sampling.mm.plot_mmphi_vs_n_lhs
sampling.mm.plot_mmphi_vs_n_lhs(
k_dim,
seed,
n_min=10,
n_max=100,
n_step=5,
q_phi=2.0,
p_phi=2.0,
)
Generates LHS designs for varying n, calculates mmphi and mmphi_intensive, and plots them against the number of samples (n).
Parameters
| k_dim |
int |
Number of dimensions for the LHS design. |
required |
| seed |
int |
Random seed for reproducibility. |
required |
| n_min |
int |
Minimum number of samples. |
10 |
| n_max |
int |
Maximum number of samples. |
100 |
| n_step |
int |
Step size for increasing n. |
5 |
| q_phi |
float |
Exponent q for the Morris-Mitchell criteria. |
2.0 |
| p_phi |
float |
Distance norm p for the Morris-Mitchell criteria. |
2.0 |
Returns
| None |
None |
Displays a plot of mmphi and mmphi_intensive vs. number of samples (n). |
Examples
>>> from spotoptim.sampling.mm import plot_mmphi_vs_n_lhs
>>> plot_mmphi_vs_n_lhs(k_dim=3, seed=42, n_min=10, n_max=50, n_step=5, q_phi=2.0, p_phi=2.0)