to 10 that interpolates
cos x on the interval [0, PI/2] at 11 equally spaced points. Study the error betwee
between the function and the polynomial at 41 equally spaced points over the
same interval. Repeat the latter but use your 11 points to be Chebyshevs.
PIECEWISE_EVAL: evaluates a piecewise function of x
usage: y = PIECEWISE_EVAL(x,breakpoints,funs)
arguments (input)
x - vector or array of points to evaluate though the function
breakpoints - list of n breakpoints, -inf and +inf are implicitly
This code converts a Galois Field array created usin GF(2^m) for a given primitive polynomial into a decimal array that can be used within typical .m file coding.
Summary: Newton-Raphson method for all real roots of the polynomial.
MATLAB Release: R11
Description: This M-file calculates all the real roots of the given polynomial. It calls syn_division, a synthetic division function, and derivate, differentiation function.
SOStools is a toolbox dedicated for positive polynomial programming, especially for Sums of Squares (S0S). A lot of chinese researchers have carried it out as a main tool in this area.
