o * i|*@szddlmZddlZddlmZddlZddlZddlm Z ddl m Z er2ddl m Z ddlmZGdd d e jZdS) ) annotationsN) TYPE_CHECKING) framework) distribution)Sequence)TensorcseZdZUdZded<d"fdd Zed#d d Zed#d d Zed#d dZ d$ddZ d$ddZ gfd%ddZ gfd%ddZ d#ddZd$ddZd&d d!ZZS)' GeometricaM Geometric distribution parameterized by probs. In probability theory and statistics, the geometric distribution is one of discrete probability distributions, parameterized by one positive shape parameter, denoted by probs. In n Bernoulli trials, it takes k+1 trials to get the probability of success for the first time. In detail, it is: the probability that the first k times failed and the kth time succeeded. The geometric distribution is a special case of the Pascal distribution when r=1. The probability mass function (pmf) is .. math:: Pr(Y=k)=(1-p)^kp where k is number of trials failed before seeing a success, and p is probability of success for each trial and k=0,1,2,3,4..., p belong to (0,1]. Args: probs (Real|Tensor): Probability parameter. The value of probs must be positive. When the parameter is a tensor, probs is probability of success for each trial. Returns: Geometric distribution for instantiation of probs. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.mean) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.) >>> print(geom.variance) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.) >>> print(geom.stddev) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.41421354) rprobsfloat | TensorreturnNonecst|tjtjtjtjjfrCt|tjrtj d|tj d}tj |j d|j d}tj |j d|j d}tj |j dt d}||k}||k}n tdt|t|j }||_t|dS)N)shapeZ fill_valuedtyperFzOExpected type of probs is Number.Real|Tensor|framework.Variable|Value, but got ) isinstancenumbersRealpaddlerrVariablepirValuefullfloat32rrbool TypeErrortypetupler super__init__)selfr all_onesZ all_zerosZ all_falseZ lessthen_0Z morethen_1Z batch_shape __class__r i/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/distribution/geometric.pyrPs2      zGeometric.__init__cCsd|jdS)zMean of geometric distribution.?)r r r r r$meanqszGeometric.meancCs"tjd|jd|j|jjdS)z#Variance of geometric distribution.r%r)rZ to_tensorr rr&r r r$variancevszGeometric.variancecCs t|jS)z-Standard deviation of Geometric distribution.)rsqrtr)r&r r r$stddev~s zGeometric.stddevk int | TensorcCsBt|tjtjtjjfrtd|j ||j St dt |)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 rrr r,r r r$pmfs z Geometric.pmfcCs:t|tjtjtjjfrt| |St dt |)aFLog probability mass function evaluated at k. .. math:: \log P(X = k) = \log(1-p)^k p Args: k (int): Value to be evaluated. Returns: Tensor: Log probability. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.log_pmf(2)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, -2.07944131) r.) rrr0rrrrrlogr3rrr2r r r$log_pmfs zGeometric.log_pmfr Sequence[int]cCs6t ||WdS1swYdS)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)rZno_gradrsample)r rr r r$samples $zGeometric.samplecCsRtjj||d}tj|ttjddjd|j j d}t t |t |j S)aGenerate samples of the specified shape. Args: shape(Sequence[int]): The shape of generated samples. Returns: Tensor: A sample tensor that fits the Geometric distribution. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> paddle.seed(2023) >>> geom = Geometric(0.5) >>> print(geom.rsample((2,2))) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0.], [1., 0.]]) )Z sample_shaperr(r%)rminmaxr)r DistributionZ _extend_shaperuniformfloatnpZfinfoZtinyr rfloorr4log1p)r rr<r r r$r7szGeometric.rsamplecCs<d|jtd|j}|jt|j}|| |jS)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 rr4)r xyr r r$entropyszGeometric.entropycCsDt|tjtjtjjfrdtd|j |dSt dt |)aDCdf of geometric distribution. .. math:: F(X \leq k) = 1 - (1-p)^(k+1), \quad k=0,1,2,\ldots Args: k: The number of trials performed. Returns: Tensor: Entropy. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom = Geometric(0.5) >>> print(geom.cdf(4)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.96875000) r%rr.r/r2r r r$cdf s z Geometric.cdfothercCsZt|tr$|j|j}}|t||d|td|d|Stdt|)aCalculate the KL divergence KL(self || other) with two Geometric instances. .. math:: KL(P \| Q) = \frac{p}{q} \log \frac{p}{q} + \log (1-p) - \log (1-q) Args: other (Geometric): An instance of Geometric. Returns: Tensor: The kl-divergence between two geometric distributions. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Geometric >>> geom_p = Geometric(0.5) >>> geom_q = Geometric(0.1) >>> print(geom_p.kl_divergence(geom_q)) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.51082563) r%z6Exacted type of other is geometric.Geometric, but got )rrr rr4rr)r rEpqr r r$ kl_divergenceBs  zGeometric.kl_divergence)r r r r )r r)r,r-r r)rr6r r)rErr r)__name__ __module__ __qualname____doc____annotations__rpropertyr'r)r+r3r5r8r7rCrDrH __classcell__r r r"r$r s" -!     "! % "r) __future__rrtypingrnumpyr>rZ paddle.baserZpaddle.distributionrcollections.abcrrr;rr r r r$s