diff toolboxes/MIRtoolbox1.3.2/somtoolbox/som_stats.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_stats.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,257 @@
+function csS = som_stats(D,varargin)
+
+%SOM_STATS Calculate descriptive statistics for the data.
+%  
+% csS = som_stats(D,[sort]); 
+% 
+%  csS = som_stats(D); 
+%  csS = som_stats(D,'nosort'); 
+%  som_table_print(som_stats_table(csS))
+%
+%  Input and output arguments ([]'s are optional): 
+%   D           (matrix) a matrix, size dlen x dim
+%               (struct) data or map struct
+%   [sort]      (string) 'sort' (default) or 'nosort'
+%                        If 'nosort' is specified, the data is not 
+%                        sorted, and therefore the values of
+%                        nunique, uvalues, ucount, fvalues, fcount, and tiles fields 
+%                        are not calculated. This may be useful if
+%                        there is a very large amount of data, and
+%                        one wants to reduce calculation time.
+%
+%   csS         (cell array) size dim x 1, of statistics structs with 
+%                        the following fields
+%      .type             (string) 'som_stat'
+%      .name             (string) name of the variable
+%      .normalization    (struct array) variable normalization (see SOM_NORMALIZE)
+%      .ntotal           (scalar) total number of values
+%      .nvalid           (scalar) number of valid values (not Inf or NaN)
+%      .min              (scalar) minimum value 
+%      .max              (scalar) maximum value 
+%      .mean             (scalar) mean value (not Inf or NaN)
+%      .std              (scalar) standard deviation (not Inf or NaN)
+%      .nunique          (scalar) number of unique values
+%      .mfvalue          (vector) most frequent value
+%      .mfcount          (vector) number of occurances of most frequent value
+%      .values           (vector) at most MAXDISCRETE (see below) sample values 
+%      .counts           (vector) number of occurances for each sampled value
+%      .tiles            (vector) NT-tile values, for example
+%                                    NT=4   for quartiles: 25%, 50% and 75%
+%                                    NT=100 for percentiles: 1%, 2%, ... and 99%
+%      .hist             (struct) histogram struct with the following fields
+%           .type        (string) 'som_hist'
+%           .bins        (vector) histogram bin centers 
+%           .counts      (vector) count of values in each bin
+%           .binlabels   (cellstr) labels for the bins (denormalized bin
+%                                  center values)
+%           .binlabels2  (cellstr) labels for the bins (denormalized bin
+%                                  edge values, e.g. '[1.4,2.5['
+%
+%   Constants: 
+%      MAXDISCRETE = 10
+%      NT          = 10
+%
+% See also  SOM_STATS_PLOT, SOM_STATS_TABLE, SOM_TABLE_PRINT, SOM_STATS_REPORT.
+
+% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto
+% Copyright (c) by Juha Vesanto
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 2.0beta juuso 311201
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
+%% arguments
+
+% default values
+nosort      = 0; 
+nbins       = 10; 
+maxdiscrete = 20; 
+ntiles      = 10; 
+
+% first argument
+if isstruct(D), 
+    switch D.type, 
+    case 'som_map',  cn = D.comp_names; sN = D.comp_norm; D = D.codebook; 
+    case 'som_data', cn = D.comp_names; sN = D.comp_norm; D = D.data; 
+    otherwise, error('Invalid first argument')
+    end    
+else
+    cn = cell(size(D,2),1); 
+    cn(:) = {'Variable'};
+    for i=1:length(cn), cn{i} = sprintf('%s%d',cn{i},i); end    
+    sN = cell(size(D,2),1); 
+end
+[dlen dim] = size(D);
+
+% other arguments
+
+if length(varargin)>0, 
+  if strcmp(varargin{1},'nosort'), nosort = 1; end
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
+%% action
+
+sStat = struct('type','som_stat','name','','normalization',[],...
+               'min',NaN,'max',NaN,'mean',NaN,'std',NaN,...
+               'nunique',NaN,'values',[],'counts',[],'mfvalue',NaN,'mfcount',NaN,'tiles',[],...
+               'ntotal',dlen,'nvalid',NaN,'hist',[]);
+csS = cell(0);
+           
+for i=1:dim, 
+    sS = sStat;
+    sS.name = cn{i};
+    sS.normalization = sN{i}; 
+    x = D(:,i); 
+    x(find(~isfinite(x))) = [];
+    % basic descriptive statistics
+    sS.nvalid = length(x);
+    if length(x), 
+        sS.min  = min(x);
+        sS.max  = max(x);
+        sS.mean = mean(x);  
+        sS.std = std(x);
+        bins = [];
+        if ~nosort, 
+            xsorted    = sort(x);
+            % number of unique values
+            repeated   = (xsorted(1:end-1)==xsorted(2:end));
+            j          = [1; find(~repeated)+1];         
+            xunique    = xsorted(j); 
+            sS.nunique = length(xunique);           
+            ucount     = diff([j; length(xsorted)+1]);
+            % most frequent value
+            [fcount,j] = max(ucount);
+            sS.mfvalue = xunique(j);
+            sS.mfcount = fcount;
+            % -tiles (k*100/ntiles % of values, k=1..)
+            pickind    = round(linspace(1,sS.nvalid,ntiles+1)); 
+            pickind    = pickind(2:end-1);
+            sS.tiles   = xsorted(pickind);
+            if sS.nunique <= sS.nvalid/2, 
+                % unique values
+                sS.values = xunique; 
+	            sS.counts = ucount; 
+                bins = sS.values; 
+            else
+                % just maxdiscrete values, evenly  picked
+                pickind    = round(linspace(1,sS.nunique,maxdiscrete));
+                sS.values  = xunique(pickind);
+                sS.counts  = ucount(pickind);
+ 	    
+                %% OPTION 2: maxdiscrete most frequent values
+                %[v,j]     = sort(ucount); 
+                %pickind   = j(1:maxdiscrete);             
+                %sS.values = xunique(pickind);
+                %sS.counts = ucount(pickind);
+
+                % OPTION 3: representative values - calculated using k-means
+                %[y,bm,qe] = kmeans(x,maxdiscrete);
+               %sS.values = y; 
+                %sS.counts = full(sum(sparse(bm,1:length(bm),1,maxdiscrete,length(bm)),2));
+            end 
+        end 
+        if isempty(bins), 
+            bins = linspace(sS.min,sS.max,nbins+1); 
+            bins = (bins(1:end-1)+bins(2:end))/2; 
+        end
+        sS.hist = som_hist(x,bins,sS.normalization);    
+    else
+        sS.hist = som_hist(x,0);
+    end
+    csS{end+1} = sS; 
+end
+
+return;
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
+%% subfunctions
+
+function sH = som_hist(x,bins,sN)
+
+    binlabels  = []; 
+    binlabels2 = []; 
+    if nargin<2 | isempty(bins) | isnan(bins), 
+        bins = linspace(min(x),max(x),10);    
+    end
+    if isstruct(bins), 
+        bins = sH.bins; 
+        binlabels  = sH.binlabels;
+        binlabels2 = sH.binlabels2;
+    end 
+    if nargin<3, sN = []; end
+
+    sH = struct('type','som_hist','bins',bins,'counts',[],...
+                'binlabels',binlabels,'binlabels2',binlabels2);                         
+            
+    if length(bins)==1,
+        sH.counts = [length(x)];
+        edges = bins;
+    elseif length(x),
+        edges = (bins(1:end-1)+bins(2:end))/2;
+        counts = histc(x,[-Inf; edges(:); Inf]);
+        sH.counts = counts(1:end-1);       
+    end 
+
+    if isempty(sH.binlabels),
+        b = som_denormalize(bins(:),sN); 
+        sH.binlabels = numtostring(b,4);
+    end 
+
+    if isempty(sH.binlabels2),
+        if length(edges)==1, 
+            sH.binlabels2 = numtostring(som_denormalize(edges,sN),2);
+            if length(bins)>1, 
+              sH.binlabels2 = sH.binlabels2([1 1]);
+              sH.binlabels2{1} = [']' sH.binlabels2{1} '['];
+              sH.binlabels2{2} = ['[' sH.binlabels2{2} '['];
+            end 
+        else
+            if size(edges,1)==1, edges = edges'; end
+            bstr = numtostring(som_denormalize(edges,sN),4);
+            sH.binlabels2 = bstr([1:end end]);
+            sH.binlabels2{1} = [bstr{1} '['];
+            for i=2:length(sH.binlabels2)-1,
+                sH.binlabels2{i} = ['[' bstr{i-1} ',' bstr{i} '[']; 
+            end 
+            sH.binlabels2{end} = ['[' bstr{end}];
+        end         
+    end 
+    
+    if 0, 
+        if length(bins)==1, sH.binlabels2 = {'constant'}; 
+        else    
+            ntiles = 10; 
+            plim = [1:ntiles-1] / ntiles; 
+            cp = cumsum(sH.counts)/sum(sH.counts);
+            [dummy,i] = histc(cp,[-Inf plim Inf]);            
+            l2 = cell(length(bins),1);            
+            for j=1:length(bins), l2{j} = sprintf('Q%d',i(j)); end
+            if i(1) > 1, l2{1} = ['...' l2{1}]; end            
+            k = 0; 
+            for j=2:length(bins), 
+                if i(j)==i(j-1), 
+                    if k==0, l2{j-1} = [l2{j-1} '.1']; k = 1; end
+                    k = k + 1; 
+                    l2{j} = [l2{j} '.' num2str(k)]; 
+                else k = 0; end
+            end 
+            if i(end) < ntiles, l2{end} = [l2{end} '...']; end
+            sH.binlabels2 = l2; 
+        end 
+    end    
+
+    return;
+
+function vstr = numtostring(v,d)
+
+    r = max(v)-min(v); 
+    if r==0, r=1; end
+    nearzero = (abs(v)/r < 10.^-d);
+    i1 = find(v > 0 & nearzero); 
+    i2 = find(v < 0 & nearzero);     
+    vstr = strrep(cellstr(num2str(v,d)),' ','');
+    vstr(i1) = {'0.0'};
+    vstr(i2) = {'-0.0'};
+    return;
+