Roadmap » History » Version 26
Version 25 (Marcus Pearce, 2014-03-26 04:08 PM) → Version 26/34 (Marcus Pearce, 2014-03-26 04:09 PM)
h1. Roadmap
h2. Installation
* -Remove absolute pathname in connect-to-database. (mtp-admin/music-data.lisp)-
* Create cache directories if they don't exist.
h2. Data import
* It is possible to import empty datasets, which cause an error when described.
h2. Viewpoints
* Zero barlengths sometimes cause divide by zero errors
* A straightforward language for specifying viewpoints, including viewpoint schemas (e.g. interval, interval size), making system more data agnostic
* Polyphonic viewpoints
h2. Viewpoint selection
* Memory errors sometimes occur with large viewpoints sets (e.g., Cpitch with no basis specified)
* When decimal places are restricted for comparison, earlier systems are preferred within a round, so use full precision to choose between ties
* 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, onset & cpitch and bioi
h2. Testing
* Include unit testing code.
h1. Longer-term goals
* 'Pace' viewpoint: measure of information rate (bits/sec), analogous to flow in speech production.
* A web service.
* Compute predictive information (PI), expected PI and PI rate (as analogs to IC, entropy and entropy rate respectively).
* Predict over more than one dataset.
* Hierarchical structure: chunk common patterns into symbols.
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)
* Provide some prepackaged models, e.g. the current model structure.
Efficiency:
** Check/extend caching of models etc.
** Use sampling to estimate mean IC during VP selection.
** Optimise viewpoint selection based on match with existing IC values.
h2. Installation
* -Remove absolute pathname in connect-to-database. (mtp-admin/music-data.lisp)-
* Create cache directories if they don't exist.
h2. Data import
* It is possible to import empty datasets, which cause an error when described.
h2. Viewpoints
* Zero barlengths sometimes cause divide by zero errors
* A straightforward language for specifying viewpoints, including viewpoint schemas (e.g. interval, interval size), making system more data agnostic
* Polyphonic viewpoints
h2. Viewpoint selection
* Memory errors sometimes occur with large viewpoints sets (e.g., Cpitch with no basis specified)
* When decimal places are restricted for comparison, earlier systems are preferred within a round, so use full precision to choose between ties
* 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, onset & cpitch and bioi
h2. Testing
* Include unit testing code.
h1. Longer-term goals
* 'Pace' viewpoint: measure of information rate (bits/sec), analogous to flow in speech production.
* A web service.
* Compute predictive information (PI), expected PI and PI rate (as analogs to IC, entropy and entropy rate respectively).
* Predict over more than one dataset.
* Hierarchical structure: chunk common patterns into symbols.
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)
* Provide some prepackaged models, e.g. the current model structure.
Efficiency:
** Check/extend caching of models etc.
** Use sampling to estimate mean IC during VP selection.
** Optimise viewpoint selection based on match with existing IC values.