\;j*^ddlZddlZddlZddlmZddlmZGddejZ y)N) framework) distributionceZdZdZfdZedZedZedZdZ dZ d dZ d d Z d Z d Zd ZxZS) 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) ct|tjtjt j frt|tjr&tjd|tj}tj|jd|j}tj|jd|j}tj|jdt}||k}||kD}ntdt|tj||r,tj||rt|j}n t!d||_t$|M|y)N)shape fill_valuedtyperFzIExpected type of probs is Number.Real|Tensor|framework.Variable, but got zvExpected parameter probs of distribution Geometric to satisfy theconstraint Interval(lower_bound=0.0, upper_bound=1.0)) isinstancenumbersRealpaddleTensorrVariablefullfloat32r r bool TypeErrortype equal_alltuple ValueErrorprobssuper__init__) selfrall_ones all_zeros all_false lessthen_0 morethen_1 batch_shape __class__s fG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/distribution/geometric.pyrzGeometric.__init__Fs6 egllFMM9;M;MN O%. fnn{{kkau{{H kkau{{I kke4I)+J)J[\`af\g[hi    J 2v7G7G  8   ,KH    %c&d|jz dz S)zMean of geometric distribution.?)rrs r&meanzGeometric.meanlsTZZ#%%r'ctjd|jz dz |jz |jjS)z#Variance of geometric distribution.r)r )r to_tensorrr r*s r&variancezGeometric.varianceqs> 4::  #tzz 1**""  r'c@tj|jS)z-Standard deviation of Geometric distribution.)rsqrtr/r*s r&stddevzGeometric.stddevys{{4==))r'ct|tjtjfr0t j d|jz ||jzStdt|)aIProbability mass funciotn evaluated at k. .. math:: P(X=k) = (1-p)^{k} p, \quad k=0,1,2,3,\ldots Args: k (int): Value to be evaluated. Returns: Tensor: Probability. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.pmf(2)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.12500000) r)>Expected type of k is number.Real|framework.Variable, but got r rIntegralrrrpowrrrrks r&pmfz Geometric.pmf~sa2 a'**I,>,>? @::sTZZ/!4tzzA APQUVWQXPYZ r'ct|tjtjfr$t j |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) r4) r rr6rrrlogr:rrr8s r&log_pmfzGeometric.log_pmfsU0 a'**I,>,>? @::dhhqk* *PQUVWQXPYZ r'cxtj5|j|cdddS#1swYyxYw)aSample from Geometric distribution with sample shape. Args: shape (tuple(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)rr s r&samplezGeometric.samples&.^^ <<&  s09c~tjj||}tj|t t jdjd|jj}tjtj|tj|j z S)aGenerate samples of the specified shape. Args: shape(tuple(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_shaperr-r))r minmaxr )r Distribution _extend_shaperuniformfloatnpfinfotinyrr floorr<log1p)rr rHs r&r@zGeometric.rsamples0))77 u8 ..bhhY/445**""  ||FJJw/&,,}2MMNNr'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))rrr<)rxys r&entropyzGeometric.entropysY,4:: C$**,>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.cdf(4)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.96875000) r)r r4r5r8s r&cdfz Geometric.cdfsa2 a'**I,>,>? @S4::%5A>> >PQUVWQXPYZ r'ct|trX|j|j}}|tj||z zd|z tjd|z d|z z zzSt dt |)aCalculate the KL divergence KL(self || other) with two Geometric instances. .. math:: KL(P \| Q) = \frac{p}{q} \log \frac{p}{q} + \log (1-p) - \log (1-q) Args: other (Geometric): An instance of Geometric. Returns: Tensor: The kl-divergence between two geometric distributions. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom_p = Geometric(0.5) >>> geom_q = Geometric(0.1) >>> print(geom_p.kl_divergence(geom_q)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.51082563) r)z6Exected type of other is geometric.Geometric, but got )r rrrr<rr)rotherpqs r& kl_divergencezGeometric.kl_divergence7s4 eY '::u{{qAvzz!a%((C!GvzzqS1W%8, He V r')r)__name__ __module__ __qualname____doc__rpropertyr+r/r2r:r=rAr@rRrTrY __classcell__)r%s@r&rrsr+Z$&L&&  **@>'4#OJ%6@"r'r) rnumpyrJr paddle.baserpaddle.distributionrrFrrr'r&rcs* !,A ))Ar'