KNN学习算法-回归算法在计算机视觉及图像处理中的应用matlab

% cvKnn - K-Nearest Neighbor classification % % Synopsis % [Class] = cvKnn(X, Proto, ProtoClass, [K], [distFunc]) % % 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(X, Proto, ProtoClass, K, distFunc) if ~exist('K', 'var') || isempty(K) K = 1;%默认为K = 1 end if ~exist('distFunc', 'var') || isempty(distFunc) distFunc = @cvEucdist; 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, [], 2); % 2 == row%每列的最小元素 Class = ProtoClass(IndexProto); if nargout == 2, % instance indices in similarity descending order [sorted, ind] = sort(d'); % PxN 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 clear d； % K closest IndexProto = IndexProto(1:K,:); KnnClass = ProtoClass(IndexProto); % 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