from typing import Optional, Union
import numpy as np
from . import common_args
from ..util import read_param_file, scale_samples, compute_groups_matrix, handle_seed
[docs]
def sample(problem, N, seed: Optional[Union[int, np.random.Generator, None]] = None):
"""Generate model inputs using Latin hypercube sampling (LHS).
Returns a NumPy matrix containing the model inputs generated by Latin
hypercube sampling. The resulting matrix contains N rows and D columns,
where D is the number of parameters.
Parameters
----------
problem : dict
The problem definition
N : int
The number of samples to generate
seed : {None, int, `numpy.random.Generator`}, optional
If `seed` is None the `numpy.random.Generator` generator is used.
If `seed` is an int, a new ``Generator`` instance is used,
seeded with `seed`.
If `seed` is already a ``Generator`` instance then that instance is
used. Default is None.
References
----------
1. McKay, M.D., Beckman, R.J., Conover, W.J., 1979.
A comparison of three methods for selecting values of input
variables in the analysis of output from a computer code.
Technometrics 21, 239-245.
https://doi.org/10.2307/1268522
2. Iman, R.L., Helton, J.C., Campbell, J.E., 1981.
An Approach to Sensitivity Analysis of Computer Models:
Part I—Introduction, Input Variable Selection and
Preliminary Variable Assessment.
Journal of Quality Technology 13, 174-183.
https://doi.org/10.1080/00224065.1981.11978748
"""
rng = handle_seed(seed)
num_samples = N
groups = problem.get("groups")
if groups:
num_groups = len(set(groups))
G, group_names = compute_groups_matrix(groups)
else:
num_groups = problem["num_vars"]
result = np.empty([num_samples, problem["num_vars"]])
temp = np.empty([num_samples])
d = 1.0 / num_samples
temp = np.array(
[
rng.uniform(low=sample * d, high=(sample + 1) * d, size=num_groups)
for sample in range(num_samples)
]
)
for group in range(num_groups):
rng.shuffle(temp[:, group])
for sample in range(num_samples):
if groups:
grouped_variables = np.where(G[:, group] == 1)
result[sample, grouped_variables[0]] = temp[sample, group]
else:
result[sample, group] = temp[sample, group]
result = scale_samples(result, problem)
return result
# No additional CLI options
cli_parse = None
[docs]
def cli_action(args):
"""Run sampling method
Parameters
----------
args : argparse namespace
"""
problem = read_param_file(args.paramfile)
param_values = sample(problem, args.samples, seed=args.seed)
np.savetxt(
args.output,
param_values,
delimiter=args.delimiter,
fmt="%." + str(args.precision) + "e",
)
if __name__ == "__main__":
common_args.run_cli(cli_parse, cli_action)
