boost_1_45_0/libs/math/doc/sf_and_dist/FAQ.qbk - nest-learning-thermostat/5.0/boost - Git at Google

 [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 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).
 ]
	[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 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).
	]