initlalize several variables at once
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I currently have a code where I am initializing many parameters, that will be solved for later.
I do this by:
C1=params(1);
C2=params(2);
C3=params(3);
C4=params(4);
C5=params(5);
C6=params(6);
C7=params(7);
C8=params(8);
C9=params(9);
C10=params(10);
C11=params(11);
C12=params(12);
C13=params(13);
There's got to be an easier way...
Additionally, I set my intial guesses like:
params0=[0.1,0.1,0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1];
Is there a way to more easily set this, assuming they all have the same initial guess? Typing them all out that way is tedious, and my code is constantly changing. How can I automate it?
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Matt J
2019-3-19
编辑:Matt J
2019-3-19
Is there a way to more easily set this, assuming they all have the same initial guess?
One way:
params0=linspace(0.1,0.1,13)
Another is:
params0=repelem(0.1,1,13);
There's got to be an easier way...
If you have a very large vector of variables, it does not make sense to assign them to separate variables. You should probably be manipulating them as a vector. But if you must index the variables individually, I would just rename params to something shorter,
C=params;
and then refer to the variables via indexing C(1), C(2),..C(13) throughout the code. Although, it is then unclear why you chose to name the vector params in the first place...
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Matt J
2019-3-19
编辑:Matt J
2019-3-19
Perhaps instead of zeroth, first,etc... you generate a cell array called "orders" like so:
T=[0 0; 1 0; 0 1]; %table of exponent combinations
[orders{1:max(d)+1}]=deal(0); %initialize
for k=1:numel(C)
i=T(k,1); j=T(k,2);
orders{i+j+1}=orders{i+j+1} + C(k) * X.^i .* Y.^j ;
end
Matt J
2019-3-20
编辑:Matt J
2019-3-20
One other thing I would point out is the model function you have shown us here is linear in the coefficients. This means that it can be represented as a matrix multiplication and the entire fit can be done with simple linear algebra instead of an iterative solver like lsqcurvefit.
A=func2mat(@(p) modelfun(p,xdata), params0);
coefficients =A\g(:);
That's it!
4 个评论
Matt J
2019-3-20
Nothing. Whatever modelfun() you were planning to use with lsqcurvefit, you simply use instead in the above 2 lines of code.
Matt J
2019-3-20
Just to sum things up here, below is my implementation of the fit, combining all my various recommendations. Both the algebraic method that I proposed and the method using lsqcurvefit are implemented and compared. You will see that the algebraic method is both faster and more accurate (gives a lower resnorm) than lsqcurvefit.
%% Data Set-up
num=xlsread('example.xlsx',1,'A2:F18110');
Np=4; %polynomial order
g = num(:,6);
otherStuff.r = num(:,4);
otherStuff.eta = num(:,3);
otherStuff.g = g;
otherStuff.eta_c = 0.6452;
otherStuff.Np=Np;
otherStuff.A1 = -0.4194;
otherStuff.A2 = 0.5812;
otherStuff.A3 = 0.6439;
otherStuff.A4 = 0.4730;
%% Fitting (2 methods)
%fit using matrix algebra
tic;
A=func2mat(@(p) modelfun(p,otherStuff), ones(Np+1,Np+1));
Cfit1 =A\g(:);
resnorm1=norm(A*Cfit1-g(:));
toc %Elapsed time is 0.124593 seconds.
%fit iteratively with lsqcurvefit
tic;
options=optimoptions(@lsqcurvefit,'MaxFunctionEvaluations',10000);
Cinitial(1:Np+1,1:Np+1)=0.1;
[Cfit2, resnorm2]=lsqcurvefit(@modelfun,Cinitial,otherStuff,g,[],[],options);
toc %Elapsed time is 1.687990 seconds.
%% Display
figure(1); showFit(Cfit1,otherStuff);
figure(2); showFit(Cfit2,otherStuff);
resnorm1,resnorm2,
function ghat=modelfun(C,otherStuff)
r = otherStuff.r;
eta = otherStuff.eta;
eta_c = otherStuff.eta_c;
Np = otherStuff.Np;
A1 = otherStuff.A1;
A2 = otherStuff.A2;
A3 = otherStuff.A3;
A4 = otherStuff.A4;
PV = 1 + 3*eta./(eta_c-eta)+ A1*(eta./eta_c) + 2*A2*(eta./eta_c).^2 +...
3*A3*(eta./eta_c).^3 + 4*A4*(eta./eta_c).^4;
rdf_contact = (PV - 1)./(4*eta);
Cflip=rot90(reshape(C,Np+1,Np+1),2);
poly_guess = polyVal2D(Cflip,r-1,eta/eta_c,Np,Np);
ghat = (poly_guess.*rdf_contact);
end
function showFit(Cfit,otherStuff)
r = otherStuff.r(:);
eta = otherStuff.eta(:);
g = otherStuff.g(:);
ghat=modelfun(Cfit,otherStuff);
tri = delaunay(r,eta);
%% Plot it with TRISURF
h=trisurf(tri, r, eta, ghat);
h.EdgeColor='b';
h.FaceColor='b';
axis vis3d
hold on;
scatter3(r,eta,g,'MarkerEdgeColor','none',...
'MarkerFaceColor','r','MarkerFaceAlpha',.05);
xlabel 'r', ylabel '\eta', zlabel 'g(r,\eta)'
hold off
end
Both methods give a similar looking surface plot:
![untitled.png](https://www.mathworks.com/matlabcentral/answers/uploaded_files/209495/untitled.png)
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