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Various implementations of Metrics (and associated Distance).
A Metric is used to measure the distance between data. Metrics are paired with a domain on which the metric can measure distance. The distance is expressed in terms of an associated type.
§Example
SymmetricDistance
can be paired with a domain: VectorDomain(AtomDomain(T))
.
In this context, the SymmetricDistance
is used to measure the distance between any two vectors of elements of type T
.
The SymmetricDistance
has an associated distance type of u32
.
This means that the symmetric distance between vectors is expressed in terms of a u32
.
Modules§
- ffi 🔒
Structs§
- The absolute distance between two scalar-valued aggregates.
- The smallest number of changes to make two equal-length datasets equivalent.
- Indicates if two elements are equal to each other.
- The number of elements that differ between two equal-length datasets.
- The smallest number of insertions or deletions to make two datasets equivalent.
- Distance between score vectors with monotonicity indicator.
- The $L_p$ distance between two vector-valued aggregates.
- The $L^0$, $L\infty$ norms of the per-partition distances between data sets.
- The $L^0$, $L^1$, $L\infty$ norms of the per-partition distances between data sets.
- The smallest number of additions or removals to make two datasets equivalent.
Type Aliases§
- The type that represents the distance between datasets. It is used as the associated
Metric
::Distance type for e.g.SymmetricDistance
,InsertDeleteDistance
, etc. - The $L_1$ distance between two vector-valued aggregates.
- The $L_2$ distance between two vector-valued aggregates.