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)