计算对数似然相似度

clc,clear;
%计算对数似然相似度
K=[1 2 3;
    1 2 3;
    1 2 3];
N=sum(sum(K));
%计算行熵
rowEntropy=[K(1,1)*log(K(1,1)/sum(K(1,:))),K(1,2)*log(K(1,2)/sum(K(1,:))),K(2,1)*log(K(2,1)/sum(K(2,:))),K(2,2)*log(K(2,2)/sum(K(2,:)))];
rowEntropy(find(isnan(rowEntropy)==1))=0;
rowEntropy=sum(rowEntropy./N);
%计算列熵
columEntropy=[K(1,1)*log(K(1,1)/sum(K(:,1))),K(1,2)*log(K(1,2)/sum(K(:,2))),K(2,1)*log(K(2,1)/sum(K(:,1))),K(2,2)*log(K(2,2)/sum(K(:,2)))];
columEntropy(find(isnan(columEntropy)==1))=0;
columEntropy=sum(columEntropy./N);
%计算矩阵熵
matrixEntropy=K./N.*log(K./N);
matrixEntropy(find(isnan(matrixEntropy)==1))=0;
matrixEntropy=sum(sum(matrixEntropy));
%计算相似度
UserSimilarity=1-2*(matrixEntropy-rowEntropy-columEntropy)

result

rowEntropy =

-0.4432

columEntropy =

-0.3662

matrixEntropy =

-2.1100

UserSimilarity =

3.6012