KML.zip_度量学习

% cvKnn - K-Nearest Neighbor classification %算距离，找邻居，做分类 % Synopsis % [Class] = cvKnn(X, Proto, ProtoClass, [K], [distFunc]) % N, ProtoClass, K, d % Description % K-Nearest Neighbor classification % % Inputs ([]s are optional) % (matrix) X D x N matrix representing column classifiee vectors % where D is the number of dimensions and N is the % number of vectors. % (matrix) Proto D x P matrix representing column prototype vectors % where D is the number of dimensions and P is the % number of vectors. % (vector) ProtoClass % 1 x P vector containing class lables for prototype % vectors. % (scalar) [K = 1] K-NN's K. Search K nearest neighbors. % (func) [distFunc = @cvEucdist] % A function handle for distance measure. The function % must have two arguments for matrix X and Y. See % cvEucdist.m (Euclidean distance) as a reference. % % Outputs ([]s are optional) % (vector) Class 1 x N vector containing classified class labels % for X. Class(n) is the class id for X(:,n). % (matrix) [Rank] Available only for NN (K = 1) now. % nClass x N vector containing ranking class labels % for X. Rank(1,n) is the 1st candidate which is % the same with Class(n), Rank(2,n) is the 2nd % candidate, Rank(3,n) is the 3rd, and so on. % % See also % cvEucdist, cvMahaldist % Authors % Naotoshi Seo <sonots(at)sonots.com> % % License % The program is free to use for non-commercial academic purposes, % but for course works, you must understand what is going inside to use. % The program can be used, modified, or re-distributed for any purposes % if you or one of your group understand codes (the one must come to % court if court cases occur.) Please contact the authors if you are % interested in using the program without meeting the above conditions. % % Changes % 04/01/2005 First Edition function [Class, Rank] = cvKnn(N, ProtoClass, K, d) if ~exist('K', 'var') || isempty(K) K = 1; end % if ~exist('distFunc', 'var') || isempty(distFunc) % distFunc = @distM; % end % if size(X, 1) ~= size(Proto, 1) % error('Dimensions of classifiee vectors and prototype vectors do not match.'); % end % [D, N] = size(X); % Calculate euclidean distances between classifiees and prototypes % d = distFunc(X, Proto); if K == 1, % sort distances only if K>1 [mini, IndexProto] = min(d, [], 1); % 2 是取每一行的最小值（若为1就是取每一列的最小值）(第一项存最小值，第二项存索引，最小值所在位置) Class = ProtoClass(IndexProto);%（把训练集的每一行的最小值放进Class里面） if nargout == 2, % instance indices in similarity descending order(按相似度降序的案例索引)输出参量为两个 [sorted, ind] = sort(d'); % PxN(sort(x),默认为按列升序排序)（d'也就是d的每一行的最小值） RankIndex = ProtoClass(ind); %,e.g., [2 1 2 3 1 5 4 1 2]' % conv into, e.g., [2 1 3 5 4]' for n = 1:N [ClassLabel, ind] = unique(RankIndex(:,n),'first'); [sorted, ind] = sort(ind); Rank(:,n) = ClassLabel(ind); end end else [sorted, IndexProto] = sort(d); % PxN（d的每一行的最小值及其位置存在矩阵里面） % K closest IndexProto = IndexProto(1:K,:);%（第1个到第k个的索引位置） KnnClass = ProtoClass(IndexProto);%（这k个训练集） % Find all class labels ClassLabel = unique(ProtoClass); nClass = length(ClassLabel); for i = 1:nClass ClassCounter(i,:) = sum(KnnClass == ClassLabel(i)); end [maxi, winnerLabelIndex] = max(ClassCounter, [], 1); % 1 == col % Future Work: Handle ties somehow Class = ClassLabel(winnerLabelIndex); end