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annotate core/magnatagatune/DistMeasureMahal.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 % ---
wolffd@0 2 % The DistMeasureMahal class states a wrapper for
wolffd@0 3 % special Mahalanobis similarity distance, and is compatible with the
wolffd@0 4 % DistMeasure class
wolffd@0 5 % ---
wolffd@0 6 classdef DistMeasureMahal < handle
wolffd@0 7
wolffd@0 8 properties (SetAccess = private)
wolffd@0 9
wolffd@0 10 mahalW;
wolffd@0 11
wolffd@0 12 featX;
wolffd@0 13
wolffd@0 14 ids;
wolffd@0 15 ret_ids;
wolffd@0 16
wolffd@0 17 % special delta parameters
wolffd@0 18 deltafun;
wolffd@0 19 deltafun_params;
wolffd@0 20 end
wolffd@0 21
wolffd@0 22 methods
wolffd@0 23
wolffd@0 24 % ---
wolffd@0 25 % constructor
wolffd@0 26 % ---
wolffd@0 27 function m = DistMeasureMahal(clips, mahalW, featX, deltafun, deltafun_params)
wolffd@0 28
wolffd@0 29 if nargin < 4 && (size(featX, 2) ~= numel(clips) || size(featX, 1) ~= length(mahalW))
wolffd@0 30 error 'wrong input format'
wolffd@0 31 end
wolffd@0 32
wolffd@0 33 % fill index and generate matrix;
wolffd@0 34 m.ids = [clips.id];
wolffd@0 35
wolffd@0 36 % reverse index
wolffd@0 37 m.ret_ids = sparse(numel(m.ids),1);
wolffd@0 38 m.ret_ids(m.ids) = 1:numel(m.ids);
wolffd@0 39
wolffd@0 40 % ---
wolffd@0 41 % save mahal Matrix and lazy-copy features
wolffd@0 42 % ---
wolffd@0 43 if size(mahalW, 1) ~= size(mahalW, 2)
wolffd@0 44
wolffd@0 45 m.mahalW = diag(mahalW);
wolffd@0 46 else
wolffd@0 47
wolffd@0 48 m.mahalW = mahalW;
wolffd@0 49 end
wolffd@0 50
wolffd@0 51 m.featX = featX;
wolffd@0 52
wolffd@0 53 % ---
wolffd@0 54 % special deltas
wolffd@0 55 % ---
wolffd@0 56 if nargin > 3
wolffd@0 57 m.deltafun = deltafun;
wolffd@0 58 m.deltafun_params = deltafun_params;
wolffd@0 59 else
wolffd@0 60 m.deltafun = [];
wolffd@0 61 end
wolffd@0 62 end
wolffd@0 63
wolffd@0 64
wolffd@0 65 % ---
wolffd@0 66 % this compability function returns the
wolffd@0 67 % mahalanobis similarity of two clip indices
wolffd@0 68 % ---
wolffd@0 69 function out = mat(m, idxa, idxb)
wolffd@0 70
wolffd@0 71 if nargin == 1
wolffd@0 72 idxa = 1:numel(m.ids);
wolffd@0 73 idxb = 1:numel(m.ids);
wolffd@0 74 end
wolffd@0 75
wolffd@0 76 % ---
wolffd@0 77 % account for different delta functions
wolffd@0 78 % ---
wolffd@0 79 if ~isempty(m.deltafun)
wolffd@0 80 out = zeros(numel(idxa),numel(idxb));
wolffd@0 81 for i=1:numel(idxa)
wolffd@0 82 for j=1:numel(idxb)
wolffd@0 83
wolffd@0 84 % calculate new distance
wolffd@0 85 tmp = m.deltafun(m.featX(:,idxa), m.featX(:,idxb),m.deltafun_params{:});
wolffd@0 86 out(i,j) = tmp' * m.mahalW * tmp;
wolffd@0 87 end
wolffd@0 88 end
wolffd@0 89 else
wolffd@0 90 % Standard Mahaldist is much faster to calculate
wolffd@0 91 out = sqdist( m.featX(:,idxa), m.featX(:,idxb), m.mahalW);
wolffd@0 92 end
wolffd@0 93 end
wolffd@0 94
wolffd@0 95 % ---
wolffd@0 96 % returns the distance for the two input clips
wolffd@0 97 % ---
wolffd@0 98 function out = distance(m, clipa, clipb)
wolffd@0 99 posa = m.get_clip_pos(clipa);
wolffd@0 100 posb = m.get_clip_pos(clipb);
wolffd@0 101
wolffd@0 102 out = m.mat(posa, posb);
wolffd@0 103 end
wolffd@0 104
wolffd@0 105 % ---
wolffd@0 106 % returns a list of n (default = 10) clips most
wolffd@0 107 % similar to the input
wolffd@0 108 % ---
wolffd@0 109 function [clips, dist] = get_nearest(m, clip, n)
wolffd@0 110 % list = get_nearest(m, clip, n)
wolffd@0 111 %
wolffd@0 112 % returns a list of n (default = 10) clips most
wolffd@0 113 % similar to the input
wolffd@0 114
wolffd@0 115 % default number of results
wolffd@0 116 if nargin == 2
wolffd@0 117
wolffd@0 118 n = 10;
wolffd@0 119 end
wolffd@0 120
wolffd@0 121 % return all clips in case n = 0
wolffd@0 122 if n == 0; n = numel(m.ids); end
wolffd@0 123
wolffd@0 124 % get clip positions
wolffd@0 125 pos = m.get_clip_pos(clip);
wolffd@0 126
wolffd@0 127 % sort according to distance
wolffd@0 128 [sc, idx] = sort( m.mat(pos, 1:numel(m.ids)), 'ascend');
wolffd@0 129
wolffd@0 130 % we only output relevant data
wolffd@0 131 idx = idx(sc < inf);
wolffd@0 132
wolffd@0 133 if numel(idx) > 0
wolffd@0 134 % create clips form best ids
wolffd@0 135 clips = MTTClip( m.ids( idx(1:min(n, end))));
wolffd@0 136 dist = m.mat(pos, idx(1:min(n, end)));
wolffd@0 137
wolffd@0 138 else
wolffd@0 139 clips = [];
wolffd@0 140 dist = [];
wolffd@0 141 end
wolffd@0 142 end
wolffd@0 143
wolffd@0 144
wolffd@0 145
wolffd@0 146 function [clips, dist] = present_nearest(m, clip, n)
wolffd@0 147 % plays and shows the n best hits for a given clip
wolffd@0 148
wolffd@0 149 % default number of results
wolffd@0 150 if nargin == 2
wolffd@0 151
wolffd@0 152 n = 3;
wolffd@0 153 end
wolffd@0 154
wolffd@0 155 % get best list
wolffd@0 156 [clips, dist] = get_nearest(m, clip, n);
wolffd@0 157
wolffd@0 158 clip.audio_features_basicsm.visualise();
wolffd@0 159 for i = 1:numel(clips)
wolffd@0 160 fprintf('\n\n\n- Rank %d, distance: %1.4f \n\n',i, dist(i));
wolffd@0 161
wolffd@0 162 clips(i).audio_features_basicsm.visualise();
wolffd@0 163 h = gcf();
wolffd@0 164 t = clips(i).play(20);
wolffd@0 165 pause(t);
wolffd@0 166 close(h);
wolffd@0 167 end
wolffd@0 168 end
wolffd@0 169
wolffd@0 170 function a = visualise(m)
wolffd@0 171
wolffd@0 172 figure;
wolffd@0 173
wolffd@0 174 % plot data
wolffd@0 175
wolffd@0 176 imagesc(m.mat);
wolffd@0 177
wolffd@0 178 a = gca;
wolffd@0 179 set(a,'YTick',[1:numel(m.ids)], 'YTickLabel',m.ids);
wolffd@0 180 set(a,'XTick',[1:numel(m.ids)], 'XTickLabel', m.ids);
wolffd@0 181
wolffd@0 182 axis xy;
wolffd@0 183 colormap(hot);
wolffd@0 184 end
wolffd@0 185
wolffd@0 186 % end methods
wolffd@0 187 end
wolffd@0 188
wolffd@0 189 % ---
wolffd@0 190 % private methods
wolffd@0 191 % ---
wolffd@0 192 methods(Access = private)
wolffd@0 193
wolffd@0 194 function out = get_clip_pos(m, clip)
wolffd@0 195 % returns position in mat for given clip
wolffd@0 196
wolffd@0 197 out = m.ret_ids(clip.id);
wolffd@0 198 end
wolffd@0 199
wolffd@0 200 end
wolffd@0 201
wolffd@0 202 end