哈夫曼编码MATLAB

好的，以下是哈夫曼编码的MATLAB代码：

function [dict, avglen] = huffmandict(symbols, prob)
% HUFFMANDICT Generate a variable-length Huffman code dictionary.
%   [DICT, AVGLEN] = HUFFMANDICT(SYMBOLS, PROB) generates a variable-length
%   Huffman code dictionary for the input symbols based on their probability
%   of occurrence. SYMBOLS is a vector of distinct symbols, and PROB is a
%   corresponding vector of probabilities. DICT is a cell array of symbol
%   codes, and AVGLEN is the average length of a code word.
%
%   Example:
%       symbols = {'A', 'B', 'C', 'D', 'E'};
%       prob = [0.35, 0.2, 0.15, 0.15, 0.15];
%       [dict, avglen] = huffmandict(symbols, prob);
%
%   See also HUFFMANENCODE, HUFFMANDECODE.

n = length(symbols);
if n ~= length(prob)
    error('symbols and prob must be the same length.');
end

% Sort symbols and probabilities in descending order of probability.
[prob, sortIndex] = sort(prob, 'descend');
symbols = symbols(sortIndex);

% Initialize the dictionary with each symbol as its own codeword.
dict = cell(n, 1);
for i = 1:n
    dict{i} = symbols{i};
end

% Build the Huffman tree.
while n > 1
    % Combine the two symbols with lowest probability.
    newSymbol = {symbols{n-1}, symbols{n}};
    newProb = [prob(n-1), prob(n)];
    n = n - 1;
    
    % Insert the new combined symbol and probability into the sorted lists.
    k = find(prob(1:n-1) <= newProb(1), 1, 'last') + 1;
    prob(k+1:n+1) = prob(k:n);
    symbols(k+1:n+1) = symbols(k:n);
    prob(k) = newProb(1);
    symbols{k} = newSymbol;
    prob(k+1) = newProb(2);
    
    % Update the dictionary.
    dict{k} = strcat('0', dict{k});
    dict{k+1} = strcat('1', dict{k+1});
end

% Calculate the average length of a code word.
avglen = 0;
for i = 1:length(dict)
    avglen = avglen + prob(i)*length(dict{i});
end

end