zj|*ddlmZddlZddlmZddlZddlZddlm Z ddl m Z er ddl m Z ddlmZGdd e jZdS) ) annotationsN) TYPE_CHECKING) framework) distribution)Sequence)TensorceZdZUdZded<dfd ZeddZedd Zedd Z dd Z ddZ gfddZ gfddZ ddZddZddZxZS) GeometricaM 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) rprobsfloat | TensorreturnNonect|tjtjt jtjjfrt|tjr!tj d|tj }tj |j d|j }tj |j d|j }tj |j dt}||k}||k}ntdt|t!|j }||_t%|dS)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 m/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/distribution/geometric.pyr#zGeometric.__init__PsC   \6=)*N O   %..  fn{kau{H kau{I ke4I)+J)JJobfglbmbmoo EK((   %%%%%cd|jz dz S)zMean of geometric distribution.?)r r$s r,meanzGeometric.meanqsTZ#%%r-chtjd|jz dz |jz |jjS)z#Variance of geometric distribution.r/r)r to_tensorr rr0s r,variancezGeometric.variancevs< 4:  #tz 1*"    r-c4tj|jS)z-Standard deviation of Geometric distribution.)rsqrtr5r0s r,stddevzGeometric.stddev~s{4=)))r-k int | Tensorct|tjtjt jjfr%t jd|j z ||j zStdt|)aIProbability mass function evaluated at k. .. math:: P(X=k) = (1-p)^{k} p, \quad k=0,1,2,3,\ldots Args: k (int): Value to be evaluated. Returns: Tensor: Probability. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.pmf(2)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.12500000) r/DExpected type of k is number.Real|framework.Variable|Value, but got rrIntegralrrrrrpowr rr r$r9s r,pmfz Geometric.pmfsv2   )"4fj6FG   :sTZ/!44tzA A`W[\]W^W^`` r-ct|tjtjt jjfr't j| |Stdt|)aFLog 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>rrrrrlogrArr r@s r,log_pmfzGeometric.log_pmfsp0   )"4fj6FG   :dhhqkk** *`W[\]W^W^`` r-r Sequence[int]ctj5||cdddS#1swxYwYdS)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$rs r,samplezGeometric.samples.^   ' '<<&& ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 's 6::cTtj||}tj|t t jdjd|j j }tj tj |tj |j z S)aGenerate samples of the specified shape. Args: shape(Sequence[int]): The shape of generated samples. Returns: Tensor: A sample tensor that fits the Geometric distribution. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> paddle.seed(2023) >>> geom = Geometric(0.5) >>> print(geom.rsample((2,2))) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0.], [1., 0.]]) ) sample_shaperr3r/)rminmaxr)r Distribution _extend_shaperuniformfloatnpfinfotinyr rfloorrClog1p)r$rrPs r,rHzGeometric.rsamples0)77 u8  .bhY///455*"    |FJw//&,}2M2MMNNNr-cd|jz tjd|jz z}|jtj|jz}||z |jz S)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 rrC)r$xys r,entropyzGeometric.entropysQ,4: C$*,>> 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=r@s r,cdfz Geometric.cdf sx2   )"4fj6FG   S4:%5A>>> >`W[\]W^W^`` r-otherct|trL|j|j}}|tj||z zd|z tjd|z d|z z zzSt dt |)aCalculate 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 rrCrr )r$r]pqs r, kl_divergencezGeometric.kl_divergenceBs4 eY ' ' :u{qAvz!a%(((C!GvzqS1W%88, VeVV r-)r r r r)r r)r9r:r r)rrEr r)r]r r r)__name__ __module__ __qualname____doc____annotations__r#propertyr1r5r8rArDrIrHrZr\ra __classcell__)r+s@r,r r s_++ZMMM&&&&&&B&&&X&   X ***X*    DB-/'''''4.0#O#O#O#O#OJ%%%%6    D""""""""r-r ) __future__rrtypingrnumpyrRr paddle.baserpaddle.distributionrcollections.abcrrrNr rr-r,ros#"""""  !!!!!!,,,,,,((((((DDDDD )DDDDDr-