www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/DSLVQ.m
function [features, targets, w] = DSLVQ(train_features, train_targets, Nmu, region, plot_on) %Reduce the number of data points using distinction sensitive linear vector quantization %Inputs: % train_features - Input features % train_targets - Input targets % Nmu - Number of output data points % region - Decision region vector (ununused) % plot_on - Plot stages of the algorithm % %Outputs % features - New features % targets - New targets % w - Weights vector if (nargin < 5), plot_on = 0; end Ndim = size(train_features, 1); alpha = 0.9; beta = 0.1; L = length(train_targets); dist = zeros(Nmu,L); label = zeros(1,L); %Initialize the mu's mu = randn(Ndim,Nmu); mu = sqrtm(cov(train_features',1))*mu + mean(train_features')'*ones(1,Nmu); mu_target= rand(1,Nmu)>.5; old_mu = zeros(Ndim,Nmu); %Initialize the weight vector w = ones(size(train_features,1),1); while (sum(sum(abs(mu - old_mu))) > 0.1), old_mu = mu; %Classify all the features to one of the mu's for i = 1:Nmu, dist(i,:) = sum(((w*ones(1,L)).*(train_features - mu(:,i)*ones(1,L))).^2); end %For each sample, ... for i = 1:L, %Find the nearest neighbor classified correctly, and the nearest one classified %incorrectly d = dist(:,i).*(mu_target'-.5)*2; dp = d;dn = d; dp(find(dp<0)) = nan; dn(find(dn>0)) = nan; ci = find(dp == min(dp)); cj = find(dn == max(dn)); if (isempty(ci) | isempty(cj)), break end di = abs(train_features(:,i) - mu(:,ci)); dj = abs(train_features(:,i) - mu(:,cj)); wn = (di-dj)/sum(abs(di-dj)); nw = w + beta*(wn - w); nw(find(nw>1)) = 1; nw(find(nw<1e-4)) = 1e-4; w = nw./sum(abs(nw)); end %Label the points [m,label] = min(dist); %Label the mu's for i = 1:Nmu, if (length(train_targets(:,find(label == i))) > 0), mu_target(i) = (sum(train_targets(:,find(label == i)))/length(train_targets(:,find(label == i))) > .5); end end %Recompute the mu's for i = 1:Nmu, indices = find(label == i); if ~isempty(indices), Q = ones(Ndim,1) * (2*(train_targets(indices) == mu_target(i)) - 1); mu(:,i) = mu(:,i) + mean(((train_features(:,indices)-mu(:,i)*ones(1,length(indices))).*Q)')'*alpha; end end alpha = 0.95 * alpha; beta = 0.95 * beta; if (plot_on == 1), plot_process(mu) end end %Make the decision region targets = zeros(1,Nmu); if (Nmu > 1), for i = 1:Nmu, if (length(train_targets(:,find(label == i))) > 0), targets(i) = (sum(train_targets(:,find(label == i)))/length(train_targets(:,find(label == i))) > .5); end end else %There is only one center targets = (sum(train_targets)/length(train_targets) > .5); end features = mu;