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1 function d=som_eucdist2(Data, Proto)
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2
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3 %SOM_EUCDIST2 Calculates matrix of squared euclidean distances between set of vectors or map, data struct
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4 %
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5 % d=som_eucdist2(D, P)
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6 %
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7 % d=som_eucdist(sMap, sData);
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8 % d=som_eucdist(sData, sMap);
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9 % d=som_eucdist(sMap1, sMap2);
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10 % d=som_eucdist(datamatrix1, datamatrix2);
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11 %
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12 % Input and output arguments ([]'s are optional):
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13 % D (matrix) size Nxd
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14 % (struct) map or data struct
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15 % P (matrix) size Pxd
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16 % (struct) map or data struct
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17 % d (matrix) distance matrix of size NxP
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18 %
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19 % IMPORTANT
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20 %
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21 % * Calculates _squared_ euclidean distances
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22 % * Observe that the mask in the map struct is not taken into account while
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23 % calculating the euclidean distance
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24 %
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25 % See also KNN, PDIST.
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26
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27 % Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg
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28 % Copyright (c) by Johan Himberg
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29 % http://www.cis.hut.fi/projects/somtoolbox/
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30
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31 % Version 2.0beta Johan 291000
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32
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33 %% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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34
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35 if isstruct(Data);
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36 if isfield(Data,'type') & ischar(Data.type),
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37 ;
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38 else
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39 error('Invalid map/data struct?');
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40 end
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41 switch Data.type
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42 case 'som_map'
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43 data=Data.codebook;
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44 case 'som_data'
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45 data=Data.data;
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46 end
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47 else
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48 % is already a matrix
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49 data=Data;
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50 end
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51
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52 % Take prototype vectors from prototype struct
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53
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54 if isstruct(Proto),
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55
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56 if isfield(Proto,'type') & ischar(Proto.type),
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57 ;
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58 else
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59 error('Invalid map/data struct?');
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60 end
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61 switch Proto.type
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62 case 'som_map'
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63 proto=Proto.codebook;
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64 case 'som_data'
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65 proto=Proto.data;
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66 end
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67 else
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68 % is already a matrix
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69 proto=Proto;
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70 end
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71
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72 % Check that inputs are matrices
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73 if ~vis_valuetype(proto,{'nxm'}) | ~vis_valuetype(data,{'nxm'}),
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74 error('Prototype or data input not valid.')
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75 end
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76
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77 % Record data&proto sizes and check their dims
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78 [N_data dim_data]=size(data);
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79 [N_proto dim_proto]=size(proto);
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80 if dim_proto ~= dim_data,
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81 error('Data and prototype vector dimension does not match.');
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82 end
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83
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84 % Calculate euclidean distances between classifiees and prototypes
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85 d=distance(data,proto);
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86
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87 %%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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88 function d=distance(X,Y);
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89
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90 % Euclidean distance matrix between row vectors in X and Y
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91
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92 U=~isnan(Y); Y(~U)=0;
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93 V=~isnan(X); X(~V)=0;
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94 d=abs(X.^2*U'+V*Y'.^2-2*X*Y');
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