AVhxdZddlZddlmZddlmZddlmZddlmZddlm Z ddlm Z dd l m Z dd l mZdd lmZd d gZedgGdd e j$ZGdd eZy)z#The Exponential distribution class.N)dtypes)ops) array_ops)math_ops)nn) random_ops)gamma) deprecation) tf_export ExponentialExponentialWithSoftplusRatezdistributions.Exponential)v1ceZdZdZej ddd d fd ZedZe dZ d Z d d Z xZ S) r aExponential distribution. The Exponential distribution is parameterized by an event `rate` parameter. #### Mathematical Details The probability density function (pdf) is, ```none pdf(x; lambda, x > 0) = exp(-lambda x) / Z Z = 1 / lambda ``` where `rate = lambda` and `Z` is the normalizaing constant. The Exponential distribution is a special case of the Gamma distribution, i.e., ```python Exponential(rate) = Gamma(concentration=1., rate) ``` The Exponential distribution uses a `rate` parameter, or "inverse scale", which can be intuited as, ```none X ~ Exponential(rate=1) Y = X / rate ``` 2019-01-01zThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.T warn_oncectt}tj||g5}tj|d|_dddt t|#tjg|j j|j |||||_ |xj|j gz c_ y#1swY|xYw)aConstruct Exponential distribution with parameter `rate`. Args: rate: Floating point tensor, equivalent to `1 / mean`. Must contain only positive values. validate_args: Python `bool`, default `False`. When `True` distribution parameters are checked for validity despite possibly degrading runtime performance. When `False` invalid inputs may silently render incorrect outputs. allow_nan_stats: Python `bool`, default `True`. When `True`, statistics (e.g., mean, mode, variance) use the value "`NaN`" to indicate the result is undefined. When `False`, an exception is raised if one or more of the statistic's batch members are undefined. name: Python `str` name prefixed to Ops created by this class. valuesratenameNdtype) concentrationrallow_nan_stats validate_argsr)dictlocalsr name_scopeconvert_to_tensor_ratesuperr __init__ronesr _parameters_graph_parentsselfrrrr parameters __class__s _/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/distributions/exponential.pyr$zExponential.__init__Fs8fhJ dV ,<((F;dj< +t%nnRtzz/?/?@ ZZ'#  & "DDJJ<'<>% 8<< #; ;;r1c^tj|gtj|jgd}t j |t j|jjjd||j}tj| |jz S)Nrg?)minvalmaxvalseedr) rconcatshaper"rrandom_uniformnpfinforas_numpy_dtypetinyrr6)r)nr<r>sampleds r, _sample_nzExponential._sample_n}s   qc9??4::#>? CE'' xx 11277 jj G LL ! !DJJ ..r1)FTr r3)__name__ __module__ __qualname____doc__r deprecatedr$ staticmethodr0propertyrr8rF __classcell__r+s@r,r r $sw@;' ##! "("(HMM  </r1cZeZdZdZej ddd dfd ZxZS)r z.Exponential with softplus transform on `rate`.rz+Use `tfd.Exponential(tf.nn.softplus(rate)).Trctt}tj||g5}tt |tj|d|||ddd||_ y#1swY||_ yxYw)Nr softplus_rater)rrrr) rrrr r#r r$rsoftplusr&r(s r,r$z$ExponentialWithSoftplusRate.__init__srfhJ dV , '7{{4o6%) 8 "D  "Ds -A**A:)FTr )rGrHrIrJr rKr$rNrOs@r,r r s96;3 ##1 "  "r1)rJnumpyr@tensorflow.python.frameworkrrtensorflow.python.opsrrrr#tensorflow.python.ops.distributionsr tensorflow.python.utilr tensorflow.python.util.tf_exportr __all__Gammar r r1r,r]so*.++*$,5.6!   *+,g/%++g/-g/T"+"r1