## Abstract

Distance functions for numeric vectors.

Each distance function is exposed as a parametric modules that lets
you control both the scalar type and the dimensionality of the
space that the vectors inhabit. This is concretely done by
specifying the representation of vectors as a module that
implements the `vector`

module type.

When using low-dimensional spaces (say, less than 10), use an
appropriately constructed statically sized vector module (see
`vector_1`

and
`cat_vector`

). For larger spaces, use
`any_vector`

.

## Synopsis

## Description

- ↑module mk_braycurtis
Bray-Curtis distance.

- ↑module mk_canberra
Canberra distance.

- ↑module mk_chebyshev
Chebyshev distance.

- ↑module mk_correlation
Correlation distance.

- ↑module mk_euclidean
Euclidean distance.

- ↑module mk_manhattan
Manhattan/cityblock distance.

- ↑module mk_minkowski
Order-2 Minkowski distance.

- ↑module mk_sqeuclidean
Squared euclidean distance.