Changeset 944 for library/doc/tutorial/005userguide0.dox
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- 05/16/10 23:13:21 (14 years ago)
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library/doc/tutorial/005userguide0.dox (modified) (2 diffs)
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library/doc/tutorial/005userguide0.dox
r853 r944 4 4 BDM is a library of basic components for Bayesian decision making, hence its direct use is not possible. In order to use BDM the components must be pulled together in order to achieve desired functionality. We expect two kinds of users: 5 5 6 - <b> Experimentators </b> who run prepared scripts with different parameterizations and analyze their results, 7 - <b> Algorithms designers </b> who are able to understand the logic of BDM and extend its functionality to new applications. 6 - <b> Experimenters </b> who run prepared scripts with different parameterizations and analyze their results, 7 - <b> Advanced users</b> who are able to extend functionality by filling prepared Matlab classes, 8 - <b> Porgrammers </b> who are able to implement algorithms withing C++ backend of BDM. 8 9 9 The primary design aim of BDM was to ease development of complex algorithms, hence the target user is the latter. 10 However, running experiments is the first task to learn for both types of users. 10 This tutorial is intended for the first two classes of users. Programmers should read it for introduction and then follow to the Doxygen maunal. 11 11 12 The logic of bdmtoolbox is that the experiment is run in C++ via mex-file. Parameterization of the task is done via Matlab structures. 13 A range of selected "callback" functions to Matlab is available. This range can be extended by a Programmer, please contact authors if available extension point are not satisfactory for you. 12 14 13 15 \section param Experiment is fully parameterized before execution 14 16 15 Experiments in BDM can be performed using either standalone applications or function bindings in high-level environment. A typical example of the latter being mex file in Matlab environment. 16 17 The main logic behind the experiment is that all necessary information about it are gathered in advance in a configuration file (for standalone applications) or in configuration structure (Matlab). 17 The main logic behind the experiment is that all necessary information about it are gathered in advance in a configuration structure. 18 18 This approach was designed especially for time consuming experiments and Monte-Carlo studies for which it suits the most. 19 19 20 For smaller decision making tasks, interactive use of the experiment can be achieved by showing the full configuration structure (or its selected parts), running the experiment on demand and showing the results. 20 For smaller decision making tasks, interactive use of the experiment can be achieved by showing the full configuration structure (or its selected parts), 21 running the experiment on demand and showing the results. 21 22 22 23 Semi-interactive experiments can be designed by sequential run of different algorithms. This topic will be covered in advanced documentation. … … 31 32 - <b>Closed loop</b>: sequantial estimation from previous step is complemneted by adaptive controller (or decision maker) that designs control strategy for the next step. 32 33 34 Mex files for some atomic operations with internal objects are provided for comfortable definition of an experiment and analysis. 35 These functions are not efficient for use in repettitive way. 36 33 37 These scenarios may serve as a starting point for advanced users who can design specific algorithms tailored for given application domain. 34 38 35 39 A tutorial how to run the scenarios are: 36 40 37 - \ref userguide 38 - \ref userguide2 41 - \ref userguide_pdf 42 - \ref userguide_sim 43 - \ref userguide_estim 39 44 40 45 */
