Roadmap » History » Version 23
Version 22 (Marcus Pearce, 2014-02-07 07:11 PM) → Version 23/34 (Marcus Pearce, 2014-03-05 11:21 AM)
h1. Roadmap
A stable release branch (<code>default</code>) is in private beta (will be v1) and will be released under the GPL. A separate development branch (<code>develop</code>) is also available.
h2. Installation Release branch (v1)
* Fix list of [[Known bugs]]
* Fix built-in examples (Conklin 95 etc.): data doesn't contain all the necessary basic viewpoints.
* Remove absolute pathname in connect-to-database. (mtp-admin/music-data.lisp)
* Create cache directories if they don't exist.
h2. Data import Short-term feature development
Make system more data agnostic:
-* Remove dependancy on amuse and mips packages. (Tested but not pushed.)-
* It is possible to import empty datasets, which cause an error when described.
h2. Viewpoints
* Zero barlengths sometimes cause divide by zero errors Separation of music viewpoints from model.
* A straightforward language for specifying viewpoints, including viewpoint schemas (e.g. interval, interval size), making system more data agnostic
* Polyphonic viewpoints size)
h2. -Include mtp-admin, ppm-star and amuse-viewpoints code in the idyom reposiory, to allow single download/checkout.-
Include unit testing code.
Viewpoint selection
* Memory errors sometimes occur with large viewpoints sets (e.g., Cpitch with no basis specified) selection:
* When decimal places are restricted for comparison, earlier systems are preferred within a round, so use full precision -* Adding viewpoint weights to choose between ties output.-
* Print trace information about VP sets being tested + mean IC values; record this to log file.
* Optionally specify: min-links
* More flexible way for user to specify constraints on viewpoint search:
** Define labelled viewpoint classes
** Pairs/triples of labels/wildcards specify acceptable combinations
** User provides whitelist or blacklist spec
* Provide recommended viewpoints for more than just cpitch and bioi
h2. Testing
* Include unit testing code.
h1. Longer-term Mid-term goals
* 'Pace' viewpoint: measure of information rate (bits/sec), analogous to flow in speech production.
*
A web service.
Efficiency:
* Compute predictive information (PI), expected PI and PI rate (as analogs Check/extend caching of models etc.
* Use sampling to IC, entropy and entropy rate respectively). estimate mean IC during VP selection.
Viewpoint selection:
* Optimise based on match with existing IC values.
* Predict over more than one dataset.
* Hierarchical structure: chunk common patterns into symbols.
basic viewpoint, and provide recommended viewpoints for each one (not just cpitch and bioi).
Allow user to specify structure of model.
* Determine order in which distributions are combined.
* Specify weights for particular combinations, e.g. weighted viewpoints, or weighted memory stores.
* Multiple memory stores.
* Specify alternative context strategies (e.g., future context) strategies.
* Provide some prepackaged models, e.g. the current model structure.
Efficiency:
** Check/extend caching of Allow models etc.
** Use sampling to estimate mean IC during VP selection.
** Optimise viewpoint selection based on match with existing IC values.
use predictive information (PI), expected PI and PI rate (as analogs to IC, entropy and entropy rate respectively).
Predict over more than one dataset.
h2. Long-term
Hierarchical structure: chunk common patterns into symbols.
A stable release branch (<code>default</code>) is in private beta (will be v1) and will be released under the GPL. A separate development branch (<code>develop</code>) is also available.
h2. Installation Release branch (v1)
* Fix list of [[Known bugs]]
* Fix built-in examples (Conklin 95 etc.): data doesn't contain all the necessary basic viewpoints.
* Remove absolute pathname in connect-to-database. (mtp-admin/music-data.lisp)
* Create cache directories if they don't exist.
h2. Data import Short-term feature development
Make system more data agnostic:
-* Remove dependancy on amuse and mips packages. (Tested but not pushed.)-
* It is possible to import empty datasets, which cause an error when described.
h2. Viewpoints
* Zero barlengths sometimes cause divide by zero errors Separation of music viewpoints from model.
* A straightforward language for specifying viewpoints, including viewpoint schemas (e.g. interval, interval size), making system more data agnostic
* Polyphonic viewpoints size)
h2. -Include mtp-admin, ppm-star and amuse-viewpoints code in the idyom reposiory, to allow single download/checkout.-
Include unit testing code.
Viewpoint selection
* Memory errors sometimes occur with large viewpoints sets (e.g., Cpitch with no basis specified) selection:
* When decimal places are restricted for comparison, earlier systems are preferred within a round, so use full precision -* Adding viewpoint weights to choose between ties output.-
* Print trace information about VP sets being tested + mean IC values; record this to log file.
* Optionally specify: min-links
* More flexible way for user to specify constraints on viewpoint search:
** Define labelled viewpoint classes
** Pairs/triples of labels/wildcards specify acceptable combinations
** User provides whitelist or blacklist spec
* Provide recommended viewpoints for more than just cpitch and bioi
h2. Testing
* Include unit testing code.
h1. Longer-term Mid-term goals
* 'Pace' viewpoint: measure of information rate (bits/sec), analogous to flow in speech production.
*
A web service.
Efficiency:
* Compute predictive information (PI), expected PI and PI rate (as analogs Check/extend caching of models etc.
* Use sampling to IC, entropy and entropy rate respectively). estimate mean IC during VP selection.
Viewpoint selection:
* Optimise based on match with existing IC values.
* Predict over more than one dataset.
* Hierarchical structure: chunk common patterns into symbols.
basic viewpoint, and provide recommended viewpoints for each one (not just cpitch and bioi).
Allow user to specify structure of model.
* Determine order in which distributions are combined.
* Specify weights for particular combinations, e.g. weighted viewpoints, or weighted memory stores.
* Multiple memory stores.
* Specify alternative context strategies (e.g., future context) strategies.
* Provide some prepackaged models, e.g. the current model structure.
Efficiency:
** Check/extend caching of Allow models etc.
** Use sampling to estimate mean IC during VP selection.
** Optimise viewpoint selection based on match with existing IC values.
use predictive information (PI), expected PI and PI rate (as analogs to IC, entropy and entropy rate respectively).
Predict over more than one dataset.
h2. Long-term
Hierarchical structure: chunk common patterns into symbols.