\;j^ddlmZddlZddlmZmZGddej ZdZdZ y))IterableN) categorical distributioncbeZdZdZfdZedZedZdZdZ d dZ dZ d Z xZ S) Multinomiala$ Multinomial distribution parameterized by :attr:`total_count` and :attr:`probs`. In probability theory, the multinomial distribution is a generalization of the binomial distribution, it models the probability of counts for each side of a k-sided die rolled n times. When k is 2 and n is 1, the multinomial is the bernoulli distribution, when k is 2 and n is grater than 1, it is the binomial distribution, when k is grater than 2 and n is 1, it is the categorical distribution. The probability mass function (PMF) for multinomial is .. math:: f(x_1, ..., x_k; n, p_1,...,p_k) = \frac{n!}{x_1!...x_k!}p_1^{x_1}...p_k^{x_k} where, :math:`n` is number of trials, k is the number of categories, :math:`p_i` denote probability of a trial falling into each category, :math:`{\textstyle \sum_{i=1}^{k}p_i=1}, p_i \ge 0`, and :math:`x_i` denote count of each category. Args: total_count (int): Number of trials. probs (Tensor): Probability of a trial falling into each category. Last axis of probs indexes over categories, other axes index over batches. Probs value should between [0, 1], and sum to 1 along last axis. If the value over 1, it will be normalized to sum to 1 along the last axis. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> multinomial = paddle.distribution.Multinomial(10, paddle.to_tensor([0.2, 0.3, 0.5])) >>> print(multinomial.sample((2, 3))) Tensor(shape=[2, 3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[[1., 5., 4.], [0., 4., 6.], [1., 3., 6.]], [[2., 2., 6.], [0., 6., 4.], [3., 3., 4.]]]) cnt|tr|dkr td|jdkr td||j ddz |_||_tj|j||_ t|1|jdd|jddy)NzBinput parameter total_count must be int type and grater than zero.z9probs parameter shoule not be none and over one dimensionT)keepdim)logits) isinstanceint ValueErrordimsumprobs total_countr Categorical_probs_to_logits _categoricalsuper__init__shape)selfrr __class__s hG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/distribution/multinomial.pyrzMultinomial.__init__Es+s+{QT  99;?K UYYr4Y88 &'33((/  Sb)5;;rs+;<c4|j|jzS)z[mean of multinomial distribuion. Returns: Tensor: mean value. )rrrs rmeanzMultinomial.meanXszzD,,,,rcT|j|jzd|jz zS)zdvariance of multinomial distribution. Returns: Tensor: variance value. r )rrrs rvariancezMultinomial.varianceas&$**,DJJ??rcJtj|j|S)zprobability mass function evaluated at value. Args: value (Tensor): value to be evaluated. Returns: Tensor: probability of value. )paddleexplog_prob)rvalues rprobzMultinomial.probjszz$--.//rctj|r*tj||jj}tj tj |j|g\}}tjrd||dk(tj|z<n:tjj||dk(tj|zd}tj|jddztj|dzjdz ||zjdzS)zprobability mass function evaluated at value Args: value (Tensor): value to be evaluated. Returns: Tensor: probability of value. rr r ) r$ is_integercastrdtypebroadcast_tensorslogin_dynamic_modeisinfstaticsetitemlgammar)rr'r s rr&zMultinomial.log_probus   U #KKtzz'7'78E00 ZZ #U +   ! ! #<=FEQJ6<<#78 9]]**! V(<=qF MM%))B-!+ ,mmEAI&**2. /v~""2& ' rct|ts td|jj |j gt |z}tjjj||jjdj|jjjdS)zdraw sample data from multinomial distribution Args: sample_shape (tuple, optional): [description]. Defaults to (). z%sample shape must be Iterable object.r r)r r TypeErrorrsamplerlistr$nn functionalone_hotrrr+r,r)rrsampless rr6zMultinomial.samples %*CD D##**   5k   II ( ($**2B2B22F G T$**"" # SV rcXtjg|j|jj}tj |jdz|jjj ddt|jjzzdd}tj|j||}||jjztj|dzz |tj|dzzjddgzS) z`entropy of multinomial distribution Returns: Tensor: entropy value )r fill_valuer,r r,)r )r Nrr )r$fullrrr,arangereshapelenrr%_binomial_logpmfrentropyr3r)rnsupport binomial_pmfs rrDzMultinomial.entropys KK!1!19I9I --   q  (8(8 '%$TZZ%5%5!666 7<zz$"7"77"CD D%%--//&--A2FF FMM'A+6 6 ; ;QG D  rc |j|jd}tj|dz}tj|dz}tj||z dz}|t |z|tj tj tj| zz|z }||z|z |z |z S)NT) is_binaryr )rrr$r3 _clip_by_zerolog1pr%abs)rcountr'r factor_nfactor_k factor_nmknorms rrCzMultinomial._binomial_logpmfs&&tzzT&B==+==+]]55=1#45  M&) )fll6::vzz&/A.A#BCC D  v~(:5<)r$r@)rMr,s r_binomial_supportrZs ==% 00rcX|jd|z|jdz dz S)Nr)min)max)clip)xs rrJrJs+ FFqFMA 1 - 22r) collections.abcrr$paddle.distributionrr DistributionrrZrJrRrrrds/% 9q=,++q=h13r