I've seen cases where the test fails simply because of the precision differ
on different platforms. That's why I wanted to see what you get when you do
print $m{ms}->sumover(). But I guess make test doesn't actually print out
the values to stdout... How about this?
Add
print STDERR t_kmeans_bad();
168> is(tapprox( t_kmeans_bad(), 0 ), 1);
169> sub t_kmeans_bad {
170> my $data = sequence 7, 3;
171> $data = $data->setbadat(4,0);
172> my %m = $data->kmeans({NCLUS=>2, NTRY=>10, V=>0});
print STDERR "$_\t$m{$_}\n" for (sort keys %m);
173> return sum( $m{ms}->sumover - pdl qw( 1.5 1.9166667 1.9166667 ) );
174> }
Best,
Maggie
On Thu, Dec 22, 2011 at 9:06 PM, Puneet Kishor <[email protected]> wrote:
> I should have added my congratulations and gratitude for taking 2.4.9 to a
> point that it builds and installs beautifully, without any heartburn.
> Perhaps because most of the dependencies got installed when I installed
> 2.4.6 via MacPorts.
>
>
> I am doing a lot of stuff using R, so am curious to try out PDL::Stats.
> For now, I am using R compiled inside Postgres using PL/R... what a
> wonderful experience to get my data out of Postgres and analyze it right
> within the database.
>
> Wrt PDL::Stats, my guess is the test failure has nothing to do with it
> being on a Mac/10.7. Perl 5.14.1 should be Perl 5.14.1 no matter what
> platform, no?
>
> On Dec 22, 2011, at 7:50 PM, chm wrote:
>
> > On 12/22/2011 7:48 PM, Puneet Kishor wrote:
> >> Inspired by the lovely blurb below, I tried to install PDL::Stats but
> failed. Here is the relevant bit from the log of my failed attempt --
> >>
> >> ----
> >> PERL_DL_NONLAZY=1 /opt/local/bin/perl "-MExtUtils::Command::MM" "-e"
> "test_harness(0, 'blib/lib', 'blib/arch')" t/*.t
> >> t/stats_basic.t ..... ok
> >> t/stats_glm.t ....... ok
> >>
> >> # Failed test at t/stats_kmeans.t line 168.
> >> # got: ''
> >> # expected: '1'
> >> # Looks like you failed 1 test of 18.
> >> t/stats_kmeans.t ....
> >> Dubious, test returned 1 (wstat 256, 0x100)
> >> Failed 1/18 subtests
> >> t/stats_ols_rptd.t .. ok
> >> t/stats_ts.t ........ ok
> >>
> >> Test Summary Report
> >> -------------------
> >> t/stats_kmeans.t (Wstat: 256 Tests: 18 Failed: 1)
> >> Failed test: 13
> >> Non-zero exit status: 1
> >> Files=5, Tests=147, 1 wallclock secs ( 0.03 usr 0.01 sys + 0.91 cusr
> 0.04 csys = 0.99 CPU)
> >> Result: FAIL
> >> Failed 1/5 test programs. 1/147 subtests failed.
> >> make: *** [test_dynamic] Error 255
> >> -> FAIL Installing PDL::Stats failed. See
> /Volumes/roller/Users/punkish/.cpanm/build.log for details.
> >> ----
> >>
> >>
> >> Here is line 168 (test #13) from t/stats_kmeans.t
> >>
> >> 168> is(tapprox( t_kmeans_bad(), 0 ), 1);
> >> 169> sub t_kmeans_bad {
> >> 170> my $data = sequence 7, 3;
> >> 171> $data = $data->setbadat(4,0);
> >> 172> my %m = $data->kmeans({NCLUS=>2, NTRY=>10, V=>0});
> >> 173> return sum( $m{ms}->sumover - pdl qw( 1.5 1.9166667 1.9166667
> ) );
> >> 174> }
> >>
> >>
> >> I am on Mac OS X Lion with Perl 5.14.1 and PDL 2.4.6 installed via
> MacPorts
> > ^^^^^
> > |||||
> >
> > My guess it the problem is you are using PDL-2.4.6 and not
> > the current PDL-2.4.9 (or even better the current CPAN
> > Developers release version).
> >
> > I know there were some fixes the are in PDL-2.4.9 that
> > helped Maggie X's stuff work. Maybe she can recall any
> > tricks to build her modules with a fairly old PDL (i.e.,
> > one not having the many fixes and features added in the
> > past couple of years).
> >
> > --Chris
> >
> >> On Dec 22, 2011, at 3:26 PM, Maggie X wrote:
> >>
> >>> Hi Chris,
> >>>
> >>> Thanks for including the info! Here's the blurb for PDL::Stats.
> >>>
> >>> --------------
> >>> This is a collection of statistics modules in Perl Data Language, with
> a
> >>
> >>> quick-start guide for non-PDL people.
> >>>
> >>> They make perldl--the simple shell for PDL--work like a teenie weenie
> R,
> >>
> >>> but with PDL threading--"the fast (and automagic) vectorised iteration
> of
> >>> 'elementary operations' over arbitrary slices of multidimensional
> data"--of
> >>> procedures including t-test, ordinary least squares regression, and
> k-means
> >>> clustering.
> >>> ---------------
> >>>
> >>> Best,
> >>> Maggie
> >>>
> >>> ..
> >>
>
>
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