Fitting a monotonically increasing spline function
24 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
Deepa Maheshvare
le 5 Sep 2022
Modifié(e) : Bruno Luong
le 6 Sep 2022
I want to fit a monotonously increasing smooth spline function for a dataset
x = [0., 0.75, 1.8, 2.25, 3.75, 4.5, 6.45, 6.75, 7.5, 8.325, 10.875, 11.25, 12.525, 12.75, 15., 20.85, 21.]
y = [2.83811035, 2.81541896, 3.14311655, 3.22373554, 3.43033456, 3.50433385, 3.66794514, 3.462296, 3.59480959,
3.56250726, 3.6209845, 3.63034523, 3.68238915, 3.69096892, 3.75560395, 3.83545191, 3.90419498]
The current fit using interp1d looks like the above. I would like to know how to fit a monotonously increasing spline function.
0 commentaires
Réponse acceptée
Bruno Luong
le 5 Sep 2022
One way is to use my https://uk.mathworks.com/matlabcentral/fileexchange/25872-free-knot-spline-approximation
x = [0., 0.75, 1.8, 2.25, 3.75, 4.5, 6.45, 6.75, 7.5, 8.325, 10.875, 11.25, 12.525, 12.75, 15., 20.85, 21.]
y = [2.83811035, 2.81541896, 3.14311655, 3.22373554, 3.43033456, 3.50433385, 3.66794514, 3.462296, 3.59480959,3.56250726, 3.6209845, 3.63034523, 3.68238915, 3.69096892, 3.75560395, 3.83545191, 3.90419498]
nknots=10;
opt=struct('shape',struct('p',1,'lo',zeros(1,nknots),'up',inf(1,nknots)));
pp=BSFK(x,y,4,nknots,[],opt); %FEX
xi=linspace(min(x),max(x),100);
yi=ppval(pp,xi);
plot(xi,yi,'-',x,y,'or')
7 commentaires
Bruno Luong
le 6 Sep 2022
Modifié(e) : Bruno Luong
le 6 Sep 2022
Monotonic polynomial
x = [0., 0.75, 1.8, 2.25, 3.75, 4.5, 6.45, 6.75, 7.5, 8.325, 10.875, 11.25, 12.525, 12.75, 15., 20.85, 21.];
y = [2.83811035, 2.81541896, 3.14311655, 3.22373554, 3.43033456, 3.50433385, 3.66794514, 3.462296, 3.59480959,3.56250726, 3.6209845, 3.63034523, 3.68238915, 3.69096892, 3.75560395, 3.83545191, 3.90419498];
% Stuff needed to normalize the data for better inversion
[xmin, xmax] = bounds(x);
xnfun = @(x)(x(:)-xmin)/(xmax-xmin);
xn=xnfun(x);
% cofficients of 2D polynomial 3d order
k = 0:7;
C = [xn.^k]; % please no comment about my use of bracket here
d = y;
% Constraint positive of 3 x 3 points in the recatagular domain to be positive,
% it should be enough
XNC = linspace(0,1,41);
A = -[k.*XNC(:).^(k-1)]; % please no comment ...
A(:,k==0)=0;
b = 0+zeros(size(A,1),1); % A*P<=0 means polynomial at (xnc,ync)>=0
P = lsqlin(C,d,A,b);
% Graphical check
% Create a grided model surface
xi=linspace(xmin,xmax,201);
Xin=xnfun(xi);
Yi=[Xin.^k]*P; % please no comment about my use of bracket here
close all
plot(xi,Yi);
hold on
plot(x,y,'or')
xlabel('x')
ylabel('y')
Plus de réponses (0)
Voir également
Catégories
En savoir plus sur Polynomials dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!