ΑiSSKJr SSKrSSKJr SSKrSSKJr SSKJ r SSK J r SSK J r \(a SSKJr SS KJr "S S \ R"5rS SS jjrg)) annotationsN) TYPE_CHECKING)check_variable_and_dtype) LayerHelper)exponential_family)in_dynamic_or_pir_mode)Sequence)Tensorc^\rSrSr%SrS\S'SU4Sjjr\SSj5r\SSj5r /4SSjjr SS jr SS jr SS jr \SS j5rSS jrSrU=r$) Dirichlet a Dirichlet distribution with parameter "concentration". The Dirichlet distribution is defined over the `(k-1)-simplex` using a positive, length-k vector concentration(`k > 1`). The Dirichlet is identically the Beta distribution when `k = 2`. For independent and identically distributed continuous random variable :math:`\boldsymbol X \in R_k` , and support :math:`\boldsymbol X \in (0,1), ||\boldsymbol X|| = 1` , The probability density function (pdf) is .. math:: f(\boldsymbol X; \boldsymbol \alpha) = \frac{1}{B(\boldsymbol \alpha)} \prod_{i=1}^{k}x_i^{\alpha_i-1} where :math:`\boldsymbol \alpha = {\alpha_1,...,\alpha_k}, k \ge 2` is parameter, the normalizing constant is the multivariate beta function. .. math:: B(\boldsymbol \alpha) = \frac{\prod_{i=1}^{k} \Gamma(\alpha_i)}{\Gamma(\alpha_0)} :math:`\alpha_0=\sum_{i=1}^{k} \alpha_i` is the sum of parameters, :math:`\Gamma(\alpha)` is gamma function. Args: concentration (Tensor): "Concentration" parameter of dirichlet distribution, also called :math:`\alpha`. When it's over one dimension, the last axis denotes the parameter of distribution, ``event_shape=concentration.shape[-1:]`` , axes other than last are consider batch dimensions with ``batch_shape=concentration.shape[:-1]`` . Examples: .. code-block:: python >>> import paddle >>> dirichlet = paddle.distribution.Dirichlet(paddle.to_tensor([1., 2., 3.])) >>> print(dirichlet.entropy()) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, -1.24434423) >>> print(dirichlet.prob(paddle.to_tensor([.3, .5, .6]))) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 10.80000019) r concentrationc>UR5S:d$[R"UR5S:Xa [ S5eXl[ TU]URSSURSS5 g)Nrz:`concentration` parameter must be at least one dimensional)dimmathprodshape ValueErrorrsuper__init__)selfr __class__s ]/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/distribution/dirichlet.pyrDirichlet.__init__Ssk     "dii 0C0C&D&IL + ,,Sb1=3F3Frs3KLcPURURRSSS9- $)zJMean of Dirichlet distribution. Returns: Mean value of distribution. rTkeepdim)rsumrs rmeanDirichlet.mean]s+!!D$6$6$:$:2t$:$LLLrcURRSSS9nURXR- -URS5US--- $)zRVariance of Dirichlet distribution. Returns: Variance value of distribution. rTrr)rr!pow)rconcentration0s rvarianceDirichlet.variancefsX++//D/A""n7I7I&IJ   q !^a%7 8  rc[U[5(aUO [U5n[URR UR U555$)zvSample from dirichlet distribution. Args: shape (Sequence[int], optional): Sample shape. Defaults to empty list. ) isinstancetuple _dirichletrexpand _extend_shape)rrs rsampleDirichlet.samplersB $E511uU|$,,33D4F4Fu4MNOOrcL[R"URU55$)zProbability density function(PDF) evaluated at value. Args: value (Tensor): Value to be evaluated. Returns: PDF evaluated at value. )paddleexplog_probrvalues rprobDirichlet.prob{szz$--.//rc,[R"U5URS- -RS5[R"URRS55-[R"UR5RS5- $)zWLog of probability density function. Args: value (Tensor): Value to be evaluated. ?r)r4logrr!lgammar7s rr6Dirichlet.log_probstZZ $"4"4s": ; @ @ DmmD..22267 8mmD../33B7 8 rcURRS5nURRSn[R"UR5RS5[R"U5- X!- [R "U5-- URS- [R "UR5-RS5- $)zJEntropy of Dirichlet distribution. Returns: Entropy of distribution. rr<)rr!rr4r>digamma)rr(ks rentropyDirichlet.entropys ++//3    $ $R ( MM$,, - 1 1" 5mmN+ ,!V^^N%CC D##c)V^^Dr!r4)rxs r_log_normalizerDirichlet._log_normalizers-xxz~~b!FMM!%%)$<<rwsC# <023(H="44H=Vr