改进熵值法matlab代码

以下是改进熵值法的MATLAB代码，其中包括了对输入数据和权重矩阵的检查：

function [score, rank] = improved_entropy(data, weight)
% Check if data and weight have the same number of rows
if size(data,1) ~= size(weight,1)
    error('Data and weight matrices must have the same number of rows');
end

% Check if weight matrix is normalized
if sum(weight,2) ~= 1
    error('Weight matrix rows must sum to 1');
end

% Calculate normalized data matrix
normalized_data = zeros(size(data));
for i = 1:size(data,2)
    col = data(:,i);
    normalized_col = (col - min(col)) / (max(col) - min(col));
    normalized_data(:,i) = normalized_col;
end

% Calculate entropy of each attribute
attribute_entropies = zeros(1,size(normalized_data,2));
for i = 1:size(normalized_data,2)
    col = normalized_data(:,i);
    p = col / sum(col);
    attribute_entropies(i) = -sum(p .* log2(p));
end

% Calculate entropy reduction of each attribute
entropy_reduction = zeros(1,size(normalized_data,2));
for i = 1:size(normalized_data,2)
    col = normalized_data(:,i);
    p = col / sum(col);
    reduced_p = p.^weight(i);
    reduced_p = reduced_p / sum(reduced_p);
    entropy_reduction(i) = attribute_entropies(i) - sum(reduced_p .* log2(reduced_p));
end

% Calculate score for each attribute
score = entropy_reduction / sum(entropy_reduction);

% Sort attributes by score in descending order
[rank,~] = sort(score,'descend');
end

在使用该函数时，需要提供一个数据矩阵和一个权重矩阵。数据矩阵应该是一个 $n \times m$ 的矩阵，其中 $n$ 是样本数量，$m$ 是属性数量。权重矩阵应该是一个 $m \times 1$ 的矩阵，其中每个元素都是一个介于 0 和 1 之间的数，表示对应属性的重要性。

函数将返回每个属性的得分和按得分降序排列的属性排名。