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fl_compose


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 -- Loadable Function: RES = fl_compose( A, B)
 -- Loadable Function: RES = fl_compose( A, B, LOCK)
 -- Loadable Function: RES = fl_compose( A, B, T)
 -- Loadable Function: RES = fl_compose( A, B, T, S)
 -- Loadable Function: RES = fl_compose( A, B, LOCK, T)
     Returns the T-Norm / S-Norm composition as basic inference
     mechanism of Fuzzy Logic.  By default, it calculates the max-min
     composition.

     A and B must be matrices with conformant dimensions as in matrix
     product.  If they are both full matrices or mixed (one full and one
     sparse), a full matrix will be returned.  If they are both sparse
     matrices, a sparse matrix will be returned.  However the best
     computation method (sparse or full) is optimally chosen at runtime.

     When true, the boolean LOCK option forces to calculate the diagonal
     results only and returns it as a column vector.

     The arguments T and S allows to specify a custom T-Norm and S-Norm
     function respectively.  They can be:

        - 'min': use the minimum function (default for T-Norm);

        - 'prod': use the product function;

        - 'max': use the maximum function (default for S-Norm);

        - 'sum': use the probabilistic sum function;

        - function_handle: a user-defined function (at most 2
          arguments).

     Note that only the predefined functions are calculated rapidly and
     in multithread mode.  Using a user-defined function as T-Norm
     and/or S-Norm will result in a long time calculation.

     Furthermore, no check is performed to be sure the provided
     functions have the T-Norm or S-Norm properties.  The results will
     be correct as expected, but the semantic correctness is only a user
     responsibility.


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Returns the T-Norm / S-Norm composition as basic inference mechanism of
Fuzzy Lo





