o "j@sLddlZddlZddlZddlZddlmZddlmZGdddeZ dS)N) framework)TransformedDistributioncsteZdZdZfddZeddZeddZedd Zd d Z d d Z ddZ ddZ ddZ ddZZS)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 s t|tjtjfstdt|t|tjtjfs$tdt|t|tjr1tjd|d}t|tjr>tjd|d}|j |j krPt ||g\|_ |_ n|||_ |_ t jdd}tjt|j t|jt|j td|j|_d|_t|j|jdS)Nz/Expected type of loc is Real|Variable, but got z1Expected type of scale is Real|Variable, but got )shape fill_valueZfloat32)dtype) isinstancenumbersRealrVariable TypeErrortypepaddlefullrZbroadcast_tensorslocscalenpfinfo distributionZUniformZ full_likefloatZtinyepsZ base_distZ transformssuper__init__)selfrrr __class__r[/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/distribution/gumbel.pyrUs,      zGumbel.__init__cCs|j|jtjS)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_gammarrrrmeantsz Gumbel.meancCs6tj|jjtjtj|jjd}t|jd|dS)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) rrrrmathpirrpow)rtemprrrvariances  zGumbel.variancecCs t|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)r rrrstddevs z Gumbel.stddevcCs*|j||j}t|t||jS)zProbability density/mass function Args: value (Tensor): The input tensor. Returns: Tensor: probability.The data type is same with value. )rrrexp)rvalueyrrrprobs z Gumbel.probcCst||S)zLog probability density/mass function. Args: value (Tensor): The input tensor. Returns: Tensor: log probability.The data type is same with value. )rlogr/rr-rrrlog_probs zGumbel.log_probcCs tt||j |j S)zCumulative distribution function. Args: value (Tensor): value to be evaluated. Returns: Tensor: cumulative probability of value. )rr,rrr1rrrcdfs z Gumbel.cdfcCst|jdtjS)z`Entropy of Gumbel distribution. Returns: Entropy of distribution. r )rr0rrrr rrrentropyszGumbel.entropycCs6t ||WdS1swYdS)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)rZno_gradrsample)rrrrrsamples $z Gumbel.samplec Csntj}tjtj|jjd|jjdt |j }tj|j|j }| | | | |j |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")rrZ ExpTransformZAffineTransformrrrrrZ ones_likeforwardZinverseZ_baser6)rrZ exp_transZaffine_trans_1Zaffine_trans_2rrrr5s"   zGumbel.rsample)__name__ __module__ __qualname____doc__rpropertyr!r)r+r/r2r3r4r6r5 __classcell__rrrrrs ;       r) r%r numpyrrZ paddle.baserZ,paddle.distribution.transformed_distributionrrrrrrs