def test_ada_exp_factory_no_asympt_more_steps(test_f: Callable[[float],
float], ):
"""Test of the adaptive exponential extrapolator."""
seeded_f = apply_seed_to_func(test_f, SEED)
fac = AdaExpFactory(steps=8, scale_factor=2.0, asymptote=None)
fac.run_classical(seeded_f)
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
def test_ada_exp_fac_with_asympt_more_steps(
test_f: Callable[[float], float], avoid_log: bool
):
"""Test of the adaptive exponential extrapolator with more steps."""
seeded_f = apply_seed_to_func(test_f, SEED)
fac = AdaExpFactory(
steps=6, scale_factor=2.0, asymptote=A, avoid_log=avoid_log
)
fac.iterate(seeded_f)
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
def test_ada_exp_factory_with_asympt(
test_f: Callable[[float], float], avoid_log: bool
):
"""Test of the adaptive exponential extrapolator."""
seeded_f = apply_seed_to_func(test_f, SEED)
fac = AdaExpFactory(
steps=3, scale_factor=2.0, asymptote=A, avoid_log=avoid_log
)
# Note: iterate calls next which calls reduce, so calling fac.iterate with
# an AdaExpFactory sets the optimal parameters as well. Hence we check that
# the opt_params are empty before AdaExpFactory.iterate is called.
assert len(fac.opt_params) == 0
fac.iterate(seeded_f)
assert np.isclose(fac.reduce(), seeded_f(0, err=0), atol=CLOSE_TOL)
# There are three parameters to fit for the (adaptive) exponential ansatz
assert len(fac.opt_params) == 3