sampling.mm.plot_mmphi_vs_points

sampling.mm.plot_mmphi_vs_points(
    X_base,
    x_min,
    x_max,
    p_min=10,
    p_max=100,
    p_step=10,
    n_repeats=5,
    figsize=(10, 6),
)

Plot the Morris-Mitchell criterion versus the number of added points.

Parameters

Name	Type	Description	Default
X_base	np.ndarray	Base design matrix	required
x_min	np.ndarray	Lower bounds for variables	required
x_max	np.ndarray	Upper bounds for variables	required
p_min	int	Minimum number of points to add	10
p_max	int	Maximum number of points to add	100
p_step	int	Step size for number of points	10
n_repeats	int	Number of repetitions for each point count	5
figsize	tuple	Size of the plot (width, height)	(10, 6)

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: Summary DataFrame with mean and std of mmphi for each number of added points.

Examples

>>> import numpy as np
>>> from spotoptim.sampling.mm import plot_mmphi_vs_points
>>> # Define base design
>>> X_base = np.array([[0.1, 0.2], [0.4, 0.5], [0.7, 0.8]])
>>> # Define variable bounds
>>> x_min = np.array([0.0, 0.0])
>>> x_max = np.array([1.0, 1.0])
>>> # Plot mmphi vs number of added points
>>> df_summary = plot_mmphi_vs_points(X_base, x_min, x_max, p_min=10, p_max=50, p_step=10, n_repeats=3)