zj,#ddlmZddlmZddlmZddlZddlmZer ddlm Z ddl m Z Gdd ej Z dS) ) annotations)Sequence) TYPE_CHECKINGN) distribution)Tensor) _DTypeLiteralceZdZUdZded<ded<ded<dfd Zdd ZeddZeddZ gfddZ ddZ ddZ ddZ ddZddZxZS) Binomiala The Binomial distribution with size `total_count` and `probs` parameters. In probability theory and statistics, the binomial distribution is the most basic discrete probability distribution defined on :math:`[0, n] \cap \mathbb{N}`, which can be viewed as the number of times a potentially unfair coin is tossed to get heads, and the result of its random variable can be viewed as the sum of a series of independent Bernoulli experiments. The probability mass function (pmf) is .. math:: pmf(x; n, p) = \frac{n!}{x!(n-x)!}p^{x}(1-p)^{n-x} In the above equation: * :math:`total\_count = n`: is the size, meaning the total number of Bernoulli experiments. * :math:`probs = p`: is the probability of the event happening in one Bernoulli experiments. Args: total_count(int|Tensor): The size of Binomial distribution which should be greater than 0, meaning the number of independent bernoulli trials with probability parameter :math:`p`. The data type will be converted to 1-D Tensor with paddle global default dtype if the input :attr:`probs` is not Tensor, otherwise will be converted to the same as :attr:`probs`. probs(float|Tensor): The probability of Binomial distribution which should reside in [0, 1], meaning the probability of success for each individual bernoulli trial. If the input data type is float, it will be converted to a 1-D Tensor with paddle global default dtype. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Binomial >>> paddle.set_device('cpu') >>> paddle.seed(100) >>> rv = Binomial(100, paddle.to_tensor([0.3, 0.6, 0.9])) >>> print(rv.sample([2])) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[31., 62., 93.], [29., 54., 91.]]) >>> print(rv.mean) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [30.00000191, 60.00000381, 90. ]) >>> print(rv.entropy()) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [2.94053698, 3.00781751, 2.51124287]) rdtyper total_countprobs int | Tensorfloat | TensorreturnNonectj|_|||\|_|_|jj}t|dS)N) paddleget_default_dtyper _to_tensorr r shapesuper__init__)selfr r batch_shape __class__s l/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/distribution/binomial.pyrzBinomial.__init__Qs[-// '+{E'J'J$$*&,  %%%%% list[Tensor]c@t|trtj||j}n |j|_t|t rtj||j}ntj||j}tj||gS)zConvert the input parameters into Tensors if they were not and broadcast them Returns: list[Tensor]: converted total_count and probs. r ) isinstancefloatr to_tensorr intcastbroadcast_tensors)rr r s rrzBinomial._to_tensorZs eU # # %$U$*===EEDJ k3 ' ' E *;djIIIKK +kDDDK'e(<===rc |j|jzS)zYMean of binomial distribution. Returns: Tensor: mean value. r r rs rmeanz Binomial.meanos$*,,rc6|j|jzd|jz zS)zaVariance of binomial distribution. Returns: Tensor: variance value. r(r)s rvariancezBinomial.variancexs$*,DJ??rr Sequence[int]ct|tstdtjd5t |}t |j}t ||z}tj|j|}tj|j |}tj tj |d|}tj ||j cdddS#1swxYwYdS)a^Generate binomial samples of the specified shape. The final shape would be ``shape+batch_shape`` . Args: shape (Sequence[int], optional): Prepended shape of the generated samples. Returns: Tensor: Sampled data with shape `sample_shape` + `batch_shape`. The returned data type is the same as `probs`. z%sample shape must be Sequence object.F)rint32r N) r!r TypeErrorrset_grad_enabledtupler broadcast_tor r binomialr%r )rrr output_shape output_size output_probsamples rr9zBinomial.samples8%** ECDD D  $U + + 3 3%LLE 011K !455L - K!-dj MMMK_ Kw777F;vtz22 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3sB.C44C8;C8c|}||}tj||zd S)aeShannon entropy in nats. The entropy is .. math:: \mathcal{H}(X) = - \sum_{x \in \Omega} p(x) \log{p(x)} In the above equation: * :math:`\Omega`: is the support of the distribution. Returns: Tensor: Shannon entropy of binomial distribution. The data type is the same as `probs`. r)_enumerate_supportlog_probrexpsum)rvaluesr<s rentropyzBinomial.entropysL ((**==((H%%055a8888rctjdtj|jz|j}|ddt |jzz}|S)zReturn the support of binomial distribution [0, 1, ... ,n] Returns: Tensor: the support of binomial distribution r,r ))r,)rarangemaxr r reshapelenr)rr?s rr;zBinomial._enumerate_supports`   4+,, ,DJ   s43C/D/D(D DEE rvaluectj||j}tj|jdztj|j|z dzz tj|dzz }tj|jjj}tj|j|d|z }tj ||tj |zz|j|z tj d|z zz| S)zLog probability density/mass function. Args: value (Tensor): The input tensor. Returns: Tensor: log probability. The data type is the same as `probs`. r g?r,)minrD)neginf) rr%r lgammar finfor epsclip nan_to_numlog)rrGlog_combrMr s rr<zBinomial.log_probs E444 M$*S0 1 1mD,u4s:;; <mECK(( )  l4:+,,0 DJCQW=== &*U+++,#e+vz!e)/D/DDE4     rcPtj||S)zProbability density/mass function. Args: value (Tensor): The input tensor. Returns: Tensor: probability. The data type is the same as `probs`. )rr=r<)rrGs rprobz Binomial.probs z$--..///rotherc|}||}||}tjtj|tj||dS)avThe KL-divergence between two binomial distributions with the same :attr:`total_count`. The probability density function (pdf) is .. math:: KL\_divergence(n_1, p_1, n_2, p_2) = \sum_x p_1(x) \log{\frac{p_1(x)}{p_2(x)}} .. math:: p_1(x) = \frac{n_1!}{x!(n_1-x)!}p_1^{x}(1-p_1)^{n_1-x} .. math:: p_2(x) = \frac{n_2!}{x!(n_2-x)!}p_2^{x}(1-p_2)^{n_2-x} Args: other (Binomial): instance of ``Binomial``. Returns: Tensor: kl-divergence between two binomial distributions. The data type is the same as `probs`. r)r;r<rmultiplyr=subtractr>)rrTsupport log_prob_1 log_prob_2s r kl_divergencezBinomial.kl_divergencest0))++]]7++ ^^G,, O :&&Z88   #a&&  r)r rr rrr)r rr rrr)rr)rr.rr)rGrrr)rTr rr)__name__ __module__ __qualname____doc____annotations__rrpropertyr*r-r9r@r;r<rSr[ __classcell__)rs@rr r sG..`MMM&&&&&&>>>>*---X-@@@X@-/3333329999(        : 0 0 0 0        rr ) __future__rcollections.abcrtypingrrpaddle.distributionrrpaddle._typing.dtype_liker Distributionr rrrjs#"""""$$$$$$ ,,,,,,8777777fffff|(fffffr