def test_distributions():
check_init_args(PDF, ["x", "p"])
check_repr(PDF([1, 2, 3], [0.1, 0.8, 0.1]))
assert (repr(PDF(
[1, 2], [0.4, 0.6])) == "PDF(x=array([1., 2.]), p=array([0.4, 0.6]))")
check_init_args(Uniform, ["low", "high", "integer"])
check_repr(Uniform(1, 3))
check_repr(Uniform(1, 4, integer=True))
assert repr(Uniform(0, 1)) == "Uniform(low=0, high=1)"
assert repr(Uniform(
0, 5, integer=True)) == "Uniform(low=0, high=5, integer=True)"
check_init_args(Gaussian, ["mean", "std"])
check_repr(Gaussian(0, 2))
assert repr(Gaussian(1, 0.1)) == "Gaussian(mean=1, std=0.1)"
check_init_args(Exponential, ["scale", "shift", "high"])
check_repr(Exponential(2.0))
check_repr(Exponential(2.0, shift=0.1))
check_repr(Exponential(2.0, shift=0.1, high=10.0))
assert repr(Exponential(2.0)) == "Exponential(scale=2.0)"
check_init_args(UniformHypersphere, ["surface", "min_magnitude"])
check_repr(UniformHypersphere())
check_repr(UniformHypersphere(surface=True))
check_repr(UniformHypersphere(min_magnitude=0.3))
assert repr(UniformHypersphere()) == "UniformHypersphere()"
assert repr(
UniformHypersphere(surface=True)) == "UniformHypersphere(surface=True)"
check_init_args(Choice, ["options", "weights"])
check_repr(Choice([3, 2, 1]))
check_repr(Choice([3, 2, 1], weights=[0.1, 0.2, 0.7]))
assert repr(Choice([1, 2, 3])) == "Choice(options=array([1., 2., 3.]))"
assert (repr(
Choice([1, 2, 3], weights=[0.1, 0.5, 0.4])
) == "Choice(options=array([1., 2., 3.]), weights=array([0.1, 0.5, 0.4]))")
check_init_args(Samples, ["samples"])
check_repr(Samples([3, 2, 1]))
assert repr(Samples([3, 2, 1])) == "Samples(samples=array([3., 2., 1.]))"
check_init_args(SqrtBeta, ["n", "m"])
check_repr(SqrtBeta(3))
check_repr(SqrtBeta(3, m=2))
assert repr(SqrtBeta(3)) == "SqrtBeta(n=3)"
assert repr(SqrtBeta(3, 2)) == "SqrtBeta(n=3, m=2)"
check_init_args(SubvectorLength, ["dimensions", "subdimensions"])
check_repr(SubvectorLength(6))
check_repr(SubvectorLength(6, 2))
assert repr(SubvectorLength(3)) == "SubvectorLength(dimensions=3)"
check_init_args(CosineSimilarity, ["dimensions"])
check_repr(CosineSimilarity(6))
assert repr(CosineSimilarity(6)) == "CosineSimilarity(dimensions=6)"
def test_pdf(rng, allclose):
s = 0.25
f = lambda x: (
np.exp(-0.5 * (x + 0.5) ** 2 / s ** 2) + np.exp(-0.5 * (x - 0.5) ** 2 / s ** 2)
)
xref = np.linspace(-2, 2, 101)
pref = f(xref)
pref /= pref.sum()
dist = PDF(xref, pref)
n = 100000
samples = dist.sample(n, rng=rng)
h, xedges = np.histogram(samples, bins=101)
x = 0.5 * (xedges[:-1] + xedges[1:])
dx = np.diff(xedges)
y = h / float(h.sum()) / dx
z = f(x)
z = z / z.sum() / dx
assert allclose(y, z, atol=0.05)
with pytest.raises(ValidationError, match="PDF must sum to one"):
dist = PDF([0, 1, 2], [0.1, 1.1, 0.1])
with pytest.raises(ValidationError, match="`x` and `p` must be the same length"):
dist = PDF([0, 1], [0, 1, 0])
def test_pdf(rng):
s = 0.25
f = lambda x: (np.exp(-0.5 *
(x + 0.5)**2 / s**2) + np.exp(-0.5 *
(x - 0.5)**2 / s**2))
xref = np.linspace(-2, 2, 101)
pref = f(xref)
pref /= pref.sum()
dist = PDF(xref, pref)
n = 100000
samples = dist.sample(n, rng=rng)
h, xedges = np.histogram(samples, bins=101)
x = 0.5 * (xedges[:-1] + xedges[1:])
dx = np.diff(xedges)
y = h / float(h.sum()) / dx
z = f(x)
z = z / z.sum() / dx
assert np.allclose(y, z, atol=0.05)
def test_argreprs():
def check_init_args(cls, args):
assert getfullargspec(cls.__init__).args[1:] == args
def check_repr(obj):
assert eval(repr(obj)) == obj
check_init_args(PDF, ['x', 'p'])
check_repr(PDF([1, 2, 3], [0.1, 0.8, 0.1]))
check_init_args(Uniform, ['low', 'high', 'integer'])
check_repr(Uniform(1, 3))
check_repr(Uniform(1, 4, integer=True))
check_init_args(Gaussian, ['mean', 'std'])
check_repr(Gaussian(0, 2))
check_init_args(Exponential, ['scale', 'shift', 'high'])
check_repr(Exponential(2.))
check_repr(Exponential(2., shift=0.1))
check_repr(Exponential(2., shift=0.1, high=10.))
check_init_args(UniformHypersphere, ['surface', 'min_magnitude'])
check_repr(UniformHypersphere())
check_repr(UniformHypersphere(surface=True))
check_repr(UniformHypersphere(min_magnitude=0.3))
check_init_args(Choice, ['options', 'weights'])
check_repr(Choice([3, 2, 1]))
check_repr(Choice([3, 2, 1], weights=[0.1, 0.2, 0.7]))
check_init_args(Samples, ['samples'])
check_repr(Samples([3, 2, 1]))
check_init_args(SqrtBeta, ['n', 'm'])
check_repr(SqrtBeta(3))
check_repr(SqrtBeta(3, m=2))
check_init_args(SubvectorLength, ['dimensions', 'subdimensions'])
check_repr(SubvectorLength(6))
check_repr(SubvectorLength(6, 2))
check_init_args(CosineSimilarity, ['dimensions'])
check_repr(CosineSimilarity(6))
def test_argreprs():
def check_init_args(cls, args):
assert getfullargspec(cls.__init__).args[1:] == args
def check_repr(obj):
assert eval(repr(obj)) == obj
check_init_args(PDF, ["x", "p"])
check_repr(PDF([1, 2, 3], [0.1, 0.8, 0.1]))
check_init_args(Uniform, ["low", "high", "integer"])
check_repr(Uniform(1, 3))
check_repr(Uniform(1, 4, integer=True))
check_init_args(Gaussian, ["mean", "std"])
check_repr(Gaussian(0, 2))
check_init_args(Exponential, ["scale", "shift", "high"])
check_repr(Exponential(2.0))
check_repr(Exponential(2.0, shift=0.1))
check_repr(Exponential(2.0, shift=0.1, high=10.0))
check_init_args(UniformHypersphere, ["surface", "min_magnitude"])
check_repr(UniformHypersphere())
check_repr(UniformHypersphere(surface=True))
check_repr(UniformHypersphere(min_magnitude=0.3))
check_init_args(Choice, ["options", "weights"])
check_repr(Choice([3, 2, 1]))
check_repr(Choice([3, 2, 1], weights=[0.1, 0.2, 0.7]))
check_init_args(Samples, ["samples"])
check_repr(Samples([3, 2, 1]))
check_init_args(SqrtBeta, ["n", "m"])
check_repr(SqrtBeta(3))
check_repr(SqrtBeta(3, m=2))
check_init_args(SubvectorLength, ["dimensions", "subdimensions"])
check_repr(SubvectorLength(6))
check_repr(SubvectorLength(6, 2))
check_init_args(CosineSimilarity, ["dimensions"])
check_repr(CosineSimilarity(6))