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_sqrt_beta(n, m, rng):
num_samples = 1000
num_bins = 5
vectors = rng.randn(num_samples, n + m)
vectors /= npext.norm(vectors, axis=1, keepdims=True)
expectation, _ = np.histogram(npext.norm(vectors[:, :m], axis=1), bins=num_bins)
dist = SqrtBeta(n, m)
samples = dist.sample(num_samples, 1, rng=rng)
histogram, _ = np.histogram(samples, bins=num_bins)
assert np.all(np.abs(np.asfarray(histogram - expectation) / num_samples) < 0.16)
def test_sqrt_beta_analytical(n, m, rng, allclose):
"""Tests pdf, cdf, and ppf of SqrtBeta distribution."""
pytest.importorskip("scipy") # beta and betainc
dt = 0.001
x = np.arange(dt, 1 + dt, dt)
dist = SqrtBeta(n, m)
pdf = dist.pdf(x)
cdf = dist.cdf(x)
ppf = dist.ppf(cdf)
# The pdf should reflect the samples
num_samples = 2500
num_bins = 5
samples = dist.sample(num_samples, rng=rng)
act_hist, _ = np.histogram(samples, bins=num_bins)
bin_points = np.linspace(0, 1, num_bins + 1)
bin_cdf = dist.cdf(bin_points)
exp_freq = bin_cdf[1:] - bin_cdf[:-1]
assert np.all(np.abs(np.asfarray(act_hist) / num_samples - exp_freq) < 0.1)
# The cdf should be the accumulated pdf
assert allclose(cdf, np.cumsum(pdf) * dt, atol=0.01)
# The ppf should give back x
assert allclose(x, ppf, atol=0.01)
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))
