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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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