[Boost-commit] svn:boost r67725 - trunk/libs/math/doc/sf_and_dist

Subject: [Boost-commit] svn:boost r67725 - trunk/libs/math/doc/sf_and_dist
From: john_at_[hidden]
Date: 2011-01-06 11:31:54

Author: johnmaddock
Date: 2011-01-06 11:31:50 EST (Thu, 06 Jan 2011)
New Revision: 67725
URL: http://svn.boost.org/trac/boost/changeset/67725

Log:
Rename to all lower case: fixes issues on case sensitive file systems.
Added:
   trunk/libs/math/doc/sf_and_dist/faq.qbk   (props changed)
      - copied unchanged from r67724, /trunk/libs/math/doc/sf_and_dist/FAQ.qbk
Removed:
   trunk/libs/math/doc/sf_and_dist/FAQ.qbk

Deleted: trunk/libs/math/doc/sf_and_dist/FAQ.qbk
==============================================================================
--- trunk/libs/math/doc/sf_and_dist/FAQ.qbk	2011-01-06 11:31:50 EST (Thu, 06 Jan 2011)
+++ (empty file)
@@ -1,93 +0,0 @@
-[section:faq Frequently Asked Questions FAQ]
-
-# ['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?] [br]
-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; [mu][sigma][super 2]). 
-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);` [br]
-#I'm a user of [@http://support.sas.com/rnd/app/da/new/probabilityfunctions.html New SAS Functions for Computing Probabilities]. [br]
-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# utility
-(that you might also find useful): see [@http://sourceforge.net/projects/distexplorer/ Statistical Distribution Explorer] [br]. 
-# ['I'm allegic to reading manuals and prefer to learn from examples.][br]
-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?][br]
-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?][br]
-They are all available (if defined for the parameters with which you constructed the distribution) via __usual_accessors.
-# ['I am a C programmer.  Can I user Boost.Math with C?][br]
-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#?] [br]
-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 [@http://sourceforge.net/projects/distexplorer/ Statistical Distribution Explorer] [br]
-# ['What these "policies" things for?] [br]
-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 __policy_section.  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?] [br]
-You can achieve what you want - see __error_policy and __user_error_handling and many examples.
-# ['I am a C user and expect to silently return a max value for overflow?] [br]
-You (and C++ users too) can return whatever you want on overflow 
-- see  __overflow_error and __error_policy and several examples.
-# ['I don't want any error message for overflow etc?] [br]
-You can control exactly what happens for all the abnormal conditions, including the values returned.
-See __domain_error, __overflow_error __error_policy __user_error_handling etc and examples.
-# ['My environment doesn't allow and/or I don't want exceptions.  Can I still user Boost.Math?] [br]
-Yes but you must customise the error handling: see __user_error_handling and __changing_policy_defaults .
-# ['The docs are several hundreds of pages long! Can I read the docs off-line or on paper?] [br]
-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?]  [br]
-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.  You might be able to
-squeeze a little more 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.
-# ['Where are the pre-built libraries?] [br]
-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.
-# ['I don't see the function or distribution that I want.] [br]
-You could try an email to ask the authors - but no promises!
-
-[endsect] [/section:faq Frequently Asked Questions]
-
-[/ 
-  Copyright 2010 John Maddock and Paul A. Bristow.
-  Distributed under the Boost Software License, Version 1.0.
-  (See accompanying file LICENSE_1_0.txt or copy at
-  http://www.boost.org/LICENSE_1_0.txt).
-]
-