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
streamline many common preprocessing and type conversion tasks.
Measure of the cardinality (#) of a set.
Trait for defining spaces containing a finite set of values.
Trait for types that can be combined in the form of an intersection.
Trait for defining geometric spaces.
Trait for types that can be combined in the form of a union.