\;jRddlZddlZddlZddlZddlmZddlmZGddeZ y)N) framework)TransformedDistributioncveZdZdZfdZedZedZedZdZ dZ dZ d Z d Z d ZxZS) 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]) c t|tjtjfst dt |t|tjtjfst dt |t|tjrtjd|}t|tjrtjd|}|j|jk7r%tj||g\|_ |_ n||c|_ |_ tjd}tjj!tj"|jt%|j&tj"|jt%d|j(z |_d|_t.|a|j*|j,y)Nz/Expected type of loc is Real|Variable, but got z1Expected type of scale is Real|Variable, but got )shape fill_valuefloat32)dtype) isinstancenumbersRealrVariable TypeErrortypepaddlefullr broadcast_tensorslocscalenpfinfo distributionUniform full_likefloattinyeps base_dist transformssuper__init__)selfrrr __class__s cG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/distribution/gumbel.pyr$zGumbel.__init__Usl# i.@.@ABA$s)M %',, 0B0B!CDCDK=Q  c7<< (++B37C eW\\ *KKbU;E 99 ##)#;#;S%L#I DHdj#& DHdjy),,44   TXXuUZZ'8 9   TXXuQ]'; <   9cV|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. )rrr euler_gammar%s r'meanz Gumbel.meants (xx$**r~~555r(c tj|jjtj tj z|j j}tj|j d|zdz S)aVariance of distribution. The variance is .. math:: variance = \sigma^2 * \pi^2 / 6 In the above equation: * :math:`scale = \sigma`: is the scale parameter. Returns: Tensor: The variance value. r r r ) rrrr mathpirr pow)r%temps r'variancezGumbel.variances[${{((..ww(**""  zz$**a(4/!33r(c@tj|jS)aStandard deviation of distribution The standard deviation is .. math:: stddev = \sqrt{\sigma^2 * \pi^2 / 6} In the above equation: * :math:`scale = \sigma`: is the scale parameter. Returns: Tensor: std value )rsqrtr5r+s r'stddevz Gumbel.stddevs {{4==))r(c|j|z |jz }tj|tj|z |jz S)zProbability density/mass function Args: value (Tensor): The input tensor. Returns: Tensor: probability.The data type is same with value. )rrrexp)r%valueys r'probz Gumbel.probs@XX  +zz!fjjm+,tzz99r(cJtj|j|S)zLog probability density/mass function. Args: value (Tensor): The input tensor. Returns: Tensor: log probability.The data type is same with value. )rlogr=r%r;s r'log_probzGumbel.log_probszz$))E*++r(ctjtj||jz |jz  S)zCumulative distribution function. Args: value (Tensor): value to be evaluated. Returns: Tensor: cumulative probability of value. )rr:rrr@s r'cdfz Gumbel.cdfs4zz6::(8&9DJJ&FGGHHr(chtj|jdztjzS)z`Entropy of Gumbel distribution. Returns: Entropy of distribution. r )rr?rrr*r+s r'entropyzGumbel.entropys%zz$**%)BNN::r(cxtj5|j|cdddS#1swYyxYw)zSample from ``Gumbel``. Args: shape (Sequence[int], optional): The sample shape. Defaults to (). Returns: Tensor: A tensor with prepended dimensions shape.The data type is float32. N)rno_gradrsample)r%r s r'samplez Gumbel.samples&^^ <<&  s09c Ntjj}tjjtj|j j d|jjtj|j  }tjj|j|j }|j|j|j|j|jj|S)zreparameterized sample Args: shape (Sequence[int]): 1D `int32`. Shape of the generated samples. Returns: Tensor: A tensor with prepended dimensions shape.The data type is float32. rr.)rr ExpTransformAffineTransformrrr rr ones_likeforwardinverse_baserI)r%r exp_transaffine_trans_1affine_trans_2s r'rHzGumbel.rsamples''446 ,,<< KKjj&&1DHHNN   djj ) )    ,,<< HHtzzk %%   &&%%djj&7&7&>?   r()__name__ __module__ __qualname____doc__r$propertyr,r5r8r=rArCrErIrH __classcell__)r&s@r'rrsi9v:>66*442**" : , I; ' r(r) r1rnumpyrr paddle.baser,paddle.distribution.transformed_distributionrrrr(r'r]s(  !Pr $r r(