diff toolboxes/FullBNT-1.0.7/KPMstats/parzen_fit_select_unif.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/KPMstats/parzen_fit_select_unif.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,45 @@
+function [mu, N, pick] = parzen_fit_select_unif(data, labels, max_proto, varargin)
+% PARZEN_FIT_SELECT_UNIF Fit a parzen density estimator by selecting prototypes uniformly from data
+% [mu, N, pick] = parzen_fit_select_unif(data, max_proto, labels, ...)
+%
+% We partition the data into different subsets based on the labels.
+% We then choose up to max_proto columns from each subset, chosen uniformly.
+%
+% INPUTS
+% data(:,t)
+% labels(t) - should be in {1,2,..,Q}
+% max_proto - max number of prototypes per partition
+%
+% Optional args
+% partition_names{m} - for debugging
+% boundary - do not choose prototypes which are within 'boundary' of the label transition
+%
+% OUTPUTS
+% mu(:, m, q) for label q, prototype m for 1 <= m <= N(q)
+% N(q) = number of prototypes for label q
+% pick{q} = identity of the prototypes
+
+nclasses = max(labels);
+[boundary, partition_names] = process_options(...
+    varargin, 'boundary', 0, 'partition_names', []);
+
+[D T] = size(data);
+mu = zeros(D, 1, nclasses); % dynamically determine num prototypes (may be less than K)
+mean_feat = mean(data,2);
+pick = cell(1,nclasses);
+for c=1:nclasses
+  ndx = find(labels==c);
+  if isempty(ndx)
+    %fprintf('no training images have label %d (%s)\n', c, partition_names{c})
+    fprintf('no training images have label %d\n', c);
+    nviews = 1;
+    mu(:,1,c) = mean_feat;
+  else
+    foo = linspace(boundary+1, length(ndx-boundary), max_proto);
+    pick{c} = ndx(unique(floor(foo)));
+    nviews = length(pick{c});
+    %fprintf('picking %d views for class %d=%s\n', nviews, c, class_names{c});
+    mu(:,1:nviews,c) = data(:, pick{c});
+  end
+  N(c) = nviews;
+end