feat: add blas/ext/base/ndarray/dcartesian-power

lib/node_modules/@stdlib/blas/ext/base/ndarray/dcartesian-power/README.md

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+<!--
+
+@license Apache-2.0
+
+Copyright (c) 2026 The Stdlib Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+-->
+
+# dcartesianPower
+
+> Compute the Cartesian power for a double-precision floating-point ndarray.
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var dcartesianPower = require( '@stdlib/blas/ext/base/ndarray/dcartesian-power' );
+```
+
+#### dcartesianPower( arrays )
+
+Computes the Cartesian power for a double-precision floating-point ndarray.
+
+```javascript
+var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+
+var x = new Float64Vector( [ 1.0, 2.0 ] );
+var out = zeros( [ 4, 2 ], {
+    'dtype': 'float64'
+});
+
+var k = scalar2ndarray( 2, {
+    'dtype': 'generic'
+});
+
+var v = dcartesianPower( [ x, out, k ] );
+// returns <ndarray>[ [ 1.0, 1.0 ], [ 1.0, 2.0 ], [ 2.0, 1.0 ], [ 2.0, 2.0 ] ]
+
+var bool = ( v === out );
+// returns true
+```
+
+The function has the following parameters:
+
+-   **arrays**: array-like object containing the following ndarrays:
+
+    -   a one-dimensional input ndarray.
+    -   a two-dimensional output ndarray.
+    -   a zero-dimensional ndarray specifying the power.
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+## Notes
+
+-   `k`-tuples are stored as rows in the output matrix, where the `j`-th column contains the `j`-th element of each tuple.
+-   For an input array of length `N`, the output array should have shape `[N^k, k]`, where `N^k` is the number of rows and `k` is the number of columns.
+-   If `N <= 0` or `k <= 0`, the function returns the output ndarray unchanged.
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var discreteUniform = require( '@stdlib/random/discrete-uniform' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var dcartesianPower = require( '@stdlib/blas/ext/base/ndarray/dcartesian-power' );
+
+var opts = {
+    'dtype': 'float64'
+};
+
+var x = discreteUniform( [ 3 ], -50, 50, opts );
+console.log( ndarray2array( x ) );
+
+var out = zeros( [ 9, 2 ], opts );
+
+var k = scalar2ndarray( 2, {
+    'dtype': 'generic'
+});
+
+var v = dcartesianPower( [ x, out, k ] );
+console.log( ndarray2array( v ) );
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
+
+<section class="related">
+
+</section>
+
+<!-- /.related -->
+
+<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="links">
+
+</section>
+
+<!-- /.links -->

lib/node_modules/@stdlib/blas/ext/base/ndarray/dcartesian-power/benchmark/benchmark.js

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+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var uniform = require( '@stdlib/random/uniform' );
+var zeros = require( '@stdlib/ndarray/zeros' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var format = require( '@stdlib/string/format' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var pkg = require( './../package.json' ).name;
+var dcartesianPower = require( './../lib' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+var K = 2;
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+	var out;
+	var x;
+	var k;
+
+	x = uniform( [ len ], -100.0, 100.0, options );
+	out = zeros( [ pow( len, K ), K ], options );
+
+	k = scalar2ndarray( K, {
+		'dtype': 'generic'
+	});
+
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var v;
+		var i;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			v = dcartesianPower( [ x, out, k ] );
+			if ( typeof v !== 'object' ) {
+				b.fail( 'should return an ndarray' );
+			}
+		}
+		b.toc();
+		if ( isnan( v.get( i%pow( len, K ), i%K ) ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var len;
+	var min;
+	var max;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 3; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		len = pow( 10, i );
+		f = createBenchmark( len );
+		bench( format( '%s:len=%d', pkg, len ), f );
+	}
+}
+
+main();

lib/node_modules/@stdlib/blas/ext/base/ndarray/dcartesian-power/docs/repl.txt

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+
+{{alias}}( arrays )
+    Computes the Cartesian power for a double-precision floating-point ndarray.
+
+    `k`-tuples are stored as rows in the output matrix, where the `j`-th column
+    contains the `j`-th element of each tuple.
+
+    For an input array of length `N`, the output array should have shape
+    [N^k, k], where `N^k` is the number of rows and `k` is the number of
+    columns.
+
+    If `N <= 0` or `k <= 0`, the function returns the output ndarray unchanged.
+
+    Parameters
+    ----------
+    arrays: ArrayLikeObject<ndarray>
+        Array-like object containing the following ndarrays:
+
+        - a one-dimensional input ndarray.
+        - a two-dimensional output ndarray.
+        - a zero-dimensional ndarray specifying the power.
+
+    Returns
+    -------
+    out: ndarray
+        Output ndarray.
+
+    Examples
+    --------
+    > var x = new {{alias:@stdlib/ndarray/vector/float64}}( [ 1.0, 2.0 ] );
+    > var out = {{alias:@stdlib/ndarray/zeros}}( [ 4, 2 ], { 'dtype': 'float64' } );
+    > var k = {{alias:@stdlib/ndarray/from-scalar}}( 2, { 'dtype': 'generic' } );
+    > {{alias}}( [ x, out, k ] );
+    > out
+    <ndarray>[ [ 1.0, 1.0 ], [ 1.0, 2.0 ], [ 2.0, 1.0 ], [ 2.0, 2.0 ] ]
+
+    See Also
+    --------
+

lib/node_modules/@stdlib/blas/ext/base/ndarray/dcartesian-power/docs/types/index.d.ts

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+/*
+* @license Apache-2.0
+*
+* Copyright (c) 2026 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+// TypeScript Version: 4.1
+
+/// <reference types="@stdlib/types"/>
+
+import { float64ndarray, typedndarray } from '@stdlib/types/ndarray';
+
+/**
+* Computes the Cartesian power for a double-precision floating-point ndarray.
+*
+* ## Notes
+*
+* -   The function expects the following ndarrays:
+*
+*     -   a one-dimensional input ndarray.
+*     -   a two-dimensional output ndarray.
+*     -   a zero-dimensional ndarray specifying the power.
+*
+* @param arrays - array-like object containing ndarrays
+* @returns output ndarray
+*
+* @example
+* var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
+* var zeros = require( '@stdlib/ndarray/zeros' );
+* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+*
+* var x = new Float64Vector( [ 1.0, 2.0 ] );
+* var out = zeros( [ 4, 2 ], {
+*     'dtype': 'float64'
+* });
+*
+* var k = scalar2ndarray( 2, {
+*     'dtype': 'generic'
+* });
+*
+* var v = dcartesianPower( [ x, out, k ] );
+* // returns <ndarray>[ [ 1.0, 1.0 ], [ 1.0, 2.0 ], [ 2.0, 1.0 ], [ 2.0, 2.0 ] ]
+*
+* var bool = ( v === out );
+* // returns true
+*/
+declare function dcartesianPower( arrays: [ float64ndarray, float64ndarray, typedndarray<number> ] ): float64ndarray;
+
+
+// EXPORTS //
+
+export = dcartesianPower;