Question

我正在使用这个生成的代码绘制两个向量之间的心理测量函数：

smqi=[0 0.504500000000000   0.540500000000000   0.574800000000000   0.459400000000000   0.500800000000000   0.533900000000000   0.401700000000000   0.462100000000000   0.511300000000000   0.291300000000000   0.347300000000000   0.419900000000000   0.491700000000000   0.552400000000000   0.598600000000000   0.392600000000000   0.426000000000000   0.451400000000000   0.334900000000000   0.359500000000000   0.371300000000000   0   0.640800000000000   0.760600000000000   0.847100000000000   0.336200000000000   0.409200000000000   0.463000000000000   0.357600000000000   0.437700000000000   0.504400000000000   0.421200000000000   0.500200000000000   0.579800000000000   0.452700000000000   0.557200000000000   0.592200000000000   0.314800000000000   0.313800000000000   0.344800000000000   0.315500000000000   0.348300000000000   0.355400000000000   0   0.558100000000000   0.636700000000000   0.702900000000000   0.361500000000000   0.427100000000000   0.469100000000000   0.389700000000000   0.457700000000000   0.489800000000000   0.446800000000000   0.528700000000000   0.589200000000000   0.441500000000000   0.475300000000000   0.499600000000000   0.325500000000000   0.360700000000000   0.364200000000000   0.338400000000000   0.368200000000000   0.396200000000000   0   0.702500000000000   0.813100000000000   0.995500000000000   0.370600000000000   0.476800000000000   0.594300000000000   0.355200000000000   0.452000000000000   0.528500000000000   0.484000000000000   0.567400000000000   0.671500000000000   0.454000000000000   0.460700000000000   0.469600000000000   0.299900000000000   0.318300000000000   0.361900000000000   0.264800000000000   0.263000000000000   0.279500000000000];
MOS=[0.240085000000000  0.558541000000000   0.861963000000000   0.935125000000000   0.276903000000000   0.634308000000000   0.838878000000000   0.268853000000000   0.274772000000000   0.724518000000000   0.285545000000000   0.238901000000000   0.477684000000000   0.338700000000000   0.655736000000000   0.656328000000000   0.138511000000000   0.388422000000000   0.582929000000000   0.368178000000000   0.364626000000000   0.341423000000000   0.237362000000000   0.728069000000000   0.894282000000000   1   0.348526000000000   0.689712000000000   0.712087000000000   0.473541000000000   0.558778000000000   0.600331000000000   0.480052000000000   0.638688000000000   0.924944000000000   0.349947000000000   0.765834000000000   0.826684000000000   0.279507000000000   0.315852000000000   0.529892000000000   0.193678000000000   0.282112000000000   0.375873000000000   0.266012000000000   0.536285000000000   0.739079000000000   0.924825000000000   0.362969000000000   0.516160000000000   0.724162000000000   0.417663000000000   0.649461000000000   0.682846000000000   0.540784000000000   0.823606000000000   0.900438000000000   0.277850000000000   0.318693000000000   0.433763000000000   0.221617000000000   0.281165000000000   0.381674000000000   0.141352000000000   0.335740000000000   0.391263000000000   0   0.659879000000000   0.834853000000000   0.953475000000000   0.502664000000000   0.609921000000000   0.818042000000000   0.402155000000000   0.543270000000000   0.755890000000000   0.470700000000000   0.621996000000000   0.748905000000000   0.375873000000000   0.501243000000000   0.682254000000000   0.126080000000000   0.294779000000000   0.353972000000000   0.223748000000000   0.369599000000000   0.261276000000000];
function [fitresult, gof] = createFit(smqi, mos)
%CREATEFIT(SMQI,MOS)
%  Create a fit.
%
%  Data for 'untitled fit 1' fit:
%      X Input : smqi 1x88
%      Y Output: mos  1x88
%  Output:
%      fitresult : a fit object representing the fit.

[xData, yData] = prepareCurveData(smqi, mos);

% Set up fittype and options.
ft = fittype( 'erfc((a+b*x)/sqrt(2))/2', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Algorithm = 'Levenberg-Marquardt';
opts.Display = 'Off';
opts.StartPoint = [0.83763959965349 0.463660782983039];

% Fit model to data.
[fitresult, gof] = fit( xData, yData, ft, opts );


% Plot fit with data.
figure( 'Name', 'untitled fit 1' );
h = plot( fitresult, xData, yData );
legend( h, 'mos vs. smqi', 'untitled fit 1', 'Location', 'NorthEast' );
% Label axes
xlabel smqi
ylabel mos
grid on

我想为每组22个矢量SMQI和MOS值指定不同的标记，同时保留相同的行（fitresult）。我负责分散功能，但我没有成功解决这个问题。

帮助？

Answer 1

最简单的方法是将xData和yData变量重塑为[X x 4]，然后将这些变量传递给绘图，为每个组创建单独的绘图对象。然后，您可以根据需要设置标记。

xData = reshape(xData, [], 4);
yData = reshape(yData, [], 4);

p = plot(xData, yData, '.');
set(p, {'Marker'}, {'.'; 'o'; 'x'; '*'})

hold on
plot(fitresult);

MATLAB中的不同标记图

1 个答案: