def setup(self):
system = WESTSystem()
system.bin_mapper = RectilinearBinMapper([[0.0, 1.0, 2.0]])
system.bin_target_counts = numpy.array([4, 4])
system.pcoord_len = 2
self.we_driver = WEDriver(system=system)
self.system = system
self._seg_id = 0
def setup(self):
system = WESTSystem()
system.bin_mapper = RectilinearBinMapper([[0.0, 1.0, 2.0]])
system.bin_target_counts = numpy.array([4,4])
system.pcoord_len = 2
self.we_driver = WEDriver(system=system)
self.system = system
self._seg_id = 0
def test_merge_by_weight(self):
selected_counts = {0: 0, 1: 0}
alpha = 0.01
nrounds = 1000
from scipy.stats import binom
# lower and upper bounds of 95% CI for selecting the segment with weight 1/3
lb = binom.ppf(alpha / 2.0, nrounds, 1.0 / 3.0)
ub = binom.ppf(1.0 - alpha / 2.0, nrounds, 1.0 / 3.0)
system = WESTSystem()
system.bin_mapper = RectilinearBinMapper([[0.0, 1.0]])
system.bin_target_counts = numpy.array([1])
system.pcoord_len = 2
self.we_driver = WEDriver(system=system)
self.system = system
self._seg_id = 0
segments = [
Segment(n_iter=1,
seg_id=0,
pcoord=numpy.array([[0], [0.25]], dtype=numpy.float32),
weight=1.0 / 3.0),
Segment(n_iter=1,
seg_id=1,
pcoord=numpy.array([[0], [0.75]], dtype=numpy.float32),
weight=2.0 / 3.0)
]
for _iround in xrange(nrounds):
for segment in segments:
segment.endpoint_type = Segment.SEG_ENDPOINT_UNSET
self.we_driver.new_iteration()
self.we_driver.assign(segments)
self.we_driver.construct_next()
assert len(self.we_driver.next_iter_binning[0]) == 1
newseg = self.we_driver.next_iter_binning[0].pop()
assert segments[
newseg.
parent_id].endpoint_type == Segment.SEG_ENDPOINT_CONTINUES
assert segments[
~newseg.parent_id].endpoint_type == Segment.SEG_ENDPOINT_MERGED
selected_counts[newseg.parent_id] += 1
print(selected_counts)
assert lb <= selected_counts[0] <= ub, (
'Incorrect proportion of histories selected.'
'this is expected about {:%} of the time; retry test.'.format(
alpha))
def test_merge_by_weight(self):
selected_counts = {0: 0, 1: 0}
alpha = 0.01
nrounds = 1000
from scipy.stats import binom
# lower and upper bounds of 95% CI for selecting the segment with weight 1/3
lb = binom.ppf(alpha/2.0, nrounds, 1.0/3.0)
ub = binom.ppf(1.0-alpha/2.0, nrounds, 1.0/3.0)
system = WESTSystem()
system.bin_mapper = RectilinearBinMapper([[0.0, 1.0]])
system.bin_target_counts = numpy.array([1])
system.pcoord_len = 2
self.we_driver = WEDriver(system=system)
self.system = system
self._seg_id = 0
segments = [Segment(n_iter=1, seg_id=0, pcoord=numpy.array([[0],[0.25]], dtype=numpy.float32),weight=1.0/3.0),
Segment(n_iter=1, seg_id=1, pcoord=numpy.array([[0],[0.75]], dtype=numpy.float32),weight=2.0/3.0)]
for _iround in xrange(nrounds):
for segment in segments:
segment.endpoint_type = Segment.SEG_ENDPOINT_UNSET
self.we_driver.new_iteration()
self.we_driver.assign(segments)
self.we_driver.construct_next()
assert len(self.we_driver.next_iter_binning[0]) == 1
newseg = self.we_driver.next_iter_binning[0].pop()
assert segments[newseg.parent_id].endpoint_type == Segment.SEG_ENDPOINT_CONTINUES
assert segments[~newseg.parent_id].endpoint_type == Segment.SEG_ENDPOINT_MERGED
selected_counts[newseg.parent_id] += 1
print(selected_counts)
assert lb <= selected_counts[0] <= ub, ('Incorrect proportion of histories selected.'
'this is expected about {:%} of the time; retry test.'.format(alpha))
