ΑiSSKJr SSKJr SSKrSSKJr SSKJrJr SSK J r SSK J r SSK Jr \(aSS KJr SS KJrJr "S S \ R&5rg) ) annotations) TYPE_CHECKINGN) distribution) check_type convert_dtype)Variable)exponential_family)in_dynamic_mode)Sequence)Tensordtypec^\rSrSr%SrS\S'S\S'S\S'SU4Sjjr\SSj5r\SS j5r SS jr SS jr SS jr /4SS jjr /4SSjjrSSjrSSjrSSjrSrU=r$)Gamma a> Gamma distribution parameterized by :attr:`concentration` (aka "alpha") and :attr:`rate` (aka "beta"). The probability density function (pdf) is .. math:: f(x; \alpha, \beta, x > 0) = \frac{\beta^{\alpha}}{\Gamma(\alpha)} x^{\alpha-1}e^{-\beta x} \Gamma(\alpha)=\int_{0}^{\infty} x^{\alpha-1} e^{-x} \mathrm{~d} x, (\alpha>0) Args: concentration (float|Tensor): Concentration parameter. It supports broadcast semantics. The value of concentration must be positive. When the parameter is a tensor, it represents multiple independent distribution with a batch_shape(refer to :ref:`api_paddle_distribution_Distribution`). rate (float|Tensor): Rate parameter. It supports broadcast semantics. The value of rate must be positive. When the parameter is tensor, it represent multiple independent distribution with a batch_shape(refer to :ref:`api_paddle_distribution_Distribution`). Example: .. code-block:: python >>> import paddle >>> # scale input >>> gamma = paddle.distribution.Gamma(0.5, 0.5) >>> print(gamma.mean) Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, 1.) >>> print(gamma.variance) Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, 2.) >>> print(gamma.entropy()) Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, 0.78375685) >>> # tensor input with broadcast >>> gamma = paddle.distribution.Gamma(paddle.to_tensor([0.2, 0.4]), paddle.to_tensor(0.6)) >>> print(gamma.mean) Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [0.33333331, 0.66666663]) >>> print(gamma.variance) Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [0.55555552, 1.11111104]) >>> print(gamma.entropy()) Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [-1.99634242, 0.17067254]) r concentrationrater c>[5(db[US[[[R R 4S5 [US[[[R R 4S5 URX5(a'XlX l [UR5Ul O8URX5uUlUl [R"5Ul [TU]=URR 5 g)Nrrr)r rfloatrpaddlepirValue_validate_argsrrrr _to_tensorget_default_dtypesuper__init__shape)selfrr __class__s Y/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/distribution/gamma.pyrGamma.__init__\s  &**"2"23   &**"2"23     } 3 3!. I&}':':;DJ.2oo/ +T  113DJ ++112c4URUR- $)z>Mean of gamma distribution. Returns: Tensor: mean value. rrrs r mean Gamma.meanzs!!DII--r"cRURURRS5- $)zFVariance of gamma distribution. Returns: Tensor: variance value. )rrpowr%s r varianceGamma.variances"!!DIIMM!$444r"cL[R"URU55$)zProbability density function evaluated at value Args: value (float|Tensor): Value to be evaluated. Returns: Tensor: Probability. )rexplog_probrvalues r prob Gamma.probszz$--.//r"c UR[R"UR5-URS- [R"U5--URU-- [R"UR5- $)zLog probability density function evaluated at value Args: value (float|Tensor): Value to be evaluated Returns: Tensor: Log probability. )rrlogrlgammar0s r r/Gamma.log_probso   DII!6 6!!A%E):: ;ii% mmD../ 0 r"cUR[R"UR5- [R"UR5-SUR- [R "UR5--$)z=Entropy of gamma distribution Returns: Tensor: Entropy. g?)rrr6rr7digammar%s r entropy Gamma.entropysh   jj# $mmD../ 0T'''6>>$:L:L+MM N r"c[R"5 URU5sSSS5 $!,(df  g=f)zGenerate samples of the specified shape. Args: shape (Sequence[int], optional): Shape of the generated samples. Returns: Tensor, A tensor with prepended dimensions shape.The data type is float32. N)rno_gradrsamplerrs r sample Gamma.samples&^^ <<&  s1 ?c[RRXS9n[R"UR R U55URR U5- $)zGenerate reparameterized samples of the specified shape. Args: shape (Sequence[int], optional): Shape of the generated samples. Returns: Tensor: A tensor with prepended dimensions shape.The data type is float32. ) sample_shape)r Distribution _extend_shaperstandard_gammarexpandrr@s r r? Gamma.rsamplesb))77 8 $$    % %e , II  U #$ $r"c>[U[5(d[S[U535eUR[ R "URUR- 5-n[ R"UR5[ R"UR5- nURUR- [ R"UR5-nURUR- URUR- -nX#-U-U-$)zThe KL-divergence between two gamma distributions. Args: other (Gamma): instance of Gamma. Returns: Tensor: kl-divergence between two gamma distributions. z/Expected type of other is Exponential, but got ) isinstancer TypeErrortyperrr6rr7r:)rothert1t2t3t4s r kl_divergenceGamma.kl_divergences%''A$u+O  6::dii%**.D#E E ]]5.. /&--   3    5#6#66&..   ;  jj499$););dii)G Hw|b  r"c:URS- UR*4$Nr5r$r%s r _natural_parametersGamma._natural_parameterss""Q& 33r"c[R"US-5US-[R"UR5*5--$rV)rr7r6 reciprocal)rxys r _log_normalizerGamma._log_normalizers4}}QU#q1u ALLN?0K&KKKr")rr r)rfloat | Tensorrr_returnNone)r`r )r1r_r`r )rz Sequence[int]r`r )rNrr`r )r[r r\r r`r )__name__ __module__ __qualname____firstlineno____doc____annotations__rpropertyr&r+r2r/r;rAr?rSrWr]__static_attributes__ __classcell__)rs@r rr s5n L L3+33A3 3<..55 0  -/ '.0$ !04LLr"r) __future__rtypingrrrpaddle.base.data_feederrrpaddle.base.frameworkrpaddle.distributionr paddle.frameworkr collections.abcr r r ExponentialFamilyrr"r rts?# =*2,($LL  0 0LLr"