Expand description
Set/space primitives for defining machine learning problems.
spaces
provides set/space primitives to be used for defining properties of
machine learning problems. Traits such as Space
, and it’s derivatives, may
be used to define state/action spaces, for example. Mappings between
different spaces may also be defined using traits such as Surjection
to
streamline many common preprocessing and type conversion tasks.
Modules
Continuous spaces module.
Discrete spaces module.
Normalisation utilities module.
Product spaces module.
Enums
Measure of the cardinality (#) of a set.
Traits
Trait for defining spaces bounded to lie on an interval I.
Trait for defining spaces containing a finite set of values.
Trait for defining geometric spaces.
Trait for types that implement a mapping from values of one set onto another.