def make_lognormal(kwargs):
kwargs['examples'] = [kwargs.pop('example')]
return Fixture(pyro_dist=(dist.lognormal, LogNormal),
scipy_dist=sp.lognorm,
scipy_arg_fn=lambda mu, sigma: ((np.array(sigma),),
{"scale": np.exp(np.array(mu))}),
**kwargs)
def lognormal():
return Fixture(pyro_dist=(dist.lognormal, LogNormal),
scipy_dist=sp.lognorm,
examples=[
{
'mu': [1.4],
'sigma': [0.4],
'test_data': [5.5]
},
],
scipy_arg_fn=lambda mu, sigma:
((np.array(sigma), ), {
"scale": np.exp(np.array(mu))
}))
continuous_dists = [
Fixture(
pyro_dist=dist.Uniform,
scipy_dist=sp.uniform,
examples=[
{
"low": [2.0],
"high": [2.5],
"test_data": [2.2]
},
{
"low": [2.0, 4.0],
"high": [3.0, 5.0],
"test_data": [[[2.5, 4.5]], [[2.5, 4.5]], [[2.5, 4.5]]],
},
{
"low": [[2.0], [-3.0], [0.0]],
"high": [[2.5], [0.0], [1.0]],
"test_data": [[2.2], [-2], [0.7]],
},
],
scipy_arg_fn=lambda low, high: (
(),
{
"loc": np.array(low),
"scale": np.array(high) - np.array(low)
},
),
),
Fixture(
pyro_dist=dist.Exponential,
super().__init__(dist.Normal(loc, scale))
continuous_dists = [
Fixture(pyro_dist=dist.Uniform,
scipy_dist=sp.uniform,
examples=[
{
'low': [2.],
'high': [2.5],
'test_data': [2.2]
},
{
'low': [2., 4.],
'high': [3., 5.],
'test_data': [[[2.5, 4.5]], [[2.5, 4.5]], [[2.5, 4.5]]]
},
{
'low': [[2.], [-3.], [0.]],
'high': [[2.5], [0.], [1.]],
'test_data': [[2.2], [-2], [0.7]]
},
],
scipy_arg_fn=lambda low, high: (
(), {
"loc": np.array(low),
"scale": np.array(high) - np.array(low)
})),
Fixture(pyro_dist=dist.Exponential,
scipy_dist=sp.expon,
examples=[
{
import scipy.stats as sp
import pyro.distributions as dist
from pyro.distributions.testing.naive_dirichlet import NaiveBeta, NaiveDirichlet
from pyro.distributions.testing.rejection_exponential import RejectionExponential
from pyro.distributions.testing.rejection_gamma import ShapeAugmentedBeta, ShapeAugmentedDirichlet, ShapeAugmentedGamma
from tests.distributions.dist_fixture import Fixture
continuous_dists = [
Fixture(pyro_dist=dist.Uniform,
scipy_dist=sp.uniform,
examples=[
{'low': [2.], 'high': [2.5],
'test_data': [2.2]},
{'low': [2., 4.], 'high': [3., 5.],
'test_data': [[[2.5, 4.5]], [[2.5, 4.5]], [[2.5, 4.5]]]},
{'low': [[2.], [-3.], [0.]],
'high': [[2.5], [0.], [1.]],
'test_data': [[2.2], [-2], [0.7]]},
],
scipy_arg_fn=lambda low, high: ((), {"loc": np.array(low),
"scale": np.array(high) - np.array(low)})),
Fixture(pyro_dist=dist.Exponential,
scipy_dist=sp.expon,
examples=[
{'rate': [2.4],
'test_data': [5.5]},
{'rate': [2.4, 5.5],
'test_data': [[[5.5, 3.2]], [[5.5, 3.2]], [[5.5, 3.2]]]},
{'rate': [[2.4, 5.5]],
'test_data': [[[5.5, 3.2]], [[5.5, 3.2]], [[5.5, 3.2]]]},
import scipy.stats as sp
import pyro.distributions as dist
from pyro.distributions import (Bernoulli, Beta, Binomial, Categorical, Cauchy, Dirichlet, Exponential, Gamma,
HalfCauchy, LogNormal, Multinomial, MultivariateNormal, Normal, OneHotCategorical,
Poisson, Uniform)
from tests.distributions.dist_fixture import Fixture
continuous_dists = [
Fixture(pyro_dist=(dist.uniform, Uniform),
scipy_dist=sp.uniform,
examples=[
{'a': [2], 'b': [2.5],
'test_data': [2.2]},
{'a': [2, 4], 'b': [3, 5],
'test_data': [[[2.5, 4.5]], [[2.5, 4.5]], [[2.5, 4.5]]]},
{'a': [[2], [-3], [0]],
'b': [[2.5], [0], [1]],
'test_data': [[2.2], [-2], [0.7]]},
],
scipy_arg_fn=lambda a, b: ((), {"loc": np.array(a),
"scale": np.array(b) - np.array(a)})),
Fixture(pyro_dist=(dist.exponential, Exponential),
scipy_dist=sp.expon,
examples=[
{'lam': [2.4],
'test_data': [5.5]},
{'lam': [2.4, 5.5],
'test_data': [[[5.5, 3.2]], [[5.5, 3.2]], [[5.5, 3.2]]]},
{'lam': [[2.4, 5.5]],
'test_data': [[[5.5, 3.2]], [[5.5, 3.2]], [[5.5, 3.2]]]},
Uniform)
from tests.distributions.dist_fixture import Fixture
continuous_dists = [
Fixture(pyro_dist=(dist.uniform, Uniform),
scipy_dist=sp.uniform,
examples=[
{
'a': [2],
'b': [2.5],
'test_data': [2.2]
},
{
'a': [2, 4],
'b': [3, 5],
'test_data': [[[2.5, 4.5]], [[2.5, 4.5]], [[2.5, 4.5]]]
},
{
'a': [[2], [-3], [0]],
'b': [[2.5], [0], [1]],
'test_data': [[2.2], [-2], [0.7]]
},
],
scipy_arg_fn=lambda a, b: ((), {
"loc": np.array(a),
"scale": np.array(b) - np.array(a)
})),
Fixture(pyro_dist=(dist.exponential, Exponential),
scipy_dist=sp.expon,
examples=[
{