MATLAB HELP NEEDED: Modify your solution to last week’s exercise (poly_curv_fit.
ID: 3589003 • Letter: M
Question
MATLAB HELP NEEDED:
Modify your solution to last week’s exercise (poly_curv_fit.m) by implementing left division rather than explicitly implementing the least-squares matrix solution.
-----------------poly_curv_fit.m BELOW-------------------
clear all
close all
clc
load noisy_poly.mat;
n = length(y);
x = 1:1:n;
PolyOrder=3;
p = polyfit(x,y,PolyOrder);
f = polyval(p,x);
figure,
plot(x,y,'.',x,f,'-');
xlabel('---- X --->');
ylabel('---- Y --->');
str = strcat('Plot between noisy-poly.mat and X at Poly Order = ',num2str(PolyOrder));
title(str);
Explanation / Answer
clc;
clear all;
close all;
x=[2.81 16.00 8.52 11.95 17.53 6.37 2.98 4.31 19.20 14.63 11.14 14.83 17.50 8.70];
y=[1.984 7.012 3.954 5.624 7.809 3.767 2.436 2.360 7.756 5.912 5.442 5.912 7.894 4.523];
n=2;% Quadratic regression
A=polyfit(x,y,n); %fit a n order polynomial
% A will be a vector of the coefficients for :
% a0+a1*x+a2*x^2+.............+an*x^n
Mn=min(x);
Mx=max(x);
xx=[Mn-1:(Mx-Mn)/100:Mx+1];
yy=polyval(A,xx);% generates expected values for each value of xx
figure;plot(x,y,'*',xx,yy,'r'); hold on
legend('Original Data Points','Quadratic Prediction');
OUTPUT:
A =
-0.0006 0.3734 1.0942
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