huffman编码实现
clcclear
symbols=1:3;
p=[.45 .35 .2];
sig=randsrc(100,1,);
tic
dict=huffmandict(symbols,p);
comp=huffmanenco(sig,dict);
toc
dsig=huffmandeco(comp,dict);
if(~isequal(sig,dsig))
print('Error!');
else
L=sum(cellfun('length',dict(:,2))'.*p);
l=length(comp)/100;
H=-sum(p.*log2(p));
eta=H/L;
fprintf('Sourse Entropy:%1.2f,\nAverage Huffman code length:%1.2f,\nCoding efficiency:%3.1f.\n',...
H,L,eta*100);
end
需要自行编写huffmandict和huffmanenco函数，后面比较效率。这两个函数怎么编写，matlab，求助！
好的，我可以帮您解决这个问题。下面是huffmandict和huffmanenco函数的示例代码：
function dict = huffmandict(symbols, p)
% HUFFMANDICT Generate Huffman dictionary.
%
% dict = HUFFMANDICT(symbols, p) generates a Huffman code dictionary
% based on the input symbol probabilities p. symbols is a vector of
% distinct data source symbols. The function returns a cell array
% containing a two-column lookup table. Column 1 contains the input
% symbols and column 2 contains their corresponding Huffman codes.
nSymbols = length(p);
dict = cell(nSymbols, 2);
if nSymbols == 1
dict{1, 1} = symbols;
dict{1, 2} = '';
else
% Construct initial binary tree nodes
counts = zeros(2*nSymbols-1, 1); % Node counts
counts(1:nSymbols) = p; % Assign leaf node counts
leftChild = zeros(2*nSymbols-1, 1); % Left children
rightChild = zeros(2*nSymbols-1, 1); % Right children
parent = zeros(2*nSymbols-1, 1); % Parent nodes
for i = (nSymbols+1):(2*nSymbols-1)
= sort(counts);
leftChild(i) = index(1);
rightChild(i) = index(2);
parent(index(1)) = i;
parent(index(2)) = i;
counts(i) = counts(1) + counts(2);
end
% Encode each symbol using its corresponding path in the binary tree
codeWord = repmat(' ', nSymbols, nSymbols);
for i = 1:nSymbols
node = i;
while parent(node) ~= 0
if leftChild(parent(node)) == node
codeWord(i, nSymbols - ceil(log2(nSymbols) - log2(node+1))) = '0';
else
codeWord(i, nSymbols - ceil(log2(nSymbols) - log2(node+1))) = '1';
end
node = parent(node);
end
dict{i, 1} = symbols(i);
dict{i, 2} = fliplr(codeWord(i, :));
end
end
function comp = huffmanenco(sig, dict)
% HUFFMANENCO Perform Huffman encoding.
%
% comp = HUFFMANENCO(sig, dict) encodes the input signal sig using the
% provided Huffman dictionary dict. The function returns a binary
% string containing the encoded message.
nBits = 0;
for i = 1:length(sig)
index = find(strcmp(dict(:, 1), sig(i)));
nBits = nBits + length(dict{index, 2});
end
comp = repmat('0', 1, nBits);
k = 1;
for i = 1:length(sig)
index = find(strcmp(dict(:, 1), sig(i)));
codeWord = dict{index, 2};
comp(k:k+length(codeWord)-1) = codeWord;
k = k + length(codeWord);
end
注意，这只是示例代码，可能并不是完全优化的。为了提高编码效率，您可以使用MATLAB内置的函数huffmandict和huffmanenco来生成哈夫曼码字典和进行哈夫曼编码。
求最佳答案 sfqxx 发表于 2023-6-5 18:05
好的，我可以帮您解决这个问题。下面是huffmandict和huffmanenco函数的示例代码：
……gpt要是能编出不报错的我也不用来这里问了 您好！Huffman编码是一种经典的无损数据压缩算法。您的代码似乎需要两个自定义函数： huffmandict 和 huffmanenco 。虽然MATLAB内置了这两个函数，但如果您想自行编写，以下是这两个函数的一种可能实现：
首先是 huffmandict 函数，该函数的任务是根据符号和它们的概率创建一个Huffman编码字典：
function dict = huffmandict(symbols, p)
% Create a cell array of all Huffman nodes, which initially are all
% 'leaf' nodes containing symbol-probability pairs.
nodes = num2cell(, 2);
while numel(nodes) > 1
% Sort all the current nodes by probability.
[~, order] = sort();
nodes = nodes(order);
% Combine the two nodes with smallest probability, and build a new
% node that points to them.
smallestTwo = nodes(1:2);
newNode = {[], sum()};
nodes = [{newNode}; nodes(3:end)];
% The new node is a 'parent' node and the two selected nodes are its 'children'.
newNode{1} = smallestTwo;
end
% The single remaining node is the root of the Huffman tree.
huffTree = nodes{1};
% Traverse the Huffman tree to produce the symbol-code dictionary.
dict = createDict(huffTree, []);
end
function dict = createDict(huffNode, prefix)
if iscell(huffNode{1})
% huffNode{1} contains two 'children', so we're not at a 'leaf' node.
dict = );
createDict(huffNode{1}{2}, )];
else
% We're at a 'leaf' node, so return the symbol and its code.
dict = {huffNode{1}, prefix};
end
end
然后是 huffmanenco 函数，它使用给定的Huffman字典来编码一个信号：
function comp = huffmanenco(sig, dict)
% Create a map from symbols to their corresponding codes.
huffLUT = containers.Map(dict(:,1), dict(:,2));
% Encode the signal.
comp = cell2mat(huffLUT.values(num2cell(sig)));
end
这两个函数应该能够满足您的需求。需要注意的是，这些实现是为了展示基本的Huffman编码过程，可能没有完全优化。如果您需要处理大数据集或需要更高的性能，可能需要使用更复杂的数据结构或算法。 G`J 发表于 2023-6-5 18:14
……gpt要是能编出不报错的我也不用来这里问了
？你是机器人吗
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