-----Original Message----- From: boost-bounces@lists.boost.org [mailto:boost-bounces@lists.boost.org] On Behalf Of Thijs van den Berg Sent: 28 November 2008 13:58 To: boost@lists.boost.org Subject: Re: [boost] [math distributions] Laplace distribution
I've done a first commit to the sandbox!
1) sandbox\math_toolkit\boost\math\distributions\laplace.cpp 2) sandbox\math_toolkit\libs\math\test\test_laplace.cpp
..more later! (doc's, equations, charts, discussion)
The unit test file was (is) quite a lot of work, ..there are *so many* things to consider/check.
;-))
How about this: We could write some generic test based on the properties of distributions...
General relations: 1) quantile(cdf(x)) == x 2) hazard(x) = pdf(x)/(1-cdf(x)) 3) pdf(x,location,scale) = pdf( (x-location)/scale, 0, 1)/scale 4) cdf(x,location,scale) = cdf( (x-location)/scale, 0, 1) 5) cdf(complement(N,x)) = cdf(N(-x)) 6) quantile(complement(N,p)) = quantile(N(-x,1-p))
perhaps some automatic checking (for all distribution) of error throwing 7) support <-> cdf, pdf 8) quantile <-> p=0, p=1
And some generic test for distributions with specific properties Symmetric distributions: pdf(x) = pdf(-x) cdf(x) = 1-cdf(-x)
etc. we could write template functions for that, that get passed a set of 'x' values etc
That would indeed be neat - but our tests just grew like Topsy ;-) And it would be quite a lot of work to change now. I also worry that the acceptable tolerances vary widely, so you would have keep passing these as parameters anyway. We also (still are) a bit schizophrenic about whether to allow infinity as parameter, so a uniform test to check throw would be difficult. I note you haven't tried to deal with the long double case. Even if your system like my MSVC only does double == long double 64 bit reals, we should have the hooks in place for systems that do proper long doubles. This rather messy code does this, if appropriate. #ifndef BOOST_MATH_NO_LONG_DOUBLE_MATH_FUNCTIONS test_spots(0.0L); // Test long double. #if !BOOST_WORKAROUND(__BORLANDC__, BOOST_TESTED_AT(0x0582)) test_spots(boost::math::concepts::real_concept(0.)); // Test real concept. #endif #else std::cout << "<note>The long double tests have been disabled on this platform " "either because the long double overloads of the usual math functions are " "not available at all, or because they are too inaccurate for these tests " "to pass.</note>" << std::cout; #endif At least a few really accurate values could be calculated using the published formula using a 100 decimal digit calculator? But round tripping should test well and I don't expect any trouble for higher precision types (in the unlikely event that someone want them - no accounting for some peoples taste). (I wonder if we can get rid of the Borland test yet?) You might also check the convenience typedef works like this? // Check that can generate lognormal distribution using the two convenience methods: boost::math::lognormal myf1(1., 2); // Using typedef lognormal_distribution<> myf2(1., 2); // Using default RealType double. http://mathworld.wolfram.com/LaplaceDistribution.html is a good link for the doc? Weisstein, Eric W. "Laplace Distribution." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/LaplaceDistribution.html <aside> (This looks rather amusing http://demonstrations.wolfram.com/SampleVersusTheoreticalDistribution/). </aside> Looking good to me. Paul PS You can write 2. 1., , 0.5, 0.25... without the L because they can be exactly represented as float, double, long double (unlike 0.1, 0.01... ) static_cast<RealType>(0.5) which saves some typing and clutter.