Source code for SALib.sample.latin
import numpy as np
from . import common_args
from ..util import read_param_file, scale_samples, compute_groups_matrix
[docs]def sample(problem, N, seed=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
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
"""
num_samples = N
if seed:
np.random.seed(seed)
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([np.random.uniform(low=sample * d,
high=(sample + 1) * d,
size=num_groups)
for sample in range(num_samples)])
for group in range(num_groups):
np.random.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)