67 lines
1.7 KiB
Matlab
67 lines
1.7 KiB
Matlab
% targets and iy order: 1) variances 2) correlations
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% 3) constraints on M_.Sigma_e itself 4) autocorrelations
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function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)
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global vx fold
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oo_.gamma_y = cell(nar+1,1);
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% M_.Sigma_e = M_.Sigma_e'*M_.Sigma_e;
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M_.Sigma_e=diag(M_.Sigma_e);
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nx = size(ghx,2);
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b=ghu1*M_.Sigma_e*ghu1';
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vx = [];
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if isempty(vx)
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vx = lyapunov_symm(A,b);
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else
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[vx,status] = bicgstab(@f_var,b(:),vx(:),1e-8,50,A,nx);
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if status
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vx = lyapunov_symm(A,b);
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else
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vx=reshape(vx,nx,nx);
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end
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end
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oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
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f = 0;
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if ~isempty(targets{1})
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e = targets{1}-sqrt(oo_.gamma_y{1}(iy{1}));
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f = e'*(var_weights{1}.*e);
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end
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sy = sqrt(diag(oo_.gamma_y{1}));
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sy = sy *sy';
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if ~isempty(targets{2})
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e = targets{2}-oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
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f = f+e'*(var_weights{2}.*e);
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end
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if ~isempty(targets{3})
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e = targets{3}-sqrt(M_.Sigma_e(iy{3}));
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f = f+e'*(var_weights{3}.*e);
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end
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% autocorrelations
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if nar > 0
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vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
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oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
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if ~isempty(targets{4})
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e = targets{4}-oo_.gamma_y{2}(iy{4});
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f = f+e'*(var_weights{4}.*e);
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end
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for i=2:nar
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vxy = A*vxy;
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oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
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if ~isempty(targets{i+3})
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e = targets{i+3}-oo_.gamma_y{i+1}(iy{i+3});
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f = f+e'*(var_weights{i+3}.*e);
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end
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end
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end
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if isempty(fold) | f < 2*fold
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fold = f;
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vxold = vx;
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end
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% 11/04/02 MJ generalized for correlations, autocorrelations and
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% constraints on M_.Sigma_e
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% 01/25/03 MJ targets std. dev. instead of variances
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