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annotate toolboxes/MIRtoolbox1.3.2/somtoolbox/som_neighf.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function H = som_neighf(sMap,radius,neigh,ntype)
wolffd@0 2
wolffd@0 3 %SOM_NEIGHF Return neighborhood function values.
wolffd@0 4 %
wolffd@0 5 % H = som_neighf(sMap,[radius],[neigh],[ntype]);
wolffd@0 6 %
wolffd@0 7 % Input and output arguments ([]'s are optional):
wolffd@0 8 % sMap (struct) map or topology struct
wolffd@0 9 % [radius] (scalar) neighborhood radius (by default, the last used value
wolffd@0 10 % in sMap.trainhist is used, or 1 if that is unavailable)
wolffd@0 11 % [neigh] (string) neighborhood function type (by default, ..., or
wolffd@0 12 % 'gaussian' if that is unavailable)
wolffd@0 13 % [ntype] (string) 'normal' (default), 'probability' or 'mirror'
wolffd@0 14 %
wolffd@0 15 % H (matrix) [munits x munits] neighborhood function values from
wolffd@0 16 % each map unit to each other map unit
wolffd@0 17 %
wolffd@0 18 % For more help, try 'type som_batchtrain' or check out online documentation.
wolffd@0 19 % See also SOM_MAKE, SOM_SEQTRAIN, SOM_TRAIN_STRUCT.
wolffd@0 20
wolffd@0 21 % Copyright (c) 1997-2000 by the SOM toolbox programming team.
wolffd@0 22 % http://www.cis.hut.fi/projects/somtoolbox/
wolffd@0 23
wolffd@0 24 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 25 %% Check arguments
wolffd@0 26
wolffd@0 27 % defaults
wolffd@0 28 rdefault = 1;
wolffd@0 29 ndefault = 'gaussian';
wolffd@0 30 tdefault = 'normal';
wolffd@0 31
wolffd@0 32 % map
wolffd@0 33 switch sMap.type,
wolffd@0 34 case 'som_map',
wolffd@0 35 sTopol = sMap.topol;
wolffd@0 36 sTrain = sMap.trainhist(end);
wolffd@0 37 if isempty(sTrain.radius_fin) | isnan(sTrain.radius_fin),
wolffd@0 38 rdefault = 1;
wolffd@0 39 else
wolffd@0 40 rdefault = sTrain.radius_fin;
wolffd@0 41 end
wolffd@0 42 if ~isempty(sTrain.neigh) & ~isnan(sTrain.neigh),
wolffd@0 43 ndefault = sTrain.neigh;
wolffd@0 44 end
wolffd@0 45 case 'som_topol', sTopol = sMap;
wolffd@0 46 end
wolffd@0 47 munits = prod(sTopol.msize);
wolffd@0 48
wolffd@0 49 % other parameters
wolffd@0 50 if nargin<2 | isempty(radius), radius = rdefault; end
wolffd@0 51 if nargin<3 | isempty(neigh), neigh = ndefault; end
wolffd@0 52 if nargin<4 | isempty(ntype), ntype = tdefault; end
wolffd@0 53
wolffd@0 54 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 55 %% initialize
wolffd@0 56
wolffd@0 57 % basic neighborhood
wolffd@0 58 Ud = som_unit_dists(sTopol);
wolffd@0 59 Ud = Ud.^2;
wolffd@0 60 radius = radius.^2;
wolffd@0 61 if radius==0, radius = eps; end % zero neighborhood radius may cause div-by-zero error
wolffd@0 62
wolffd@0 63 switch ntype,
wolffd@0 64 case 'normal',
wolffd@0 65 H = neighf(neigh,Ud,radius);
wolffd@0 66 case 'probability',
wolffd@0 67 H = neighf(neigh,Ud,radius);
wolffd@0 68 for i=1:munits, H(i,:) = H(i,:)/sum(H(i,:)); end
wolffd@0 69 case 'mirror', % only works for 2-dim grid!!!
wolffd@0 70 H = zeros(munits,munits);
wolffd@0 71 Co = som_unit_coords(sTopol);
wolffd@0 72 for i=-1:1,
wolffd@0 73 for j=-1:1,
wolffd@0 74 Ud = gridmirrordist(Co,i,j);
wolffd@0 75 H = H + neighf(neigh,Ud,radius);
wolffd@0 76 end
wolffd@0 77 end
wolffd@0 78 end
wolffd@0 79
wolffd@0 80 return;
wolffd@0 81
wolffd@0 82 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 83 %% subfunctions
wolffd@0 84
wolffd@0 85 function H = neighf(neigh,Ud,radius)
wolffd@0 86
wolffd@0 87 switch neigh,
wolffd@0 88 case 'bubble', H = (Ud<=radius);
wolffd@0 89 case 'gaussian', H = exp(-Ud/(2*radius));
wolffd@0 90 case 'cutgauss', H = exp(-Ud/(2*radius)) .* (Ud<=radius);
wolffd@0 91 case 'ep', H = (1-Ud/radius) .* (Ud<=radius);
wolffd@0 92 end
wolffd@0 93 return;
wolffd@0 94
wolffd@0 95 function Ud = gridmirrordist(Co,mirrorx,mirrory)
wolffd@0 96
wolffd@0 97 [munits,mdim] = size(Co);
wolffd@0 98 if mdim>2, error('Mirrored neighborhood only works for 2-dim map grids.'); end
wolffd@0 99
wolffd@0 100 % width and height of the grid
wolffd@0 101 dx = max(Co(:,1))-min(Co(:,1));
wolffd@0 102 dy = max(Co(:,2))-min(Co(:,2));
wolffd@0 103
wolffd@0 104 % calculate distance from each location to each other location
wolffd@0 105 Ud = zeros(munits,munits);
wolffd@0 106 for i=1:munits,
wolffd@0 107 inds = [i:munits];
wolffd@0 108 coi = Co(i,:); % take hexagonal shift into account
wolffd@0 109 coi(1) = coi(1)*(1-2*(mirrorx~=0)) + 2*dx*(mirrorx==1); % +mirrorx * step
wolffd@0 110 coi(2) = coi(2)*(1-2*(mirrory~=0)) + 2*dy*(mirrory==1); % +mirrory * step
wolffd@0 111 Dco = (Co(inds,:) - coi(ones(munits-i+1,1),:))';
wolffd@0 112 Ud(i,inds) = sqrt(sum(Dco.^2));
wolffd@0 113 Ud(inds,i) = Ud(i,inds)';
wolffd@0 114 end
wolffd@0 115 return;
wolffd@0 116
wolffd@0 117