| Age | Commit message (Collapse) | Author | Lines | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
They may be important for dtree generation.
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
model.build_dtree does belong into attr, but that's a different commit
 | 
 | 
The tree variant is used for attributes which depend on a non-numeric
parameter (which can't be modeled as a function)
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
This speeds up analysis and adds support for models with more than 32
parameters.
 | 
 | 
 | 
 | 
Still TODO: Ignore codependent parameters when partitioning data for
analytic modeling / regression
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
TODO: fitting and submodel usage in parent
 | 
 | 
Iteration over states/transitions and model attributes is no longer hardcoded
into most model generation code. This should make support for decision trees
and sub-states much easier.
 | 
 | 
 | 
 | 
This should speed up analysis quite a bit and also reduce memory usage
significantly
 | 
 | 
 | 
 | 
 | 
 | 
It doesn't work and is not methodically sound. Decision/Regression Trees
seem to be the way to go
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 |