spacefilling

spacefilling

Bases: designs

Source code in spotPython/design/spacefilling.py

class spacefilling(designs):
    def __init__(self, k=2, seed=123):
        """
        Spacefilling design class

        Args:
            k (int, optional): number of design variables (dimensions). Defaults to 2.
            seed (int, optional): random seed. Defaults to 123.
        """
        self.k = k
        self.seed = seed
        super().__init__(k, seed)
        self.sampler = LatinHypercube(d=self.k, seed=self.seed)

    def scipy_lhd(self, n, repeats=1, lower=None, upper=None):
        """
        Latin hypercube sampling based on scipy.

        Args:
            n (int): number of samples
            repeats (int): number of repeats (replicates)
            lower (int, optional): lower bound. Defaults to 0.
            upper (int, optional): upper bound. Defaults to 1.

        Returns:
            (numpy.ndarray): Latin hypercube design.
        """
        if lower is None:
            lower = zeros(self.k)
        if upper is None:
            upper = ones(self.k)
        sample = self.sampler.random(n=n)
        des = scale(sample, lower, upper)
        return repeat(des, repeats, axis=0)

__init__(k=2, seed=123)

Spacefilling design class

Parameters:

Name	Type	Description	Default
k	int	number of design variables (dimensions). Defaults to 2.	2
seed	int	random seed. Defaults to 123.	123

Source code in spotPython/design/spacefilling.py

def __init__(self, k=2, seed=123):
    """
    Spacefilling design class

    Args:
        k (int, optional): number of design variables (dimensions). Defaults to 2.
        seed (int, optional): random seed. Defaults to 123.
    """
    self.k = k
    self.seed = seed
    super().__init__(k, seed)
    self.sampler = LatinHypercube(d=self.k, seed=self.seed)

scipy_lhd(n, repeats=1, lower=None, upper=None)

Latin hypercube sampling based on scipy.

Parameters:

Name	Type	Description	Default
n	int	number of samples	required
repeats	int	number of repeats (replicates)	1
lower	int	lower bound. Defaults to 0.	None
upper	int	upper bound. Defaults to 1.	None

Returns:

Type	Description
numpy.ndarray	Latin hypercube design.

Source code in spotPython/design/spacefilling.py

def scipy_lhd(self, n, repeats=1, lower=None, upper=None):
    """
    Latin hypercube sampling based on scipy.

    Args:
        n (int): number of samples
        repeats (int): number of repeats (replicates)
        lower (int, optional): lower bound. Defaults to 0.
        upper (int, optional): upper bound. Defaults to 1.

    Returns:
        (numpy.ndarray): Latin hypercube design.
    """
    if lower is None:
        lower = zeros(self.k)
    if upper is None:
        upper = ones(self.k)
    sample = self.sampler.random(n=n)
    des = scale(sample, lower, upper)
    return repeat(des, repeats, axis=0)