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classification.m
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classification.m
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function results = classification(data)
all_classifiers = dir('classifiers');
all_classifiers = all_classifiers(3: end);
%all_classifiers(6:9) = [];
all_classifiers = all_classifiers(2);
results = [];
for i = 1 : length(all_classifiers)
file = all_classifiers(i).name(1: end-2);
classifier = str2func( file )
[training_indexes, testing_indexes] = crossval(size(data,1), 5);
result.Errors = [];
result.FP = [];
result.FN = [];
for j = 1 : 5
fprintf('Test Number %g\n', j)
training_set = build_set( data, training_indexes(j) );
testing_set = build_set( data, testing_indexes(j) );
aux = classifier(training_set, testing_set);
result.Errors = [result.Errors; aux.Errors];
result.FP = aux.FP;
result.FN = aux.FN;
end
results.(char(file)).Error = mean( result.Errors )
results.(char(file)).STD = std( result.Errors )
results.(char(file)).FP = result.FP
results.(char(file)).FN = result.FN
results
results.(char(file))
%results.(char(file)) = result;
pause
end
end
%sensitivity vs 1-specificity