Blurb::
Ensures that the samples of discrete variables with finite support are unique
Description::
Traditional LHS can generate replicate samples when applied to
discrete variables. This keyword enforces uniqueness, which is
determined only over the set of discrete variables with finite
support. This allows one to generate LHS for a mixed set of continuous
and discrete variables whilst still enforcing that the set of discrete
LHS components of all the samples are unique.

*Default Behavior*

Uniqueness of samples over discrete variables is not enforced.

*Usage Tips*

Uniqueness can be useful when applying discrete LHS to simulations
without noise.
Topics::

Examples::

.. code-block::

    method,
            sampling
              samples = 12
              seed = 123456
              sample_type lhs backfill
    
    variables,
            active all
            uniform_uncertain = 1
              lower_bounds =  0.
              upper_bounds =  1.
              descriptors  = 'continuous-uniform'
    
            discrete_uncertain_set
              integer = 1
               elements_per_variable = 4
                elements  1 3 5 7
                descriptors =    'design-set-int'
              real = 1
                initial_point = 0.50
                set_values  =  0.25 0.50 0.75 1.00
                descriptors =  'design-set-real'
    
    interface,
            direct analysis_driver = 'text_book'
    
    responses,
            response_functions = 3
            no_gradients
            no_hessians


Theory::

Faq::

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