def test_mixed_2(histogram):
conditions = (
HistogramCondition(histogram["xs"], histogram["densities"]),
IntervalCondition(p=0.4, max=1),
IntervalCondition(p=0.45, max=1.2),
IntervalCondition(p=0.48, max=1.3),
IntervalCondition(p=0.5, max=2),
IntervalCondition(p=0.7, max=2.2),
IntervalCondition(p=0.9, max=2.3),
)
dist = LogisticMixture.from_conditions(conditions,
num_components=3,
verbose=True)
assert dist.pdf1(-5) == pytest.approx(0, abs=0.1)
assert dist.pdf1(6) == pytest.approx(0, abs=0.1)
my_cache = {}
my_cache[conditions] = 3
conditions_2 = (
HistogramCondition(histogram["xs"], histogram["densities"]),
IntervalCondition(p=0.4, max=1),
IntervalCondition(p=0.45, max=1.2),
IntervalCondition(p=0.48, max=1.3),
IntervalCondition(p=0.5, max=2),
IntervalCondition(p=0.7, max=2.2),
IntervalCondition(p=0.9, max=2.3),
)
assert hash(conditions) == hash(conditions_2)
assert my_cache[conditions_2] == 3
def test_normalization_histogram_condition(histogram):
original = HistogramCondition(histogram["xs"], histogram["densities"])
normalized_denormalized = original.normalize(10,
1000).denormalize(10, 1000)
for (density,
norm_denorm_density) in zip(histogram["densities"],
normalized_denormalized.densities):
assert density == pytest.approx(
norm_denorm_density,
rel=0.001,
)
for (x, norm_denorm_x) in zip(histogram["xs"], normalized_denormalized.xs):
assert x == pytest.approx(
norm_denorm_x,
rel=0.001,
)
# half-assed test that xs and densities are at least
# getting transformed in the right direction
normalized = original.normalize(1, 4)
for idx, (normalized_x, normalized_density) in enumerate(
zip(normalized.xs, normalized.densities)):
orig_x = histogram["xs"][idx]
orig_density = histogram["densities"][idx]
assert orig_x > normalized_x
assert orig_density < normalized_density
def test_mixture_from_histogram(histogram):
conditions = [HistogramCondition(histogram["xs"], histogram["densities"])]
mixture = LogisticMixture.from_conditions(conditions,
num_components=3,
verbose=True)
for (x, density) in zip(histogram["xs"], histogram["densities"]):
assert mixture.pdf1(x) == pytest.approx(density, abs=0.2)
def compile_mixture_loss_functions(num_bins: int = 201):
print("Compiling mixture loss functions")
target_dist = HistogramDist(np.array([-float(num_bins)] * num_bins))
condition = HistogramCondition(*target_dist.to_arrays())
LogisticMixture.from_conditions([condition], num_components=3)