📄 plotf.m
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%PLOTF Plot feature distribution, special version% % h = PLOTF(A,N)% % Produces 1-D density plots for all the features in dataset A. The % densities are estimated using PARZENML. N is the number of % feature density plots on a row. % % See also DATASETS, PARZENML% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: plotf.m,v 1.5 2007/04/23 12:17:19 duin Exp $function h_out = plotf(a,n)%DXD make a standard setting for n, I'm getting crazy!if nargin<2 n = 1;end prtrace(mfilename); [m,k,c] = getsize(a); % Define the color for each of the classes: if c == 2 map = [0 0 1; 1 0 0]; else map = hsv(c); end % Make subplots for each feature, so a grid of p x q subplots is % defined h = []; if k >= n p = ceil(k/n); q = n; else p = k; q = 1; end % Get the feature names feats = getfeatlab(a,'string'); if size(feats,2) == 1 feats = [repmat('Feature ',size(feats,1),1) feats]; end % Make the plot for each of the features: for j = 1:k b = a(:,j); s = zeros(1,c); d = zeros(121,c); bb = [-0.10:0.01:1.10]' * (max(b)-min(b)) + min(b); ex = 0; % Make a density estimate of each of the classes: for i = 1:c I = findnlab(a,i); D = +distm(bb,b(I,:)); s(i) = parzenml(b(I,:)); % Compute the density function d(:,i) = sum(exp(-D/(s(i).^2)),2)./(length(I)*s(i));; end % Create the subplots with the correct sizes: subplot(p,q,j) plot(bb,zeros(size(bb)),'w.'); hold on h = []; % Scatter the data and plot the density functions for each of the % classes: for i = 1:c I = findnlab(a,i); hh = plot(b(I),zeros(size(b(I))),'x',bb,+d(:,i)); set(hh,'color',map(i,:)); h = [h;hh]; end legend(h(1:2:end)',num2str(getlablist(a))); %does not work properly title([getname(a) ': ' feats(j,:)]); V = axis; axis([bb(1) bb(end) V(3) V(4)]); set(gca,'xtick',[]); set(gca,'ytick',[]); xlabel(feats(j,:)); hold off end % The last details to take care of: if k == 1, title(''); end if nargout > 0 h_out = h; end return
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