Data size mismatch..

Question

Anandh 2011-7-29

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/12646-data-size-mismatch

Dear friends, I have tried to execute the below mentioned code. But am getting following error: _*E_rror using ==> mle at 208 DATA must be a vector.

Error in ==> MixtureModelExample_test at 31 p = mle(data, 'pdf', mixtureGauss, 'start', [0.5 0.1 0.5 0.1 0.5], ..._ _*

How to correct the code? whats wrong in this? Please help me out. thanks.

function MixtureModelExample() % % This script generates some data from two different Gaussians and then % combines the data into one big vector. It then fits a mixture model of % two Gaussians to the data to try to recover the original Gaussians that % generated the data (it uses the matlab function mle() to get the maximum % likelihood mixture). % %

    % Generate some data drawn from two Gaussians
    a=[8.3  13.9  12.5  22.2  8.3  11.1  18.1  11.1  6.9  6.9  6.9  19.4  9.7  4.2  5.6  12.5  11.1  6.9  8.3  8.3  13.9  9.7  6.9  6.9  8.3  5.6  12.5  4.2  18.1  11.1  4.2  8.3  12.5  15.3  5.6  6.9  13.9  13.9  18.1  12.5  15.3  29.2  36.1  30.6  22.2  40.3  41.7  52.8  50  52.8  61.1  55.6  72.2  69.4  68.1  68.1  76.4  94.4  77.8  101.4  81.9  73.6  93.1  76.4  48.6  52.8  41.7  44.4  43.1  25  26.4  19.4  25  19.4  16.7  13.9  8.3  15.3  5.6  5.6  5.6  5.6  9.7  6.9  2.8  8.3  9.7  8.3  11.1  12.5  15.3  8.3  13.9  4.2  16.7  5.6  8.3  16.7  4.2  11.1  2.8  8.3  5.6  8.3  4.2  11.1  12.5  8.3  8.3  9.7  13.9  15.3  19.4  20.8  25  23.6  25  25  25  33.3  26.4  23.6  27.8  19.4  22.2  19.4  23.6  26.4  15.3  23.6  15.3  26.4  13.9  9.7  15.3  11.1  11.1  18.1  9.7  16.7  18.1  9.7  11.1  22.2  18.1  13.9  19.4  20.8  18.1  13.9  15.3  19.4  13.9  16.7  20.8  12.5  18.1  15.3  15.3  12.5  8.3  12.5  20.8  15.3  15.3  12.5  13.9  9.7  18.1  8.3  19.4  16.7  12.5  12.5  13.9  6.9  9.7  11.1  16.7  5.6  8.3  11.1  4.2  12.5  2.8  12.5  11.1  8.3  9.7  8.3  8.3  18.1  12.5  11.1  8.3  9.7  6.9  12.5  5.6  8.3  0];
    b=[a.*1.1];
    data = [a;b]';
    data(data < 0.05) = 0.05;
    [n,x] = hist(data);
    bar(x,n);
    % Make the mixture model pdf
    mixtureGauss = ...
        @(x,m1,s1,m2,s2,theta) (theta*normpdf(x,m1,s1) + (1-theta)*normpdf(x,m2,s2));
    
    % Set up parameters for the MLE function
    options = statset('mlecustom');
    options.MaxIter     = 20000;
    options.MaxFunEvals = 20000;
    % Get max likilihood parameters for our mixture model (start with some
    %   reasonable guesses about the parameters)
    p = mle(data, 'pdf', mixtureGauss, 'start', [0.5 0.1 0.5 0.1 0.5], ...
        'lowerbound', [-Inf 0 -Inf 0 0], 'upperbound', [Inf Inf Inf Inf 1], ...
        'options', options);
    % Plot and print information
    hold on;
    x = linspace(min(data),max(data),100);
    plot(x, mixtureGauss(x,p(1),p(2),p(3),p(4),p(5))*max(n), 'r', 'LineWidth', 2);
    fprintf('Gauss 1: %0.2f (+/- %0.2f)\n', p(1), p(2));
    fprintf('Gauss 2: %0.2f (+/- %0.2f)\n', p(3), p(4));
    fprintf('Mix: %0.2f proportion first gaussian\n', p(5));