def test_run():
from ultranest import NestedSampler
def loglike(y):
z = np.log10(y)
a = np.array([-0.5 * sum([((xi - 0.83456 + i*0.1)/0.5)**2 for i, xi in enumerate(x)]) for x in z])
b = np.array([-0.5 * sum([((xi - 0.43456 - i*0.1)/0.5)**2 for i, xi in enumerate(x)]) for x in z])
loglike.ncalls += len(a)
return np.logaddexp(a, b)
loglike.ncalls = 0
def transform(x):
return 10**(10. * x - 5.)
paramnames = ['Hinz', 'Kunz']
sampler = NestedSampler(paramnames, loglike, transform=transform, num_live_points=400, vectorized=True)
r = sampler.run(log_interval=50)
ncalls = loglike.ncalls
if sampler.mpi_size > 1:
ncalls = sampler.comm.gather(ncalls, root=0)
if sampler.mpi_rank == 0:
print("ncalls on the different MPI ranks:", ncalls)
ncalls = sum(sampler.comm.bcast(ncalls, root=0))
assert abs(r['ncall'] - ncalls) <= 2 * sampler.mpi_size, (r['ncall'], ncalls)
open('nestedsampling_results.txt', 'a').write("%.3f\n" % r['logz'])
sampler.plot()
def main(args):
from ultranest import NestedSampler
#def loglike(z):
# return np.array([-sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0) for x in z])
def loglike_(z):
return np.array([
-sum(100.0 * (x[1::2] - x[::2]**2.0)**2.0 + (1 - x[::2])**2.0)
for x in z
])
def loglike(z):
a = np.array([
-0.5 * sum([((xi - 0.83456 + i * 0.1) / 0.01)**2
for i, xi in enumerate(x)]) for x in z
])
b = np.array([
-0.5 * sum([((xi - 0.43456 - i * 0.1) / 0.01)**2
for i, xi in enumerate(x)]) for x in z
])
return np.logaddexp(a, b)
def transform(x):
return 10. * x - 5.
import string
paramnames = list(string.ascii_lowercase)[:args.x_dim]
sampler = NestedSampler(paramnames,
loglike,
transform=transform,
vectorized=True,
log_dir=args.log_dir)
sampler.run(log_interval=20, num_live_points=args.num_live_points)
sampler.plot()