zjddlmZddlmZddlZddlmZddlmZmZddl m Z ddl m Z ddl mZerdd lmZdd lmZmZGd d e jZdS) ) annotations) TYPE_CHECKINGN) distribution) check_type convert_dtype)Variable)exponential_family)in_dynamic_mode)Sequence)TensordtypeceZdZUdZded<ded<ded<dfd Zedd Zedd Zdd Z ddZ ddZ gfddZ gfddZ ddZddZddZxZS) Gammaa 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 float | TensorreturnNonec$ts\t|dtttjjfdt|dtttjjfd|||r(||_||_ t|j |_ n;| ||\|_|_ t j |_ t|jjdS)Nrrr)r rfloatrpaddlepirValue_validate_argsrrrr _to_tensorget_default_dtypesuper__init__shape)selfrr __class__s i/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/distribution/gamma.pyrzGamma.__init__\s   &*"23     &*"23       }d 3 3 4!.D DI&}':;;DJJ.2oot// +T  133DJ +122222c |j|jz S)zVMean of gamma distribution. Returns: Tensor: mean value. rrr s r"meanz Gamma.meanzs!DI--r#cF|j|jdz S)z^Variance of gamma distribution. Returns: Tensor: variance value. )rrpowr&s r"variancezGamma.variances !DIMM!$4$444r#valuecPtj||S)zProbability density function evaluated at value Args: value (float|Tensor): Value to be evaluated. Returns: Tensor: Probability. )rexplog_probr r,s r"probz Gamma.probs z$--..///r#c|jtj|jz|jdz tj|zz|j|zz tj|jz S)zLog probability density function evaluated at value Args: value (float|Tensor): Value to be evaluated Returns: Tensor: Log probability. )rrlogrlgammar0s r"r/zGamma.log_probse  DI!6!6 6!A%E):):: ;i% mD.// 0 r#c|jtj|jz tj|jzd|jz tj|jzzS)zUEntropy of gamma distribution Returns: Tensor: Entropy. g?)rrr4rr5digammar&s r"entropyz Gamma.entropys]  j## $mD.// 0T''6>$:L+M+MM N r#r Sequence[int]ctj5||cdddS#1swxYwYdS)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_gradrsampler rs r"samplez Gamma.samples^   ' '<<&& ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 's 6::ctj||}tj|j||j|z S)aGenerate 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<z Gamma.rsamplesi)77 u8  $   % %e , ,  I  U # #$ $r#otherct|tstdt||jt j|j|jz z}t j|jt j|jz }|j|jz t j |jz}|j|jz |j|jz z}||z|z|zS)zThe KL-divergence between two gamma distributions. Args: other (Gamma): instance of Gamma. Returns: Tensor: kl-divergence between two gamma distributions. z/Expected type of other is Exponential, but got ) isinstancer TypeErrortyperrr4rr5r7)r rEt1t2t3t4s r" kl_divergencezGamma.kl_divergences%'' O$u++OO  6:di%*.D#E#E E ]5. / /&-  3 3   5#66&.  ; ;  j49$);di)G HBw|b  r#c&|jdz |j fSNr3r%r&s r"_natural_parameterszGamma._natural_parameterss"Q& 33r#xyctj|dz|dztj| zzSrP)rr5r4 reciprocal)r rRrSs r"_log_normalizerzGamma._log_normalizers8}QU##q1u ALLNN?0K0K&KKKr#)rrrrrr)rr )r,rrr )rr9rr )rErrr )rRr rSr rr )__name__ __module__ __qualname____doc____annotations__rpropertyr'r+r1r/r8r>r<rNrQrV __classcell__)r!s@r"rr sf55nLLLLLL333333<...X.555X5 0 0 0 0        -/ ' ' ' ' '.0$$$$$ !!!!04444LLLLLLLLr#r) __future__rtypingrrrpaddle.base.data_feederrrpaddle.base.frameworkrpaddle.distributionr paddle.frameworkr collections.abcr r r ExponentialFamilyrr#r"rgs#""""" ========******222222,,,,,,%(((((($$$$$$$$LLLLLLLLLL  0LLLLLLLLLLr#