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 Kommentare
Jan
am 26 Okt. 2012
Bearbeitet: Jan
am 26 Okt. 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].
Akzeptierte Antwort
Jan
am 26 Okt. 2012
Bearbeitet: Jan
am 26 Okt. 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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Weitere Antworten (1)
Vrushabh Bhangod
am 19 Mai 2018
Bearbeitet: Walter Roberson
am 10 Jun. 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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