Distance metrics for strided arrays.
var ns = require( '@stdlib/stats/strided/distances' );Namespace containing APIs for computing distance metrics on strided arrays.
var o = ns;
// returns {...}The namespace exports the following:
dchebyshev( N, x, strideX, y, strideY ): compute the Chebyshev distance between two double-precision floating-point strided arrays.dcityblock( N, x, strideX, y, strideY ): compute the city block (Manhattan) distance between two double-precision floating-point strided arrays.dcorrelation( N, x, strideX, y, strideY ): compute the correlation distance between two double-precision floating-point strided arrays.dcosineDistance( N, x, strideX, y, strideY ): compute the cosine distance between two double-precision floating-point strided arrays.dcosineSimilarity( N, x, strideX, y, strideY ): compute the cosine similarity of two double-precision floating-point strided arrays.deuclidean( N, x, strideX, y, strideY ): compute the Euclidean distance between two double-precision floating-point strided arrays.dminkowski( N, p, x, strideX, y, strideY ): compute the Minkowski distance between two double-precision floating-point strided arrays.dsquaredEuclidean( N, x, strideX, y, strideY ): compute the squared Euclidean distance between two double-precision floating-point strided arrays.
var objectKeys = require( '@stdlib/utils/keys' );
var ns = require( '@stdlib/stats/strided/distances' );
console.log( objectKeys( ns ) );