diff core/tools/machine_learning/save_svmlight_inequalities.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/core/tools/machine_learning/save_svmlight_inequalities.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,57 @@
+function success = save_svmlight_inequalities(lhs, rhs, factors, file)
+% success = save_svmlight_inequalities(lhs, rhs, file)
+%
+% saves the optimisation problem given by lhs and rhs to
+% a svmlight data file. the individual equations can 
+% be weighted using 
+%
+% success = save_svmlight_inequalities(lhs, rhs, factors, file)
+
+if nargin == 3
+    file = factors;
+    factors = [];
+end
+
+% open file
+fid = fopen(file, 'w+');
+if fid < 1 
+    success = 0;
+    return;
+end
+
+try 
+    % write individual constraint rows
+    for i = 1:size(lhs,1)
+
+        % ---
+        % print rows:" rhs #fnum:#fval #fnum:#fval #fnum:#fval ..."
+        % ---
+
+        % print right hand side
+        fprintf(fid,'%d ', rhs(i));
+
+        % print cost factors if availablefactor
+        if (numel(lhs{i,1}) > 0) && (numel(factors) >= i)
+
+            fprintf(fid,'cost:%f ', factors(i));
+        end
+
+        % print left hand side
+        for j = 1:numel(lhs{i,1})
+
+            fprintf(fid,'%d:%2.16f ', lhs{i,1}(j), lhs{i,2}(j));
+        end
+
+        % finish line
+        fprintf(fid,'\n');
+    end  
+catch
+    success = 0;
+    fclose(fid);
+    return;
+end
+success = 1;
+fclose(fid);
+
+    
+    
\ No newline at end of file