Compute the Euclidean distance between two double-precision floating-point strided arrays.
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> Compute the Euclidean distance between two double-precision floating-point strided arrays.
The [Euclidean distance][wikipedia-euclidean-distance] is defined as
where x_i and y_i are the _ith_ components of vectors X and Y, respectively.
``bash`
npm install @stdlib/stats-strided-distances-deuclidean
`javascript`
var deuclidean = require( '@stdlib/stats-strided-distances-deuclidean' );
#### deuclidean( N, x, strideX, y, strideY )
Computes the Euclidean distance between two double-precision floating-point strided arrays.
`javascript
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var y = new Float64Array( [ 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 ] );
var z = deuclidean( x.length, x, 1, y, 1 );
// returns ~8.485
`
The function has the following parameters:
- N: number of indexed elements.
- x: input [Float64Array][@stdlib/array/float64].x
- strideX: stride length of .Float64Array
- y: input [][@stdlib/array/float64].y
- strideY: stride length of .
The N and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to calculate the Euclidean distance between every other element in x and the first N elements of y in reverse order,
`javascript
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var z = deuclidean( 3, x, 2, y, -1 );
// returns ~4.472
`
Note that indexing is relative to the first index. To introduce an offset, use [typed array][mdn-typed-array] views.
`javascript
var Float64Array = require( '@stdlib/array-float64' );
// Initial arrays...
var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y0 = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
var z = deuclidean( 3, x1, 1, y1, 1 );
// returns ~13.856
`
#### deuclidean.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )
Computes the Euclidean distance between two double-precision floating-point strided arrays using alternative indexing semantics.
`javascript
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var y = new Float64Array( [ 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 ] );
var z = deuclidean.ndarray( x.length, x, 1, 0, y, 1, 0 );
// returns ~8.485
`
The function has the following additional parameters:
- offsetX: starting index for x.y
- offsetY: starting index for .
While [typed array][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to calculate the Euclidean distance between every other element in x starting from the second element with the last 3 elements in y in reverse order
`javascript
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var z = deuclidean.ndarray( 3, x, 2, 1, y, -1, y.length-1 );
// returns ~12.845
`
- If N <= 0, both functions return NaN.
`javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var deuclidean = require( '@stdlib/stats-strided-distances-deuclidean' );
var opts = {
'dtype': 'float64'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );
var y = discreteUniform( x.length, 0, 10, opts );
console.log( y );
var out = deuclidean.ndarray( x.length, x, 1, 0, y, -1, y.length-1 );
console.log( out );
`
*
`c`
#include "stdlib/stats/strided/distances/deuclidean.h"
#### stdlib_strided_deuclidean( N, \X, strideX, \Y, strideY )
Computes the Euclidean distance between two double-precision floating-point strided arrays.
`c
const double x[] = { 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 };
const double y[] = { 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 };
double v = stdlib_strided_deuclidean( 8, x, 1, y, 1 );
// returns ~8.485
`
The function accepts the following arguments:
- N: [in] CBLAS_INT number of indexed elements.[in] double*
- X: first input array.[in] CBLAS_INT
- strideX: stride length of X.[in] double*
- Y: second input array.[in] CBLAS_INT
- strideY: stride length of Y.
`c`
double stdlib_strided_deuclidean( const CBLAS_INT N, const double X, const CBLAS_INT strideX, const double Y, const CBLAS_INT strideY );
#### stdlib_strided_deuclidean_ndarray( N, \X, strideX, offsetX, \Y, strideY, offsetY )
Computes the Euclidean distance between two double-precision floating-point strided arrays using alternative indexing semantics.
`c
const double x[] = { 4.0, 2.0, -3.0, 5.0, -1.0 };
const double y[] = { 2.0, 6.0, -1.0, -4.0, 8.0 };
double v = stdlib_strided_deuclidean_ndarray( 5, x, -1, 4, y, -1, 4 );
// returns ~13.638
`
The function accepts the following arguments:
- N: [in] CBLAS_INT number of indexed elements.[in] double*
- X: first input array.[in] CBLAS_INT
- strideX: stride length of X.[in] CBLAS_INT
- offsetX: starting index for X.[in] double*
- Y: second input array.[in] CBLAS_INT
- strideY: stride length of Y.[in] CBLAS_INT
- offsetY: starting index for Y.
`c`
double stdlib_strided_deuclidean_ndarray( const CBLAS_INT N, const double X, const CBLAS_INT strideX, const CBLAS_INT offsetX, const double Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );
`c
#include "stdlib/stats/strided/distances/deuclidean.h"
#include
int main( void ) {
// Create strided arrays:
const double x[] = { 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0 };
const double y[] = { 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0 };
// Specify the number of elements:
const int N = 8;
// Specify strides:
const int strideX = 1;
const int strideY = -1;
// Compute the Euclidean distance between x and y:
double d = stdlib_strided_deuclidean( N, x, strideX, y, strideY );
// Print the result:
printf( "Euclidean distance: %lf\n", d );
// Compute the Euclidean distance between x and y with offsets:
d = stdlib_strided_deuclidean_ndarray( N, x, strideX, 0, y, strideY, N-1 );
// Print the result:
printf( "Euclidean distance: %lf\n", d );
}
`
*
This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].
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