:i[ddlZddlmZmZddlZddlmZmZmZddlm Z m Z ddl m Z ddl mZmZddlmZdgZGd de ZdS) N)OptionalUnion)infnanTensor)Chi2 constraints) Distribution)_standard_normal broadcast_all)_sizeStudentTc ReZdZdZejejejdZejZdZ e de fdZ e de fdZ e de fdZ dd ee efd ee efd ee efdeedd f fd Zdfd Zejfdede fdZdZdZxZS)ra Creates a Student's t-distribution parameterized by degree of freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale`. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = StudentT(torch.tensor([2.0])) >>> m.sample() # Student's t-distributed with degrees of freedom=2 tensor([ 0.1046]) Args: df (float or Tensor): degrees of freedom loc (float or Tensor): mean of the distribution scale (float or Tensor): scale of the distribution )dflocscaleTreturncv|jtj}t||jdk<|S)N memory_format)rclonetorchcontiguous_formatrrselfms O/var/www/icac/venv/lib/python3.11/site-packages/torch/distributions/studentT.pymeanz StudentT.mean+s. HNN)@N A A$'Q,c|jSN)r)rs rmodez StudentT.mode1s xr c|jtj}|j|jdkd|j|jdkz|j|jdkdz z ||jdk<t ||jdk|jdkz<t||jdk<|S)Nrr)rrrrrpowrrrs rvariancezStudentT.variance5s GMM(?M @ @ Jtw{ # ' ' * *gdgk" #wtw{#a' ) $'A+ -047arhs """""""" """"""""""11111111999999EEEEEEEE ,p p p p p |p p p p p r