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Copyright (C) 2008 Matteo Vescovi <matteo.vescovi@yahoo.co.uk>
___________________ The Presage project ~~~~~~~~~~~~~~~~~~~ NEWS ---- Presage 0.8.1 released ---------------------- Presage 0.8.1 is now available for download. Presage 0.8.1 sports significant performance improvements in its smoothed n-gram predictor. Runtime execution was sped up by approximately a factor of 5 by tuning some expensive SQL queries to the embedded SQLite database. Presage 0.8.1 comes with refactored configuration and profile handling subsystems. Configuration is read from system-level, installation-level, user-level XML profiles and from an optional user-specified profile. Changes to configuration variables made at runtime through the config() API can now be persisted to file by calling the new save_profile() API method. Presage components are also immediately notified of configuration changes and adjust accordingly. Presage 0.8.1 also includes other bug fixes and enhancements. Compilation problems on recent stricter compilers, compilation warnings, and minor memory leaks were fixed in this release. Please see the ChangeLog for more details. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Presage should follow the (easy) steps needed to build presage, as detailed in the README file and in the documentation available in the doc/ directory. Presage 0.8.1 has been built and tested on various Linux platforms (including 32-bit and 64-bit architectures), Solaris 10, Windows (Cygwin and MinGW/MSYS), and MacOS X. If you encounter any issues while building or running presage, please report them to the author. Presage 0.8 released ---------------------- Presage 0.8 is now available for download. Presage 0.8 includes two new predictive applications, gprompter and pypresagemate. Gprompter is a cross-platform predictive text editor. Gprompter displays predictions in a contextual pop-up box as each letter is typed. Predictions can be easily selected and inserted in the document. Pypresagemate is a universal predictive text companion. Pypresagemate works alongside any AT-SPI aware application. The Assistive Technology Service Provider Interface (AT-SPI) is a toolkit-neutral way of providing accessibility facilities in applications. Pypresagemate works in the background by tracking what keystrokes are typed and displaying predictions in its window. When a prediction is selected, text is sent to the active application. Presage 0.8 provides a new callback-aware programming interface to make it easier to develop interactive presage applications. Presage applications no longer need to track user interaction by explicitly updating the context. Instead, a callback object decouples the user application from the chosen text buffer (which might be a simple string, a graphical text widget, a file stream, etc.) and frees the caller from having to explicitly notify of any updates to the context. with presage and this allows presage to retrieve contextual information from the application whenever needed. Presage 0.8 also includes other bug fixes and enhancements. Please see the ChangeLog for more details. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Presage should follow the (easy) steps needed to build presage, as detailed in the README file and in the documentation available in the doc/ directory. Presage 0.8 has been built and tested on various Linux platforms (including 32-bit and 64-bit architectures), Solaris 10, Windows (Cygwin and MinGW/MSYS), and MacOS X. If you encounter any issues while building or running presage, please report them to the author. Presage 0.7.3 released ---------------------- Presage 0.7.3 is now available for download. Presage 0.7.3 includes the new predictive ARPA plugin. The ARPA plugin enables the use of statistical language modelling data in the ARPA N-gram format. In the ARPA format each N-gram is stored with its discounted log probability and its Katz backoff weight. Probabilities are estimated by applying Katz backoff smoothing to the maximum likelihood estimates based on n-gram counts data. Presage 0.7.3 also provides (in a separate tarball) an extensive language model generated from the British National Corpus (BNC) containing 20.001 unigrams, 517.537 bigrams, and 1.648.226 trigrams. This language model was constructed by computing the smoothed Katz backoff trigram model using the CMU-Cambridge Statistical Language Modeling toolkit. Presage 0.7.3 also includes other bug fixes and enhancements. Please see the ChangeLog for more details. Presage 0.7.3 is a beta release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Presage should follow the (easy) steps needed to build presage, as detailed in the README file and in the documentation available in the doc/ directory. Presage 0.7.3 has been built and tested on various Linux platforms (including 32-bit and 64-bit architectures), Solaris 10, Windows XP/Cygwin, and Windows XP/MinGW/MSYS. If you encounter any issues while building or running presage, please report them to the author. Presage 0.7.2 released ---------------------- Presage 0.7.2 is now available for download. Presage 0.7.2 adds support for predictive plugins filters and a new predict method returning an ordered multimap of probability-token pairs. The filters feature enables lookahead prediction. Presage 0.7.2 incorporates a number of bug fixes, such as handling of utf-8 encoded text, initialization bug in presage demo program, build problems fixes in gpresagemate. Prompter comes with user interface improvements, such as text size controls. Presage 0.7.2 also includes other bug fixes and enhancements. Please see the ChangeLog for more details. Presage 0.7.2 is a beta release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Presage should follow the (easy) steps needed to build presage, as detailed in the README file and in the documentation available in the doc/ directory. Presage 0.7.2 has been built and tested on various Linux platforms (including 32-bit and 64-bit architectures), Solaris 10, Windows XP/Cygwin, and Windows XP/MinGW/MSYS. If you encounter any issues while building or running presage, please report it to the author. As always, there is still a lot more work to be done. Currently, the installed presage system is trained on a very small training corpus. Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate statistical predictive resources using the text2ngram tool on a custom training text corpus. New predictive plugins are also in the works, which will take advantage of the multiple predictive source architecture. Please refer to our TODO list for details on what needs to be done. Presage 0.7.1 released ---------------------- Presage 0.7.1 is now available for download. Presage 0.7.1 is able to learn "on the fly" from the context and the text currently being entered. The smoothed n-gram predictive plugin dynamically learns from the current context, while generating new predictions. An n-gram count consistency bug triggered by the dynamic learning capability of the smoothed n-gram predictive plugin has been fixed in this release. Presage 0.7.1 incorporates a number of bug fixes: completion validation routine case sensitiveness, fixed various compilation warnings, prompter UTF8 encoding. Presage 0.7.1 also includes other bug fixes and enhancements. Please see the ChangeLog for more details. Presage 0.7.1 is a beta release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Presage will need to follow the (easy) steps required to build presage on their machine, as detailed in the README file. Please note that SQLite is required to build presage. CPPUnit is optional, but be aware that no unit tests will be built nor run when running `make check', unless CPPUnit is installed. Presage 0.7.1 has been built and tested on various Linux platforms (including 32-bit and 64-bit architectures), Solaris 10, Windows XP/Cygwin, and Windows XP/MinGW/MSYS. If you encounter any issues while building or running presage, please report it to the author. As always, there is still a lot more work to be done. Currently, the installed presage system is trained on a very small training corpus. Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate statistical predictive resources using the text2ngram tool on a custom training text corpus. New predictive plugins are also in the works, which will take advantage of the multiple predictive source architecture. Please refer to our TODO list for details on what needs to be done. Presage 0.7 released -------------------- Presage 0.7 is now available for download. Presage 0.7 is the first release that uses the new project name. Presage was formerly known as Soothsayer. The Soothsayer project was renamed to Presage in order to avoid clashes with a similarly named commercial software product. Presage 0.7 is now able to learn "on the fly" from the context and the text currently being entered. The smoothed n-gram predictive plugin dynamically learns from the current context, while generating new predictions. Presage 0.7 also includes a new dejavu plugin, which reproduces previously entered text sequences once its memory trigger is activated. Presage 0.7 provides better predictions by incrementally increasing depth of prediction generation while previous predictions did not match desired token. Presage 0.7 delivers an improved Python GUI demo application, Prompter: * added prompt functionality, which allows user to request a new prediction on-demand * added function keys mode, which allows user to select desired prediction by pressing the corresponding function key * added ability to toggle autopunctuation functionality * added toolbar (can be hidden/shown) * added ability to toggle learning mode on or off * added edit menu with cut, copy, paste, undo, redo, select all operations * improved editor layout * added modern about dialog box Presage 0.7 incorporates several bug fixes to the context changes detection code, including fixing bugs triggered by empty string updates and bugs where multiple separators triggered spurious context changes. Presage 0.7 includes a new GTK application which aims to augment any other application with presage predictive functionality. Presage 0.7 also includes bug fixes and improvements to the build framework. See ChangeLog for more details. Presage 0.7 is a beta release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Presage will need to follow the (easy) steps required to build presage on their machine, as detailed in the README file. Please note that SQLite is required to build presage. CPPUnit is optional, but be aware that no unit tests will be built nor run when running `make check', unless CPPUnit is installed. Presage 0.7 has been built and tested on various Linux platforms (including 32-bit and 64-bit architectures), Solaris 10, Windows XP/Cygwin, and Windows XP/MinGW/MSYS. If you encounter any issues while building or running presage, please report it to the author. As always, there is still a lot more work to be done. Currently, the installed presage system is trained on a very small training corpus. Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate statistical predictive resources using the text2ngram tool on a custom training text corpus. New predictive plugins are also in the works, which will take advantage of the multiple predictive source architecture. Please refer to our TODO list for details on what needs to be done. Soothsayer 0.6.3 released ------------------------- Soothsayer 0.6.3 is now available for download. Soothsayer 0.6.3 comes with a number of packaging and distribution improvements. The build system has received minor fixes and configurability enhancements. Soothsayer 0.6.3 ships with improved demonstration programs. The existing C++ demonstration programs have been renamed from from capitalized style names to underscore separated style names. Python demonstration programs incorporate various improvements, including new command line switches and manual pages. Soothsayer 0.6.3 adds improved UTF-8 support. Distributed text resources used to generate sample statistical data are now UTF-8 encoded. Soothsayer 0.6.3 also includes a few bug fixes and documentation updates. See ChangeLog for more details. Soothsayer 0.6.3 is a beta release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Soothsayer will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README file. Please note that SQLite is required to build soothsayer. CPPUnit is optional, but be aware that no unit tests will be built nor run when running `make check', unless CPPUnit is installed. Soothsayer 0.6.3 has been built and tested on various Linux platforms (including 32-bit and 64-bit architectures), Solaris 10, Windows XP/Cygwin, and Windows XP/MinGW/MSYS. If you encounter any issues while building or running soothsayer, please report it to the author. As always, there is still a lot more work to be done. Currently, the installed soothsayer system is trained on a very small training corpus. Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate statistical predictive resources using the text2ngram tool on a custom training text corpus. New predictive plugins are also in the works, which will take advantage of the multiple predictive source architecture. Please refer to our TODO list for details on what needs to be done. Soothsayer 0.6.2 released ------------------------- Soothsayer 0.6.2 is now available for download. Soothsayer 0.6.2 comes with a number of new features. Most notably, a new statistical predictive plugin, based on recency promotion, is available. The new recency plugin generates predictions by assigning exponentially decaying probability values to previously encountered word tokens, thereby promoting context recency. Soothsayer 0.6.2 also ships a brand new simple GUI demonstration program, prompter. Prompter is a soothsayer-enabled text editor. Prompter displays predictions generated by soothsayer through a pop-up autocompletion list. Prompter also provides an autopunctuation feature that saves key pressing by intelligently handling punctuation and whitespace. Prompter is a Python application (wxPython) and uses soothsayer's python binding. Soothsayer 0.6.2 adds native Windows support by supporting the MinGW/MSYS platform. It is now possible to build soothsayer in native Win32 mode. Detailed instructions to build soothsayer on MinGW/MSYS are included in the doc/ directory. Soothsayer 0.6.2 includes enhancements to the build system, a restructured soothsayer exception hierarchy, additional range checking in core classes, and improved logging subsystem. Soothsayer 0.6.2 also includes a number of bug fixes. See ChangeLog for more details. Soothsayer 0.6.2 is a beta release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Soothsayer will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README file. Please note that SQLite is required to build soothsayer. CPPUnit is optional, but be aware that no unit tests will be built nor run when running `make check', unless CPPUnit is installed. Soothsayer 0.6.2 has been built and tested on various Linux platforms (including 32-bit and 64-bit architectures), Solaris 10, Windows XP/Cygwin, and Windows XP/MinGW/MSYS. If you encounter any issues while building or running soothsayer, please report it to the author. As always, there is still a lot more work to be done. Currently, the installed soothsayer system is trained on a very small training corpus. Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate statistical predictive resources using the text2ngram tool on a custom training text corpus. New predictive plugins are also in the works, which will take advantage of the multiple predictive source architecture. Please refer to our TODO list for details on what needs to be done. Soothsayer 0.6.1 released ------------------------- Soothsayer 0.6.1 is now available for download. Soothsayer 0.6.1 includes a number of under-the-hood changes. The focus of this release has been on refactoring, restructuring, and cleaning up, rather than adding new functionality. The source directory layout was changed to better reflect the logical structure. Improvements were made to the configuration system and the logging subsystem, which underwent a complete overhaul and rewrite. Soothsayer 0.6.1 ships with man pages for the tools and demo programs. This release also includes bug fixes and improvements to the build system. All GCC generated compilation warnings were fixed. Library dependencies have been cleaned up. Soothsayer 0.6.1 is a beta release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Soothsayer will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README file. Please note that SQLite is required to build soothsayer. CPPUnit is optional, but be aware that no unit tests will be built nor run when running `make check', unless CPPUnit is installed. Soothsayer 0.6.1 has been built and tested on various Linux platforms (including 64 bit architectures) and Windows XP + Cygwin platform. If you encounter any issues while building or running soothsayer, please report it to the author. As always, there is still a lot of work to be done. Currently, Soothsayer is trained on a very small training corpus. Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate statistical predictive resources using the text2ngram tool on a custom training text corpus. New predictive plugins will also be added to take advantage of the multiple predictive source architecture. Please refer to our TODO list for details on what needs to be done. Soothsayer 0.6 released ----------------------- Soothsayer 0.6 is now available for download. Soothsayer 0.6 includes a new Python binding module, which enables Python applications to natively call into soothsayer. Soothsayer 0.6 has also been ported to Solaris 10 platform, and built with Sun Studio 10 and 11 compilers. This release also includes bug fixes and improvements to the build system. Library dependencies have been cleaned up. Shared libraries are now built on all supported platforms, including Windows/Cygwin targets. Soothsayer 0.6 is a beta release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Soothsayer will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README file. Please note that SQLite is required to build soothsayer. CPPUnit is optional, but be aware that no unit tests will be built nor run when running `make check', unless CPPUnit is installed. Soothsayer 0.6 has been built and tested on various Linux platforms (including 64 bit architectures) and Windows XP + Cygwin platform. If you encounter any issues while building or running soothsayer, please report it to the author. There is still a lot of work to be done. Currently, Soothsayer is training on a very small training corpus. Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate statistical predictive resources using the text2ngram tool on a custom training text corpus. New predictive plugins should also be added to take advantage of the multiple predictive source architecture. Please refer to our TODO list for more details on what needs to be done. Soothsayer 0.5 released ----------------------- Soothsayer 0.5 is now available for download. Soothsayer 0.5 includes the new generalized smoothed n-gram statistical predictive plugin, which supports arbitrary order n-grams. Used in combination with the text2ngram tool, statistical predictions can be generated by n-gram language models of arbitrary cardinality. The new generalized smoothed n-gram predictive plugin also uses an improved heuristic to generate initial completion candidates, by using highest order n-gram statistics, and falling back on lower order n-grams if initial completion set is smaller than required. This release also includes notable bug fixes and improvements to soothsayer simulator. A bug in the simulator caused the reported Key Stroke Reduction rate to much much lower than the actual KSR achieved by soothsayer. Soothsayer 0.5 marks a change in project status from alpha to beta. However, it is still to be considered a developer's preview release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Soothsayer will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README file. Please note that SQLite is required to build soothsayer. CPPUnit is optional, but be aware that no unit tests will be built nor run when running `make check', unless CPPUnit is installed. Soothsayer 0.5 has been built and tested on various Linux platforms (including 64 bit architectures) and Windows XP + Cygwin platform. If you encounter any issues while building or running soothsayer, please report it to the author. There is still a lot of work to be done. Currently, Soothsayer is training on a very small training corpus. Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate statistical predictive resources using the text2ngram tool on a custom training text corpus. New predictive plugins should also be added to take advantage of the multiple predictive source architecture. Please refer to our TODO list for more details on what needs to be done. Soothsayer 0.4 released ----------------------- Soothsayer 0.4 is now available for download. Soothsayer 0.4 includes the new abbreviation expansion predictive plugin. This plugin allows users to specify a file containing a list of abbreviations/expansions pairs. When an abbreviation is entered, the next generated prediction will contain the associated expansion, which is typically a commonly used word or phrase. This release also includes bug fixes and documentation improvements. Soothsayer 0.4 is a developer's preview release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try out Soothsayer will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README file. Please note that SQLite is required to build soothsayer. CPPUnit is optional, but be aware that no unit tests will be built nor run when running `make check', unless CPPUnit is installed. Soothsayer 0.4 has been built and tested on various Linux platforms (including 64 bit architectures) and Windows XP + Cygwin platform. If you encounter any issues while building or running soothsayer, please report it to the author. There is still a lot of work to be done. Currently, the build process trains Soothsayer predictive plugin with a very limited training corpus (a single novel). Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate predictive resources using a custom text corpus and the extraction tools provided. Currently, the main predictive plugin is a smoothed uni-bi-tri-gram predictive plugin. The plugin should be extended to be able to support n-grams of any cardinality. More plugins should also be added to take advantage of the multiple predictive source architecture. Please refer to our TODO list for more details on what needs to be done. Soothsayer 0.3 released ----------------------- Soothsayer 0.3 is now available for download. Soothsayer 0.3 adds initial support for simultaneous use of multiple predictive plugins. Multiple predictions combination is carried out according to a meritocracy policy. This release also includes bug fixes, memory leak fixes, and improvements to the demo programs (visual context change cues). Soothsayer 0.3 adds new configuration variables to control its runtime predictive behaviour. Configuration handling has been refactored. Please consult the ChangeLog for more details. Soothsayer 0.3 is a developer's preview release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try Soothsayer out will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README file. Please note that SQLite is required to build soothsayer. CPPUnit is optional, but no unit tests will be built when running `make check', unless CPPUnit is installed. Soothsayer 0.3 has been built and tested on various Linux platforms (including 64 bit architectures) and Windows XP + Cygwin platform. If you encounter any issues while building or running soothsayer, please report it to the author. There is still a lot of work to be done. Currently, the build process trains Soothsayer predictive plugin with a very limited training corpus (a single novel). Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate predictive resources using a custom text corpus and the extraction tools provided. Currently, only one predictive plugin is provided, a smoothed n-gram predictive plugin. The plugin should be extended to be able to support n-grams of any cardinality. More plugins should also be added to take advantage of the multiple predictive source architecture. Please refer to our TODO list for more details on what needs to be done. Soothsayer 0.2 released ----------------------- Soothsayer 0.2 is now available for download. Soothsayer 0.2 adds support for SQLite 2.x and SQLite 3.x. The soothsayer build system will autodetect which SQLite version is available and use the most recent version. This release also includes bug fixes and improvements to the command line utilities and demo programs. Please consult the ChangeLog for more details. Soothsayer 0.2 is a developer's preview release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try Soothsayer out will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README file. Please note that SQLite is required to build soothsayer. CPPUnit is optional, but no unit tests will be built when running `make check', unless CPPUnit is installed. Soothsayer 0.2 has been built and tested on various Linux platforms (including 64 bit architectures) and Windows XP + Cygwin platform. If you encounter any issues while building or running soothsayer, please report it to the author. There is still a lot of work to be done. Currently, the build process trains Soothsayer predictive plugin with a very limited training corpus (a single book). Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate predictive resources using a custom text corpus and the extraction tools provided. Currently, only one predictive plugin is provided, a smoothed n-gram predictive plugin. The plugin should be extended to be able to support n-grams of any cardinality. More plugins should also be added to take advantage of the multiple predictive source architecture. There is also more work to be done on various modules, including the combiner module and the configuration module. Please refer to our TODO list for more details on what needs to be done. Soothsayer 0.1.1 released ------------------------- Soothsayer 0.1.1 is now available for download. Soothsayer 0.1.1 fixes a problem that caused build failures in a Windows + Cygwin environment. Soothsayer 0.1.1 is a developer's preview release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try Soothsayer out will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README. Please note that Sqlite is required to build soothsayer. CPPUnit is optional, but no unit tests will be built when running `make test', unless CPPUnit is installed. Soothsayer 0.1.1 has been built and tested on various Linux platforms (including 64 bit architectures) and Windows XP + Cygwin platform. If you encounter any issues while building or running soothsayer, please report it to the author. There is still a lot of work to be done. Currently, the build process trains Soothsayer predictive plugin with a very limited training corpus (a single book). Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate predictive resources using a custom text corpus and the extraction tools provided. Currently, only one predictive plugin is provided, a smoothed n-gram predictive plugin. The plugin should be extended to be able to support n-grams of any cardinality. More plugins should also be added to take advantage of the multiple predictive source architecture. There is also more work to be done on various modules, including the combiner module and the configuration module. Please refer to our TODO file for more information on what needs to be done. Soothsayer 0.1 released ----------------------- I am proud to announce the first ever release of Soothsayer. Soothsayer 0.1 is now available for download. Soothsayer 0.1 is a developer's preview release. This is a source release only. No precompiled packages or installers are provided. Users wishing to try Soothsayer out will need to follow the (easy) steps required to build soothsayer on their machine, as detailed in the README. Please note that Sqlite is required to build soothsayer. CPPUnit is optional, but no unit tests will be built when running `make test', unless CPPUnit is installed. Soothsayer 0.1 has been built and tested on various Linux platforms (including 64 bit architectures). Building on Windows + Cygwin is temporarily broken. If you encounter any issues while building or running soothsayer, please report it to the author. There is still a lot of work to be done. Currently, the build process trains Soothsayer predictive plugin with a very limited training corpus (a single book). Predictive performance can be greatly increased by using a larger training corpus. Users can easily generate predictive resources using a custom text corpus and the extraction tools provided. Currently, only one predictive plugin is provided, a smoothed n-gram predictive plugin. The plugin should be extended to be able to support n-grams of any cardinality. More plugins should also be added to take advantage of the multiple predictive source architecture. There is also more work to be done on various modules, including the combiner module and the configuration module. Please refer to our TODO file for more information on what needs to be done. ########/ Copyright (C) 2008 Matteo Vescovi <matteo.vescovi@yahoo.co.uk> Soothsayer is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. ########\ |