规范化的互信息以评估聚类特征

Question

我是Matlab的新手，我正在尝试实现规范化的互信息，但是我收到了一个错误。 我正在使用MATHWorks提供的规范化互信息函数：归一化互信息 Mo Chen的1.0版（734字节） 完全矢量化的实现NMI。 NMI通常用于评估聚类结果。

这是我正在使用的代码：

function nmi1
    addpath('C:\Users\user\Desktop\Normalization')
     FeaturesFile = dlmread('test.txt');
     [x,y]=size(FeaturesFile);
       z = nmi(x,y);
end

这是函数nmi

    function z = nmi(x, y)
    % Compute normalized mutual information I(x,y)/sqrt(H(x)*H(y)) of two discrete variables x and y.
    % Input:
    %   x, y: two integer vector of the same length 
    % Ouput:
    %   z: normalized mutual information z=I(x,y)/sqrt(H(x)*H(y))
    % Written by Mo Chen (sth4nth@gmail.com).
    assert(numel(x) == numel(y));
    n = numel(x);
    x = reshape(x,1,n);
    y = reshape(y,1,n);

    l = min(min(x),min(y));
    x = x-l+1;
    y = y-l+1;
    k = max(max(x),max(y));

    idx = 1:n;
    Mx = sparse(idx,x,1,n,k,n);
    My = sparse(idx,y,1,n,k,n);
    Pxy = nonzeros(Mx'*My/n); %joint distribution of x and y
    Hxy = -dot(Pxy,log2(Pxy));


    % hacking, to elimative the 0log0 issue
    Px = nonzeros(mean(Mx,1));
    Py = nonzeros(mean(My,1));

    % entropy of Py and Px
    Hx = -dot(Px,log2(Px));
    Hy = -dot(Py,log2(Py));

    % mutual information
    MI = Hx + Hy - Hxy;

    % normalized mutual information
    z = sqrt((MI/Hx)*(MI/Hy));
    z = max(0,z);

你知道我做错了吗？

先谢谢