IDyOM Project

h1. Roadmap

The development code has been made compatible with sbcl 1.1,

the immediate goal is to release a version that works with the built-in

examples (Conklin 95 etc.).  Need to fix problems created by datasets with

incomplete viewpoints.

h2. Short-term

New basic viewpoints:

* cents - a higher resolution representation of pitch. [High priority for Makam data.]

* comma (implemented?)

* metrical contour

Remove dependancy on amuse, so the system is data agnostic.

Viewpoint selection:

* Adding viewpoint weights to output.

* Optionally specify: start point for search, 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

Efficiency:

* Check/extend caching of models etc.

* Use sampling to estimate mean IC during VP selection.

Minor:

* Some basic benchmarks to ensure stability of future development versions.

* Show all viewpoints in describe-dataset.

* Zero barlength (e.g. in Makam data) causes divide by zero error.

h2. Mid-term

Viewpoint selection:

* Optimise based on match with existing IC values.

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.

* Provide some prepackaged models, e.g. the current model structure.

Allow models to use predictive information (PI), expected PI and PI rate (as analogs to IC, entropy and entropy rate respectively).

Predict more than one basic viewpoint

h2. Long-term

Hierarchical structure.

Parallel implementation.