Copyright (C) 2014  Matteo Vescovi <matteo.vescovi@yahoo.co.uk>
___________________
The Presage project
~~~~~~~~~~~~~~~~~~~

NEWS
----


Presage 0.9.1 released
----------------------
Presage 0.9.1 is now available for download.

Presage 0.9.1 introduces a new offline learning API method. Active
predictors in presage are capable of online learning (dynamic learning
triggered by context changes - context-awareness enables presage to
train predictors on changes in context dynamically). This new offline
learning method provides a way to instruct presage to learn from a
specific body of text, separate from the context. A corresponding
ONLINE_LEARNING configuration variable has been added to control
whether online machine learning is enabled or disabled for
applications that require control over what is learnt. Moreover,
learning performance has been improved by a factor of 10 in most
scenarios.

Presage 0.9.1 provides a new Windows Communication Foundation
service. Applications can now consume presage's predictive
functionality by invoking on the WCF service process rather than
directly linking to presage library. The presage WCF service
application is automatically started at logon and displays a
notification icon in the Windows system tray. The notification icon
can be used to shutdown the presage WCF service or to display further
information. The presage WCF service can only be consumed by the same
user that started the service and that is currently logged on the
local system. The service uses the NetNamedPipeBinding
binding/transport, "a secure, reliable, optimized binding that is
suitable for on-machine communication between WCF applications".
The presage predictive service endpoint is exposed at this address:
net.pipe://localhost/PresageService/v1/presage
The presage Metadata Exchange endpoint is exposed at this address:
net.pipe://localhost/PresageService/v1/mex

Presage 0.9.1 adds support for in-memory SQLite databases. Users
wishing to store a temporary language model in memory can do so by
using the started in-memory database filename designator ":memory:".

Presage 0.9.1 brings several fixes and improvements to Windows
installer/uninstaller.

Presage 0.9.1 also includes other bug fixes and enhancements. Please
see the ChangeLog for further details.


Presage 0.9 released
--------------------
Presage 0.9 is now available for download.

Presage 0.9 features a new .NET binding. .NET applications can now
easily integrate with presage by using the presage.NET assembly. The
native C++ presage core is wrapped using P/Invoke functionality
provided by the .NET framework. A C# demo application illustrates how
simple it is to use presage.net in .NET (bindings/c#).

Presage 0.9 now supports the MinGW-w64 64-bit and 32-bit toolchains
[http://mingw-w64.sourceforge.net/] from the MinGW-builds
project. Detailed instructions on how to build with the MinGW-w64
toolchains are available in doc/INSTALL_MinGW64_MSYS_dev_env.txt and
doc/INSTALL_MinGW32_MSYS_dev_env.txt. Pre-built Windows installers are
also available.

Presage 0.9 fixes several issues uncovered by static code analysis
tools. Enhancements to the Notepad++ plugin, the Windows installer,
and the build system have been added.

Presage 0.9 also includes other bug fixes and enhancements. Please see
the ChangeLog for further details.


Presage 0.8.9 released
----------------------
Presage 0.8.9 is now available for download.

Presage 0.8.9 comes with a number of small enhancements and several
fixes. No major features were added in this release. Presage 0.8.9 is
a minor mantainance release.

Presage 0.8.9 fixes several issues uncovered by static code analysis
tools. Enhancements to the Notepad++ plugin and the Windows installer
have been added. Bugs affecting the concurrent execution of the
internal testsuite have been fixed. Issues affecting the build system
have been fixed.

Presage 0.8.9 also includes other bug fixes and enhancements. Please
see the ChangeLog for more details.


Presage 0.8.8 released
----------------------
Presage 0.8.8 is now available for download.

Presage 0.8.8 allows to enabled multiple instances of the same
predictor class at runtime through configuration. Each predictor
instance is independently configured to allow greater
flexibility. Predictive performance can be more finely tailor to the
users' specific needs by tuning the resources and configuration of
each predictor. The default configuration has been changed to add a
custom user smoothed ngram predictor, which adaptively learns its
language model. The user-specific default configuration file location
is now ~/.presage/presage.xml (~/.presage.xml is no longer used). The
configuration now supports and expands environment variables.

Presage 0.8.8 adds support for filters to dictionary predictor,
recency predictor, abbreviation expansion predictor, and dejavu
predictor. The smoothed n-gram predictor will create and initialize a
language model database if it does not already exist. Learning
performance of the smoothed n-gram predictor has been improved. A new
configuration variable has been added to control context tracking case
sensitivity.

Presage 0.8.8 fixes a bug that caused learning of spurious text. A bug
affecting gprompter when learning is enabled was fixed. A memory
allocation bug and a memory leak were fixed in the Notepad++ plugin.

Presage 0.8.8 ships with an updated embedded XML configuration parser
library and update text editing component.

Presage 0.8.8 comes with a number of other enhancements and
fixes. Please see the ChangeLog for more details.


Presage 0.8.7 released
----------------------
Presage 0.8.7 is now available for download.

Presage 0.8.7 now integrates into the Notepad++, a powerful Windows
text editor, thanks to the new presage predictive Notepad++ plugin,
NppPresage. NppPresage is able to detect a presage installation and
dynamically load presage to add predictive text functionality to
Notepad++.

Presage 0.8.7 comes with improved configuration profile handling on
Windows: presage now locates the system profile configuration
directory from HKCU/Software/Presage registry key on Windows, and
correctly locates the user profile directory.

Presage 0.8.7 brings improvements to gprompter, including an updated
text editing widget and a new invert colours feature. gprompter and
pyprompter also come with new (and somewhat ugly) icons on the GNOME
desktop.

Presage 0.8.7 comes with a number of other enhancements and
fixes. Please see the ChangeLog for more details.


Presage 0.8.6 released
----------------------
Presage 0.8.6 is now available for download.

Presage 0.8.6 brings improved support for Visual Studio compilers on
Windows through the presage C API.

Presage 0.8.6 also comes with improvements to the predictive text
editor gprompter.

Presage 0.8.6 comes with a number of other enhancements and
fixes. 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 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 0.8.5 released
----------------------
Presage 0.8.5 is now available for download.

Presage 0.8.5 now offers a new C API to libpresage, in addition to the
C++ and Python APIs. gprompter is now written in plain C and uses the
new libpresage C API.

Presage 0.8.5 brings improvements to the experimental D-BUS service
interface and start-stop scripts. A D-BUS python example client is
also provided.

Presage 0.8.5 comes with a number of other enhancements and
fixes. 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 0.8.5 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 0.8.4 released
----------------------
Presage 0.8.4 is now available for download.

Presage 0.8.4 comes with a number of improvements and fixes to its
build system.

Presage 0.8.4 adds shared library symbol versioning. Symbols exported
by libpresage are limited to public API symbols and versioned.

Presage 0.8.4 removes its dependency on embedded convenience copy of
XML configuration parser library, when a system installed libtinyxml
is available.

Presage 0.8.4 also provides an experimental DBUS prediction service
and Spanish language model generation.

Presage 0.8.4 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 0.8.4 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 0.8.3 released
----------------------
Presage 0.8.3 is now available for download.

Presage 0.8.3 comes with a number of improvements and fixes to its
build system. No major features were added in this release. Presage
0.8.3 is a minor mantainance release.

Presage 0.8.3 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 0.8.3 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 0.8.2 released
----------------------
Presage 0.8.2 is now available for download.

Presage 0.8.2 comes with improved gprompter and pypresagemate demo
applications. gprompter new features include autopunctuation
implementation, support for keyboard accelerators to access menu
items, bug fixes, and updates to the text editing
component. pypresagemate now supports standard command line
parameters.

Presage 0.8.2 delivers a number of fixes and improvements to the core
predictive engine, such as a fix for a defect in the learning code and
the prediction with multimaps. The internal core predictor hierarchy
has been refactored and the XML parsing code subsystem
updated. Compilations issues on Solaris and various compilation
warning have also been fixed.

Presage 0.8.2 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 0.8.2 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 0.8.1 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) 2014  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.

########\