| [5] | 1 | function out = lessfx(par,x) |
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| 2 | %LESSFX simple dataset mappings |
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| 3 | % |
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| 4 | % PAR = LESSFX(TYPE,X) |
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| 5 | % |
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| 6 | % 'Train' or define a mapping of different types. |
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| 7 | % TYPE can be: |
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| 8 | % 1: (x - m_2).^2 - (x - m_1).^2 normal nearest means |
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| 9 | % 2: ((x-m_2).^2)/s1 - ((x-m_1).^2)/s2 weighted nearest means |
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| 10 | % 3: (x-|m|_2).^2 - (x-|m|_1).^2 nearest medians |
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| 11 | % 4: exp(-((x-M1).^2)./S1) - exp(-((x-M2).^2)./S2); |
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| 12 | % 5: " " with medians instead of % avergs |
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| 13 | % |
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| 14 | % New data is mapped using: |
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| 15 | % Y = LESSFX(PAR,X) |
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| 16 | % where X in the input dataset, and PAR is obtained as above... |
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| 17 | % |
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| 18 | % This is used in LESS.M |
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| 19 | |
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| 20 | |
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| 21 | if ~isstruct(par) % we train the parameters of the function |
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| 22 | out = []; |
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| 23 | out.type = par; |
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| 24 | switch out.type |
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| 25 | case 0 |
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| 26 | out.bla = []; |
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| 27 | case 1 % basic version |
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| 28 | out.u = +meancov(x); |
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| 29 | case 2 % mean-var |
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| 30 | [u,g] = meancov(x); |
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| 31 | out.u = +u; |
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| 32 | out.g(1,:) = diag(g(:,:,1)) + mean(diag(g(:,:,1))); |
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| 33 | out.g(2,:) = diag(g(:,:,2)) + mean(diag(g(:,:,2))); |
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| 34 | case 3 % median |
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| 35 | out.u(1,:) = med(+seldat(x,1)); |
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| 36 | out.u(2,:) = med(+seldat(x,2)); |
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| 37 | case 4 % mean-var |
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| 38 | [u,g] = meancov(x); |
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| 39 | out.u = +u; |
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| 40 | out.g(1,:) = diag(g(:,:,1)) + mean(diag(g(:,:,1))); |
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| 41 | out.g(2,:) = diag(g(:,:,2)) + mean(diag(g(:,:,2))); |
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| 42 | case 5 % median-MSD |
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| 43 | [u,g] = meancov(x); |
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| 44 | X1 = seldat(x,1); |
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| 45 | X2 = seldat(x,2); |
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| 46 | out.u(1,:) = med(+X1); |
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| 47 | out.u(2,:) = med(+X2); |
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| 48 | out.g(1,:) = medstd(+X1,out.u(1,:)) + mean(diag(g(:,:,1))); |
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| 49 | out.g(2,:) = medstd(+X2,out.u(2,:)) + mean(diag(g(:,:,2))); |
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| 50 | case 6 % median-MSD |
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| 51 | X1 = seldat(x,1); |
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| 52 | X2 = seldat(x,2); |
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| 53 | out.u(1,:) = med(+X1); |
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| 54 | out.u(2,:) = med(+X2); |
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| 55 | out.g(1,:) = medstd(+X1,out.u(1,:)); |
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| 56 | out.g(2,:) = medstd(+X2,out.u(2,:)); |
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| 57 | otherwise |
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| 58 | error('This function is not defined'); |
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| 59 | end |
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| 60 | else % we evaluate the function on new data: |
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| 61 | [m,k] = size(x); |
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| 62 | switch par.type |
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| 63 | case 0 |
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| 64 | out = +x; |
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| 65 | case 1 |
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| 66 | M1 = repmat(par.u(1,:),m,1); |
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| 67 | M2 = repmat(par.u(2,:),m,1); |
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| 68 | out = -(2*(+x).*(M2-M1) + M1.*M1 - M2.*M2); |
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| 69 | case 2 |
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| 70 | M1 = repmat(par.u(1,:),m,1); |
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| 71 | M2 = repmat(par.u(2,:),m,1); |
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| 72 | S1 = repmat(par.g(1,:),m,1); |
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| 73 | S2 = repmat(par.g(2,:),m,1); |
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| 74 | out = ((x-M2).^2)./S2 - ((x-M1).^2)./S1; |
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| 75 | case 3 |
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| 76 | M1 = repmat(par.u(1,:),m,1); |
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| 77 | M2 = repmat(par.u(2,:),m,1); |
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| 78 | out = -(2*(+x).*(M2-M1) + M1.*M1 - M2.*M2); |
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| 79 | case 4 |
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| 80 | M1 = repmat(par.u(1,:),m,1); |
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| 81 | M2 = repmat(par.u(2,:),m,1); |
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| 82 | S1 = repmat(par.g(1,:),m,1); |
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| 83 | S2 = repmat(par.g(2,:),m,1); |
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| 84 | out = exp(-((x-M1).^2)./S1) - exp(-((x-M2).^2)./S2); |
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| 85 | case 5 |
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| 86 | M1 = repmat(par.u(1,:),m,1); |
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| 87 | M2 = repmat(par.u(2,:),m,1); |
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| 88 | S1 = repmat(par.g(1,:),m,1); |
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| 89 | S2 = repmat(par.g(2,:),m,1); |
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| 90 | out = ((x-M2).^2)./S2 - ((x-M1).^2)./S1; |
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| 91 | case 6 |
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| 92 | M1 = repmat(par.u(1,:),m,1); |
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| 93 | M2 = repmat(par.u(2,:),m,1); |
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| 94 | S1 = repmat(par.g(1,:),m,1); |
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| 95 | S2 = repmat(par.g(2,:),m,1); |
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| 96 | %out = ((x-M2).^2)./S2 - ((x-M1).^2)./S1; |
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| 97 | out = (sigm(10-((x-M1).^2)./(1*S1)) - sigm(10-((x-M2).^2)./(1*S2))); |
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| 98 | otherwise |
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| 99 | error('This function is not defined'); |
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| 100 | end |
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| 101 | |
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| 102 | end |
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| 103 | |
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| 104 | return |
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