o Me%@sddlmZddlZddlZddlZddlZddlmZmZddl m Z ddl m Z m Z mZmZddlmZmZmZddlmZmZmZmZmZmZmZmZddlmZmZm Z m!Z!Gd d d e"Z#dS) )print_functionN)_C_ops _legacy_C_ops)core) check_dtype check_typecheck_variable_and_dtype convert_dtype)_non_static_modein_dygraph_mode_in_legacy_dygraph) control_flowelementwise_addelementwise_divelementwise_mulelementwise_subnnopstensor)arangeconcat gather_nd multinomialcseZdZdZd(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]. csBt|tr|nt||_t|tr|nt||_tt|dS)N) isinstancetuple _batch_shape _event_shapesuperr__init__)self batch_shape event_shape __class__rPD:\Projects\ConvertPro\env\Lib\site-packages\paddle/distribution/distribution.pyr EszDistribution.__init__cC|jS)zeReturns batch shape of distribution Returns: Sequence[int]: batch shape )rr!rrr&r"TzDistribution.batch_shapecCr')zeReturns event shape of distribution Returns: Sequence[int]: event shape )rr(rrr&r#]r)zDistribution.event_shapecCt)zMean of distributionNotImplementedErrorr(rrr&meanfzDistribution.meancCr*)zVariance of distributionr+r(rrr&variancekr.zDistribution.variancecCr*)zSampling from the distribution.r+r!shaperrr&samplepzDistribution.samplecCr*)zreparameterized sampler+r0rrr&rsampletr3zDistribution.rsamplecCr*)z The entropy of the distribution.r+r(rrr&entropyxr3zDistribution.entropycCr*)z7The KL-divergence between self distributions and other.r+)r!otherrrr& kl_divergence|r3zDistribution.kl_divergencecCs||S)zProbability density/mass function evaluated at value. Args: value (Tensor): value which will be evaluated )log_probexpr!valuerrr&probszDistribution.probcCr*)z&Log probability density/mass function.r+r:rrr&r8r3zDistribution.log_probcCr*)zProbability density/mass function. Note: This method will be deprecated in the future, please use `prob` instead. r+r:rrr&probsszDistribution.probscCs||j|jS)zcompute shape of the sample Args: sample_shape (Tensor): sample shape Returns: Tensor: generated sample data shape )rr)r!Z sample_shaperrr& _extend_shapes zDistribution._extend_shapecGs<d}d}|D] }t|tjrd}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)rrVariable ValueError)r!argsZ is_variableZ is_numberargrrr&_validate_argss zDistribution._validate_argsc Gsg}g}d}|D]E}t|tr|g}t|tttjtjfs&td t |t |}|j }t |dkrDt |dkr?td|d}||}||q|j }|D]}t||\} } tj|d} t| | || qSt|S)z Argument convert args to Tensor Args: value (float, list, numpy.ndarray, Tensor) Returns: Tensor of args. gzUType of input args must be float, list, numpy.ndarray or Tensor, but received type {}float32float64zadata type of argument only support float32 and float64, your argument will be convert to float32.dtype)rfloatlistrnpZndarrayrr? TypeErrorformattypearrayrGstrwarningswarnZastypeappendZbroadcast_arraysZ create_tensorZassign) r!rAZ numpy_argsZ variable_argstmprBZarg_npZ arg_dtyperGZarg_broadcasted_Z arg_variablerrr& _to_tensors:          zDistribution._to_tensorcCstr/|j|jkr-t|jdvr-tdtrt||jStr-t |d|jd|jS|St |dddgd|j|jkrKtdt j||jd S|S) a Log_prob and probs methods have input ``value``, if value's dtype is different from param, convert value's dtype to be consistent with param's dtype. Args: param (Tensor): low and high in Uniform class, loc and scale in Normal class. value (Tensor): The input tensor. Returns: value (Tensor): Change value's dtype if value's dtype is different from param. )rDrEztdtype of input 'value' needs to be the same as parameters of distribution class. dtype of 'value' will be converted.Zin_dtypeZ out_dtyper;rDrEr8rF) r rGr rPrQr rcastr rrr)r!paramr;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. )paddleloglog1p)r!r= is_binaryrrr&_probs_to_logits s  zDistribution._probs_to_logitscCs$|r tjj|Stjjj|ddS)z Converts logits into probabilities. For the binary, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities of the events. )Zaxis)rYrZ functionalZsigmoidZsoftmax)r!Zlogitsr\rrr&_logits_to_probss zDistribution._logits_to_probs)rr)r)F)__name__ __module__ __qualname____doc__r propertyr"r#r-r/r2r4r5r7r<r8r=r>rCrUrXr]r_ __classcell__rrr$r&r7s0         . & r)$ __future__rmathrPnumpyrJrYrrZ paddle.fluidrZpaddle.fluid.data_feederrrrr Zpaddle.fluid.frameworkr r r Zpaddle.fluid.layersr rrrrrrrZ paddle.tensorrrrrobjectrrrrr&s  (