sampling.mm.mm_improvement

sampling.mm.mm_improvement(
    x,
    X_base,
    phi_base=None,
    J_base=None,
    d_base=None,
    q=2,
    p=2,
    normalize_flag=False,
    verbose=False,
    exponential=True,
)

Calculates the Morris-Mitchell improvement for a candidate point x.

Parameters

Name	Type	Description	Default
x	np.ndarray	Candidate point (1D array).	required
X_base	np.ndarray	Existing design points.	required
J_base	np.ndarray	Multiplicities of distances for X_base.	None
d_base	np.ndarray	Unique distances for X_base.	None
q	int	Number of nearest neighbors for MM metric.	2
p	int	Power for MM metric.	2
normalize_flag	bool	If True, normalizes the X array and candidate point before computing distances. Defaults to False.	False
exponential	bool	If True, the exponential is applied.	True

Returns

Name	Type	Description
float	float	Morris-Mitchell improvement.

Examples

>>> import numpy as np
>>> from spotoptim.sampling.mm import mm_improvement
>>> X_base = np.array([[0.1, 0.2], [0.4, 0.5], [0.7, 0.8]])
>>> x = np.array([0.5, 0.5])
>>> improvement = mm_improvement(x, X_base, q=2, p=2)
>>> print(improvement)
0.123456789