class Beta(Distribution):
r"""
Beta distribution parameterized by `concentration1` and `concentration0`.
Example::
>>> m = Beta(torch.Tensor([0.5]), torch.Tensor([0.5]))
>>> m.sample() # Beta distributed with concentration concentration1 and concentration0
0.1046
[torch.FloatTensor of size 1]
Args:
concentration1 (float or Tensor or Variable): 1st concentration parameter of the distribution
(often referred to as alpha)
concentration0 (float or Tensor or Variable): 2nd concentration parameter of the distribution
(often referred to as beta)
"""
params = {'concentration1': constraints.positive, 'concentration0': constraints.positive}
support = constraints.unit_interval
has_rsample = True
def __init__(self, concentration1, concentration0):
if isinstance(concentration1, Number) and isinstance(concentration0, Number):
concentration1_concentration0 = torch.Tensor([concentration1, concentration0])
else:
concentration1, concentration0 = broadcast_all(concentration1, concentration0)
concentration1_concentration0 = torch.stack([concentration1, concentration0], -1)
self._dirichlet = Dirichlet(concentration1_concentration0)
super(Beta, self).__init__(self._dirichlet._batch_shape)
def rsample(self, sample_shape=()):
value = self._dirichlet.rsample(sample_shape).select(-1, 0)
if isinstance(value, Number):
value = self._dirichlet.concentration.new([value])
return value
def log_prob(self, value):
self._validate_log_prob_arg(value)
heads_tails = torch.stack([value, 1.0 - value], -1)
return self._dirichlet.log_prob(heads_tails)
def entropy(self):
return self._dirichlet.entropy()
@property
def concentration1(self):
result = self._dirichlet.concentration[..., 0]
if isinstance(result, Number):
return torch.Tensor([result])
else:
return result
@property
def concentration0(self):
result = self._dirichlet.concentration[..., 1]
if isinstance(result, Number):
return torch.Tensor([result])
else:
return result
class Beta(Distribution):
r"""
Creates a Beta distribution parameterized by concentration `alpha` and `beta`.
Example::
>>> m = Beta(torch.Tensor([0.5]), torch.Tensor([0.5]))
>>> m.sample() # Beta distributed with concentration alpha and beta
0.1046
[torch.FloatTensor of size 1]
Args:
alpha (float or Tensor or Variable): 1st concentration parameter of the distribution
beta (float or Tensor or Variable): 2nd concentration parameter of the distribution
"""
params = {'alpha': constraints.positive, 'beta': constraints.positive}
support = constraints.unit_interval
has_rsample = True
def __init__(self, alpha, beta):
if isinstance(alpha, Number) and isinstance(beta, Number):
alpha_beta = torch.Tensor([alpha, beta])
else:
alpha, beta = broadcast_all(alpha, beta)
alpha_beta = torch.stack([alpha, beta], -1)
self._dirichlet = Dirichlet(alpha_beta)
super(Beta, self).__init__(self._dirichlet._batch_shape)
def rsample(self, sample_shape=()):
value = self._dirichlet.rsample(sample_shape).select(-1, 0)
if isinstance(value, Number):
value = self._dirichlet.alpha.new([value])
return value
def log_prob(self, value):
self._validate_log_prob_arg(value)
heads_tails = torch.stack([value, 1.0 - value], -1)
return self._dirichlet.log_prob(heads_tails)
def entropy(self):
return self._dirichlet.entropy()
@property
def alpha(self):
result = self._dirichlet.alpha[..., 0]
if isinstance(result, Number):
return torch.Tensor([result])
else:
return result
@property
def beta(self):
result = self._dirichlet.alpha[..., 1]
if isinstance(result, Number):
return torch.Tensor([result])
else:
return result
class Beta(Distribution):
r"""
Creates a Beta distribution parameterized by concentration `alpha` and `beta`.
Example::
>>> m = Beta(torch.Tensor([0.5]), torch.Tensor([0.5]))
>>> m.sample() # Beta distributed with concentrarion alpha
0.1046
[torch.FloatTensor of size 2]
Args:
alpha (Tensor or Variable): concentration parameter of the distribution
"""
params = {'alpha': constraints.positive, 'beta': constraints.positive}
support = constraints.unit_interval
has_rsample = True
def __init__(self, alpha, beta):
if isinstance(alpha, Number) and isinstance(beta, Number):
alpha_beta = torch.Tensor([alpha, beta])
else:
alpha, beta = broadcast_all(alpha, beta)
alpha_beta = torch.stack([alpha, beta], -1)
self._dirichlet = Dirichlet(alpha_beta)
super(Beta, self).__init__(self._dirichlet._batch_shape)
def rsample(self, sample_shape=()):
value = self._dirichlet.rsample(sample_shape).select(-1, 0)
if isinstance(value, Number):
value = self._dirichlet.alpha.new([value])
return value
def log_prob(self, value):
self._validate_log_prob_arg(value)
heads_tails = torch.stack([value, 1.0 - value], -1)
return self._dirichlet.log_prob(heads_tails)
def entropy(self):
return self._dirichlet.entropy()
class Beta(Distribution):
r"""
Creates a Beta distribution parameterized by concentration `alpha` and `beta`.
Example::
>>> m = Beta(torch.Tensor([0.5]), torch.Tensor([0.5]))
>>> m.sample() # Beta distributed with concentrarion alpha
0.1046
[torch.FloatTensor of size 2]
Args:
alpha (Tensor or Variable): concentration parameter of the distribution
"""
has_rsample = True
def __init__(self, alpha, beta):
if isinstance(alpha, Number) and isinstance(beta, Number):
alpha_beta = torch.Tensor([alpha, beta])
else:
alpha, beta = broadcast_all(alpha, beta)
alpha_beta = torch.stack([alpha, beta], -1)
self._dirichlet = Dirichlet(alpha_beta)
super(Beta, self).__init__(self._dirichlet._batch_shape)
def rsample(self, sample_shape=()):
value = self._dirichlet.rsample(sample_shape).select(-1, 0)
if isinstance(value, Number):
value = self._dirichlet.alpha.new([value])
return value
def log_prob(self, value):
self._validate_log_prob_arg(value)
heads_tails = torch.stack([value, 1.0 - value], -1)
return self._dirichlet.log_prob(heads_tails)
def entropy(self):
return self._dirichlet.entropy()
class Beta(ExponentialFamily):
r"""
Beta distribution parameterized by `concentration1` and `concentration0`.
Example::
>>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
>>> m.sample() # Beta distributed with concentration concentration1 and concentration0
0.1046
[torch.FloatTensor of size 1]
Args:
concentration1 (float or Tensor): 1st concentration parameter of the distribution
(often referred to as alpha)
concentration0 (float or Tensor): 2nd concentration parameter of the distribution
(often referred to as beta)
"""
arg_constraints = {
'concentration1': constraints.positive,
'concentration0': constraints.positive
}
support = constraints.unit_interval
has_rsample = True
def __init__(self, concentration1, concentration0, validate_args=None):
if isinstance(concentration1, Number) and isinstance(
concentration0, Number):
concentration1_concentration0 = torch.tensor(
[float(concentration1),
float(concentration0)])
else:
concentration1, concentration0 = broadcast_all(
concentration1, concentration0)
concentration1_concentration0 = torch.stack(
[concentration1, concentration0], -1)
self._dirichlet = Dirichlet(concentration1_concentration0)
super(Beta, self).__init__(self._dirichlet._batch_shape,
validate_args=validate_args)
@property
def mean(self):
return self.concentration1 / (self.concentration1 +
self.concentration0)
@property
def variance(self):
total = self.concentration1 + self.concentration0
return (self.concentration1 * self.concentration0 / (total.pow(2) *
(total + 1)))
def rsample(self, sample_shape=()):
value = self._dirichlet.rsample(sample_shape).select(-1, 0)
if isinstance(value, Number):
value = self._dirichlet.concentration.new_tensor(value)
return value
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
heads_tails = torch.stack([value, 1.0 - value], -1)
return self._dirichlet.log_prob(heads_tails)
def entropy(self):
return self._dirichlet.entropy()
@property
def concentration1(self):
result = self._dirichlet.concentration[..., 0]
if isinstance(result, Number):
return torch.Tensor([result])
else:
return result
@property
def concentration0(self):
result = self._dirichlet.concentration[..., 1]
if isinstance(result, Number):
return torch.Tensor([result])
else:
return result
@property
def _natural_params(self):
return (self.concentration1, self.concentration0)
def _log_normalizer(self, x, y):
return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y)
class Beta(ExponentialFamily):
r"""
Beta distribution parameterized by `concentration1` and `concentration0`.
Example::
>>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
>>> m.sample() # Beta distributed with concentration concentration1 and concentration0
tensor([ 0.1046])
Args:
concentration1 (float or Tensor): 1st concentration parameter of the distribution
(often referred to as alpha)
concentration0 (float or Tensor): 2nd concentration parameter of the distribution
(often referred to as beta)
"""
arg_constraints = {'concentration1': constraints.positive, 'concentration0': constraints.positive}
support = constraints.unit_interval
has_rsample = True
def __init__(self, concentration1, concentration0, validate_args=None):
if isinstance(concentration1, Number) and isinstance(concentration0, Number):
concentration1_concentration0 = torch.tensor([float(concentration1), float(concentration0)])
else:
concentration1, concentration0 = broadcast_all(concentration1, concentration0)
concentration1_concentration0 = torch.stack([concentration1, concentration0], -1)
self._dirichlet = Dirichlet(concentration1_concentration0)
super(Beta, self).__init__(self._dirichlet._batch_shape, validate_args=validate_args)
@property
def mean(self):
return self.concentration1 / (self.concentration1 + self.concentration0)
@property
def variance(self):
total = self.concentration1 + self.concentration0
return (self.concentration1 * self.concentration0 /
(total.pow(2) * (total + 1)))
def rsample(self, sample_shape=()):
value = self._dirichlet.rsample(sample_shape).select(-1, 0)
if isinstance(value, Number):
value = self._dirichlet.concentration.new_tensor(value)
return value
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
heads_tails = torch.stack([value, 1.0 - value], -1)
return self._dirichlet.log_prob(heads_tails)
def entropy(self):
return self._dirichlet.entropy()
@property
def concentration1(self):
result = self._dirichlet.concentration[..., 0]
if isinstance(result, Number):
return torch.tensor([result])
else:
return result
@property
def concentration0(self):
result = self._dirichlet.concentration[..., 1]
if isinstance(result, Number):
return torch.tensor([result])
else:
return result
@property
def _natural_params(self):
return (self.concentration1, self.concentration0)
def _log_normalizer(self, x, y):
return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y)
class Beta(Distribution):
r"""
Beta distribution parameterized by `concentration1` and `concentration0`.
Example::
>>> m = Beta(torch.Tensor([0.5]), torch.Tensor([0.5]))
>>> m.sample() # Beta distributed with concentration concentration1 and concentration0
0.1046
[torch.FloatTensor of size 1]
Args:
concentration1 (float or Tensor or Variable): 1st concentration parameter of the distribution
(often referred to as alpha)
concentration0 (float or Tensor or Variable): 2nd concentration parameter of the distribution
(often referred to as beta)
"""
params = {'concentration1': constraints.positive, 'concentration0': constraints.positive}
support = constraints.unit_interval
has_rsample = True
def __init__(self, concentration1, concentration0):
if isinstance(concentration1, Number) and isinstance(concentration0, Number):
concentration1_concentration0 = variable([concentration1, concentration0])
else:
concentration1, concentration0 = broadcast_all(concentration1, concentration0)
concentration1_concentration0 = torch.stack([concentration1, concentration0], -1)
self._dirichlet = Dirichlet(concentration1_concentration0)
super(Beta, self).__init__(self._dirichlet._batch_shape)
@property
def mean(self):
return self.concentration1 / (self.concentration1 + self.concentration0)
@property
def variance(self):
total = self.concentration1 + self.concentration0
return (self.concentration1 * self.concentration0 /
(total.pow(2) * (total + 1)))
def rsample(self, sample_shape=()):
value = self._dirichlet.rsample(sample_shape).select(-1, 0)
if isinstance(value, Number):
value = self._dirichlet.concentration.new([value])
return value
def log_prob(self, value):
self._validate_log_prob_arg(value)
heads_tails = torch.stack([value, 1.0 - value], -1)
return self._dirichlet.log_prob(heads_tails)
def entropy(self):
return self._dirichlet.entropy()
@property
def concentration1(self):
result = self._dirichlet.concentration[..., 0]
if isinstance(result, Number):
return torch.Tensor([result])
else:
return result
@property
def concentration0(self):
result = self._dirichlet.concentration[..., 1]
if isinstance(result, Number):
return torch.Tensor([result])
else:
return result
class Beta(ExponentialFamily):
r"""
Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`.
Example::
>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
>>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
>>> m.sample() # Beta distributed with concentration concentration1 and concentration0
tensor([ 0.1046])
Args:
concentration1 (float or Tensor): 1st concentration parameter of the distribution
(often referred to as alpha)
concentration0 (float or Tensor): 2nd concentration parameter of the distribution
(often referred to as beta)
"""
arg_constraints = {
'concentration1': constraints.positive,
'concentration0': constraints.positive
}
support = constraints.unit_interval
has_rsample = True
def __init__(self, concentration1, concentration0, validate_args=None):
if isinstance(concentration1, Real) and isinstance(
concentration0, Real):
concentration1_concentration0 = torch.tensor(
[float(concentration1),
float(concentration0)])
else:
concentration1, concentration0 = broadcast_all(
concentration1, concentration0)
concentration1_concentration0 = torch.stack(
[concentration1, concentration0], -1)
self._dirichlet = Dirichlet(concentration1_concentration0,
validate_args=validate_args)
super(Beta, self).__init__(self._dirichlet._batch_shape,
validate_args=validate_args)
def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(Beta, _instance)
batch_shape = torch.Size(batch_shape)
new._dirichlet = self._dirichlet.expand(batch_shape)
super(Beta, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new
@property
def mean(self):
return self.concentration1 / (self.concentration1 +
self.concentration0)
@property
def mode(self):
return self._dirichlet.mode[..., 0]
@property
def variance(self):
total = self.concentration1 + self.concentration0
return (self.concentration1 * self.concentration0 / (total.pow(2) *
(total + 1)))
def rsample(self, sample_shape=()):
return self._dirichlet.rsample(sample_shape).select(-1, 0)
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
heads_tails = torch.stack([value, 1.0 - value], -1)
return self._dirichlet.log_prob(heads_tails)
def entropy(self):
return self._dirichlet.entropy()
@property
def concentration1(self):
result = self._dirichlet.concentration[..., 0]
if isinstance(result, Number):
return torch.tensor([result])
else:
return result
@property
def concentration0(self):
result = self._dirichlet.concentration[..., 1]
if isinstance(result, Number):
return torch.tensor([result])
else:
return result
@property
def _natural_params(self):
return (self.concentration1, self.concentration0)
def _log_normalizer(self, x, y):
return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y)
