代码搜索：Problem
找到约 10,000 项符合「Problem」的源代码
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www.eeworm.com/read/366702/2871255
c p2846b.c
// { dg-do run }
// Shows that problem of initializing one object's secondary base from
// another object via a user defined copy constructor for that base,
// the pointer for the secondary vtable is
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f90 derived_comp_array_ref_3.f90
! { dg-do run }
! Tests the fix for PR33337, which was partly associated with
! the problem in PR31564 and, in addition, the parentheses in
! the initialization expression for the_chi_square.
!
! Cont
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f90 interface_14.f90
! { dg-do compile }
! Checks the fix for a regression PR32526, which was caused by
! the patch for PR31494. The problem here was that the symbol
! 'new' was determined to be ambiguous.
!
! Contribute
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x 930529-1.x
# The problem on Alpha at -O3 is that when dd is inlined, we have
# division by a constant, which gets converted to multiplication
# by a large constant, which gets turned into an induction variable.
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c 20001116-1.c
/* This looks like a warning test, but it's actually a regression test for a
nasty ICE due to messed up parser context. Problem originally found
during bootstrap; this is synthetic. -zw */
/*
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c builtin-sin-mpfr-1.c
/* Version 2.2.0 of MPFR had bugs in sin rounding. This test checks
to see if that buggy version was installed. The problem is fixed
in version 2.2.1 and presumably later MPFR versions.
Or
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m demev1.m
%DEMEV1 Demonstrate Bayesian regression for the MLP.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by c
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m demhmc1.m
%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians.
%
% Description
% The problem consists of generating data from a mixture of two
% Gaussians in two dimensions using a
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m demev3.m
%DEMEV3 Demonstrate Bayesian regression for the RBF.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by c
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m demknn1.m
%DEMKNN1 Demonstrate nearest neighbour classifier.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian distributions with