comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/som_drmake.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [sR,best,sig,Cm] = som_drmake(D,inds1,inds2,sigmea,nanis)
+
+% SOM_DRMAKE Make descriptive rules for given group within the given data. 
+%
+% sR = som_drmake(D,[inds1],[inds2],[sigmea],[nanis]) 
+% 
+%  D        (struct) map or data struct
+%           (matrix) the data, of size [dlen x dim]
+%  [inds1]  (vector) indeces belonging to the group
+%                    (the whole data set by default)
+%  [inds2]  (vector) indeces belonging to the contrast group
+%                    (the rest of the data set by default)
+%  [sigmea] (string) significance measure: 'accuracy', 
+%                    'mutuconf' (default), or 'accuracyI'.
+%                    (See definitions below).
+%  [nanis]  (scalar) value given for NaNs: 0 (=FALSE, default),
+%                    1 (=TRUE) or NaN (=ignored)
+%
+%  sR      (struct array) best rule for each component. Each 
+%                   struct has the following fields:
+%    .type     (string) 'som_rule'
+%    .name     (string) name of the component
+%    .low      (scalar) the low end of the rule range
+%    .high     (scalar) the high end of the rule range
+%    .nanis    (scalar) how NaNs are handled: NaN, 0 or 1
+%
+%  best    (vector) indeces of rules which make the best combined rule
+%  sig     (vector) significance measure values for each rule, and for the combined rule
+%  Cm      (matrix) A matrix of vectorized confusion matrices for each rule, 
+%                   and for the combined rule: [a, c, b, d] (see below). 
+% 
+% For each rule, such rules sR.low <= x < sR.high are found 
+% which optimize the given significance measure. The confusion
+% matrix below between the given grouping (G: group - not G: contrast group) 
+% and rule (R: true or false) is used to determine the significance values:
+%
+%          G    not G    
+%       ---------------    accuracy  = (a+d) / (a+b+c+d)
+% true  |  a  |   b   |    
+%       |--------------    mutuconf  =  a*a  / ((a+b)(a+c)) 
+% false |  c  |   d   | 
+%       ---------------    accuracyI =   a   / (a+b+c)
+%
+% See also  SOM_DREVAL, SOM_DRTABLE.
+
+% Contributed to SOM Toolbox 2.0, January 7th, 2002 by Juha Vesanto
+% Copyright (c) by Juha Vesanto
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 2.0beta juuso 070102
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% input arguments
+
+if isstruct(D), 
+  switch D.type, 
+   case 'som_data', cn = D.comp_names; D = D.data; 
+   case 'som_map',  cn = D.comp_names; D = D.codebook; 
+  end  
+else
+  cn = cell(size(D,2),1);
+  for i=1:size(D,2), cn{i} = sprintf('Variable%d',i); end
+end
+
+[dlen,dim] = size(D);
+if nargin<2 | isempty(inds1), inds1 = 1:dlen; end
+if nargin<3 | isempty(inds2), i = ones(dlen,1); i(inds1) = 0; inds2 = find(i); end
+if nargin<4, sigmea = 'mutuconf'; end
+if nargin<5, nanis = 0; end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% input arguments
+
+sig = zeros(dim+1,1); 
+Cm  = zeros(dim+1,4); 
+
+sR1tmp = struct('type','som_rule','name','','low',-Inf,'high',Inf,'nanis',nanis,'lowstr','','highstr','');
+sR = sR1tmp;  
+
+% single variable rules
+for i=1:dim,
+    
+    % bin edges
+    mi = min(D(:,i)); 
+    ma = max(D(:,i)); 
+    [histcount,bins] = hist([mi,ma],10); 
+    if size(bins,1)>1, bins = bins'; end
+    edges = [-Inf, (bins(1:end-1)+bins(2:end))/2, Inf];
+    
+    % find the rule for this variable
+    [low,high,s,cm] = onevar_descrule(D(inds1,i),D(inds2,i),sigmea,nanis,edges);
+    sR1 = sR1tmp;      
+    sR1.name = cn{i}; 
+    sR1.low = low; 
+    sR1.high = high; 
+    sR(i) = sR1; 
+    sig(i) = s; 
+    Cm(i,:) = cm; 
+    
+end  
+
+% find combined rule
+[dummy,order] = sort(-sig);
+maxsig = sig(order(1)); bestcm = Cm(order(1),:);
+best  = order(1);
+for i=2:dim,    
+    com = [best, order(i)];
+    [s,cm,truex,truey] = som_dreval(sR(com),D(:,com),sigmea,inds1,inds2,'and');
+    if s>maxsig, best = com; maxsig = s; bestcm = cm; end
+end   
+sig(end) = maxsig;
+Cm(end,:) = cm; 
+
+return;
+    
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
+%% descriptive rules
+
+function [low,high,sig,cm] = onevar_descrule(x,y,sigmea,nanis,edges)
+
+  % Given a set of bin edges, find the range of bins with best significance.
+  %
+  %  x          data values in cluster
+  %  y          data values not in cluster
+  %  sigmea     significance measure
+  %  bins       bin centers
+  %  nanis      how to handle NaNs 
+
+  % histogram counts
+  if isnan(nanis), x = x(~isnan(x)); y = y(~isnan(y)); end
+  [xcount,xbin] = histc(x,edges); 
+  [ycount,ybin] = histc(y,edges); 
+  xcount = xcount(1:end-1);
+  ycount = ycount(1:end-1); 
+  xnan=sum(isnan(x));
+  ynan=sum(isnan(y));
+    
+  % find number of true items in both groups in all possible ranges
+  n = length(xcount);
+  V = zeros(n*(n+1)/2,4); 
+  s1 = cumsum(xcount);
+  s2 = cumsum(xcount(end:-1:1)); s2 = s2(end:-1:1);       
+  m  = s1(end);      
+  Tx = triu(s1(end)-m*log(exp(s1/m)*exp(s2/m)')+repmat(xcount',[n 1])+repmat(xcount,[1 n]),0); 
+  s1 = cumsum(ycount); 
+  s2 = cumsum(ycount(end:-1:1)); s2 = s2(end:-1:1);        
+  Ty = triu(s1(end)-m*log(exp(s1/m)*exp(s2/m)')+repmat(ycount',[n 1])+repmat(ycount,[1 n]),0); 
+  [i,j] = find(Tx+Ty);
+  k = sub2ind(size(Tx),i,j);
+  V = [i, j, Tx(k), Ty(k)];
+  tix = V(:,3) + nanis*xnan; 
+  tiy = V(:,4) + nanis*ynan; 
+  
+  % select the best range
+  nix   = length(x);
+  niy   = length(y);
+  Cm    = [tix,nix-tix,tiy,niy-tiy];
+  [s,k] = max(som_drsignif(sigmea,Cm));
+
+  % output
+  low  = edges(V(k,1));
+  high = edges(V(k,2)+1);
+  sig  = s;   
+  cm   = Cm(k,:);
+
+  return;
+