def test_adding_points_and_skip_one_point():
learner = IntegratorLearner(f24, bounds=(0, 3), tol=1e-10)
xs, _ = learner.ask(17)
skip_x = xs[1]
for x in xs:
if x != skip_x:
learner.tell(x, learner.function(x))
for i in range(1000):
xs, _ = learner.ask(1)
for x in xs:
if x != skip_x:
learner.tell(x, learner.function(x))
# Now add the point that was skipped
learner.tell(skip_x, learner.function(skip_x))
# Create a learner with the same number of points, which should
# give an identical igral value.
learner2 = IntegratorLearner(f24, bounds=(0, 3), tol=1e-10)
for i in range(1017):
xs, _ = learner2.ask(1)
for x in xs:
learner2.tell(x, learner2.function(x))
np.testing.assert_almost_equal(learner.igral, learner2.igral)
def test_removed_ask_one_by_one():
with pytest.raises(RuntimeError):
# This test should raise because integrating np.exp should be done
# after the 33th point
learner = IntegratorLearner(np.exp, bounds=(0, 1), tol=1e-15)
n = ns[-1] + 2 * (ns[0] - 2) # first + two children (33+6=39)
for _ in range(n):
xs, _ = learner.ask(1)
for x in xs:
learner.tell(x, learner.function(x))
def test_removed_choose_mutiple_points_at_once():
"""Given that a high-precision interval that was split into 2 low-precision ones,
we should use the high-precision interval.
"""
learner = IntegratorLearner(np.exp, bounds=(0, 1), tol=1e-15)
n = ns[-1] + 2 * (ns[0] - 2) # first + two children (33+6=39)
xs, _ = learner.ask(n)
for x in xs:
learner.tell(x, learner.function(x))
assert list(learner.approximating_intervals)[0] == learner.first_ival
def test_choosing_and_adding_multiple_points_at_once():
learner = IntegratorLearner(f24, bounds=(0, 3), tol=1e-10)
xs, _ = learner.ask(100)
for x in xs:
learner.tell(x, learner.function(x))
def test_choosing_and_adding_points_one_by_one():
learner = IntegratorLearner(f24, bounds=(0, 3), tol=1e-10)
for _ in range(1000):
xs, _ = learner.ask(1)
for x in xs:
learner.tell(x, learner.function(x))