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includes

Test whether an ndarray contains a specified value along one or more dimensions.

Installation

npm install @stdlib/ndarray-includes

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 includes = require( '@stdlib/ndarray-includes' );

includes( x, searchElement[, options] )

Tests whether an ndarray contains a specified value along one or more dimensions.

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

// Create a data buffer:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

// Define the shape of the input array:
var sh = [ 3, 1, 2 ];

// Define the array strides:
var sx = [ 4, 4, 1 ];

// Define the index offset:
var ox = 1;

// Create an input ndarray:
var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );

// Perform reduction:
var out = includes( x, 6.0 );
// returns <ndarray>

var v = out.get();
// returns true

The function accepts the following arguments:

• x: input ndarray.
• searchElement: search element. May be either a scalar or an ndarray. Must be broadcast-compatible with the non-reduced dimensions of input ndarray. Must have a data type which can be (mostly) safely cast to the data type of the input ndarray.
• options: function options (optional).

The function accepts the following options:

• dims: list of dimensions over which to perform a reduction.
• keepdims: boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions. Default: false.

By default, the function performs a reduction over all elements in a provided ndarray. To reduce specific dimensions, set the dims option.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );

// Create a data buffer:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

// Define the shape of the input array:
var sh = [ 3, 1, 2 ];

// Define the array strides:
var sx = [ 4, 4, 1 ];

// Define the index offset:
var ox = 1;

// Create an input ndarray:
var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );

// Perform reduction:
var out = includes( x, 6.0, {
    'dims': [ 1, 2 ]
});
// returns <ndarray>

var v = ndarray2array( out );
// returns [ false, true, false ]

By default, the function returns an ndarray having a shape matching only the non-reduced dimensions of the input ndarray (i.e., the reduced dimensions are dropped). To include the reduced dimensions as singleton dimensions in the output ndarray, set the keepdims option to true.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );

// Create a data buffer:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

// Define the shape of the input array:
var sh = [ 3, 1, 2 ];

// Define the array strides:
var sx = [ 4, 4, 1 ];

// Define the index offset:
var ox = 1;

// Create an input ndarray:
var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );

// Perform reduction:
var out = includes( x, 6.0, {
    'dims': [ 1, 2 ],
    'keepdims': true
});
// returns <ndarray>

var v = ndarray2array( out );
// returns [ [ [ false ] ], [ [ true ] ], [ [ false ] ] ]

includes.assign( x, searchElement, out[, options] )

Tests whether an ndarray contains a specified value along one or more dimensions and assigns results to a provided output ndarray.

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

// Create a data buffer:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

// Define the shape of the input array:
var sh = [ 3, 1, 2 ];

// Define the array strides:
var sx = [ 4, 4, 1 ];

// Define the index offset:
var ox = 1;

// Create an input ndarray:
var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );

// Create an output ndarray:
var y = empty( [], {
    'dtype': 'bool'
});

// Perform reduction:
var out = includes.assign( x, 6.0, y );
// returns <ndarray>

var bool = ( out === y );
// returns true

var v = y.get();
// returns true

The function accepts the following arguments:

• x: input ndarray.
• searchElement: search element. May be either a scalar or an ndarray. Must be broadcast-compatible with the non-reduced dimensions of input ndarray. Must have a data type which can be (mostly) safely cast to the data type of the input ndarray.
• out: output ndarray. The output ndarray must have a shape matching the non-reduced dimensions of the input ndarray.
• options: function options (optional).

The function accepts the following options:

• dims: list of dimensions over which to perform a reduction.

By default, the function performs a reduction over all elements in a provided ndarray. To reduce specific dimensions, set the dims option.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var empty = require( '@stdlib/ndarray-empty' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );

// Create a data buffer:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

// Define the shape of the input array:
var sh = [ 3, 1, 2 ];

// Define the array strides:
var sx = [ 4, 4, 1 ];

// Define the index offset:
var ox = 1;

// Create an input ndarray:
var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );

// Create an output ndarray:
var y = empty( [ 3 ], {
    'dtype': 'bool'
});

// Perform reduction:
var out = includes.assign( x, 6.0, y, {
    'dims': [ 1, 2 ]
});

var bool = ( out === y );
// returns true

var v = ndarray2array( y );
// returns [ false, true, false ]

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var fillBy = require( '@stdlib/ndarray-fill-by' );
var zeros = require( '@stdlib/ndarray-zeros' );
var includes = require( '@stdlib/ndarray-includes' );

var x = zeros( [ 2, 4, 5 ], {
    'dtype': 'float64'
});
x = fillBy( x, discreteUniform( 0, 10 ) );
console.log( ndarray2array( x ) );

var y = includes( x, 1 );
console.log( 'includes(x[:,:,:], 1) =' );
console.log( y.get() );

y = includes( x, 2, {
    'dims': [ 0 ],
    'keepdims': true
});
console.log( 'includes(x[:,j,k], 2) =' );
console.log( ndarray2array( y ) );

y = includes( x, 3, {
    'dims': [ 1 ],
    'keepdims': true
});
console.log( 'includes(x[i,:,k], 3) =' );
console.log( ndarray2array( y ) );

y = includes( x, 4, {
    'dims': [ 2 ],
    'keepdims': true
});
console.log( 'includes(x[i,j,:], 4) =' );
console.log( ndarray2array( y ) );

y = includes( x, 5, {
    'dims': [ 0, 1 ],
    'keepdims': true
});
console.log( 'includes(x[:,:,k], 5) =' );
console.log( ndarray2array( y ) );

y = includes( x, 6, {
    'dims': [ 0, 2 ],
    'keepdims': true
});
console.log( 'includes(x[:,j,:], 6) =' );
console.log( ndarray2array( y ) );

y = includes( x, 7, {
    'dims': [ 1, 2 ],
    'keepdims': true
});
console.log( 'includes(x[i,:,:], 7) =' );
console.log( ndarray2array( y ) );

y = includes( x, 8, {
    'dims': [ 0, 1, 2 ],
    'keepdims': true
});
console.log( 'includes(x[:,:,:], 8) =' );
console.log( ndarray2array( y ) );

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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.

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License

See LICENSE.

Copyright

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