Description of auto

0001 function varargout = auto_classify(varargin)
0002 % AUTO_CLASSIFY M-file for auto_classify.fig
0003 %      AUTO_CLASSIFY, by itself, creates a new AUTO_CLASSIFY or raises the existing
0004 %      singleton*.
0005 %
0006 %      H = AUTO_CLASSIFY returns the handle to a new AUTO_CLASSIFY or the handle to
0007 %      the existing singleton*.
0008 %
0009 %      AUTO_CLASSIFY('CALLBACK',hObject,eventData,handles,...) calls the local
0010 %      function named CALLBACK in AUTO_CLASSIFY.M with the given input arguments.
0011 %
0012 %      AUTO_CLASSIFY('Property','Value',...) creates a new AUTO_CLASSIFY or raises the
0013 %      existing singleton*.  Starting from the left, property value pairs are
0014 %      applied to the GUI before auto_classify_OpeningFunction gets called.  An
0015 %      unrecognized property name or invalid value makes property application
0016 %      stop.  All inputs are passed to auto_classify_OpeningFcn via varargin.
0017 %
0018 %      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
0019 %      instance to run (singleton)".
0020 %
0021 % See also: GUIDE, GUIDATA, GUIHANDLES
0022 
0023 % Copyright 2002-2003 The MathWorks, Inc.
0024 
0025 % Edit the above text to modify the response to help auto_classify
0026 
0027 % Last Modified by GUIDE v2.5 21-Jun-2006 00:34:13
0028 
0029 % Begin initialization code - DO NOT EDIT
0030 gui_Singleton = 1;
0031 gui_State = struct('gui_Name',       mfilename, ...
0032                    'gui_Singleton',  gui_Singleton, ...
0033                    'gui_OpeningFcn', @auto_classify_OpeningFcn, ...
0034                    'gui_OutputFcn',  @auto_classify_OutputFcn, ...
0035                    'gui_LayoutFcn',  [] , ...
0036                    'gui_Callback',   []);
0037 if nargin && ischar(varargin{1})
0038     gui_State.gui_Callback = str2func(varargin{1});
0039 end
0040 
0041 if nargout
0042     [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
0043 else
0044     gui_mainfcn(gui_State, varargin{:});
0045 end
0046 % End initialization code - DO NOT EDIT
0047 
0048 
0049 % --- Executes just before auto_classify is made visible.
0050 function auto_classify_OpeningFcn(hObject, eventdata, handles, varargin)
0051 % This function has no output args, see OutputFcn.
0052 % hObject    handle to figure
0053 % eventdata  reserved - to be defined in a future version of MATLAB
0054 % handles    structure with handles and user data (see GUIDATA)
0055 % varargin   command line arguments to auto_classify (see VARARGIN)
0056 
0057 % input parameters
0058 
0059 if isempty(varargin)
0060     handles.matrix2classify = [];
0061     handles.ncepstral = 5;
0062 else
0063     handles.matrix2classify = varargin{1};
0064     handles.ncepstral = varargin{2};
0065 end
0066 
0067 handles.distancemeasure = 'sqEuclidean';
0068 
0069 handles.cluster_method = 'kmeans';
0070 
0071 set(handles.CepstralPopupMenu,'String',num2str([0:handles.ncepstral]'));
0072 set(handles.CepstralPopupMenu,'Value',handles.ncepstral); %handles.ncepstral +1 );
0073 % set(handles.CepstralPopupMenu,'Value',handles.ncepstral-1);
0074 
0075 % Choose default command line output for auto_classify
0076 % handles.output = hObject;
0077 
0078 % Update handles structure
0079 guidata(hObject, handles);
0080 
0081 % UIWAIT makes auto_classify wait for user response (see UIRESUME)
0082 % uiwait(handles.figure1);
0083 
0084 uiwait(handles.figure1);
0085     
0086 % --- Outputs from this function are returned to the command line.
0087 function varargout = auto_classify_OutputFcn(hObject, eventdata, handles) 
0088 % varargout  cell array for returning output args (see VARARGOUT);
0089 % hObject    handle to figure
0090 % eventdata  reserved - to be defined in a future version of MATLAB
0091 % handles    structure with handles and user data (see GUIDATA)
0092 
0093 % Get default command line output from handles structure
0094 varargout{1} = handles.output;
0095 
0096 close;
0097 
0098 function clusters = clusterdata(X,k,dmeasure)
0099 % A wrapper function for clustering
0100 
0101 try
0102     clusters = kmeans(X,k,'distance',dmeasure,'EmptyAction','singleton','replicates',30);
0103 catch
0104     clusters = k;
0105 end
0106 
0107 % --- Executes on button press in AutoClassifyButton.
0108 function AutoClassifyButton_Callback(hObject, eventdata, handles)
0109 % hObject    handle to AutoClassifyButton (see GCBO)
0110 % eventdata  reserved - to be defined in a future version of MATLAB
0111 % handles    structure with handles and user data (see GUIDATA)
0112 
0113 set(handles.AcceptButton,'Enable','off');
0114 guidata(gcbo,handles);
0115 
0116 cncepstral = get(handles.CepstralPopupMenu,'Value');
0117 
0118 matrix2classify = handles.matrix2classify;
0119 matrix2classify = madnormalize(matrix2classify,1); % normalize the columns
0120 matrix2classify = matrix2classify(:,1:cncepstral+1); % include the cepstral coefficients that you want
0121 
0122 if length(matrix2classify(1,:)) > 3
0123     matrix2classify = matrix2classify(:,[1,3:cncepstral+1]); % exclude the first cepestral coefficient
0124 end
0125 
0126 try
0127     segweight = str2num(get(handles.SegLenWeight,'String'));
0128 catch
0129     segweight = 1;
0130     set(handles.SegLenWeight,'String','1');
0131 end
0132 
0133 % Weight the segment lengths by square root of the weight
0134 matrix2classify(:,1) = matrix2classify(:,1)*sqrt(segweight);
0135  
0136 % Other weightings can be added here but the cepestral coefficients have a
0137 % natural exponential decline which acts a weighting
0138 
0139 %Handle transformations to segment length
0140 
0141 contents = get(handles.TransformPopupMenu,'String');
0142 value = get(handles.TransformPopupMenu,'Value');
0143 
0144 switch contents{value}
0145     case 'None'
0146         matrix2classify = matrix2classify;
0147     case 'Exclude'
0148         matrix2classify = matrix2classify(:,2:length(matrix2classify(1,:)));
0149     case 'Log'
0150         matrix2classify(:,1) = log(matrix2classify(:,1));
0151     case 'Exp'
0152         matrix2classify(:,1) = exp(matrix2classify(:,1));
0153 end
0154 
0155 if length(matrix2classify(1,:)) == 0 % matrix to classify has no information
0156     return
0157 end
0158 
0159 rangestate = get(handles.RangeSpecify,'Value');
0160 diagnosticstate = get(handles.DiagnosticCheckbox,'Value');
0161 
0162 if strcmp(handles.cluster_method,'hierarchical')
0163     handles.classification = cluster_hierarchical(matrix2classify);
0164     nclusters = length(unique(handles.classification)) - 1;
0165     set(handles.KClasses,'String',nclusters);
0166     
0167     if diagnosticstate
0168         pcaplot(matrix2classify,handles.classification);
0169     end
0170     
0171 else if strcmp(handles.cluster_method, 'kmeans')
0172 
0173 if not(rangestate)
0174 
0175     kclassstr = get(handles.KClasses,'String');
0176 
0177     if not(isempty(kclassstr))    
0178         try
0179             nclasses = str2num(kclassstr);
0180         catch
0181             return;
0182         end
0183     
0184         classification =  clusterdata(matrix2classify,nclasses,handles.distancemeasure);% this can be changed to a different clustering algorithm
0185     
0186         % clusterdata shows how a clustering algorithm can be hooked in where the number of
0187         % classes need to be assigned.
0188         
0189         if length(classification) == 1
0190             msgbox('Error in clustering. Try changing the number of target clusters or rerunning.');
0191         else
0192             if diagnosticstate
0193                 figure();
0194                 [silh h] = silhouette(matrix2classify,classification);
0195                 pcaplot(matrix2classify,classification);
0196             end
0197         handles.classification = classification;
0198         end
0199     else
0200         return;
0201     end
0202 else
0203     try
0204         minclust = str2num(get(handles.MinClusters,'String'));
0205     catch
0206         return;
0207     end
0208     try
0209         maxclust = str2num(get(handles.MaxClusters,'String'));
0210     catch
0211         return;
0212     end 
0213     
0214     resultsclust = cell((1+ maxclust)-minclust,1);
0215     
0216     hw = waitbar(0,'Clustering data set ');
0217     for i = 1:(1+maxclust)-minclust
0218         resultsclust{i} = clusterdata(matrix2classify,minclust + (i-1),handles.distancemeasure);
0219         if length(resultsclust{i}) == 1
0220             msgbox(['Clustering failed at ' num2str(resultsclust{i}) ' clusters. Try changing the range of clusters or rerunning.']);
0221             return;
0222         end
0223         waitbar(i/(1+maxclust-minclust));
0224     end
0225     close(hw);
0226     
0227     silhmean = zeros((1+ maxclust)-minclust,1);
0228     
0229     for i = 1:1+(maxclust)-minclust
0230         
0231         if diagnosticstate
0232             figure();
0233             [silh h] = silhouette(matrix2classify,resultsclust{i});
0234             pcaplot(matrix2classify,resultsclust{i});
0235         else
0236             silh = silhouette(matrix2classify,resultsclust{i});
0237         end
0238             
0239         silhmean(i) = mean(silh);
0240     end
0241     
0242     [x I] = max(silhmean);
0243     handles.classification = resultsclust{I(1)};
0244     
0245     set(handles.KClasses,'String',num2str(minclust + (I(1)-1)));
0246 end
0247 end
0248     end
0249 
0250 
0251 set(handles.AcceptButton,'Enable','on');
0252 
0253 guidata(gcbo,handles);
0254 
0255 % --- Executes on button press in CancelButton.
0256 function CancelButton_Callback(hObject, eventdata, handles)
0257 % hObject    handle to CancelButton (see GCBO)
0258 % eventdata  reserved - to be defined in a future version of MATLAB
0259 % handles    structure with handles and user data (see GUIDATA)
0260 
0261 handles.output = 0;
0262 guidata(hObject,handles);
0263 uiresume(handles.figure1);
0264 
0265 % --- Executes on button press in AcceptButton.
0266 function AcceptButton_Callback(hObject, eventdata, handles)
0267 % hObject    handle to AcceptButton (see GCBO)
0268 % eventdata  reserved - to be defined in a future version of MATLAB
0269 % handles    structure with handles and user data (see GUIDATA)
0270 
0271 handles.output = handles.classification;
0272 guidata(hObject,handles);
0273 uiresume(handles.figure1);
0274 
0275 
0276 function CepstralEdit_Callback(hObject, eventdata, handles)
0277 % hObject    handle to CepstralEdit (see GCBO)
0278 % eventdata  reserved - to be defined in a future version of MATLAB
0279 % handles    structure with handles and user data (see GUIDATA)
0280 
0281 % Hints: get(hObject,'String') returns contents of CepstralEdit as text
0282 %        str2double(get(hObject,'String')) returns contents of CepstralEdit as a double
0283 
0284 
0285 % --- Executes during object creation, after setting all properties.
0286 function CepstralEdit_CreateFcn(hObject, eventdata, handles)
0287 % hObject    handle to CepstralEdit (see GCBO)
0288 % eventdata  reserved - to be defined in a future version of MATLAB
0289 % handles    empty - handles not created until after all CreateFcns called
0290 
0291 % Hint: edit controls usually have a white background on Windows.
0292 %       See ISPC and COMPUTER.
0293 if ispc
0294     set(hObject,'BackgroundColor','white');
0295 else
0296     set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
0297 end
0298 
0299 
0300 
0301 function MinClusters_Callback(hObject, eventdata, handles)
0302 % hObject    handle to MinClusters (see GCBO)
0303 % eventdata  reserved - to be defined in a future version of MATLAB
0304 % handles    structure with handles and user data (see GUIDATA)
0305 
0306 % Hints: get(hObject,'String') returns contents of MinClusters as text
0307 %        str2double(get(hObject,'String')) returns contents of MinClusters as a double
0308 
0309 
0310 % --- Executes during object creation, after setting all properties.
0311 function MinClusters_CreateFcn(hObject, eventdata, handles)
0312 % hObject    handle to MinClusters (see GCBO)
0313 % eventdata  reserved - to be defined in a future version of MATLAB
0314 % handles    empty - handles not created until after all CreateFcns called
0315 
0316 % Hint: edit controls usually have a white background on Windows.
0317 %       See ISPC and COMPUTER.
0318 if ispc
0319     set(hObject,'BackgroundColor','white');
0320 else
0321     set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
0322 end
0323 
0324 
0325 
0326 function MaxClusters_Callback(hObject, eventdata, handles)
0327 % hObject    handle to MaxClusters (see GCBO)
0328 % eventdata  reserved - to be defined in a future version of MATLAB
0329 % handles    structure with handles and user data (see GUIDATA)
0330 
0331 % Hints: get(hObject,'String') returns contents of MaxClusters as text
0332 %        str2double(get(hObject,'String')) returns contents of MaxClusters as a double
0333 
0334 
0335 % --- Executes during object creation, after setting all properties.
0336 function MaxClusters_CreateFcn(hObject, eventdata, handles)
0337 % hObject    handle to MaxClusters (see GCBO)
0338 % eventdata  reserved - to be defined in a future version of MATLAB
0339 % handles    empty - handles not created until after all CreateFcns called
0340 
0341 % Hint: edit controls usually have a white background on Windows.
0342 %       See ISPC and COMPUTER.
0343 if ispc
0344     set(hObject,'BackgroundColor','white');
0345 else
0346     set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
0347 end
0348 
0349 
0350 % --- Executes on selection change in TransformPopupMenu.
0351 function TransformPopupMenu_Callback(hObject, eventdata, handles)
0352 % hObject    handle to TransformPopupMenu (see GCBO)
0353 % eventdata  reserved - to be defined in a future version of MATLAB
0354 % handles    structure with handles and user data (see GUIDATA)
0355 
0356 % Hints: contents = get(hObject,'String') returns TransformPopupMenu contents as cell array
0357 %        contents{get(hObject,'Value')} returns selected item from TransformPopupMenu
0358 
0359 % value = get(hObject,'Value');
0360 %
0361 % if value == 2
0362 %     handles.matrix2classify(:,1) = log(handles.matrix2classify(:,1));
0363 % else
0364 %     handles.matrix2classify(:,1) = exp(handles.matrix2classify(:,1));
0365 % end
0366 
0367 % --- Executes during object creation, after setting all properties.
0368 function TransformPopupMenu_CreateFcn(hObject, eventdata, handles)
0369 % hObject    handle to TransformPopupMenu (see GCBO)
0370 % eventdata  reserved - to be defined in a future version of MATLAB
0371 % handles    empty - handles not created until after all CreateFcns called
0372 
0373 % Hint: popupmenu controls usually have a white background on Windows.
0374 %       See ISPC and COMPUTER.
0375 if ispc
0376     set(hObject,'BackgroundColor','white');
0377 else
0378     set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
0379 end
0380 
0381 function KClasses_Callback(hObject, eventdata, handles)
0382 % hObject    handle to KClasses (see GCBO)
0383 % eventdata  reserved - to be defined in a future version of MATLAB
0384 % handles    structure with handles and user data (see GUIDATA)
0385 
0386 % Hints: get(hObject,'String') returns contents of KClasses as text
0387 %        str2double(get(hObject,'String')) returns contents of KClasses as a double
0388 
0389 
0390 % --- Executes during object creation, after setting all properties.
0391 function KClasses_CreateFcn(hObject, eventdata, handles)
0392 % hObject    handle to KClasses (see GCBO)
0393 % eventdata  reserved - to be defined in a future version of MATLAB
0394 % handles    empty - handles not created until after all CreateFcns called
0395 
0396 % Hint: edit controls usually have a white background on Windows.
0397 %       See ISPC and COMPUTER.
0398 if ispc
0399     set(hObject,'BackgroundColor','white');
0400 else
0401     set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
0402 end
0403 
0404 
0405 % --- Executes on selection change in CepstralPopupMenu.
0406 function CepstralPopupMenu_Callback(hObject, eventdata, handles)
0407 % hObject    handle to CepstralPopupMenu (see GCBO)
0408 % eventdata  reserved - to be defined in a future version of MATLAB
0409 % handles    structure with handles and user data (see GUIDATA)
0410 
0411 % Hints: contents = get(hObject,'String') returns CepstralPopupMenu contents as cell array
0412 %        contents{get(hObject,'Value')} returns selected item from CepstralPopupMenu
0413 
0414 
0415 % --- Executes during object creation, after setting all properties.
0416 function CepstralPopupMenu_CreateFcn(hObject, eventdata, handles)
0417 % hObject    handle to CepstralPopupMenu (see GCBO)
0418 % eventdata  reserved - to be defined in a future version of MATLAB
0419 % handles    empty - handles not created until after all CreateFcns called
0420 
0421 % Hint: popupmenu controls usually have a white background on Windows.
0422 %       See ISPC and COMPUTER.
0423 if ispc
0424     set(hObject,'BackgroundColor','white');
0425 else
0426     set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
0427 end
0428 
0429 
0430 
0431 % --- Executes on button press in RangeSpecify.
0432 function RangeSpecify_Callback(hObject, eventdata, handles)
0433 % hObject    handle to RangeSpecify (see GCBO)
0434 % eventdata  reserved - to be defined in a future version of MATLAB
0435 % handles    structure with handles and user data (see GUIDATA)
0436 
0437 % Hint: get(hObject,'Value') returns toggle state of RangeSpecify
0438 
0439 state = get(hObject,'Value');
0440 
0441 if state
0442     set(handles.KClasses,'Enable','off');
0443     set(handles.text3,'Visible','on');
0444     set(handles.MinClusters,'Visible','on');
0445     set(handles.text4,'Visible','on');
0446     set(handles.MaxClusters,'Visible','on');
0447 else
0448     set(handles.KClasses,'Enable','on');
0449     set(handles.text3,'Visible','off');
0450     set(handles.MinClusters,'Visible','off');
0451     set(handles.text4,'Visible','off');
0452     set(handles.MaxClusters,'Visible','off');
0453 end
0454 
0455 
0456 % --- Executes on selection change in DistancePopupMenu.
0457 function DistancePopupMenu_Callback(hObject, eventdata, handles)
0458 % hObject    handle to DistancePopupMenu (see GCBO)
0459 % eventdata  reserved - to be defined in a future version of MATLAB
0460 % handles    structure with handles and user data (see GUIDATA)
0461 
0462 % Hints: contents = get(hObject,'String') returns DistancePopupMenu contents as cell array
0463 %        contents{get(hObject,'Value')} returns selected item from DistancePopupMenu
0464 
0465 contents = get(hObject,'String');
0466 
0467 dstring = contents{get(hObject,'Value')};
0468 
0469 switch dstring
0470     case 'Squared Euclidean'
0471         handles.distancemeasure = 'sqEuclidean'
0472     case 'City Block (L1)'
0473         handles.distancemeasure = 'cityblock'
0474     case 'Cosine'
0475         handles.distancemeasure = 'cosine'
0476     case 'Correlation'
0477         handles.distancemeasure = 'correlation'
0478 end
0479 
0480 guidata(gcbo,handles);
0481 
0482 % --- Executes during object creation, after setting all properties.
0483 function DistancePopupMenu_CreateFcn(hObject, eventdata, handles)
0484 % hObject    handle to DistancePopupMenu (see GCBO)
0485 % eventdata  reserved - to be defined in a future version of MATLAB
0486 % handles    empty - handles not created until after all CreateFcns called
0487 
0488 % Hint: popupmenu controls usually have a white background on Windows.
0489 %       See ISPC and COMPUTER.
0490 if ispc
0491     set(hObject,'BackgroundColor','white');
0492 else
0493     set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
0494 end
0495 
0496 handles.distancemeasure = 'sqEuclidean';
0497 
0498 guidata(gcbo,handles);
0499 
0500 % --- Executes on button press in DiagnosticCheckbox.
0501 function DiagnosticCheckbox_Callback(hObject, eventdata, handles)
0502 % hObject    handle to DiagnosticCheckbox (see GCBO)
0503 % eventdata  reserved - to be defined in a future version of MATLAB
0504 % handles    structure with handles and user data (see GUIDATA)
0505 
0506 % Hint: get(hObject,'Value') returns toggle state of DiagnosticCheckbox
0507 
0508 function pcaplot(matrix2classify,classification)
0509 % Plots the first two principal components uses different shapes selected
0510 % randomly and different colors selected randomly to show classes.
0511 
0512 nclusters = length(unique(classification));
0513 
0514 if not(isempty(find(classification == 0)))
0515     nclusters = nclusters -1;
0516 end
0517 
0518 colors = rand(nclusters,3);
0519 mark = mod(1:nclusters,13)+1;
0520 
0521 [coef, score] = princomp(matrix2classify);
0522 
0523 figure();
0524 
0525 for i = 1:length(classification)
0526     h = line(score(i,1),score(i,2));
0527     markers = set(h,'Marker');
0528     if classification(i) == 0 % For segments which are not classified
0529         set(h,'Marker',markers{4});   
0530         set(h,'Color',[0 0 0]);
0531     else
0532         set(h,'Marker',markers{mark(classification(i))});   
0533         set(h,'Color',colors(classification(i),:));
0534     end
0535 end
0536 
0537 xlabel('PCA 1');
0538 ylabel('PCA 2');
0539 
0540 function classifymatrix = madnormalize(classifymatrix, cols2normalize)
0541   for i = cols2normalize
0542     classifymatrix(:,i) = (classifymatrix(:,i) - median(classifymatrix(:,i))) / mad(classifymatrix(:,i));
0543   end
0544   
0545   
0546 % This code is modified from http://phys.columbia.edu/~aylin/clustering
0547 
0548 function c=cluster_aylin(p1,p2);
0549 %enter the p1,p2 found from hneighbors.m and get the struct array c.
0550 %c(i).c will contain the indices of the objects that is in cluster i.
0551 %c(1).c will be the cluster that has the maximum number of objects.
0552 nn=size(p1,2);
0553 p=std(p1);g=p(p2);p=p';
0554 z=zeros(nn,1);zz=zeros(nn,1);
0555 n=zeros(1,nn);
0556 for i=1:nn
0557     n(i)=max(max(p1(:,p2(:,i))));
0558 end
0559 gg=[1:nn; max(g); n]';n=find(n<1);
0560 [q1,q1]=sort(gg(n,2)');
0561 g=gg(n(q1),1);
0562 t=cputime;
0563 j=1;m=1
0564     for i=1:length(n)
0565         ii=g(i);b=p2(:,ii);a=b;aa=a(find(gg(a,2)<1));
0566         if length([0; unique(z(aa))])<=2;a1=1;a2=0;
0567             while a1~=a2;a1=length(aa);
0568                 a=unique(p2(:,a));a=a(find(gg(a,2)<1));
0569                 a=unique(a(find(gg(a,2)<=mean(gg(aa,2))+std(gg(aa,2)))));
0570                 a=a(find(ismember(a,aa)==0));
0571                 if ~isempty(a);aa=[aa;a];
0572                     jj=aa(find(z(aa)));
0573                     if ~isempty(jj);;
0574                          u=unique(z(jj));
0575                          if length(u)==1;
0576                             zz(aa(find(~z(aa))))=m;z(aa)=u;m=m+1;
0577                          end
0578                          break;
0579                     end
0580                 end;a2=length(aa);
0581             end;a=aa;
0582             jj=a(find(z(a)));
0583             if isempty(jj);
0584                 z(a)=j;j=j+1;
0585                 zz(a)=m;zz(b)=m-.1;zz(ii)=m-.2;m=m+1;
0586             end
0587         end
0588     end
0589     u=unique(z)
0590     u=u(find(u));v=length(u);vv=floor(1/v);if vv;vv=' is';else vv='s      are';end
0591     fprintf([int2str(v) ' cluster' vv ' found in ' num2str(cputime-t) 'sec\n\n'])
0592     q0=[];for i=1:v;qq=find(z==u(i));q0=[q0 length(qq)];end;
0593     [q1,q2]=sort(q0);%q2=q2(find(100*q1/nn>1));v=length(q2);
0594     c=[];for i=1:v;qq=find(z==u(q2(i)));[j,j]=sort(zz(qq));c(v-i+1).c=qq(j);end;
0595     if isempty(c);fprintf('\tNo cluster was found. \n \tscale the data (step size must be 1)\n');end 
0596     
0597     
0598     function [p1,p2]=hneighbors(e);
0599     % this function finds the neighbors of each object in 'e' within a unit hypercube
0600     % and returns the sorted object distances to q.q1 and their identities to q.q2 , 'e' is a
0601     % matrix where i th row and j th column represents the j th component of the i th object.
0602     s='find(';i='abs(e(:,%d)-e(i,%d))<1&';
0603     % assuming that the data is given scaled and the characteristic step size is 1, variable s
0604     % keeps a script to find the objects that lie within a unit hypercube around the i th object.
0605     for j=1:size(e,2);
0606         s=[s sprintf(i,j,j)];
0607     end;s([end end+1])=');';
0608      % runs the script s for each of the objects and stores the sorted distances
0609      % from the i th object in q(i).q1 and their indentities in q(i).q2
0610     nn=size(e,1);m=ceil(nn^(1/4));p1=ones(m,nn);p2=kron(ones(m,1),1:nn);
0611     for i=1:nn;
0612         j=eval(s);
0613         [q,qq]=sort(sqrt(sum((e(j,:)-kron(ones(length(j),1),e(i,:)))'.^2)));q=q(find(q<1));mm=length(q);
0614         qq=j(qq(1:mm))';mn=min([m mm]);
0615         p1(1:mn,i)=q(1:mn)';
0616         p2(1:mn,i)=qq(1:mn)';
0617     end
0618     
0619     
0620 function classification = cluster_hierarchical(matrix2classify)
0621     [p1,p2] = hneighbors(matrix2classify);
0622     c = cluster_aylin(p1,p2);
0623     
0624     nclusters = size(c,2); 
0625     
0626     total_classified = 0
0627     
0628     nsegments = size(matrix2classify,1);
0629             
0630     classification = zeros(nsegments,1);
0631     
0632     for i = 1:nclusters
0633         for j = 1:length(c(i).c)
0634             classification(c(i).c(j)) = i;
0635         end
0636     end
0637 ;
0638 
0639 
0640 % --- Executes on button press in HierarchicalRadioButton.
0641 function HierarchicalRadioButton_Callback(hObject, eventdata, handles)
0642 % hObject    handle to HierarchicalRadioButton (see GCBO)
0643 % eventdata  reserved - to be defined in a future version of MATLAB
0644 % handles    structure with handles and user data (see GUIDATA)
0645 
0646 % Hint: get(hObject,'Value') returns toggle state of HierarchicalRadioButton
0647 
0648 selected = get(hObject,'Value');
0649 
0650 if selected
0651     handles.cluster_method = 'hierarchical';
0652 end
0653 
0654 guidata(gcbo,handles);
0655 
0656 
0657 % --- Executes on button press in KmeansRadioButton.
0658 function KmeansRadioButton_Callback(hObject, eventdata, handles)
0659 % hObject    handle to KmeansRadioButton (see GCBO)
0660 % eventdata  reserved - to be defined in a future version of MATLAB
0661 % handles    structure with handles and user data (see GUIDATA)
0662 
0663 % Hint: get(hObject,'Value') returns toggle state of KmeansRadioButton
0664 
0665 selected = get(hObject,'Value');
0666 
0667 if selected
0668     handles.cluster_method = 'kmeans';
0669 end
0670 
0671 guidata(gcbo,handles);
0672 
0673 
0674 
0675 
0676 function SegLenWeight_Callback(hObject, eventdata, handles)
0677 % hObject    handle to SegLenWeight (see GCBO)
0678 % eventdata  reserved - to be defined in a future version of MATLAB
0679 % handles    structure with handles and user data (see GUIDATA)
0680 
0681 % Hints: get(hObject,'String') returns contents of SegLenWeight as text
0682 %        str2double(get(hObject,'String')) returns contents of SegLenWeight as a double
0683 
0684 
0685 % --- Executes during object creation, after setting all properties.
0686 function SegLenWeight_CreateFcn(hObject, eventdata, handles)
0687 % hObject    handle to SegLenWeight (see GCBO)
0688 % eventdata  reserved - to be defined in a future version of MATLAB
0689 % handles    empty - handles not created until after all CreateFcns called
0690 
0691 % Hint: edit controls usually have a white background on Windows.
0692 %       See ISPC and COMPUTER.
0693 if ispc
0694     set(hObject,'BackgroundColor','white');
0695 else
0696     set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
0697 end
0698 
0699
auto_classify

PURPOSE

SYNOPSIS

DESCRIPTION

CROSS-REFERENCE INFORMATION

SUBFUNCTIONS

SOURCE CODE
