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liangboliu

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[交流] 【原创】分享一个matlab编写的模糊KNN的例子，经过验证效果不错。 已有12人参与

分享一个模糊KNN的例子，经过测试效果不错。
建议大家在使用的时候，引用一下作者的文章。
function [predicted,memberships, numhits] = fknn(data, labels, test, ...
            testlabels, k_values, info, fuzzy)
% FKNN Fuzzy k-nearest neighbor classification algorithm.
%         Y = FKNN(DATA, LABELS, TEST, TESTLABELS, K, INFO) Runs fuzzy
%         k-nearest neighbors on the given data. DATA is a N-by-D data matrix
%         consisting of N patterns each of which is D-dimensional. LABELS is a
%         N-vector containing the class labels (1,2,...,C) for each pattern.
%        TEST is a M-by-D matrix consisting of M test patterns. TESTLABELS
%        is an optional M-vector for the true class labels of the given test
%        data. If you don't have true labels, just give an empty matrix for this
%        TESTLABELS.
%        K is the number of nearest neighbors to look at.
%        The algorithm will print an information line at every INFO test
%        patterns, if INFO>0. If INFO is zero, nothing will be printed.
%        Y is a M-vector containing the predicted class labels for the given test
%        patterns.
%
%         [Y,MEMS,HITS] = FKNN(DATA, LABELS, TEST, TESTLABELS, K, INFO) returns
%         the fuzzy class-memberships values in MEMS, for each test pattern. It is
%         a M-by-C matrix, C being the number of classes.
%        HITS is the number of correctly predicted test patterns. Note that HITS
%        is meaningless if TESTLABELS is not provided.
%
%         [Y,MEMS,HITS] = FKNN(DATA, LABELS, TEST, TESTLABELS, K, INFO, FUZZY) If
%         you don't want to do "fuzzy" k-nn, then give FUZZY as 'false'.
%
%        This m-file is capable of testing several k-values simultaneously. If
%        you pass a vector of k-values, rather than a single scalar, in K, then
%        each output variable is populated accordingly. So, if you give K as
%        [5 10 15], then Y becomes M-by-3, MEMS M-by-C-by-3 and HITS  3-by-1.
%
%        References:
%        J. M. Keller, M. R. Gray, and J. A. Givens, Jr.,
%        "A Fuzzy K-Nearest Neighbor Algorithm",
%        IEEE Transactions on Systems, Man, and Cybernetics,
%        Vol. 15, No. 4, pp. 580-585.  
%
% TODO: Generalize this m-file to Lp norm
%
% Emre Akbas [eakbas2 @ uiuc.edu]  Dec 2006.
%

if nargin<7
    fuzzy = true;
end

num_train = size(data,1);
num_test  = size(test,1);

% scaling factor for fuzzy weights. see [1] for details
m = 2;

% convert class labels to unary membership vectors (of 1s and 0s)
max_class = max(labels);
temp = zeros(length(labels),max_class);
for i=1:num_train
    temp(i, = [zeros(1, labels(i)-1) 1 zeros(1,max_class - labels(i))];
end
labels = temp;
clear temp;

% allocate space for storing predicted labels
predicted = zeros(num_test, length(k_values));

% allocate space for 'numhits'. This will only be used if 'testlabels' is
% provided
numhits = zeros(length(k_values),1);

% will the memberships be stored? if yes, allocate space
store_memberships = false;
if nargout > 1,
    store_memberships=true;
    memberships = zeros(num_test, max_class, length(k_values));
end

%% BEGIN kNN
% for each test point, do:
t0=clock;
tstart = t0;
for i=1:num_test
    distances = (repmat(test(i,, num_train,1) - data).^2;
    % for efficiency, no need to take sqrt since it is a non-decreasing function
    distances = sum(distances,2)';

    % sort the distances
    [junk, indeces] = sort(distances);

    for k=1:length(k_values)
        neighbor_index = indeces(1:k_values(k));
        weight = ones(1,length(neighbor_index));
        if fuzzy,
            % originally, this weight calculation should be:
            % weight = distances(neighbor_index).^(-2/(m-1));
            % but since we didn't take sqrt above and the inverse 2th power
            % the weights are:
            % weight = sqrt(distances(neighbor_index)).^(-2/(m-1));
            % which is equaliavent to:
            weight = distances(neighbor_index).^(-1/(m-1));

            % set the Inf (infite) weights, if there are any, to  1.
            if max(isinf(weight))
                warning(['Some of the weights are Inf for sample: ' ...
                        num2str(i) '. These weights are set to 1.']);
                weight(isinf(weight))=1;
            end
        end
        test_out = weight*labels(neighbor_index,/(sum(weight));

        if store_memberships, memberships(i,:,k) = test_out; end;

        % find predicted class (the one with the max. fuzzy vote)
        [junk, index_of_max] = max(test_out');

        predicted(i,k) = index_of_max;

        % compute current hit rate, if test labels are given
        if ~isempty(testlabels) && predicted(i,k)==testlabels(i)
            numhits(k) = numhits(k)+1;
        end
    end

    % print info
    if mod(i,info)==0
        elapsed = etime(clock, t0);
        fprintf(1,['%dth sample done.  Elapsed (from previous info): %.2f' ...
            ' sn.  Estimated left: %.2f sn.\n\tHit rate(s) so far:   '], ...
            i, elapsed, etime(clock, tstart)*((num_test-i)/i) );
        for k=1:length(k_values)
            fprintf(1,'%3d: %.3f\t',k_values(k), 100*numhits(k)/i);
        end
        fprintf(1,'\n');

        t0=clock; % start timer again
    end
end
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顶下！比我用C++写的那个简易的好！
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不错，谢谢分享！
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不错，下来看看！ ，谢谢分享！
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谢谢分享
[zeros(1, labels(i)-1) 1 zeros(1,max_class - labels(i))];
这一句是不是有问题？

[ Last edited by 苏素2006 on 2013-1-8 at 21:44 ]
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