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61 lines
1.7 KiB
61 lines
1.7 KiB
6 years ago
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%----------------------------------------------------------------------
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% abs() search with a linear, ampl scaling, and slope term
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function [idx contrib errors b_log2] = vq_search_slope(vq, data, closed_quant_fn, open_quant_fn)
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[nVec nCols] = size(vq);
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nRows = rows(data);
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diff = zeros(nVec, nCols);
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idx = errors = zeros(1, nRows);
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error = zeros(1, nVec);
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contrib = zeros(nRows, nCols);
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b_log = zeros(nVec, 3);
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b_log2 = [];
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weights = ones(1,nCols);
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A = zeros(3,3,nVec);
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for i=1:nVec
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A(:,:,i) = [ sum(vq(i,:)) sum(1:nCols) nCols; ...
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(1:nCols)*vq(i,:)' (1:nCols)*(1:nCols)' sum(1:nCols); ...
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vq(i,:)*vq(i,:)' (1:nCols)*vq(i,:)' sum(vq(i,:)) ];
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end
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for f=1:nRows
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target = data(f,:);
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for i=1:nVec
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c = [sum(target) target*(1:nCols)' target*vq(i,:)' ]';
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b = inv(A(:,:,i))*c;
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if nargin >= 3;
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b = feval(closed_quant_fn,b);
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end
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b_log(i,:) = b;
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diff(i,:) = target - (b(1)*vq(i,:) + b(2)*(1:nCols) + b(3));
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error(i) = diff(i,:) * diff(i,:)';
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%printf("f: %d i: %d mg: %f g: %f sl: %f error: %f\n", f, i, b(1), b(2), b(3), error(i));
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end
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[mn min_ind] = min(error);
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errors(f) = mn;
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idx(f) = min_ind(1);
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b = b_log(min_ind,:);
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% optional quantisation performed after error min loop
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% - note we recalc gain to match energy after quantisation
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if nargin == 4
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b = feval(open_quant_fn, b);
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b(3) = (sum(target) - sum(b(1)*vq(min_ind,:) + b(2)*(1:nCols)))/nCols;
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end
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printf("f: %d i: %d mg: %f sl: %f g: %f\n", f, idx(f), b(1), b(2), b(3));
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b_log2(f,:) = b;
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contrib(f,:) = b(1)*vq(min_ind,:) + b(2)*(1:nCols) + b(3);
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end
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endfunction
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