Adding New Latin Hypercube Design Criteria
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The :class:`.LatinHypercubeSampling` class offers a variety of methods for pairing the samples in a Latin hypercube
design. These are specified by the `criterion` parameter (i.e. Random, Centered, MaxiMin, MinCorrelation).
Each one of the Criteria classes can be found in :py:mod:`.latin_hypercube_criteria` folder, with the :class:`.Criterion` baseclass
defining their common interface. As a result, adding a new method is straightforward.
This is done by creating a new class that implements the :class:`.Criterion` abstract baseclass. The base class requires the
creation of a :meth:`generate_samples` method that contains the algorithm for pairing the samples.
This method retrieves the randomly generated samples generated in the baseclass in equal probability bins in each
dimension and returns a set of samples that is paired according to the user's desired criterion.
The user may also pass criterion-specific parameters into the :meth:`__init__` method of the generated class.
The output of this function should be a numpy array of at least two-dimensions with the first dimension being the
number of samples and the second dimension being the number of variables.

The :class:`.Criterion` class is imported using the following command:

>>> from UQpy.sampling.stratified_sampling.latin_hypercube_criteria.baseclass.Criterion import Criterion

.. autoclass:: UQpy.sampling.Criterion
    :members:

An example of a user-defined criterion is given below:


    >>> class UserCriterion(Criterion):
    >>>
    >>>     def __init__(self):
    >>>         super().__init__()
    >>>
    >>>     def generate_samples(self, random_state):
    >>>         lhs_samples = np.zeros_like(self.samples)
    >>>         samples_number = len(self._samples)
    >>>         for j in range(self.samples.shape[1]):
    >>>             if random_state is not None:
    >>>                 order = random_state.permutation(samples_number)
    >>>             else:
    >>>                 order = np.random.permutation(samples_number)
    >>>             lhs_samples[:, j] = self.samples[order, j]
    >>>
    >>>         return lhs_samplesAn example of a user-defined criterion is given below: