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
Structs
Finite, uniformly partitioned interval.
Generalisation of a interval.
Enums
Measure of the cardinality (#) of a set.
Traits
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.