MATLAB: Parfor. Error: The variable eavg in a parfor cannot be classified.

classclassificationerrorParallel Computing Toolboxparforvariablevariables

I have already read other topic on Errors and parfor but i don't find the answer to my problem.

My code give me an Error: "Error: The variable eavg in a parfor cannot be classified."

The pice of code with parfor is:

eavg = zeros(size(coeff_d,2),size(coeff_c,2),size(f,1));
parfor i0 = 1:size(vett,2)
    i1 = vett(1,i0);
    i2 = vett(2,i0);
    i3 = vett(3,i0);
            if curva~=2
                if curva==4
                    z_rel = (i3-1)/(size(y,1)-1);
                    newcoeff = (-1.817*(z_rel)^2+2.776*z_rel)*0.7;
                else
                    newcoeff = coeff_c(i2);
                end
                suptemp = EsponenzialeSingola(y(i3,:), f(i3,:),...
                    coeff_d(i1), newcoeff, plottacurve);
            else
                eavg(i1,i2,i3) = EsponenzialeDoppia(y(i3,:), f(i3,:),...
                    coeff_d(i1), coeff_c(i2), plottacurve);
            end
end

the variables coeff_c, coeff_d, f, coeff_c, vett, curva, y and plottacurva are declared befor parfor-loop.

Can you help e to fix the Error?

Best Answer

your parfor is based on i0 eavg is not being sliced by that.

So it doesn't know how to slice eavg and then once it comes out of parfor it doesn't know how to combins it.

Related Solutions

MATLAB: The input character is not valid in MATLAB statements or expressions.

Check this line:

M2_min(i,1) = M0_min(i,1).*esponenziale(i,1);_

There is some _ at the end of line after semi colon......delete it.

M2_min(i,1) = M0_min(i,1).*esponenziale(i,1);

This should work:

for i=1:righe
    isp1=isp(i,1);
    isp2=isp(i,2);
      [m0_min,dv1_opt]=codice_parametrico(ks1,ks2,mpl,v,isp1,isp2);
      M0_min(i,1)=m0_min;
      Dv1_opt(i,1)=dv1_opt;
      esponenziale(i,1)=exp(-((1-Dv1_opt(i,1)/100).*v)./(isp2.*9.807));
      M2_min(i,1) = M0_min(i,1).*esponenziale(i,1);
      acceleration(i,1) = thrust(i,1)./M0_min(i,1);
      acceleration(i,2) = thrust(i,2)./M2_min(i,1);
  end

MATLAB: Solving equation with for loop is slow

solve() generically outside of the loop and then subs() or matlabFunction() to get code executed for each loop instance.

t = roots( [Coeff(6)*A(i, 1)^5+Coeff(11)*A(i, 1)^4*A(i, 2)+Coeff(15)*A(i, 1)^3*A(i, 2)^2+Coeff(18)*A(i, 1)^2*A(i, 2)^3+Coeff(20)*A(i, 1)*A(i, 2)^4+Coeff(21)*A(i, 2)^5,
            (5*Coeff(6)*B(i, 1)+Coeff(11)*B(i, 2)+Coeff(5))*A(i, 1)^4+(2*(2*Coeff(11)*B(i, 1)+Coeff(15)*B(i, 2)+(1/2)*Coeff(10)))*A(i, 1)^3*A(i, 2)+(3*Coeff(15)*B(i, 1)+3*Coeff(18)*B(i, 2)+Coeff(14))*A(i, 1)^2*A(i, 2)^2+(2*Coeff(18)*B(i, 1)+4*Coeff(20)*B(i, 2)+Coeff(17))*A(i, 1)*A(i, 2)^3+(Coeff(20)*B(i, 1)+5*Coeff(21)*B(i, 2)+Coeff(19))*A(i, 2)^4,
            (10*Coeff(6)*B(i, 1)^2+Coeff(15)*B(i, 2)^2+(4*Coeff(11)*B(i, 2)+4*Coeff(5))*B(i, 1)+Coeff(10)*B(i, 2)+Coeff(4))*A(i, 1)^3+(3*(2*Coeff(11)*B(i, 1)^2+Coeff(18)*B(i, 2)^2+(2*Coeff(15)*B(i, 2)+Coeff(10))*B(i, 1)+(2/3)*Coeff(14)*B(i, 2)+(1/3)*Coeff(9)))*A(i, 1)^2*A(i, 2)+(3*Coeff(15)*B(i, 1)^2+6*Coeff(20)*B(i, 2)^2+(6*Coeff(18)*B(i, 2)+2*Coeff(14))*B(i, 1)+3*Coeff(17)*B(i, 2)+Coeff(13))*A(i, 1)*A(i, 2)^2+(Coeff(18)*B(i, 1)^2+10*Coeff(21)*B(i, 2)^2+(4*Coeff(20)*B(i, 2)+Coeff(17))*B(i, 1)+4*Coeff(19)*B(i, 2)+Coeff(16))*A(i, 2)^3,
            (10*Coeff(6)*B(i, 1)^3+Coeff(18)*B(i, 2)^3+(6*Coeff(11)*B(i, 2)+6*Coeff(5))*B(i, 1)^2+Coeff(14)*B(i, 2)^2+(3*Coeff(15)*B(i, 2)^2+3*Coeff(10)*B(i, 2)+3*Coeff(4))*B(i, 1)+Coeff(9)*B(i, 2)+Coeff(3))*A(i, 1)^2+(4*Coeff(11)*B(i, 1)^3+4*Coeff(20)*B(i, 2)^3+(6*Coeff(15)*B(i, 2)+3*Coeff(10))*B(i, 1)^2+3*Coeff(17)*B(i, 2)^2+(6*Coeff(18)*B(i, 2)^2+4*Coeff(14)*B(i, 2)+2*Coeff(9))*B(i, 1)+2*Coeff(13)*B(i, 2)+Coeff(8))*A(i, 1)*A(i, 2)+(Coeff(15)*B(i, 1)^3+10*Coeff(21)*B(i, 2)^3+(3*Coeff(18)*B(i, 2)+Coeff(14))*B(i, 1)^2+6*Coeff(19)*B(i, 2)^2+(6*Coeff(20)*B(i, 2)^2+3*Coeff(17)*B(i, 2)+Coeff(13))*B(i, 1)+3*Coeff(16)*B(i, 2)+Coeff(12))*A(i, 2)^2,
            (5*Coeff(6)*A(i, 1)+Coeff(11)*A(i, 2))*B(i, 1)^4+(Coeff(20)*A(i, 1)+5*Coeff(21)*A(i, 2))*B(i, 2)^4+(4*Coeff(5)*A(i, 1)+Coeff(10)*A(i, 2)+(4*Coeff(11)*A(i, 1)+2*Coeff(15)*A(i, 2))*B(i, 2))*B(i, 1)^3+(Coeff(17)*A(i, 1)+4*Coeff(19)*A(i, 2))*B(i, 2)^3+((3*Coeff(15)*A(i, 1)+3*Coeff(18)*A(i, 2))*B(i, 2)^2+3*Coeff(4)*A(i, 1)+Coeff(9)*A(i, 2)+(3*Coeff(10)*A(i, 1)+2*Coeff(14)*A(i, 2))*B(i, 2))*B(i, 1)^2+(Coeff(13)*A(i, 1)+3*Coeff(16)*A(i, 2))*B(i, 2)^2+Coeff(2)*A(i, 1)+Coeff(7)*A(i, 2)+A(i, 3)+((2*Coeff(18)*A(i, 1)+4*Coeff(20)*A(i, 2))*B(i, 2)^3+(2*Coeff(14)*A(i, 1)+3*Coeff(17)*A(i, 2))*B(i, 2)^2+2*Coeff(3)*A(i, 1)+Coeff(8)*A(i, 2)+(2*Coeff(9)*A(i, 1)+2*Coeff(13)*A(i, 2))*B(i, 2))*B(i, 1)+(Coeff(8)*A(i, 1)+2*Coeff(12)*A(i, 2))*B(i, 2),
             Coeff(6)*B(i, 1)^5+Coeff(21)*B(i, 2)^5+(Coeff(11)*B(i, 2)+Coeff(5))*B(i, 1)^4+Coeff(19)*B(i, 2)^4+(Coeff(15)*B(i, 2)^2+Coeff(10)*B(i, 2)+Coeff(4))*B(i, 1)^3+Coeff(16)*B(i, 2)^3+(Coeff(18)*B(i, 2)^3+Coeff(14)*B(i, 2)^2+Coeff(9)*B(i, 2)+Coeff(3))*B(i, 1)^2+Coeff(12)*B(i, 2)^2-Thickness+(Coeff(20)*B(i, 2)^4+Coeff(17)*B(i, 2)^3+Coeff(13)*B(i, 2)^2+Coeff(8)*B(i, 2)+Coeff(2))*B(i, 1)+Coeff(7)*B(i, 2)+B(i, 3)+Coeff(1) ] );

and then do the filtering like you had before.

Best Answer

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