zj;ddlmZddlmZddlZddlZddlmZddlm Z m Z ddl m Z ddl mZddlmZerdd lmZdd lmZmZGd d ejZdS) ) annotations) TYPE_CHECKINGN) distribution) check_type convert_dtype)Variable)exponential_family)in_dynamic_mode)Sequence)TensordtypeceZdZUdZded<ded<dfd Zedd Zedd Zgfdd Z gfddZ ddZ ddZ ddZ ddZddZddZed dZd!dZxZS)" Exponentiala Exponential distribution parameterized by :attr:`rate`. The probability density function (pdf) is .. math:: f(x; \theta) = \theta e^{- \theta x }, (x \ge 0) $$ In the above equation: * :math:`rate = \theta`: is the rate parameter. Args: rate (float|Tensor): Rate parameter. The value of rate must be positive. Example: .. code-block:: python >>> import paddle >>> expon = paddle.distribution.Exponential(paddle.to_tensor([0.5])) >>> print(expon.mean) Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True, [2.]) >>> print(expon.variance) Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True, [4.]) >>> print(expon.entropy()) Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True, [1.69314718]) r rater float | TensorreturnNonects.t|dtttjjfd||r!||_t|j |_ n4| |\|_t j |_ t|jjdS)Nrr)r rfloatrpaddlepirValue_validate_argsrrr _to_tensorget_default_dtypesuper__init__shape)selfr __class__s o/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/distribution/exponential.pyrzExponential.__init__Is    &*"23       t $ $ 4DI&tz22DJJ//$//KTY133DJ )))))c4|jS)z\Mean of exponential distribution. Returns: Tensor: mean value. )r reciprocalrs r!meanzExponential.mean\sy##%%%r"c6|jdS)zdVariance of exponential distribution. Returns: Tensor: variance value. )rpowr%s r!variancezExponential.varianceesy}}R   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_gradrsample)rrs r!samplezExponential.samplens^   ' '<<&& ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 's 6::c tj||}tj|t t jdjd|j j }tj | |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_shapefloat32)r ?)rminmaxr ) r Distribution _extend_shaperuniformrnpfinfotinyrr log)rrr8s r!r.zExponential.rsamplezs)77 u8  .bhY///455)/     7###di//r"valuecL|jtj|j |zzS)a Probability density function evaluated at value. .. math:: { f(x; \theta) = \theta e^{- \theta x}, (x \ge 0 ) } Args: value (float|Tensor): Value to be evaluated. Returns: Tensor: Probability. )rrexprr=s r!probzExponential.probs$y6:tyj5&89999r"cJtj|j|j|zz S)zLog probability density function evaluated at value. Args: value (float|Tensor): Value to be evaluated Returns: Tensor: Log probability. rr<rr@s r!log_probzExponential.log_probs"z$)$$ty5'888r"c:dtj|jz S)z\Entropy of exponential distribution. Returns: Tensor: Entropy. r3rCr%s r!entropyzExponential.entropys VZ ****r"cBdtj|j |zz S)aHCumulative distribution function(CDF) evaluated at value. .. math:: { cdf(x; \theta) = 1 - e^{- \theta x }, (x \ge 0) } Args: value (float|Tensor): Input value to evaluate the cumulative probability. Returns: Tensor: The evaluated cumulative probability. r3)rr?rr@s r!cdfzExponential.cdfs"VZ U 23333r"c>tj|  |jz S)aOInverse cumulative distribution function(CDF) evaluated at value. .. math:: { icdf(x; \theta) = -\frac{ 1 }{ \theta } ln(1 - x), (0 < x < 1) } Args: value (float|Tensor): Input probability to evaluate the quantile. Returns: Tensor: The evaluated quantile value. )rlog1prr@s r!icdfzExponential.icdfs  eV$$$ty00r"otherct|tstdt||j|jz }t j| }||zdz S)zThe KL-divergence between two exponential distributions. Args: other (Exponential): instance of Exponential. Returns: Tensor: kl-divergence between two exponential distributions. z/Expected type of other is Exponential, but got ) isinstancer TypeErrortyperrr<)rrL rate_ratiot1s r! kl_divergencezExponential.kl_divergencesj%-- O$u++OO Z$)+ j$$ $J""r" tuple[Tensor]c|j fSN)rr%s r!_natural_parameterszExponential._natural_parameterss }r"xc.tj|  SrW)rr<)rrYs r!_log_normalizerzExponential._log_normalizers A2r")rrrr)rr )rr+rr )r=rrr )rLrrr )rrU)rYr rr )__name__ __module__ __qualname____doc____annotations__rpropertyr&r*r/r.rArDrFrHrKrTrXr[ __classcell__)r s@r!rr"s!!FLLLLLL******&&&&X&!!!X!-/ ' ' ' ' '.000000, : : : : 9 9 9 9++++4444 1111 ####$Xr"r) __future__rtypingrnumpyr9rrpaddle.base.data_feederrrpaddle.base.frameworkrpaddle.distributionr paddle.frameworkr collections.abcr r r ExponentialFamilyrr"r!rms#"""""  ========******222222,,,,,,%(((((($$$$$$$$GGGGG$6GGGGGr"