o "j "@s^ddlZddlZddlZddlmZddlmZmZddlm Z ddl m Z GdddZ dS)N)_C_ops)check_variable_and_dtype convert_dtype)Variable)in_dynamic_modecseZdZdZd(fdd ZeddZeddZed d Zed d Z d)d dZ d)ddZ ddZ ddZ ddZddZddZddZddZdd Zd!d"Zd*d$d%Zd*d&d'ZZS)+ Distributiona> The abstract base class for probability distributions. Functions are implemented in specific distributions. Args: batch_shape(Sequence[int], optional): independent, not identically distributed draws, aka a "collection" or "bunch" of distributions. event_shape(Sequence[int], optional): the shape of a single draw from the distribution; it may be dependent across dimensions. For scalar distributions, the event shape is []. For n-dimension multivariate distribution, the event shape is [n]. cs>t|tr|nt||_t|tr|nt||_tdS)N) isinstancetuple _batch_shape _event_shapesuper__init__)self batch_shape event_shape __class__ra/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/distribution/distribution.pyr/szDistribution.__init__cC|jS)zeReturns batch shape of distribution Returns: Sequence[int]: batch shape )r rrrrr=zDistribution.batch_shapecCr)zeReturns event shape of distribution Returns: Sequence[int]: event shape )r rrrrrFrzDistribution.event_shapecCt)zMean of distributionNotImplementedErrorrrrrmeanOzDistribution.meancCr)zVariance of distributionrrrrrvarianceTrzDistribution.variancecCr)zSampling from the distribution.rrshaperrrsampleYzDistribution.samplecCr)zreparameterized samplerrrrrrsample]r!zDistribution.rsamplecCr)z The entropy of the distribution.rrrrrentropyar!zDistribution.entropycCr)z7The KL-divergence between self distributions and other.r)rotherrrr kl_divergenceer!zDistribution.kl_divergencecCs||S)zProbability density/mass function evaluated at value. Args: value (Tensor): value which will be evaluated )log_probexprvaluerrrprobiszDistribution.probcCr)z&Log probability density/mass function.rr(rrrr&qr!zDistribution.log_probcCr)zProbability density/mass function. Note: This method will be deprecated in the future, please use `prob` instead. rr(rrrprobsuszDistribution.probscCst|t|jt|jS)zcompute shape of the sample Args: sample_shape (Tensor): sample shape Returns: Tensor: generated sample data shape )r r r )rZ sample_shaperrr _extend_shapes  zDistribution._extend_shapecGs:d}d}|D] }t|trd}qd}q|r|rtd|S)z Argument validation for distribution args Args: value (float, list, numpy.ndarray, Tensor) Raises ValueError: if one argument is Tensor, all arguments should be Tensor FTz9if one argument is Tensor, all arguments should be Tensor)r r ValueError)rargsZ is_variableZ is_numberargrrr_validate_argss zDistribution._validate_argsc Gsg}g}d}|D]=}t|ttttjtfstdt |t |}|j }t |dkrrcastrr@)rparamr)rrr_check_values_dtype_in_probss    z)Distribution._check_values_dtype_in_probsFcCs$|r t|t| St|S)a Converts probabilities into logits. For the binary, probs denotes the probability of occurrence of the event indexed by `1`. For the multi-dimensional, values of last axis denote the probabilities of occurrence of each of the events. )r@loglog1p)rr+ is_binaryrrr_probs_to_logitss  zDistribution._probs_to_logitscCs$|r tjj|Stjjj|ddS)z Converts logits into probabilities. For the binary, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities of the events. )Zaxis)r@nnZ functionalZsigmoidZsoftmax)rZlogitsrIrrr_logits_to_probss zDistribution._logits_to_probs)rr)r)F)__name__ __module__ __qualname____doc__rpropertyrrrrr r"r#r%r*r&r+r,r0rCrFrJrM __classcell__rrrrr!s0        , ! r) r=numpyr7r@rZpaddle.base.data_feederrrZpaddle.base.frameworkrZpaddle.frameworkrrrrrrs