# GENERATED FILE, DO NOT EDIT!
"""
Bivariate copulas.
"""
import jax.numpy as np
import tensorflow_probability.substrates.jax.bijectors as tfb
import tensorflow_probability.substrates.jax.distributions as tfd
from tensorflow_probability.substrates.jax.internal import parameter_properties
from ..bijectors import AlgebraicSigmoid
__docformat__ = "google"
[docs]class GaussianCopula(tfd.TransformedDistribution):
"""A bivariate Gaussian copula."""
def __init__(
self,
dependence=None,
validate_args=False,
allow_nan_stats=True,
name="GaussianCopula",
):
"""
Constructs the distribution.
Args:
dependence: The correlation parameter.
"""
parameters = dict(locals())
batch_shape = np.shape(dependence)
loc = np.zeros(batch_shape + (2,))
if dependence is None:
scale_tril = None
else:
if validate_args:
assert np.all(dependence >= 0.0)
assert np.all(dependence <= 1.0)
tril11 = np.broadcast_to(1.0, batch_shape)
tril12 = np.broadcast_to(0.0, batch_shape)
tril22 = np.sqrt(1.0 - dependence**2)
tril1 = np.stack([tril11, tril12], axis=-1)
tril2 = np.stack([dependence, tril22], axis=-1)
scale_tril = np.stack([tril1, tril2], axis=-2)
distribution = tfd.MultivariateNormalTriL(
loc=loc,
scale_tril=scale_tril,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
)
bijector = tfb.NormalCDF(validate_args=validate_args)
super().__init__(
distribution=distribution,
bijector=bijector,
validate_args=validate_args,
name=name,
)
self._parameters = parameters
@classmethod
def _parameter_properties(cls, dtype, num_classes=None):
return {
"dependence": parameter_properties.ParameterProperties(
shape_fn=lambda sample_shape: sample_shape[:-1],
default_constraining_bijector_fn=lambda: AlgebraicSigmoid(),
)
}
