o * i@sddlmZddlmZddlZddlZddlmZddlm Z m Z ddl m Z ddl mZddlmZerDdd lmZdd lmZmZGd d d ejZdS) ) annotations) TYPE_CHECKINGN) distribution) check_type convert_dtype)Variable)exponential_family)in_dynamic_mode)Sequence)TensordtypecseZdZUdZded<ded<d(fdd Zed)d d Zed)d d Zgfd*ddZ gfd*ddZ d+ddZ d+ddZ d)ddZ d+ddZd+ddZd,d d!Zed-d#d$Zd.d&d'ZZS)/ 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 | TensorreturnNonecshtst|dtttjjfd||r||_t |j |_ n | |\|_t |_ t |jjdS)Nrr )r rfloatrpaddleZpirValueZ_validate_argsrrr Z _to_tensorZget_default_dtypesuper__init__shape)selfr __class__k/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/distribution/exponential.pyrIs   zExponential.__init__cCs |jS)z\Mean of exponential distribution. Returns: Tensor: mean value. )rZ reciprocalrrrrmean\s zExponential.meancCs |jdS)zdVariance of exponential distribution. Returns: Tensor: variance value. )rpowrrrrvariancees zExponential.variancer Sequence[int]cCs6t ||WdS1swYdS)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)rZno_gradrsample)rrrrrsamplens $zExponential.samplecCsFtjj||d}tj|ttjddjd|j j d}t | |j 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. )Z sample_shapeZfloat32)r ?)rminmaxr ) r DistributionZ _extend_shaperuniformrnpZfinfoZtinyrr log)rrr)rrrr#zs zExponential.rsamplevaluecCs|jt|j |S)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,rrrprobs zExponential.probcCst|j|j|S)zLog probability density function evaluated at value. Args: value (float|Tensor): Value to be evaluated Returns: Tensor: Log probability. rr+rr.rrrlog_probs zExponential.log_probcCsdt|jS)z\Entropy of exponential distribution. Returns: Tensor: Entropy. r%r0rrrrentropyszExponential.entropycCsdt|j |S)aCumulative distribution function(CDF) evaluated at value. .. math:: { cdf(x; \theta) = 1 - e^{- \theta x }, (x \ge 0) } Args: value (float|Tensor): Value to be evaluated. Returns: Tensor: CDF evaluated at value. r%)rr-rr.rrrcdfszExponential.cdfcCst|  |jS)a2Inverse cumulative distribution function(CDF) evaluated at value. .. math:: { icdf(x; \theta) = -\frac{ 1 }{ \theta } ln(1 + x), (x \ge 0) } Args: value (float|Tensor): Value to be evaluated. Returns: Tensor: CDF evaluated at value. )rlog1prr.rrricdfszExponential.icdfothercCs@t|tstdt||j|j}t| }||dS)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+)rr6Z rate_ratiot1rrr kl_divergences     zExponential.kl_divergence tuple[Tensor]cCs |j fSN)rrrrr_natural_parameterss zExponential._natural_parametersxcCst|  Sr>)rr+)rr@rrr_log_normalizerszExponential._log_normalizer)rrrr)rr )rr"rr )r,rrr )r6r rr )rr=)r@r rr )__name__ __module__ __qualname____doc____annotations__rpropertyrr!r$r#r/r1r2r3r5r<r?rA __classcell__rrrrr "s( #        r ) __future__rtypingrnumpyr*rrZpaddle.base.data_feederrrZpaddle.base.frameworkrZpaddle.distributionrZpaddle.frameworkr collections.abcr r r ZExponentialFamilyr rrrrs