def marginal_mixture_logcdf(op, value, rng, weights, *components, **kwargs):
# single component
if len(components) == 1:
# Need to broadcast value across mixture axis
mix_axis = -components[0].owner.op.ndim_supp - 1
components_logcdf = logcdf(components[0],
at.expand_dims(value, mix_axis))
else:
components_logcdf = at.stack(
[logcdf(component, value) for component in components],
axis=-1,
)
mix_logcdf = at.logsumexp(at.log(weights) + components_logcdf, axis=-1)
# Squeeze stack dimension
# There is a Aesara bug in squeeze with negative axis
# https://github.com/aesara-devs/aesara/issues/830
# mix_logp = at.squeeze(mix_logp, axis=-1)
mix_logcdf = at.squeeze(mix_logcdf, axis=mix_logcdf.ndim - 1)
mix_logcdf = check_parameters(
mix_logcdf,
0 <= weights,
weights <= 1,
at.isclose(at.sum(weights, axis=-1), 1),
msg="0 <= weights <= 1, sum(weights) == 1",
)
return mix_logcdf
def marginal_mixture_logcdf(op, value, rng, weights, *components, **kwargs):
# single component
if len(components) == 1:
# Need to broadcast value across mixture axis
mix_axis = -components[0].owner.op.ndim_supp - 1
components_logcdf = logcdf(components[0],
at.expand_dims(value, mix_axis))
else:
components_logcdf = at.stack(
[logcdf(component, value) for component in components],
axis=-1,
)
mix_logcdf = at.logsumexp(at.log(weights) + components_logcdf, axis=-1)
mix_logcdf = check_parameters(
mix_logcdf,
0 <= weights,
weights <= 1,
at.isclose(at.sum(weights, axis=-1), 1),
msg="0 <= weights <= 1, sum(weights) == 1",
)
return mix_logcdf
def logp(value, n, p):
return check_parameters(
factln(n) - factln(value).sum() + (value * at.log(p)).sum(),
at.all(value >= 0),
at.all(0 <= p),
at.all(p <= 1),
at.isclose(p.sum(), 1),
)
def logp(value, n, p):
return check_parameters(
factln(n) - factln(value).sum() + (value * at.log(p)).sum(),
value >= 0,
0 <= p,
p <= 1,
at.isclose(p.sum(), 1),
)
def logp(value, n, p):
return bound(
factln(n) - factln(value).sum() + (value * at.log(p)).sum(),
value >= 0,
0 <= p,
p <= 1,
at.isclose(p.sum(), 1),
broadcast_conditions=False,
)
def logp(self, value):
n = self.n
p = self.p
return bound(
factln(n) - factln(value).sum() + (value * at.log(p)).sum(),
at.all(value >= 0),
at.all(0 <= p),
at.all(p <= 1),
at.isclose(p.sum(), 1),
broadcast_conditions=False,
)
def logp(self, value):
n = self.n
p = self.p
return bound(
factln(n) - factln(value).sum() + (value * aet.log(p)).sum(),
value >= 0,
0 <= p,
p <= 1,
aet.isclose(p.sum(), 1),
broadcast_conditions=False,
)
