ΑiSSKJr SSKrSSKJr SSKrSSKJrJr \(a SSK J r SSKJ r "SS\R5r g) ) annotationsN) TYPE_CHECKING) dirichletexponential_family)Sequence)Tensorc^\rSrSr%SrS\S'S\S'SU4Sjjr\SSj5r\SSj5r SS jr SS jr /4SS jjr SS jr \SS j5rSSjrSrU=r$)BetaaH Beta distribution parameterized by alpha and beta. In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] parameterized by two positive shape parameters, denoted by alpha and beta, that appear as exponents of the random variable and control the shape of the distribution. The generalization to multiple variables is called a Dirichlet distribution. The probability density function (pdf) is .. math:: f(x; \alpha, \beta) = \frac{1}{B(\alpha, \beta)}x^{\alpha-1}(1-x)^{\beta-1} where the normalization, B, is the beta function, .. math:: B(\alpha, \beta) = \int_{0}^{1} t^{\alpha - 1} (1-t)^{\beta - 1}\mathrm{d}t Args: alpha (float|Tensor): Alpha parameter. It supports broadcast semantics. The value of alpha must be positive. When the parameter is a tensor, it represents multiple independent distribution with a batch_shape(refer to ``Distribution`` ). beta (float|Tensor): Beta parameter. It supports broadcast semantics. The value of beta must be positive(>0). When the parameter is tensor, it represent multiple independent distribution with a batch_shape(refer to ``Distribution`` ). Examples: .. code-block:: python >>> import paddle >>> # scale input >>> beta = paddle.distribution.Beta(alpha=0.5, beta=0.5) >>> print(beta.mean) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.50000000) >>> print(beta.variance) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.12500000) >>> print(beta.entropy()) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, -0.24156499) >>> # tensor input with broadcast >>> beta = paddle.distribution.Beta(alpha=paddle.to_tensor([0.2, 0.4]), beta=0.6) >>> print(beta.mean) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [0.25000000, 0.40000001]) >>> print(beta.variance) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [0.10416666, 0.12000000]) >>> print(beta.entropy()) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [-1.91923141, -0.38095081]) ralphabetac>[U[R5(a[R"/US9n[U[R5(a[R"/US9n[R "X/5uUlUl[R"[R"UR UR/S55Ul [TU]5URR5 g)N)shape fill_value) isinstancenumbersRealpaddlefullbroadcast_tensorsr r r Dirichletstack _dirichletsuper__init__ _batch_shape)selfr r __class__s X/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/distribution/beta.pyr Beta.__init__ds eW\\ * *KKbU;E dGLL ) );;RD9D & 8 8% G DI#-- LL$**dii0" 5  556cNURURUR-- $)zMean of beta distribution.r r rs r mean Beta.meanss zzTZZ$))344r"cURUR-nURUR-URS5US--- $)zVariance of beat distribution)r r pow)rsums r variance Beta.variancexs?jj499$zzDII%sQw)?@@r"cL[R"URU55$)zProbability density function evaluated at value Args: value (Tensor): Value to be evaluated. Returns: Tensor: Probability. )rexplog_probrvalues r prob Beta.prob~szz$--.//r"clURR[R"USU- /S55$)zLog probability density function evaluated at value Args: value (Tensor): Value to be evaluated Returns: Tensor: Log probability. g?r)rr1rrr2s r r1 Beta.log_probs-'' eS5[5I2(NOOr"c[U[5(aUO [U5n[R"URR U5SSS9$)zSample from beta distribution with sample shape. Args: shape (Sequence[int], optional): Sample shape. Returns: Tensor, Sampled data with shape `sample_shape` + `batch_shape` + `event_shape`. ).rr)axis)rtuplersqueezersample)rrs r r< Beta.samples@$E511uU|~~doo44U;FC"MMr"c6URR5$)zAEntropy of dirichlet distribution Returns: Tensor: Entropy. )rentropyr%s r r? Beta.entropys &&((r"c2URUR4$Nr$r%s r _natural_parametersBeta._natural_parameterss DII&&r"c[R"U5[R"U5-[R"X-5- $rB)rlgamma)rxys r _log_normalizerBeta._log_normalizers/}}Q&--"22V]]155IIIr")rr r )r float | Tensorr rKreturnNone)rLr)r3rrLr)rz Sequence[int]rLr)rLztuple[Tensor, Tensor])rGrrHrrLr)__name__ __module__ __qualname____firstlineno____doc____annotations__rpropertyr&r-r4r1r<r?rCrI__static_attributes__ __classcell__)rs@r r r sBH M L 755AA 0 P-/ N)''JJr"r ) __future__rrtypingrrpaddle.distributionrrcollections.abcrrExponentialFamilyr r"r r]s6# =(QJ  / /QJr"