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ddot

Calculate the dot product of two double-precision floating-point vectors.

The dot product (or scalar product) is defined as

$$\mathbf{x}\cdot\mathbf{y} = \sum_{i=0}^{N-1} x_i y_i = x_0 y_0 + x_1 y_1 + \ldots + x_{N-1} y_{N-1}$$

Installation

npm install @stdlib/blas-ddot

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var ddot = require( '@stdlib/blas-ddot' );

ddot( x, y[, dim] )

Calculates the dot product of two double-precision floating-point vectors x and y.

var Float64Array = require( '@stdlib/array-float64' );
var array = require( '@stdlib/ndarray-array' );

var x = array( new Float64Array( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] ) );
var y = array( new Float64Array( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] ) );

var z = ddot( x, y );
// returns <ndarray>

var v = z.get();
// returns -5.0

The function has the following parameters:

• x: a non-zero-dimensional ndarray whose underlying data type is float64. Must be broadcast-compatible with y.
• y: a non-zero-dimensional ndarray whose underlying data type is float64. Must be broadcast-compatible with x.
• dim: dimension for which to compute the dot product. Must be a negative integer. Negative indices are resolved relative to the last array dimension, with the last dimension corresponding to -1. Default: -1.

If provided at least one input ndarray having more than one dimension, the input ndarrays are broadcasted to a common shape. For multi-dimensional input ndarrays, the function performs batched computation, such that the function computes the dot product for each pair of vectors in x and y according to the specified dimension index.

var Float64Array = require( '@stdlib/array-float64' );
var array = require( '@stdlib/ndarray-array' );

var opts = {
    'shape': [ 2, 3 ]
};
var x = array( new Float64Array( [ 4.0, 2.0, -3.0, 5.0, -1.0, 3.0 ] ), opts );
var y = array( new Float64Array( [ 2.0, 6.0, -1.0, -4.0, 8.0, 2.0 ] ), opts );

var z = ddot( x, y );
// returns <ndarray>

var v1 = z.get( 0 );
// returns 23.0

var v2 = z.get( 1 );
// returns -22.0

Notes

• The size of the contracted dimension must be the same for both input ndarrays.
• The function resolves the dimension index for which to compute the dot product before broadcasting.
• Negative indices are resolved relative to the last ndarray dimension, with the last dimension corresponding to -1.
• The output ndarray has the same data type as the input ndarrays and has a shape which is determined by broadcasting and excludes the contracted dimension.
• If provided empty vectors, the dot product is 0.
• ddot() provides a higher-level interface to the BLAS level 1 function ddot.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var array = require( '@stdlib/ndarray-array' );
var ddot = require( '@stdlib/blas-ddot' );

var opts = {
    'dtype': 'float64'
};

var x = array( discreteUniform( 10, 0, 100, opts ), {
    'shape': [ 5, 2 ]
});
console.log( ndarray2array( x ) );

var y = array( discreteUniform( 10, 0, 10, opts ), {
    'shape': x.shape
});
console.log( ndarray2array( y ) );

var z = ddot( x, y, -1 );
console.log( ndarray2array( z ) );

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See Also

• @stdlib/blas-base/ddot: calculate the dot product of two double-precision floating-point vectors.
• @stdlib/blas-gdot: calculate the dot product of two vectors.
• @stdlib/blas-sdot: calculate the dot product of two single-precision floating-point vectors.

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Notice

This package is part of 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, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.