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.

<b> Default Behavior </b>

Uniqueness of samples over discrete variables is not enforced.

<b> Usage Tips </b>

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

Topics::
Examples::
\verbatim
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
\endverbatim
Theory::
Faq::
See_Also::	method-sampling-sample_type-lhs