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§
- Equipartition
- Finite, uniformly partitioned interval.
- Interval
- Generalisation of a interval.
Enums§
- Card
- Measure of the cardinality (#) of a set.
Traits§
- Finite
Space - Trait for defining spaces containing a finite set of values.
- Intersect
- Trait for types that can be combined in the form of an intersection.
- Ordered
Space - Space
- Trait for defining geometric spaces.
- Union
- Trait for types that can be combined in the form of a union.