Writing my own polyfit function
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How would one write their own polyfit function using only mldivide and for loops?
I have a basic idea:
function [A,e] = MyPolyRegressor(x, y, n)
c=ones(n,1);
for i=1:n;
c(:,i)=x.^(i-1);
end
A=c\y
e=c*A-y
But it doesnt quite work.
3 commentaires
Jan
le 26 Oct 2012
Modifié(e) : Jan
le 26 Oct 2012
Because you didn't format your code properly (please learn how to do this...), it is not possible to find out, which one is the "line 4".
But with some guessing: "ones(n,1)" and even "ones(size(x))" create vectors, while the required Vandermonde-matrix needs the dimensions [length(x), n+1].
Réponse acceptée
Jan
le 26 Oct 2012
Modifié(e) : Jan
le 26 Oct 2012
function p = fPolyFit(x, y, n)
% Construct Vandermonde matrix:
x = x(:);
V = ones(length(x), n + 1);
for j = n:-1:1
V(:, j) = V(:, j + 1) .* x;
end
% Solve least squares problem:
[Q, R] = qr(V, 0);
p = transpose(R \ (transpose(Q) * y(:)));
% Equivalent: (V \ y)'
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Plus de réponses (1)
Vrushabh Bhangod
le 19 Mai 2018
Modifié(e) : Walter Roberson
le 10 Juin 2018
function [p,mu,f] = plofit(x,y,n)
% x = input samples
% y = output function,n = order
m = length(x); %number of rows in the Vandermonde Matrix
V = zeros(m,n);
a = n;
for i = 1:m
v = zeros(1,n);
for j = a:-1:1
v(n+1-j) = realpow(x(i),j);
end
V(i,:) = v;
end
V(:,n+1)=ones(m,1);% adding 1 column to ones to the vandermonde matrix
%%QR method to compute the least squares solution for the coefficients,'p'
[Q,R] = qr(V,0);
p = transpose(R \ (transpose(Q) * y'));
f = polyval(p,x);
%%to find mean
mean = sum(x)/length(x);
sq = 0;
for i =1:length(x)
sq = sq + (x(i)-mean)^2;
end
sd = (sq/length(x))^0.5;
mu = [mean;sd];
end
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