48 lines
1.4 KiB
Matlab
Executable file
48 lines
1.4 KiB
Matlab
Executable file
donnees;
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n_tests = 500;
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% Estimation de la droite de regression par le maximum de vraisemblance :
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[a_DYX_1,b_DYX_1] = estimation_1(x_donnees_bruitees,y_donnees_bruitees,n_tests);
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% Affichage de la droite de regression estimee par le maximum de vraisemblance :
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if abs(a_DYX_1)<1
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x_DYX_1 = x_D;
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y_DYX_1 = a_DYX_1*x_DYX_1+b_DYX_1;
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else
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y_DYX_1 = y_D;
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x_DYX_1 = (y_DYX_1-b_DYX_1)/a_DYX_1;
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end
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plot(x_DYX_1,y_DYX_1,'b','LineWidth',3);
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axis(bornes);
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lg = legend('~Droite', ...
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'~Donnees bruitees', ...
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'~$D_{YX}$ (maximum de vraisemblance)', ...
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'Location','Best');
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set(lg,'Interpreter','Latex');
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% Calcul et affichage de l'ecart angulaire :
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theta_DYX_1 = atan2(b_DYX_1,-a_DYX_1*b_DYX_1);
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EA_DYX_1 = abs(theta_DYX_1-theta_D);
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fprintf('D_YX (maximum de vraisemblance) : ecart angulaire = %.2f degres\n',EA_DYX_1/pi*180);
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function [a_DYX_1,b_DYX_1] = estimation_1(x_donnees_bruitees,y_donnees_bruitees,n_tests)
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G = mean( [ x_donnees_bruitees.' y_donnees_bruitees.' ] );
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x_centre = x_donnees_bruitees - G(1);
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y_centre = y_donnees_bruitees - G(2);
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x = repmat(x_centre.', 1, n_tests);
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y = repmat(y_centre.', 1, n_tests);
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phi = rand(1, n_tests)*pi - pi/2;
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dist = ( y - bsxfun(@times, x, tan(phi)) ).^2;
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[min_val, min_index] = min( sum( dist ) );
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a_DYX_1 = tan(phi(min_index));
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b_DYX_1 = G(2) - a_DYX_1*G(1);
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end |