The programm loopoptims needs 2 arguments: 
	* the name of the FIS configuration file;
	* the name of the data file used for tuning;
	* default only optimize rule conclusions if no other option is given

loopoptims combines FIS optimization and sampling. If asked for (default 10 sample pairs), it generates n pairs of sample pairs (learning and test), with the same parameters as the data sampling menu option. (Check seed=0)

Then it iterates on each learning sample to optimize the FIS elements (default=optimize rule conclusions). A secondary loop performs as many steps as asked by the -u parameter. 
At each iteration, the program evaluates the optimized FIS accuracy on the learning sample, and also on the corresponding test sample. 
Finally a median FIS is built using all of the optimized FIS and written into the "med.fis" file.

A perf file is also written, with (nxu)+1 rows. The first row gives the accuracy of the initial and median FIS on the whole data set. Then n rows are written for each step.
Each of them includes, for the uth step, the initial FIS accuracy on each learning and test sample, the accuracy of the corresponding optimized FIS, and the relative accuracy gain.

Command line options:
	* -b the basename of the resulting FIS configuration file;(default optim)
	* -it the number of iterations; (default 100)
	* -e the range of the gaussian noise; (default 0.005)
	* -v the max number of constraints violations before it counts as an iteration (default 1000);
	* -f the max number of failure steps in an algorithm (default 1000);
	* -d the equality center distance threshold (default 1e-6);
	* -ns Number of sample pairs to generate from data file (default 10-put -ns 0 for no sampling);
	* -cs Draw samples with respect to class ratio in data file (class is last column, default no));
	* -rs Ratio learning/all pairs (default 0.75, maximum 0.9);
	* -s   integer to set seed value for parker miller random;(default 0:new sampling each time);
	* -in "x y ", the string of input numbers to optimize (starting at 1, order is important, default no input optimization);
	* -r optimize  rule conclusions;
	* -o optimize fuzzy output (default false)
	* -n   Output number to consider (default 0: first output);
	* -m   minimum membership (default 0.1);
	* -l1  Solis Wetts Constant 1 (default 0.4);
	* -l2  Solis Wetts Constant 2 (default 0.2);
	* -l3  Solis Wetts Constant 3 (default 0.5);
	* -u   Number of loops for optimizing (default 2);
	* -c relative tolerated loss of coverage (default 0.10 ; 1.=10.0%).
	* -g create intermediate files (default false);
	* -a for intermediate display (default false);
	* -wl for wordless.(default not silent)
