70 lines
1.9 KiB
Mathematica
70 lines
1.9 KiB
Mathematica
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function part = learn_simple_partition(ordered_points, fii)
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% Goes through all the ways to divide the points into two or three groups.
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% Chooses the partition which obtains highest logml.
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npoints = length(ordered_points);
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% One cluster:
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val = calculatePopLogml(ordered_points,fii);
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bestValue = val;
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best_type = 'single';
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% Two clusters:
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for i=1:npoints-1
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% The right endpoint of the first cluster.
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val_1 = calculatePopLogml(ordered_points(1:i),fii);
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val_2 = calculatePopLogml(ordered_points(i+1:end),fii);
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total = val_1 + val_2;
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if total>bestValue
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bestValue = total;
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best_type = 'double';
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best_i = i;
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end
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end
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% Three clusters:
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for i=1:npoints-2
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for j=i+1:npoints-1
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val_1 = calculatePopLogml(ordered_points(1:i),fii);
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val_2 = calculatePopLogml(ordered_points(i+1:j),fii);
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val_3 = calculatePopLogml(ordered_points(j+1:end),fii);
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total = val_1 + val_2 + val_3;
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if total>bestValue
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bestValue = total;
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best_type = 'triple';
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best_i = i;
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best_j = j;
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end
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end
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end
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part = zeros(npoints,1);
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switch best_type
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case 'single'
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part = ones(npoints,1);
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case 'double'
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part(1:best_i) = 1;
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part(best_i+1:end) = 2;
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case 'triple'
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part(1:best_i) = 1;
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part(best_i+1:best_j) = 2;
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part(best_j+1:end) = 3;
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end
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%------------------------------------------
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function val = calculatePopLogml(points,fii)
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% Calculates fuzzy (log) marginal likelihood for a population of real
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% values using estimate "fii" for the dispersion value, and Jeffreys prior
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% for the mean parameter.
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n = length(points);
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fuzzy_ones = sum(points);
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fuzzy_zeros = n-fuzzy_ones;
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val = gammaln(1) - gammaln(1 + n/fii) ...
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+ gammaln(0.5 + fuzzy_ones/fii) + gammaln(0.5 + fuzzy_zeros/fii) ...
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- gammaln(0.5) - gammaln(0.5);
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