Daniel@0: function [mu, N, pick] = parzen_fit_select_unif(data, labels, max_proto, varargin)
Daniel@0: % PARZEN_FIT_SELECT_UNIF Fit a parzen density estimator by selecting prototypes uniformly from data
Daniel@0: % [mu, N, pick] = parzen_fit_select_unif(data, max_proto, labels, ...)
Daniel@0: %
Daniel@0: % We partition the data into different subsets based on the labels.
Daniel@0: % We then choose up to max_proto columns from each subset, chosen uniformly.
Daniel@0: %
Daniel@0: % INPUTS
Daniel@0: % data(:,t)
Daniel@0: % labels(t) - should be in {1,2,..,Q}
Daniel@0: % max_proto - max number of prototypes per partition
Daniel@0: %
Daniel@0: % Optional args
Daniel@0: % partition_names{m} - for debugging
Daniel@0: % boundary - do not choose prototypes which are within 'boundary' of the label transition
Daniel@0: %
Daniel@0: % OUTPUTS
Daniel@0: % mu(:, m, q) for label q, prototype m for 1 <= m <= N(q)
Daniel@0: % N(q) = number of prototypes for label q
Daniel@0: % pick{q} = identity of the prototypes
Daniel@0: 
Daniel@0: nclasses = max(labels);
Daniel@0: [boundary, partition_names] = process_options(...
Daniel@0:     varargin, 'boundary', 0, 'partition_names', []);
Daniel@0: 
Daniel@0: [D T] = size(data);
Daniel@0: mu = zeros(D, 1, nclasses); % dynamically determine num prototypes (may be less than K)
Daniel@0: mean_feat = mean(data,2);
Daniel@0: pick = cell(1,nclasses);
Daniel@0: for c=1:nclasses
Daniel@0:   ndx = find(labels==c);
Daniel@0:   if isempty(ndx)
Daniel@0:     %fprintf('no training images have label %d (%s)\n', c, partition_names{c})
Daniel@0:     fprintf('no training images have label %d\n', c);
Daniel@0:     nviews = 1;
Daniel@0:     mu(:,1,c) = mean_feat;
Daniel@0:   else
Daniel@0:     foo = linspace(boundary+1, length(ndx-boundary), max_proto);
Daniel@0:     pick{c} = ndx(unique(floor(foo)));
Daniel@0:     nviews = length(pick{c});
Daniel@0:     %fprintf('picking %d views for class %d=%s\n', nviews, c, class_names{c});
Daniel@0:     mu(:,1:nviews,c) = data(:, pick{c});
Daniel@0:   end
Daniel@0:   N(c) = nviews;
Daniel@0: end