Αi;SSKJr SSKJr SSKrSSKrSSKJr SSKJ r J r SSK J r SSK Jr SSKJr \(aSS KJr SS KJrJr "S S \R*5rg) ) annotations) TYPE_CHECKINGN) distribution) check_type convert_dtype)Variable)exponential_family)in_dynamic_mode)Sequence)Tensordtypec^\rSrSr%SrS\S'S\S'SU4Sjjr\SSj5r\SSj5r /4SS jjr /4SS jjr SS jr SS jr SS jrSSjrSSjrSSjr\SSj5rSSjrSrU=r$) Exponential"at 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 c>[5(d1[US[[[R R 4S5 URU5(a!Xl[UR5Ul O2URU5uUl[R"5Ul [TU]9URR5 g)Nrr)r rfloatrpaddlepirValue_validate_argsrrr _to_tensorget_default_dtypesuper__init__shape)selfr __class__s _/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/distribution/exponential.pyrExponential.__init__Is  &**"2"23     t $ $I&tzz2DJ//$/KTY113DJ )c6URR5$)zDMean of exponential distribution. Returns: Tensor: mean value. )r reciprocalrs rmeanExponential.mean\syy##%%r!c8URRS5$)zLVariance of exponential distribution. Returns: Tensor: variance value. )rpowr$s rvarianceExponential.varianceesyy}}R  r!c[R"5 URU5sSSS5 $!,(df  g=f)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 rsampleExponential.samplens&^^ <<&  s1 ?c $[RRXS9n[R"U[ [ R"SS9R5SURRS9n[R"U5*UR- $)zGenerate 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)rrr9s rr.Exponential.rsamplezsw))77 8 ..bhhY/445))//   7##dii//r!cdUR[R"UR*U-5-$)zProbability 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. )rrexprvalues rprobExponential.probs'yy6::tyyj5&8999r!cb[R"UR5URU-- $)zLog probability density function evaluated at value. Args: value (float|Tensor): Value to be evaluated Returns: Tensor: Log probability. rr=rrAs rlog_probExponential.log_probs%zz$))$tyy5'888r!cHS[R"UR5- $)zDEntropy of exponential distribution. Returns: Tensor: Entropy. r4rFr$s rentropyExponential.entropys VZZ ***r!cPS[R"UR*U-5- $)aCumulative 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. r4)rr@rrAs rcdfExponential.cdfs#VZZ U 2333r!cL[R"U*5*UR- $)aInverse 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. )rlog1prrAs ricdfExponential.icdfs! eV$$tyy00r!c[U[5(d[S[U535eURUR- n[ R "U5*nX2-S- $)zThe KL-divergence between two exponential distributions. Args: other (Exponential): instance of Exponential. Returns: Tensor: kl-divergence between two exponential distributions. z/Expected type of other is Exponential, but got ) isinstancer TypeErrortyperrr=)rother rate_ratiot1s r kl_divergenceExponential.kl_divergences^%--A$u+O ZZ$))+ jj$ $""r!cUR*4$N)rr$s r_natural_parametersExponential._natural_parameterss }r!c2[R"U*5*$r^)rr=)rxs r_log_normalizerExponential._log_normalizers A2r!)r r)rfloat | TensorreturnNone)rfr )rz Sequence[int]rfr )rBrerfr )rXrrfr )rfz tuple[Tensor])rbr rfr )__name__ __module__ __qualname____firstlineno____doc____annotations__rpropertyr%r*r/r.rCrGrJrMrQr[r_rc__static_attributes__ __classcell__)rs@rrr"s!F L L*&&&!!-/ '.00, : 9+4 1 #$r!r) __future__rtypingrnumpyr:rrpaddle.base.data_feederrrpaddle.base.frameworkrpaddle.distributionr paddle.frameworkr collections.abcr r r ExponentialFamilyrr!rr{s@#  =*2,($G$66Gr!