zj ddlmZddlZddlZddlmZddlZddlZddl m Z ddl m Z erddl mZddlmZddlmZmZGd d e ZdS) ) annotationsN) TYPE_CHECKING) framework)TransformedDistribution)Sequence)Tensor) TransformUniformceZdZUdZded<ded<ded<ded<dfd Zedd ZeddZeddZ ddZ ddZ ddZ ddZ gfddZgfddZxZS)GumbelaThe Gumbel distribution with location `loc` and `scale` parameters. Mathematical details The probability density function (pdf) is .. math:: pdf(x; mu, sigma) = exp(-(x - mu) / sigma - exp(-(x - mu) / sigma)) / sigma In the above equation: * :math:`loc = \mu`: is the mean. * :math:`scale = \sigma`: is the std. Args: loc(int|float|tensor): The mean of gumbel distribution.The data type is int, float, tensor. scale(int|float|tensor): The std of gumbel distribution.The data type is int, float, tensor. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution.gumbel import Gumbel >>> # Gumbel distributed with loc=0, scale=1 >>> dist = Gumbel(paddle.full([1], 0.0), paddle.full([1], 1.0)) >>> # doctest: +SKIP >>> print(dist.sample([2])) Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.40484068], [3.19400501]]) >>> print(dist.rsample([2])) Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.95093185], [ 0.32422572]]) >>> # doctest: -SKIP >>> value = paddle.full([1], 0.5) >>> print(dist.prob(value)) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.33070430]) >>> print(dist.log_prob(value)) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [-1.10653067]) >>> print(dist.cdf(value)) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.54523921]) >>> print(dist.entropy()) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.57721567]) rlocscaler base_distztuple[Transform, ...] transformsfloat | TensorreturnNonec *t|tjtjt jjfstdt|t|tjtjt jjfstdt|t|tjrt j d|}t|tjrt j d|}|j |j kr$t j ||g\|_ |_n||c|_ |_tjd}t jt j|j t)|jt j|j t)d|jz |_d|_t3|j|jdS)Nz5Expected type of loc is Real|Variable|Value, but got z7Expected type of scale is Real|Variable|Value, but got )shape fill_valuefloat32)dtype) isinstancenumbersRealrVariablepaddlepirValue TypeErrortypefullrbroadcast_tensorsr rnpfinfo distributionr full_likefloattinyepsrrsuper__init__)selfr rr' __class__s j/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/distribution/gumbel.pyr.zGumbel.__init__cs ', 2FJ4DE   SS SS  GL)"4fj6FG   W$u++WW  c7< ( ( 8+B3777C eW\ * * <KbU;;;E 9 # ##)#;S%L#I#I DHdjj#& DHdjy))),44  TXuUZ'8'8 9 9  TXuQ]';'; < <    99999c:|j|jtjzzS)u`Mean of distribution The mean is .. math:: mean = \mu + \sigma * γ In the above equation: * :math:`loc = \mu`: is the location parameter. * :math:`scale = \sigma`: is the scale parameter. * :math:`γ`: is the euler's constant. Returns: Tensor: mean value. )r rr& euler_gammar/s r1meanz Gumbel.means(x$*r~555r2ctj|jjtjtjz|jj}tj|jd|zdz S)aVariance of distribution. The variance is .. math:: variance = \sigma^2 * \pi^2 / 6 In the above equation: * :math:`scale = \sigma`: is the scale parameter. Returns: Tensor: The variance value. rrr) rr$r rmathpirrpow)r/temps r1variancezGumbel.variancesU${(.w(*"    z$*a((4/!33r2c4tj|jS)aStandard deviation of distribution The standard deviation is .. math:: stddev = \sqrt{\sigma^2 * \pi^2 / 6} In the above equation: * :math:`scale = \sigma`: is the scale parameter. Returns: Tensor: std value )rsqrtr?r5s r1stddevz Gumbel.stddevs {4=)))r2valuec,|j||jjz |j|jjz }t j|t j|z |j|jz S)zProbability density/mass function Args: value (Tensor): The input tensor. Returns: Tensor: probability.The data type is same with value. )r astyperrrexp)r/rCys r1probz Gumbel.probsvX TX^44 4 8I8I HN9 9  z!fjmm+,,tz/@/@/I/IIIr2cPtj||S)zLog probability density/mass function. Args: value (Tensor): The input tensor. Returns: Tensor: log probability.The data type is same with value. )rlogrHr/rCs r1log_probzGumbel.log_probs z$))E**+++r2ctjtj||j|jz |j|jz  S)zCumulative distribution function. Args: value (Tensor): value to be evaluated. Returns: Tensor: cumulative probability of value. )rrFr rErrrKs r1cdfz Gumbel.cdfsaz Z$(//%+6667*##EK001    r2cTtj|jdztjzS)z`Entropy of Gumbel distribution. Returns: Entropy of distribution. r)rrJrr&r4r5s r1entropyzGumbel.entropys"z$*%%)BN::r2r Sequence[int]ctj5||cdddS#1swxYwYdS)zSample from ``Gumbel``. Args: shape (Sequence[int], optional): The sample shape. Defaults to []. Returns: Tensor: A tensor with prepended dimensions shape.The data type is float32. N)rno_gradrsample)r/rs r1samplez Gumbel.samples^   ' '<<&& ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 's 6::c "tj}tjtj|jjd|jjtj |j }tj|j|j }| | | | |j |S)areparameterized sample Args: shape (Sequence[int], optional): 1D `int32`. Shape of the generated samples. Defaults to []. Returns: Tensor: A tensor with prepended dimensions shape.The data type is float32. rr8)rr( ExpTransformAffineTransformr$rrr r ones_likeforwardinverse_baserU)r/r exp_transaffine_trans_1affine_trans_2s r1rTzGumbel.rsample s'4466 ,<< Kj&1DHN    dj ) ) )     ,<< Htzk  %%   &&%%dj&7&7&>&>??     r2)r rrrrr)rr)rCrrr)rrQrr)__name__ __module__ __qualname____doc____annotations__r.propertyr6r?rBrHrLrNrPrUrT __classcell__)r0s@r1r r "sg99vKKKMMM%%%%!:!:!:!:!:!:F666X6*444X42***X*"JJJJ , , , ,    ;;;;-/ ' ' ' ' '.0         r2r ) __future__rr;rtypingrnumpyr&r paddle.baser,paddle.distribution.transformed_distributionrcollections.abcrrpaddle.distributionr r r rr2r1rns#"""""   !!!!!!PPPPPP7((((((66666666B B B B B $B B B B B r2