diff toolboxes/MIRtoolbox1.3.2/MIRToolbox/mircluster.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mircluster.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,238 @@
+function [a d] = mircluster(a,varargin)
+%   c = mircluster(a,f) clusters the segments in the audio sequence(s) 
+%       contained in the audio object a, along the analytic feature(s) 
+%       f, using the k-means strategy. Multiple analytic features have to
+%       be grouped into one array of cells.
+%       Example:
+%           sg = mirsegment(a);
+%           mircluster(sg, mirmfcc(sg))
+%           mircluster(sg, {mirmfcc(sg), mircentroid(sg)})
+%   c = mircluster(d) clusters the frame-decomposed data d into groups
+%       using K-means clustering.
+%       Example:
+%           cc = mirmfcc(a,'Frame');
+%           mircluster(cc)
+%   Optional argument:
+%       mircluster(...,n) indicates the maximal number of clusters.
+%           Default value: n = 2.
+%       mircluster(...,'Runs',r) indicates the maximal number of runs.
+%           Default value: r = 5.
+%
+%   Requires SOM Toolbox (included in the MIRtoolbox distribution).
+
+
+        nruns.key = 'Runs';
+        nruns.type = 'Integer';
+        nruns.default = 5;
+    option.nruns = nruns;
+     
+        nclust.position = 2;
+        nclust.type = 'Integer';
+        nclust.default = 2;
+    option.nclust = nclust;
+
+specif.option = option;
+
+specif.nochunk = 1;
+    
+d = a;
+if isa(a,'mirdesign')
+    if not(get(a,'Eval'))
+        % During bottom-up construction of the general design
+
+        [unused option] = miroptions(@mircluster,a,specif,varargin);
+        type = get(a,'Type');
+        a = mirdesign(@mircluster,a,option,{},struct,type);  
+        a = set(a,'NoChunk',1);
+    else
+        % During top-down evaluation initiation
+        
+        e = evaleach(a);
+        if iscell(e)
+            e = e{1};
+        end
+        a = mircluster(e,varargin{:});
+    end
+else
+    if not(isa(a,'mirdata'))
+        mirerror('mircluster','The input should be either frame- or segment-decomposed.');
+    end
+   
+    if isempty(varargin) || (not(isa(varargin{1},'mirdata') || ...
+                             (iscell(varargin{1}) && ...
+                              isa(varargin{1}{1},'mirdata'))))
+        % mircluster version for frame-decomposed data:
+        % frames are clustered into groups using K-means clustering.
+        [unused option] = miroptions(@mircluster,a,specif,varargin);
+        da = get(a,'Data');
+        lva = length(da); % Number of audio files in the audio object.
+        c = cell(1,lva);
+        display('Clustering frames...');
+        if mirwaitbar
+            handle = waitbar(0,'Clustering frames...');
+        else
+            handle = 0;
+        end
+        for j = 1:lva           % For each audio file,...
+            va = [];            % Data transmitted to the kmeans_cluster function.
+            v = da{j};
+            if iscell(v)
+                v = uncell(v,-Inf); %v{1};
+            end
+            if size(v,4)>1
+                v(end+1:2*end,:,:,1) = v(:,:,:,2);
+                v(:,:,:,2) = [];
+            end
+            % Standardization
+            %stv = std(v,0,2);
+            %stv(find(stv == 0)) = 1;
+            va(end+1:end+size(v,1),:,:) = v;%...
+                %(v - repmat(mean(v,2),[1 size(v,2) ])) ...
+                %./ repmat(stv,[1 size(v,2) ]);
+            if isa(a,'mirscalar')
+                m = get(a,'Mode');
+                if not(isempty(m))
+                    m = m{j};
+                    val = [];
+                    for l = 1:nseg
+                        vl = m{l};
+                        if iscell(vl)
+                            vl = vl{1};
+                        end
+                        val(:,l) = vl;
+                    end
+                    stv = std(val,0,2);
+                    stv(find(stv == 0)) = 1;
+                    va(end+1:end+size(val,1),:) = ...
+                        (val - repmat(mean(val,2),[1 size(val,2) ])) ...
+                            ./ repmat(stv,[1 size(val,2) ]);
+                end
+            end
+            if size(va,3)>1
+                mel = 1;
+                va = reshape(va,size(va,2),size(va,3))';
+            else
+                mel = 0;
+            end
+            [cc, p, err, ind] = kmeans_clusters(va',option.nclust,option.nruns);
+            [minind select] = min(ind);
+            c{j}.centr = cc{select}';
+            c{j}.index = p{select};
+            c{j}.weight = zeros(1,size(cc{select},1));
+            c{j}.covar = zeros(size(cc{select}'));
+            ii = 1;
+            for i = 1:size(c{j}.centr,2)
+                clus = va(:,c{j}.index == ii);
+                if isempty(clus)
+                    higher = find(c{j}.index > ii);
+                    c{j}.index(higher) = c{j}.index(higher)-1;
+                    c{j}.centr(:,ii) = [];
+                    c{j}.weight(ii) = [];
+                    c{j}.covar(:,ii) = [];
+                else
+                    c{j}.weight(ii) = size(clus,2)/size(va,2);
+                    if c{j}.weight(ii) == 0
+                        pause
+                    end
+                    c{j}.covar(:,ii) = mean((clus'-ones(1,size(clus,1))*c{j}.centr(:,ii)).^2);
+                    ii = ii+1;
+                end
+            end
+            if handle
+                waitbar(j/lva,handle);
+            end
+        end
+        if handle
+           delete(handle)
+        end
+        a = set(a,'Clusters',c);
+    else
+        % mircluster version for segmented audio:
+        % segments are clustered into groups using K-means clustering.
+        da = varargin{1};
+        varargin(1) = [];
+        [unused option] = miroptions(@mircluster,a,specif,varargin);
+        display('Clustering segments...');
+        if isa(da,'mirdata') || (iscell(da) && isa(da{1},'mirdata'))
+            if not(iscell(da))
+                da = {da};
+            end
+            vala = get(a,'Data');    % Data contained in the audio object a.
+            lva = length(vala); % Number of audio files in the audio object.
+            clus = cell(1,lva);
+            for j = 1:lva           % For each audio file,...
+                va = [];            % Data transmitted to the kmeans_cluster function.
+                nseg = length(vala{j}); % Number of segments in the audio file.
+                for i = 1:length(da)    % For each analytic feature,...
+                    v = get(da{i},'Data');
+                    v = v{j};
+                    if iscell(v)
+                        v = uncell(v,-Inf); %v{1};
+                    end
+                    val = [];           
+                    if size(v,4)>1
+                        v(end+1:2*end,:,:,1) = v(:,:,:,2);
+                        v(:,:,:,2) = [];
+                    end
+
+                    % Standardization
+                    stv = std(v,0,2);
+                    stv(find(stv == 0)) = 1;
+                    va(end+1:end+size(v,1),:) = ...
+                        (v - repmat(mean(v,2),[1 size(v,2) ])) ...
+                        ./ repmat(stv,[1 size(v,2) ]);
+                    if isa(da{i},'mirscalar')
+                        m = get(da{i},'Mode');
+                        if not(isempty(m))
+                            m = m{j};
+                            val = [];
+                            for l = 1:nseg
+                                vl = m{l};
+                                if iscell(vl)
+                                    vl = vl{1};
+                                end
+                                val(:,l) = vl;
+                            end
+                            stv = std(val,0,2);
+                            stv(find(stv == 0)) = 1;
+                            va(end+1:end+size(val,1),:) = ...
+                                (val - repmat(mean(val,2),[1 size(val,2) ])) ...
+                                    ./ repmat(stv,[1 size(val,2) ]);
+                        end
+                    end
+
+                end
+                [cc, p, err, ind] = kmeans_clusters(va',min(option.nclust,nseg),option.nruns);
+                clus{j} = p{end};
+            end
+            a = set(a,'Clusters',clus);
+            t = get(a,'Time'); 
+            fp = get(a,'FramePos'); 
+            for j = 1:lva           % For each audio file,...
+                aj = vala{j};
+                tj = t{j};
+                fpj = fp{j};
+                clj = clus{j};
+                k = 2;
+                while k <= length(aj)
+                    if clj(k) == clj(k-1)
+                        aj{k-1} = [aj{k-1};aj{k}];
+                        aj(k) = [];
+                        tj{k-1} = [tj{k-1};tj{k}];
+                        tj(k) = [];
+                        fpj{k-1} = [fpj{k-1}(1);fpj{k}(2)];
+                        fpj(k) = [];
+                        clj(k) = [];
+                        k = k-1;
+                    end
+                    k = k+1;
+                end
+                vala{j} = aj;
+                t{j} = tj;
+                fp{j} = fpj;
+                cl{j} = clj;
+            end
+            a = set(a,'Data',vala,'Time',t,'FramePos',fp,'Clusters',cl);
+        end
+    end
+end
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