[docs]classBeta(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_intervalhas_rsample=Truedef__init__(self,concentration1,concentration0,validate_args=None):ifisinstance(concentration1,Real)andisinstance(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().__init__(self._dirichlet._batch_shape,validate_args=validate_args)
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