Changeset 79 for distools

Timestamp:

07/23/13 12:22:18 (11 years ago)

Author:

bduin

Message:

 

Location:

distools

Files:

• aucdlpc.m (modified) (1 diff)
• augpsem.m (modified) (2 diffs)
• checkeucl.m (modified) (1 diff)
• checksubeucl.m (modified) (1 diff)
• corrdistm.m (modified) (1 diff)
• cosdistm.m (modified) (1 diff)
• discc.m (modified) (4 diffs)
• disnorm.m (modified) (3 diffs)
• dispc.m (modified) (3 diffs)
• disrc.m (modified) (3 diffs)
• dissimt.m (modified) (1 diff)
• dlpc.m (modified) (3 diffs)
• dmstspm.m (modified) (2 diffs)
• dprocrustdm.m (modified) (1 diff)
• drsscc.m (modified) (1 diff)
• expdistm.m (modified) (1 diff)
• fastmapd.m (modified) (3 diffs)
• genballd.m (modified) (1 diff)
• genddat.m (modified) (1 diff)
• genrep.m (modified) (2 diffs)
• ikfd.m (modified) (1 diff)
• ikpca.m (modified) (4 diffs)
• ispe_em.m (modified) (1 diff)
• ispsem.m (modified) (1 diff)
• jacsimdistm.m (modified) (1 diff)
• kcenterm.m (modified) (2 diffs)
• kem.m (modified) (4 diffs)
• kfd.m (modified) (1 diff)
• knndc.m (modified) (3 diffs)
• kpca.m (modified) (4 diffs)
• ksvc.m (modified) (3 diffs)
• ksvc_nu.m (modified) (3 diffs)
• mclassdc.m (modified) (4 diffs)
• parzenddc.m (modified) (2 diffs)
• pe_affine.m (modified) (1 diff)
• pe_distm.m (modified) (2 diffs)
• pe_em.m (modified) (4 diffs)
• pe_kernelm.m (modified) (3 diffs)
• pe_knnc.m (modified) (2 diffs)
• pe_libsvc.m (modified) (2 diffs)
• pe_nmc.m (modified) (1 diff)
• pe_parzenc.m (modified) (2 diffs)
• private/auclpm.m (modified) (2 diffs)
• protselfd.m (modified) (1 diff)
• proxxm.m (modified) (2 diffs)
• prtbunke.m (modified) (1 diff)
• psem.m (modified) (3 diffs)
• pspca.m (modified) (3 diffs)
• setsig.m (modified) (2 diffs)
• simdistm.m (modified) (1 diff)
• sphem.m (modified) (4 diffs)
• tqdc.m (modified) (2 diffs)

distools/aucdlpc.m

@@ -43 +43 @@
 
 if nargin < 1 | isempty(d)
-    w = mapping(mfilename,C);
+    w = prmapping(mfilename,C);
     w = setname(w,'AUCDLPC');
     return

distools/augpsem.m

@@ -67 +67 @@
 if nargin < 2 | isempty(alf), alf = inf; end
 if nargin < 1 | isempty(d)
-   W = mapping(mfilename,alf,pzero);
+   W = prmapping(mfilename,alf,pzero);
    W = setname(W,'Augmented PE embedding');
    return
@@ -196 +196 @@
 %%%%% ---------------------------------------------- %%%%%
 
-W = mapping(mfilename,'trained',{Wp,Wn,dme,pzero,tsig,sig,tol},[],m,sum(sig));
+W = prmapping(mfilename,'trained',{Wp,Wn,dme,pzero,tsig,sig,tol},[],m,sum(sig));
 W = setname(W,'Augmented PE embedding');
 return

distools/checkeucl.m

@@ -49 +49 @@
         end
 
-        D = dataset(D,1);  % we are not interested in labels here.
+        D = prdataset(D,1);  % we are not interested in labels here.
         D = setfeatlab(D,ones(m,1));
         

distools/checksubeucl.m

@@ -20 +20 @@
 discheck(d)
 m = size(d,1);
-d = dataset(+d,1);
+d = prdataset(+d,1);
 d = setfeatlab(d,ones(m,1));
 

distools/corrdistm.m

@@ -10 +10 @@
 %
 % OUTPUT
-%   D   NxM dissimilarity matrix or dataset; D in [0,1]
+%   D   NxM dissimilarity matrix or prdataset; D in [0,1]
 %
 % DESCRIPTION

distools/cosdistm.m

@@ -10 +10 @@
 %
 % OUTPUT
-%   D   NxM Dissimilarity matrix or dataset; D in [0,1]
+%   D   NxM Dissimilarity matrix or prdataset; D in [0,1]
 %
 % DESCRIPTION

distools/discc.m

@@ -34 +34 @@
 if nargin < 1 | isempty(d)
         if isstr(par)   % untrained classifier, par = 'loo' or 'all'
-                W = mapping('discc',par);
+                W = prmapping('discc',par);
         else                    % fixed classifier, par = knn
-                W = mapping('discc','fixed',par);
+                W = prmapping('discc','fixed',par);
         end
         return
 end
 
-[nlab,lablist,m,k,c,p,featlist] = dataset(d);
+[nlab,lablist,m,k,c,p,featlist] = prdataset(d);
 [clab,classlist] = renumlab(featlist);
 c = size(classlist,1);
@@ -78 +78 @@
                         K = K+k;
                 end
-                w = +(dataset(s,2-(t==r))*loglc);
-                W = mapping('discc',w,lablist,k,c,1);
+                w = +(prdataset(s,2-(t==r))*loglc);
+                W = prmapping('discc',w,lablist,k,c,1);
         elseif strcmp(par,'all')
                 n = m*k;
@@ -92 +92 @@
                         K = K+k;
                 end
-                w = +(dataset(s,2-(t==r))*loglc);
-                W = mapping('discc',w,lablist,k,c,1);
+                w = +(prdataset(s,2-(t==r))*loglc);
+                W = prmapping('discc',w,lablist,k,c,1);
         else
                 error(['Unknown option ''' par ''''])
@@ -102 +102 @@
         % testing for given mapping
                 
-        if isa(par,'mapping')
+        if isa(par,'prmapping')
                 w = +par;
                 W = d*w(1) + w(2);

distools/disnorm.m

@@ -35 +35 @@
 
 if nargin == 0 | isempty(D)
-  V = mapping(mfilename,{opt});
+  V = prmapping(mfilename,{opt});
   V = setname(V,'Disnorm');
   return
@@ -41 +41 @@
 
 if ~isdataset(D)
-        D = dataset(D,1);
+        D = prdataset(D,1);
         D = setfeatlab(D,getlabels(D));
 end
@@ -60 +60 @@
                 D = D./m;
         end     
-    V = mapping(mfilename,'trained',{m},[],size(D,2),size(D,2));
+    V = prmapping(mfilename,'trained',{m},[],size(D,2),size(D,2));
         return;
 end

distools/dispc.m

@@ -15 +15 @@
 
 if nargin < 1 | isempty(a) % empty call (untrained classifier)
-        w = mapping('dispc');
+        w = prmapping('dispc');
                 
 elseif nargin == 1    % training
-        [nlab,lablist,m,k,c,p] = dataset(a);
+        [nlab,lablist,m,k,c,p] = prdataset(a);
         [nn,nf,fl] = renumlab(lablist,getfeat(a));
         if c < 2
@@ -37 +37 @@
 
                 b = {+a(find(nlab==nf(j)),j) +a(find(nlab~=nf(j)),j)};
-                w = [w; mapping('distrc',b,fl(nf(j),:),1,1,1)];
+                w = [w; prmapping('distrc',b,fl(nf(j),:),1,1,1)];
         end
 
@@ -54 +54 @@
         w2(R2(L2)-n2) = (n2+2-(L2-[1:m]'))/(n2+2);
 %       disp([w1;w2])
-        w = invsig(dataset((w1+w2)/2,getlab(a),getfeat(v)));
+        w = invsig(prdataset((w1+w2)/2,getlab(a),getfeat(v)));
 else
         error('Illegal call')

distools/disrc.m

@@ -29 +29 @@
 
 if nargin < 1 | isempty(a) % empty call (untrained classifier)
-        w = mapping('disrc');
+        w = prmapping('disrc');
                 
 elseif nargin == 1    % training
-        [nlab,lablist,m,k,c,p] = dataset(a);
+        [nlab,lablist,m,k,c,p] = prdataset(a);
         [nn,nf,fl] = renumlab(lablist,getfeat(a));
         if c < 2
@@ -43 +43 @@
         for j = 1:k
                 b = {+a(find(nlab==nf(j)),j) +a(find(nlab~=nf(j)),j)};
-                w = [w; mapping('disrc',b,fl(nf(j),:),1,1,1)];
+                w = [w; prmapping('disrc',b,fl(nf(j),:),1,1,1)];
         end
 
@@ -60 +60 @@
         w2(R2(L2)-n2) = (n2+2-(L2-[1:m]'))/(n2+2);
 %       disp([w1;w2])
-        w = invsig(dataset((w1+w2)/2,getlab(a),getfeat(v)));
+        w = invsig(prdataset((w1+w2)/2,getlab(a),getfeat(v)));
 else
         error('Illegal call')

distools/dissimt.m

@@ -58 +58 @@
 end
 if nargin == 0 | isempty(D)
-  W = mapping(mfilename,'fixed',{type,p});
+  W = prmapping(mfilename,'fixed',{type,p});
   W = setname(W,'Fixed proximity transformation');
   return

distools/dlpc.m

@@ -25 +25 @@
 %   W1  LP-Classifier in the complete dissimilarity space
 %   W2  LP-Classifier in a reduced dissimilarity space
-%   W3  Object selection mapping; the indices of support objects are in +W3.
+%   W3  Object selection prmapping; the indices of support objects are in +W3.
 %
 % DEFAULTS
@@ -125 +125 @@
 if nargin < 2 | isempty(is_w0), is_w0 = 1; end
 if nargin < 1 | isempty(d)
-  W1 = mapping(mfilename,{is_w0,type,par,usematlab});
+  W1 = prmapping(mfilename,{is_w0,type,par,usematlab});
   W1 = setname(W1,'DLPC');
   W2 = [];
@@ -176 +176 @@
 % If more than 2 classes, do one against all others.
 if C > 2,
-% W1 = mclassc(d,mapping(mfilename,{is_w0,type,par,usematlab}));
+% W1 = mclassc(d,prmapping(mfilename,{is_w0,type,par,usematlab}));
 
   W1 = [];

distools/dmstspm.m

@@ -43 +43 @@
 end
 if nargin < 1 | isempty(D)
-   W = mapping(mfilename,{K,p});
+   W = prmapping(mfilename,{K,p});
    W = setname(W,'dmstspm');
    return
@@ -140 +140 @@
 
     [Lmst,d] = kdmst(D,K,p);
-    W = mapping(mfilename,'trained',{Lmst,d,K},[],n,n);
+    W = prmapping(mfilename,'trained',{Lmst,d,K},[],n,n);
     W = setname(W,'dmstspm');
   end

distools/dprocrustdm.m

@@ -54 +54 @@
 %
 % SEE ALSO
-% MDS, PCA, PSEM, KPCA, MAPPINGS, DATASETS
+% MDS, PCAM, PSEM, KPCA, MAPPINGS, DATASETS
 %
 % REFERENCE

distools/drsscc.m

@@ -44 +44 @@
 end
 if nargin < 1 | isempty(D)
-  W = mapping('DRSSCC',{v,N,M,crule});
+  W = prmapping('DRSSCC',{v,N,M,crule});
   return
 end

distools/expdistm.m

@@ -64 +64 @@
 end
 
-isda = isa(A,'dataset');
-isdb = isa(B,'dataset');
+isda = isa(A,'prdataset');
+isdb = isa(B,'prdataset');
 a = +A;
 b = +B;

distools/fastmapd.m

@@ -40 +40 @@
 end
 if nargin < 1 | isempty(D)
-   W = mapping(mfilename,K); 
+   W = prmapping(mfilename,K); 
    W = setname(W,'Fastmapd');
    return
@@ -66 +66 @@
       end
                 end             
-    W = dataset(Y,nlab);
+    W = prdataset(Y,nlab);
     return
   end
@@ -133 +133 @@
 end
 
-W = mapping(mfilename,'trained',{P,dd,X,KK},[],n,KK);
+W = prmapping(mfilename,'trained',{P,dd,X,KK},[],n,KK);
 W = setname(W,'Fastmapd');
 return

distools/genballd.m

@@ -45 +45 @@
 
 labs = genlab(n);
-d = dataset(d,labs);
+d = prdataset(d,labs);
 d = setfeatlab(d,labs);
 d = setprior(d,0);

distools/genddat.m

@@ -100 +100 @@
 end
 
-[ja,jb] = gendat(dataset([1:n]',nlab),m);
+[ja,jb] = gendat(prdataset([1:n]',nlab),m);
 ja = +ja; 
 jb = +jb;

distools/genrep.m

@@ -86 +86 @@
     L = [R; setdiff(L,R)];
   end
-  DR = dataset(D(L,R),labels(L,:),'featlab',labels(R,:));
+  DR = prdataset(D(L,R),labels(L,:),'featlab',labels(R,:));
 else
   if strcmp(strl,'exclude')
@@ -96 +96 @@
     ;
   end
-  DR = dataset(D(L,R),labels(L,:),'featlab',labels(R,:));
-  DT = dataset(D(T,R),labels(T,:),'featlab',labels(R,:));
+  DR = prdataset(D(L,R),labels(L,:),'featlab',labels(R,:));
+  DT = prdataset(D(T,R),labels(T,:),'featlab',labels(R,:));
 end
 return

distools/ikfd.m

@@ -43 +43 @@
 end
 if nargin < 1 | isempty(K),
-  W = mapping(mfilename,{alf,isb0});
+  W = prmapping(mfilename,{alf,isb0});
   W = setname(W,'IKFD');
   return

distools/ikpca.m

@@ -44 +44 @@
 %
 % SEE ALSO
-% MAPPINGS, DATASETS, PCA, PSEM
+% MAPPINGS, DATASETS, PCAM, PSEM
 %
 % REFERENCE
@@ -71 +71 @@
 end
 if nargin < 1 | isempty(K),
-  W = mapping(mfilename,alf,prec);
+  W = prmapping(mfilename,alf,prec);
   W = setname(W,'IKPCA');
   return
@@ -117 +117 @@
   L       = L(J);                  % Eigenvalues
 
-  W = mapping(mfilename,'trained',{QL,pars{2},L,sig,pars{5}},[],m,length(J));
+  W = prmapping(mfilename,'trained',{QL,pars{2},L,sig,pars{5}},[],m,length(J));
   W = setname(W,'IKPCA');
   return
@@ -173 +173 @@
 
 % Determine the mapping
-W = mapping(mfilename,'trained',{QL,Kme,L,sig,prec},[],m,sum(sig));
+W = prmapping(mfilename,'trained',{QL,Kme,L,sig,prec},[],m,sum(sig));
 W = setname(W,'IKPCA');
 return

distools/ispe_em.m

@@ -22 +22 @@
         prtrace(mfilename);
         
-        if isa(w,'mapping') & strcmp(w.mapping_file,'pe_em')
+        if isa(w,'prmapping') & strcmp(w.mapping_file,'pe_em')
                 n = 1;
         else

distools/ispsem.m

@@ -22 +22 @@
         prtrace(mfilename);
         
-        if isa(w,'mapping') & strcmp(w.mapping_file,'psem')
+        if isa(w,'prmapping') & strcmp(w.mapping_file,'psem')
                 n = 1;
         else

distools/jacsimdistm.m

@@ -10 +10 @@
 %
 % OUTPUT
-%   D   NxM Dissimilarity matrix or dataset; D in [0,1]
+%   D   NxM Dissimilarity matrix or prdataset; D in [0,1]
 %
 % DESCRIPTION

distools/kcenterm.m

@@ -39 +39 @@
 
 if nargin < 1 | isempty(K)
-   V = mapping(mfilename);
+   V = prmapping(mfilename);
    V = setname(V,'kcenterm');
    return
@@ -101 +101 @@
 end
 
-V = mapping(mfilename,'trained',{v,Kwm},[],m,m);
+V = prmapping(mfilename,'trained',{v,Kwm},[],m,m);
 V = setname(V,'kcenterm');
 return

distools/kem.m

@@ -47 +47 @@
 %
 % SEE ALSO
-% MAPPINGS, DATASETS, PE_EM, AUGPSEM, PCA, PE_PCA, PE_SPEC, GETSIG, SETSIG
+% MAPPINGS, DATASETS, PE_EM, AUGPSEM, PCAM, PE_PCA, PE_SPEC, GETSIG, SETSIG
 %
 % LITERATURE
@@ -73 +73 @@
 
 if nargin < 1 | isempty(d),
-  W = mapping(mfilename,{alf,pzero,prec});
+  W = prmapping(mfilename,{alf,pzero,prec});
   W = setname(W,'PE embedding');
   return
@@ -146 +146 @@
   data.eval = L(J);          % Eigenvalues
  
-  W = mapping(mfilename,'trained',data,[],m,length(J));
+  W = prmapping(mfilename,'trained',data,[],m,length(J));
   W = setname(W,'PE embedding');
   return
@@ -204 +204 @@
 % Determine the mapping depending on pzero
 if pzero == 0,
-  W = mapping(mfilename,'trained',data,[],m,sum(sig));
+  W = prmapping(mfilename,'trained',data,[],m,sum(sig));
 else
   data.mdis = +d(:,pzero)';
-  W = mapping(mfilename,'trained',data,[],m,sum(sig));
+  W = prmapping(mfilename,'trained',data,[],m,sum(sig));
 end
 W = setname(W,'PE embedding');

distools/kfd.m

@@ -43 +43 @@
 end
 if nargin < 1 | isempty(K),
-  W = mapping(mfilename,{alf,isb0});
+  W = prmapping(mfilename,{alf,isb0});
   W = setname(W,'KFD');
   return

distools/knndc.m

@@ -59 +59 @@
 % empty call, to handle d*knnd, or d*knnd([],par)                       
 if nargin < 1 | isempty(d)
-        W = mapping(mfilename,'untrained',knn,par);
+        W = prmapping(mfilename,'untrained',knn,par);
         W = setname(W,'KNND');
         return
@@ -70 +70 @@
 p        = getprior(d);
 
-%[nlab,lablist,m,k,c,p,featlist] = dataset(d);
+%[nlab,lablist,m,k,c,p,featlist] = prdataset(d);
 [clab,classlist] = renumlab(featlist);
 [cl,nc] = renumlab(classlist,lablist);
@@ -131 +131 @@
         end
         
-        W = mapping(mfilename,'trained',{knn,JJ},lablist,k,c);
+        W = prmapping(mfilename,'trained',{knn,JJ},lablist,k,c);
         W = setname(W,'KNNDC');
         

distools/kpca.m

@@ -31 +31 @@
 %
 % SEE ALSO
-% MAPPINGS, DATASETS, PCA, PSEM
+% MAPPINGS, DATASETS, PCAM, PSEM
 %
 % REFERENCE
@@ -48 +48 @@
 end
 if nargin < 1 | isempty(K),
-  W = mapping(mfilename,alf);
+  W = prmapping(mfilename,alf);
   W = setname(W,'Kernel PCA');
   return
@@ -100 +100 @@
   L = L(J);                 % Eigenvalues
 
-  W = mapping(mfilename,'trained',{Q,pars{2},L},[],m,length(J));
+  W = prmapping(mfilename,'trained',{Q,pars{2},L},[],m,length(J));
   W = setname(W,'KPCA');
   return
@@ -154 +154 @@
 
 % Determine the mapping
-W = mapping(mfilename,'trained',{Q,Kme,L},[],m,length(J));
+W = prmapping(mfilename,'trained',{Q,Kme,L},[],m,length(J));
 W = setname(W,'KPCA');
 return

distools/ksvc.m

@@ -95 +95 @@
 end
 if nargin < 1 | isempty(K),
-  W = mapping(mfilename,{C,kc,r});
+  W = prmapping(mfilename,{C,kc,r});
   W = setname(W,'Kernel Support Vector Classifier (KSVC)');
   return;
@@ -115 +115 @@
 
 % TRAIN THE CLASSIFIER
-if ~isa(C,'mapping')
+if ~isa(C,'prmapping')
   islabtype(K,'crisp');
   isvaldset(K,1,2);      % Expect at least 1 object per class and 2 classes
@@ -167 +167 @@
 
     % Store the results
-    W = mapping(mfilename,'trained',{me,J,T,v,reg},getlablist(K),k,2);
+    W = prmapping(mfilename,'trained',{me,J,T,v,reg},getlablist(K),k,2);
     % W = cnormc(W,K);
     W = setname(W,'Kernel Support Vector Classifier (KSVC)');

distools/ksvc_nu.m

@@ -113 +113 @@
 end
 if nargin < 1 | isempty(K),
-  W = mapping(mfilename,{nu,kc,r});
+  W = prmapping(mfilename,{nu,kc,r});
   W = setname(W,'Kernel Support Vector Classifier (KSVC-NU)');
   return;
@@ -134 +134 @@
 
 % TRAIN THE CLASSIFIER
-if ~isa(nu,'mapping')
+if ~isa(nu,'prmapping')
   islabtype(K,'crisp');
   isvaldset(K,1,2);      % Expect at least 1 object per class and 2 classes
@@ -187 +187 @@
 
     % Store the results
-    W = mapping(mfilename,'trained',{me,J,T,v},getlablist(K),k,2);
+    W = prmapping(mfilename,'trained',{me,J,T,v},getlablist(K),k,2);
     %W = cnormc(W,a);
     W = setname(W,'Kernel Support Vector Classifier (KSVC-NU)');

distools/mclassdc.m

@@ -50 +50 @@
 end
 if nargin < 1 | isempty(D)
-        w = mapping(mfilename,{classf,mode});
+        w = prmapping(mfilename,{classf,mode});
         return
 end
@@ -65 +65 @@
 varout = {};
 if c == 2
-        varargout = map(D,classf);
+        varargout = prmap(D,classf);
         return
 end
@@ -82 +82 @@
                           Dtargets = gettargets(D);
                           targets  = [Dtargets(:,i) 1-Dtargets(:,i)]; 
-                          DD                     = dataset(+D,mlab,targets,'lablist',[1 2]');
+                          DD                     = prdataset(+D,mlab,targets,'lablist',[1 2]');
                   end
                         if nargout(classf.mapping_file) > 1
-                                [v,varo] = map(DD,classf);
+                                [v,varo] = prmap(DD,classf);
                                 varout   = [varout {varo}];
                         else
-                                v = map(DD,classf);
+                                v = prmap(DD,classf);
                         end
                         w = [w,setlabels(v(:,1),lablist(i,:))];
@@ -114 +114 @@
                   I1 = setdiff([1:c],z);
                         if nargout(classf.mapping_file) > 1
-                                [v,varo] = map(DD,classf);
+                                [v,varo] = prmap(DD,classf);
                                 varout   = [varout {varo}];
                         else
-                                v = map(DD,classf);
+                                v = prmap(DD,classf);
                         end
                         w = [w,setlabels(v(:,1),lab)];

distools/parzenddc.m

@@ -29 +29 @@
   % No input arguments: return an untrained mapping.
   if nargin == 0 | isempty(d)
-    W = mapping(mfilename,h); 
+    W = prmapping(mfilename,h); 
     W = setname(W,'Parzen DisRep Classifier');
     return; 
@@ -146 +146 @@
   par.weights = ones(m,1);
     
-  W = mapping('parzend_map','trained',par,getlablist(d),k,c);
+  W = prmapping('parzend_map','trained',par,getlablist(d),k,c);
   W = setname(W,'Parzen DisRep Classifier');
   W = setcost(W,d);

distools/pe_affine.m

@@ -18 +18 @@
         par.map = arg1;
         par.sig = arg2;
-        argout = mapping(mfilename,'trained',par,getlabels(arg1),n1,n2);
+        argout = prmapping(mfilename,'trained',par,getlabels(arg1),n1,n2);
 else
         isdataset(arg1);

distools/pe_distm.m

@@ -32 +32 @@
 %
 % SEE ALSO
-% DATASET, SETSIG, DISTM
+% PRDATASET, SETSIG, DISTM
 
 % Copyright: Elzbieta Pekalska, ela.pekalska@googlemail.com
@@ -47 +47 @@
   sig = getsig(A);
 
-  if ~isdataset(B), B = dataset(B,1); end
+  if ~isdataset(B), B = prdataset(B,1); end
 
   a    = +A;

distools/pe_em.m

@@ -43 +43 @@
 %
 % SEE ALSO
-% MAPPINGS, DATASETS, AUGPSEM, PCA, GETSIG, SETSIG
+% MAPPINGS, DATASETS, AUGPSEM, PCAM, GETSIG, SETSIG
 %
 % LITERATURE
@@ -70 +70 @@
 
 if nargin < 1 | isempty(d),
-  W = mapping(mfilename,{alf,pzero,prec});
+  W = prmapping(mfilename,{alf,pzero,prec});
   W = setname(W,'PE embedding');
   return
@@ -143 +143 @@
   data.eval = L(J);          % Eigenvalues
  
-  W = mapping(mfilename,'trained',data,[],m,length(J));
+  W = prmapping(mfilename,'trained',data,[],m,length(J));
   W = setname(W,'PE embedding');
   return
@@ -200 +200 @@
 % Determine the mapping depending on pzero
 if pzero == 0,
-  W = mapping(mfilename,'trained',data,[],m,sum(sig));
+  W = prmapping(mfilename,'trained',data,[],m,sum(sig));
 else
   data.mdis = +d(:,pzero)';
-  W = mapping(mfilename,'trained',data,[],m,sum(sig));
+  W = prmapping(mfilename,'trained',data,[],m,sum(sig));
 end
 W = setname(W,'PE embedding');

distools/pe_kernelm.m

@@ -53 +53 @@
         if (nargin < 1) | (isempty(a) & nargin == 1)  
                 % Definition: pe kernel mapping.
-                w = mapping(mfilename,{b});
+                w = prmapping(mfilename,{b});
                 w = setname(w,mapname);
   elseif isempty(a)
-    w = mapping(mfilename,'trained',b,getlab(b),size(b,2),size(b,1));
+    w = prmapping(mfilename,'trained',b,getlab(b),size(b,2),size(b,1));
                 w = setname(w,mapname);
   elseif isdataset(a) & ~isempty(a)
     if isempty(b)   % store a as rep set, 'training'
-      w = mapping(mfilename,'trained',a,getlab(a),size(a,2),size(a,1));
+      w = prmapping(mfilename,'trained',a,getlab(a),size(a,2),size(a,1));
                   w = setname(w,mapname);
     elseif isdataset(b); % compute kernel between a and b
@@ -66 +66 @@
     elseif ismapping(b)
       if isuntrained(b) % nothing stored yet, do it now, a is rep set
-        w = mapping(mfilename,'trained',a,getlab(a),size(a,2),size(a,1));
+        w = prmapping(mfilename,'trained',a,getlab(a),size(a,2),size(a,1));
                     w = setname(w,mapname);
       else % we have already a rep set: compute kernel matrix
@@ -77 +77 @@
     
   else  % may be double ???
-    a = dataset(a);
+    a = prdataset(a);
     w = feval(mfilename,a,b);
     

distools/pe_knnc.m

@@ -31 +31 @@
 
         if nargin == 0 | isempty(a)
-                w = mapping(mfilename,'untrained',{k});
+                w = prmapping(mfilename,'untrained',{k});
                 w = setname(w,'PE K-NN Classifier');
                 
@@ -45 +45 @@
         e = testkd(pe_distm(a),k,'loo');
                         end
-                        w = mapping(mfilename,'trained',{a,k},getlablist(a),size(a,2),getsize(a,3));
+                        w = prmapping(mfilename,'trained',{a,k},getlablist(a),size(a,2),getsize(a,3));
                 end
                 

distools/pe_libsvc.m

@@ -42 +42 @@
 
         if nargin == 0 | isempty(a)
-                w = mapping(mfilename,'untrained',{c});
+                w = prmapping(mfilename,'untrained',{c});
                 w = setname(w,'PE LIBSVC');
                 
@@ -53 +53 @@
                         ktrain = pe_kernelm(a,a)
                         v = libsvc(ktrain,0);
-                        w = mapping(mfilename,'trained',{v,a},getlablist(a),size(a,2),getsize(a,3));
+                        w = prmapping(mfilename,'trained',{v,a},getlablist(a),size(a,2),getsize(a,3));
                 end
                 

distools/pe_nmc.m

@@ -24 +24 @@
 
   if nargin == 0 | isempty(a)
-    w = mapping(mfilename,'untrained');
+    w = prmapping(mfilename,'untrained');
     w = setname(w,'PE Nearest Mean');
     

distools/pe_parzenc.m

@@ -31 +31 @@
 
   if nargin == 0 | isempty(a)
-    w = mapping(mfilename,'untrained',{h});
+    w = prmapping(mfilename,'untrained',{h});
     w = setname(w,'PE Parzen Classifier');
     
@@ -46 +46 @@
         e = testpd(sqrt(pe_distm(a)),h,'loo');
       end
-      w = mapping(mfilename,'trained',{a,h},getlablist(a),size(a,2),getsize(a,3));
+      w = prmapping(mfilename,'trained',{a,h},getlablist(a),size(a,2),getsize(a,3));
     end
     

distools/private/auclpm.m

@@ -55 +55 @@
                 error('Please check your input parameters: C should be a double.');
         end
-        w = mapping(mfilename,{C,rtype,par,unitnorm,usematlab});
+        w = prmapping(mfilename,{C,rtype,par,unitnorm,usematlab});
         w = setname(w,defauclpmname(C,rtype,par,unitnorm));
         return
@@ -204 +204 @@
         W.rel = rel;
         W.C = C;
-        w = mapping(mfilename,'trained',W,tlab,dim,1);
+        w = prmapping(mfilename,'trained',W,tlab,dim,1);
         w = setname(w,defauclpmname(C,rtype,par,unitnorm));
         

distools/protselfd.m

@@ -67 +67 @@
 
   if nargin < 1 || isempty(D)  % allow for D*protselfd([],pars)
-    R = mapping(mfilename,'untrained',{ksel,type});
+    R = prmapping(mfilename,'untrained',{ksel,type});
     R = setname(R,'Forward Prototype Sel');
     return

distools/proxxm.m

@@ -80 +80 @@
 % No data, return an UNTRAINED mapping
 if (nargin < 1) | (isempty(A))
-  W = mapping(mfilename,{type,s,weights});
+  W = prmapping(mfilename,{type,s,weights});
   W = setname(W,'Proximity mapping');
   return;
@@ -104 +104 @@
   [m,k] = size(A);
   if isdataset(A)
-    W = mapping(mfilename,'trained',{A,type,s,weights},getlab(A),getfeatsize(A),getobjsize(A));
+    W = prmapping(mfilename,'trained',{A,type,s,weights},getlab(A),getfeatsize(A),getobjsize(A));
   else
-    W = mapping(mfilename,'trained',{A,type,s,weights},[],k,m);
+    W = prmapping(mfilename,'trained',{A,type,s,weights},[],k,m);
   end
   W = setname(W,'Proximity mapping');

distools/prtbunke.m

@@ -34 +34 @@
       end
       if ~isempty(lab),
-        D = dataset(D,lab,'featlab',lab);
+        D = prdataset(D,lab,'featlab',lab);
         C = classsizes(D);
         % Should it be class frequencies or equal priors?

distools/psem.m

@@ -79 +79 @@
 end
 if nargin < 1 | isempty(d),
-  W = mapping(mfilename,{alf,pzero,prec});
+  W = prmapping(mfilename,{alf,pzero,prec});
   W = setname(W,'PE embedding');
   return
@@ -133 +133 @@
   L = L(J);          % Eigenvalues
 
-  W = mapping(mfilename,'trained',{Q, pars{2},pars{3},L,pars{5},pars{6}},[],m,length(J));
+  W = prmapping(mfilename,'trained',{Q, pars{2},pars{3},L,pars{5},pars{6}},[],m,length(J));
   W = setname(W,'PE embedding');
   return
@@ -186 +186 @@
 % Determine the mapping depending on pzero
 if pzero == 0,
-  W = mapping(mfilename,'trained',{Q,mean(+d,2)',pzero,L,sig,prec},[],m,sum(sig));
+  W = prmapping(mfilename,'trained',{Q,mean(+d,2)',pzero,L,sig,prec},[],m,sum(sig));
 else
-  W = mapping(mfilename,'trained',{Q,+d(:,pzero)',pzero,L,sig,prec},[],m,sum(sig));
+  W = prmapping(mfilename,'trained',{Q,+d(:,pzero)',pzero,L,sig,prec},[],m,sum(sig));
 end
 W = setname(W,'PE embedding');

distools/pspca.m

@@ -49 +49 @@
 %
 % SEE ALSO
-% MAPPINGS, DATASETS, PCA, KPSEM, PSEM
+% MAPPINGS, DATASETS, PCAM, KPSEM, PSEM
 %
 % LITERATURE
@@ -69 +69 @@
 if nargin < 2 | isempty(sig), sig = [size(a,1) 0]; end
 if nargin < 1 | isempty(a),
-  W = mapping(mfilename,sig,alf,prec);
+  W = prmapping(mfilename,sig,alf,prec);
   W = setname(W,'Pseudo-Euclidean PCA');
   return
@@ -153 +153 @@
 
 % Determine the mapping
-W = mapping(mfilename,'trained',{v,Q,outsig,sig},[],k,sum(outsig));
+W = prmapping(mfilename,'trained',{v,Q,outsig,sig},[],k,sum(outsig));
 W = setname(W,'Pseudo-Euclidean PCA');
 return

distools/setsig.m

@@ -40 +40 @@
   if nargin < 2, sig = []; end
   if nargin < 1 | isempty(a)
-    b = mapping(mfilename,'fixed',{sig});
+    b = prmapping(mfilename,'fixed',{sig});
     b = setname(b,'Set signature');
     return
@@ -58 +58 @@
                 b = setdata(a,data);
         else
-                a = dataset(a);
+                a = prdataset(a);
                 b = setuser(a,sig,'pe_signature');
         end

distools/simdistm.m

@@ -8 +8 @@
 %
 % OUTPUT
-%   D   NxM dissimilarity matrix or dataset; D in [0,1]
+%   D   NxM dissimilarity matrix or prdataset; D in [0,1]
 %
 % DESCRIPTION

distools/sphem.m

@@ -62 +62 @@
 if nargin < 2 | isempty(R), R = 1; end
 if nargin < 1 | isempty(d),
-  W = mapping(mfilename,{R,alf,prec});
+  W = prmapping(mfilename,{R,alf,prec});
   W = setname(W,'Spherical embedding');
   return
@@ -82 +82 @@
     W = R^2*(cos(+d/R)) * Q * diag(sqrt(abs(L))./L);
     if isdataset(d),
-      W = dataset(W,getlab(d),'prior',getprior(d));
+      W = prdataset(W,getlab(d),'prior',getprior(d));
 
       % Store signature in the USER field
@@ -109 +109 @@
   L = L(J);          % Eigenvalues
 
-  W = mapping(mfilename,'trained',{R,Q,L,pars{4},pars{5}},[],m,length(J));
+  W = prmapping(mfilename,'trained',{R,Q,L,pars{4},pars{5}},[],m,length(J));
   W = setname(W,'Spherical embedding');
   return
@@ -150 +150 @@
 Q = Q(:,J);                   % Eigenvectors
 
-W = mapping(mfilename,'trained',{R,Q,L,sig,prec},[],m,sum(sig));
+W = prmapping(mfilename,'trained',{R,Q,L,sig,prec},[],m,sum(sig));
 W = setname(W,'Spherical  embedding');
 return

distools/tqdc.m

@@ -77 +77 @@
 % No input arguments: return an UNTRAINED mapping
 if (nargin < 1) | (isempty(a))
-  w = mapping(mfilename,{alf,r,s,dim});
+  w = prmapping(mfilename,{alf,r,s,dim});
   w = setname(w,'Trade-off Bayes-Normal-2');
   return
@@ -144 +144 @@
 end
 
-w = mapping('normal_map','trained',pars,getlab(U),k,c);
+w = prmapping('normal_map','trained',pars,getlab(U),k,c);
 w = setname(w,'Trade-off Bayes-Normal-2');
 w = setcost(w,a);