Dear Boosters, dear Jens,
After some debugging I realized another bug in the new version of the
Boost random number library. uniform_real has the following operator():
result_type operator()(Engine& eng) { return eng() * (_max - _min) +
_min; }
This does not work if the underlying engine doe
A quick last minute contribution to this thread.
There was a fair bit of incidental information in the discussion. If
anyone wants to formalise things a little further one good place to put
is the Boost Wiki. There is already a page there regarding uBLAS and a
page on uBLAS and Linear Algebra whic
> From:
> "Neal D. Becker" <[EMAIL PROTECTED]>
>
>Does ublas require matrix storage be managed by ublas? Is it possible to
>construct a ublas matrix that references a plain-old-C-style array?
>
>If not, what is a simple way construct a ublas matrix from a C-style
array?
>
>
>
There is an undocume
t))/*(.*)" :
$(rpath) : 1 2 ] ;
if ! $(pruned_path) {
I don't have a fix as am unsure of the Jam syntax for a simple (non
regex) match to do this job.
Any ideas of best solution?
Michael Stevens
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Dave,
Could you please suggest a patch for the regression tests which will
detect this problem? It's always better to get the test to fail
first before we check in any fixes.
Yep. I'll work on something to pass on to Jens for inclusion latter.
There is no simple patch for the regression test.
m is NOT picked up by regression tests
in random_test.cpp. The current test procedure
void instantiate_dist(const char * name, const Dist& dist)
uses a reference to a constant distribution. It therefore fails to
pickup on the return of non constant member reference.
All the best,
Mic
to
argument, the reference will be to the temporary and will thus fail.
2. The additional copy put additional requirements on 'members'
arguments types. They may not actually be copiable!
3. The runtime cost of the copy.
Hopefully there are no disadvanatage!
Michael Stevens
_
There is a small correction required for lognormal_distribution.
The problem is flagged under GCC3.2
explicit lognormal_distribution(base_type & rng,
result_type mean = result_type(1),
result_type sigma = result_type(1))
...
RealT
