Αi|*SSKJr SSKrSSKJr SSKrSSKrSSKJ r SSK J r \(a SSK J r SSKJr "SS \ R5rg) ) annotationsN) TYPE_CHECKING) framework) distribution)Sequence)Tensorc^\rSrSr%SrS\S'SU4Sjjr\SSj5r\SSj5r \SSj5r SS jr SS jr /4SS jjr /4SS jjrSS jrSSjrSSjrSrU=r$) Geometric a Geometric distribution parameterized by probs. In probability theory and statistics, the geometric distribution is one of discrete probability distributions, parameterized by one positive shape parameter, denoted by probs. In n Bernoulli trials, it takes k+1 trials to get the probability of success for the first time. In detail, it is: the probability that the first k times failed and the kth time succeeded. The geometric distribution is a special case of the Pascal distribution when r=1. The probability mass function (pmf) is .. math:: Pr(Y=k)=(1-p)^kp where k is number of trials failed before seeing a success, and p is probability of success for each trial and k=0,1,2,3,4..., p belong to (0,1]. Args: probs (Real|Tensor): Probability parameter. The value of probs must be positive. When the parameter is a tensor, probs is probability of success for each trial. Returns: Geometric distribution for instantiation of probs. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.mean) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.) >>> print(geom.variance) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) >>> print(geom.stddev) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.41421354) rprobsc>[U[R[R[ R [RR45(a[U[R5(a$[R"SU[RS9n[R"URSURS9n[R"URSURS9n[R"URS[S9nX:*nX:nO[S[U535e[!UR5nXl[$TU]MU5 g)N)shape fill_valuedtyperFzOExpected type of probs is Number.Real|Tensor|framework.Variable|Value, but got ) isinstancenumbersRealpaddlerrVariablepirValuefullfloat32rrbool TypeErrortypetupler super__init__) selfr all_ones all_zeros all_false lessthen_0 morethen_1 batch_shape __class__s ]/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/distribution/geometric.pyr!Geometric.__init__Ps   \\6==)*<*N>N O  %.. fnn{{kkau{{H kkau{{I kke4I+J)Jabfglbmano EKK(   %c&SUR- S- $)zMean of geometric distribution.?r r"s r*meanGeometric.meanqsTZZ#%%r,c[R"SUR- S- UR- URRS9$)z#Variance of geometric distribution.r.r)r to_tensorr rr0s r*varianceGeometric.variancevs> 4::  #tzz 1**""  r,cB[R"UR5$)z-Standard deviation of Geometric distribution.)rsqrtr6r0s r*stddevGeometric.stddev~s{{4==))r,c"[U[R[R[ R R45(a1[ R"SUR- U5UR-$[S[U535e)aProbability mass function evaluated at k. .. math:: P(X=k) = (1-p)^{k} p, \quad k=0,1,2,3,\ldots Args: k (int): Value to be evaluated. Returns: Tensor: Probability. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.pmf(2)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.12500000) r.DExpected type of k is number.Real|framework.Variable|Value, but got rrIntegralrrrrrpowr rrr"ks r*pmf Geometric.pmfsw2    )"4"4fjj6F6FG  ::sTZZ/!4tzzA AVW[\]W^V_` r,c [U[R[R[ R R45(a%[ R"URU55$[S[U535e)aLog probability mass function evaluated at k. .. math:: \log P(X = k) = \log(1-p)^k p Args: k (int): Value to be evaluated. Returns: Tensor: Log probability. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.log_pmf(2)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, -2.07944131) r=) rrr?rrrrrlogrCrrrAs r*log_pmfGeometric.log_pmfsk0    )"4"4fjj6F6FG  ::dhhqk* *VW[\]W^V_` r,c[R"5 URU5sSSS5 $!,(df  g=f)aSample from Geometric distribution with sample shape. Args: shape (Sequence[int]): Sample shape. Returns: Sampled data with shape `sample_shape` + `batch_shape` + `event_shape`. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> paddle.seed(2023) >>> geom = Geometric(0.5) >>> print(geom.sample((2,2))) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0.], [1., 0.]]) N)rno_gradrsample)r"rs r*sampleGeometric.samples&.^^ <<&  s1 ?c t[RRXS9n[R"U[ [ R"SS9R5SURRS9n[R"[R"U5[R"UR*5- 5$)aGenerate samples of the specified shape. Args: shape(Sequence[int]): The shape of generated samples. Returns: Tensor: A sample tensor that fits the Geometric distribution. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> paddle.seed(2023) >>> geom = Geometric(0.5) >>> print(geom.rsample((2,2))) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0.], [1., 0.]]) ) sample_shaperr4r.)rminmaxr)r Distribution _extend_shaperuniformfloatnpfinfotinyr rfloorrFlog1p)r"rrTs r*rKGeometric.rsamples0))77 8 ..bhhY/445**""  ||FJJw/&,,}2MMNNr,cSUR- [R"SUR- 5-nUR[R"UR5-nX-*UR- $)aEntropy of dirichlet distribution. .. math:: H(X) = -\left[\frac{1}{p} \log p + \frac{1-p}{p^2} \log (1-p) \right] Returns: Tensor: Entropy. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.entropy()) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.38629425) r.)r rrF)r"xys r*entropyGeometric.entropysW,4:: C$**,>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.cdf(4)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.96875000) r.rr=r>rAs r*cdf Geometric.cdf sw2    )"4"4fjj6F6FG  S4::%5A>> >VW[\]W^V_` r,c [U[5(aXURURp2U[R"X#- 5-SU- [R"SU- SU- - 5--$[ S[ U535e)axCalculate the KL divergence KL(self || other) with two Geometric instances. .. math:: KL(P \| Q) = \frac{p}{q} \log \frac{p}{q} + \log (1-p) - \log (1-q) Args: other (Geometric): An instance of Geometric. Returns: Tensor: The kl-divergence between two geometric distributions. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom_p = Geometric(0.5) >>> geom_q = Geometric(0.1) >>> print(geom_p.kl_divergence(geom_q)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.51082563) r.z6Exacted type of other is geometric.Geometric, but got )rr r rrFrr)r"otherpqs r* kl_divergenceGeometric.kl_divergenceBs4 eY ' '::u{{qvzz!%((C!GvzzqS1W%8, He V r,r/)r zfloat | TensorreturnNone)rjr)rBz int | Tensorrjr)rz Sequence[int]rjr)rer rjr)__name__ __module__ __qualname____firstlineno____doc____annotations__r!propertyr1r6r:rCrGrLrKr_rbrh__static_attributes__ __classcell__)r)s@r*r r s+Z M&B&&  ** DB-/'4.0#OJ%6 D""r,r ) __future__rrtypingrnumpyrVr paddle.baserpaddle.distributionrcollections.abcrrrRr rr,r*r{s9#  !,(D ))Dr,