-
I'm a FORTRAN/NAG/SPSS/SAS/Cephes/MathCad/R user and I don't
see where the functions like dnorm(mean, sd) are in Boost.Math?
Nearly all are provided, and many more like mean, skewness, quantiles,
complements ... but Boost.Math makes full use of C++, and it looks a bit
different. But do not panic! See section on construction and the many examples.
Briefly, the distribution is constructed with the parameters (like location
and scale) (things after the | in representation like P(X=k|n, p) or ;
in a common represention of pdf f(x; μσ2). Functions like pdf, cdf are called
with the name of that distribution and the random variate often called
x or k. For example, normal my_norm(0, 1); pdf(my_norm, 2.0);
-
I'm a user of New
SAS Functions for Computing Probabilities.
You will find
the interface more familar, but to be able to select a distribution (perhaps
using a string) see the Extras/Future Directions section, and /boost/libs/math/dot_net_example/boost_math.cpp
for an example that is used to create a C# (C sharp) utility (that you
might also find useful): see Statistical
Distribution Explorer.
-
I'm allegic to reading manuals and prefer to learn from examples.
Fear not - you are not alone! Many examples are available for functions
and distributions. Some are referenced directly from the text. Others can
be found at \boost_latest_release\libs\math\example. If you are a Visual
Studio user, you should be able to create projects from each of these,
making sure that the Boost library is in the include directories list.
-
How do I make sure that the Boost library is in the Visual Studio
include directories list?
You can add an include path,
for example, your Boost place /boost-latest_release, for example X:/boost_1_45_0/ if you have a separate partition X for
Boost releases. Or you can use an environment variable BOOST_ROOT set to
your Boost place, and include that. Visual Studio before 2010 provided
Tools, Options, VC++ Directories to control directories: Visual Studio
2010 instead provides property sheets to assist. You may find it convenient
to create a new one adding \boost-latest_release; to the existing include
items in $(IncludePath).
-
I'm a FORTRAN/NAG/SPSS/SAS/Cephes/MathCad/R user and I don't
see where the properties like mean, median, mode, variance, skewness of
distributions are in Boost.Math?
They are all available
(if defined for the parameters with which you constructed the distribution)
via Cumulative Distribution
Function, Probability
Density Function, Quantile,
Hazard Function,
Cumulative Hazard Function,
mean, median,
mode, variance,
standard deviation,
skewness, kurtosis, kurtosis_excess,
range and support.
-
I am a C programmer. Can I user Boost.Math with C?
Yes you can, including all the special functions, and TR1 functions like
isnan. They appear as C functions, by being declared as "extern C".
-
I am a C# (Basic? F# FORTRAN? Other CLI?) programmer. Can I use
Boost.Math with C#? (or ...)?
Yes you can, including
all the special functions, and TR1 functions like isnan. But you must build the Boost.Math as a dynamic library (.dll) and compile
with the /CLI option. See the boost/math/dot_net_example folder
which contains an example that builds a simple statistical distribution
app with a GUI. See Statistical
Distribution Explorer
-
What these "policies" things for?
Policies are a powerful (if necessarily complex) fine-grain mechanism that
allow you to customise the behaviour of the Boost.Math library according
to your precise needs. See Policies. But
if, very probably, the default behaviour suits you, you don't need to know
more.
-
I am a C user and expect to see global C-style
::errno set for overflow/errors etc?
You can achieve what you want - see error
handling policies and user
error handling and many examples.
-
I am a C user and expect to silently return a max value for overflow?
You (and C++ users too) can return whatever you want on overflow
- see overflow_error
and error
handling policies and several examples.
-
I don't want any error message for overflow etc?
You can control exactly what happens for all the abnormal conditions,
including the values returned. See domain_error,
overflow_error
error handling
policies user
error handling etc and examples.
-
My environment doesn't allow and/or I don't want exceptions.
Can I still user Boost.Math?
Yes but you must customise
the error handling: see user
error handling and changing
policies defaults .
-
The docs are several hundreds of pages long! Can I read the docs
off-line or on paper?
Yes - you can download the Boost
current release of most documentation as a zip of pdfs (including Boost.Math)
from Sourceforge, for example https://sourceforge.net/projects/boost/files/boost-docs/1.45.0/boost_pdf_1_45_0.tar.gz/download.
And you can print any pages you need (or even print all pages - but be
warned that there are several hundred!). Both html and pdf versions are
highly hyperlinked. The entire Boost.Math pdf can be searched with Adobe
Reader, Edit, Find ... This can often find what you seek, a partial substitute
for a full index.
-
I want a compact version for an embedded application. Can I use
float precision?
Yes - by selecting RealType template
parameter as float: for example normal_distribution<float> your_normal(mean,
sd); (But double may still be used internally, so space saving may be less
that you hope for). You can also change the promotion policy, but accuracy
might be much reduced.
-
I seem to get somewhat different results compared to other programs.
Why? We hope Boost.Math to be more accurate: our priority is
accuracy (over speed). See the section on accuracy. But for evaluations
that require iterations there are parameters which can change the required
accuracy (see Policies). You might be able
to squeeze a little more (or less) accuracy at the cost of runtime.
-
Will my program run more slowly compared to other math functions
and statistical libraries? Probably, thought not always, and
not by too much: our priority is accuracy. For most functions, making sure
you have the latest compiler version with all optimisations switched on
is the key to speed. For evaluations that require iteration, you may be
able to gain a little more speed at the expense of accuracy. See detailed
suggestions and results on performance.
-
How do I handle infinity and NaNs portably?
See nonfinite fp_facets for
Facets for Floating-Point Infinities and NaNs.
-
Where are the pre-built libraries?
Good news
- you probably don't need any! - just #include
<boost/math/distribution_you_want>.
But in the unlikely event that you do, see building
libraries.
-
I don't see the function or distribution that I want.
You could try an email to ask the authors - but no promises!
-
I need more decimal digits for values/computations.
You can use Boost.Math with Boost.Multiprecision:
typically cpp_dec_float
is a useful user-defined type to provide a fixed number of decimal digits,
usually 50 or 100.
-
Why can't I write something really simple like
cpp_int
one(1); cpp_dec_float_50
two(2); one
* two; Because cpp_int
might be bigger than cpp_dec_float
can hold,
so you must make an explicit conversion.
See mixed
multiprecision arithmetic and conversion.
