multiclass.rar_ multiclass_matlab多分类_multiclass matlab_分类算法_多分类算

function [test_targets, coding_matrix] = multiclass(train_patterns, train_targets, test_patterns, params) % Multiclass classification using two-class classification % % train_patterns - Train patterns % train_targets - Train targets % test_patterns - Test patterns % params - [Type of matrix, balance classes?, classification algorithm, algorithm parameters] % The type of matrix can be: % OAA - One against all % all-pairs % Hamming % If the balance classes flag is set, the examples in the two-class % problems will be replicated so as to form % approximately balanced classes % %Outputs % test_targets - Predicted targets % coding_matrix - The coding matrix used [matrix_type, balance, class_alg, class_alg_params] = process_params(params); Uc = unique(train_targets); Nc = length(Uc); switch lower(matrix_type) case {'oaa', 'one against all'} coding_matrix = 2*eye(Nc)-1; case 'all-pairs' coding_matrix = zeros(Nc, Nc*(Nc-1)/2); count = 0; for i = 1:Nc-1, for j = i+1:Nc, count = count + 1; coding_matrix(i, count) = 1; coding_matrix(j, count) = -1; end end case 'hamming' coding_matrix = ones(Nc, 2*Nc-1); coding_matrix(1:Nc, 1:Nc) = eye(Nc); coding_matrix(2:end, Nc+1:end) = ~eye(Nc-1); coding_matrix = 2*coding_matrix-1; otherwise error('Unknown coding matrix'); end output = zeros(size(coding_matrix, 2), size(test_patterns,2)); for i = 1:size(coding_matrix, 2) %Build a two-class classifier labels0 = find(ismember(train_targets, Uc(find(coding_matrix(:,i)==-1)))); labels1 = find(ismember(train_targets, Uc(find(coding_matrix(:,i)==1)))); N0 = length(labels0); N1 = length(labels1); if ~isempty(labels0) & ~isempty(labels1) if balance if (N0 < N1) %Replicate class 0 if N1-N0 < N0, new_in = randperm(N0); new_in = new_in(1:N1-N0); else new_in = rem(randperm(N1-N0), N0-1)+1; end labels0 = [labels0, labels0(new_in)]; else %Replicate class 1 if N0-N1 < N1, new_in = randperm(N1); new_in = new_in(1:N0-N1); else new_in = rem(randperm(N0-N1), N1-1)+1; end labels1 = [labels1, labels1(new_in)]; end end new_targets = [zeros(1, length(labels0)), ones(1, length(labels1))]; new_patterns = train_patterns(:, [labels0, labels1]); %Mix the new data in = randperm(size(new_patterns, 2)); new_targets = new_targets(in); new_patterns = new_patterns(:, in); output(i,:) = 2*feval(class_alg, new_patterns, new_targets, test_patterns, class_alg_params)-1; elseif isempty(labels0) output(i, :) = 1; else output(i, :) = -1; end end %For each pattern in the test patterns, find the closest coding matrix vector test_targets = zeros(1,size(test_patterns, 2)); for i = 1:length(test_targets) if ~strcmp(lower(matrix_type), 'all-pairs') dist = sum((coding_matrix - ones(size(coding_matrix,1),1)*output(:,i)')'.^2); [m, closest] = min(dist); test_targets(i) = Uc(closest); else [x, y] = find(coding_matrix==ones(size(coding_matrix,1),1)*output(:,i)'); %dist = (ones(size(coding_matrix,1),1)*output(:,i)').*coding_matrix; %[x,y] = ind2sub(size(dist), find(dist~=0)); %sums = zeros(1, Nc); %for j = 1:Nc, % sums(j) = sum(x==j); %end sums = hist(x, Uc); in = find(sums == max(sums)); if length(in) > 1, in = in(randperm(length(in))); in = in(1); end test_targets(i) = Uc(in); end end