(self, seed)
return self._shrink(X, self.size)
Creates an LHD with the Translational propagation algorithm.
         
        Uses the specified seed and design size N specified during construction.

        :param seed: seed design, size S x D
        :return: data matrix, size N x D
Creates an LHD with the Translational propagation algorithm.
         
        Uses the specified seed and design size N specified during construction.
Creates an LHD with the Translational propagation algorithm . Uses the specified seed and design size N specified during construction .
def _tplhd_design(self, seed):
        """
        Creates an LHD with the Translational propagation algorithm.
         
        Uses the specified seed and design size N specified during construction.

        :param seed: seed design, size S x D
        :return: data matrix, size N x D
        """
        ns, nv = seed.shape

        # Start by computing two quantities.
        # 1) the number of translation steps in each dimension
        nd = np.power(self.size / float(ns), 1 / float(nv))
        ndStar = np.ceil(nd)

        # 2) the total amount of points we'll be generating.
        # Typically, npStar > self.size, although sometimes npStar == self.size
        npStar = np.power(ndStar, nv) * ns if ndStar > nd else self.size

        # First rescale the seed, then perform translations and propagations.
        seed = self._rescale_seed(seed, npStar, ndStar)
        X = self._translate_propagate(seed, npStar, ndStar)

        # In case npStar > N, shrink the design to the requested size specified in __init__
        return self._shrink(X, self.size)
def _tplhd_design ( self , seed ) : ns , nv = seed . shape # Start by computing two quantities. # 1) the number of translation steps in each dimension nd = np . power ( self . size / float ( ns ) , 1 / float ( nv ) ) ndStar = np . ceil ( nd ) # 2) the total amount of points we'll be generating. # Typically, npStar > self.size, although sometimes npStar == self.size npStar = np . power ( ndStar , nv ) * ns if ndStar > nd else self . size # First rescale the seed, then perform translations and propagations. seed = self . _rescale_seed ( seed , npStar , ndStar ) X = self . _translate_propagate ( seed , npStar , ndStar ) # In case npStar > N, shrink the design to the requested size specified in __init__ return self . _shrink ( X , self . size )