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leave_one_out_single_vp.m
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leave_one_out_single_vp.m
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% leave one out train and test
function [accuracy, confn, f] = leave_one_out_single_vp(dataset, pool, person_ids, left_out_ind)
%%% leave one out train and test
protate = 0;
spatial_cuts = 1;
dim = struct('start_frame', 0, 'end_frame', 1000, 'xlen', 1280, 'ylen', 960, 'protate', protate, 'spatial_cuts', spatial_cuts);
labels = unique(dataset.label);
n_label = length(labels);
assert(n_label == length(dataset.valid_labels));
conf = zeros(n_label, n_label);
left_out = person_ids(left_out_ind);
f1 = find(dataset.person ~= left_out);
traindata = dataset.sub(f1);
y_train = traindata.label;
f1 = find(dataset.person == left_out);
testdata = dataset.sub(f1);
y_test = testdata.label;
%{
%%% repeat samples to be balanced
f3 = [];
for i = 1:n_label
f1 = find(y_train == i);
f1_n = length(f1);
if f1_n == 0
continue
end
f2 = repmat(f1, [1 ceil(50/f1_n)]);
f3 = [f3 f2(1:50)];
end
traindata = traindata.sub(f3);
%}
% train...
f = boost_varyrounds(traindata, pool, .8, dim, 'poly', 5);
% get application of each partition scheme to be used by the classifier
for pat_ind = 1:length(f.min_pat_inds)
pool_num = f.min_pat_inds(pat_ind);
partition = pool{pool_num};
partitioned_feats{pool_num} = testdata.compute_histograms(partition, dim);
end
% now test
y_pred = strong_classify_all(f, partitioned_feats, dataset.valid_labels);
conf1 = zeros(n_label, n_label);
for j = 1:length(y_test)
conf1(y_test(j), y_pred(j)) = conf1(y_test(j), y_pred(j)) + 1;
end
conf = conf + conf1;
[left_out sum(diag(conf1))/sum(conf1(:))]
clear partitioned_feats;
%confn = bsxfun(@rdivide, conf, sum(conf, 2) + eps); %% normalize the confusion matrix
%accuracy = sum(diag(confn)/sum(confn(:)))
confn = conf1;
accuracy = sum(diag(conf1)) / sum(conf1(:));