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Add generalized BLAS routine to multiply a vector by a constant
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‎lib/node_modules/@stdlib/blas/base/gscal/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2020 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# gscal
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> Multiply a vector `x` by a constant `alpha`.
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<section class="usage">
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## Usage
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```javascript
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var gscal = require( '@stdlib/blas/base/gscal' );
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```
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#### gscal( N, alpha, x, stride )
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Multiplies a vector `x` by a constant `alpha`.
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```javascript
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var x = [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ];
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gscal( x.length, 5.0, x, 1 );
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// x => [ -10.0, 5.0, 15.0, -25.0, 20.0, 0.0, -5.0, -15.0 ]
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```
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The function accepts the following parameters:
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- **N**: number of indexed elements.
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- **alpha**: `numeric` constant
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- **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
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- **stride**: index increment.
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The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to multiply every other value by a constant
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```javascript
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var floor = require( '@stdlib/math/base/special/floor' );
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var x = [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ];
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var N = floor( x.length / 2 );
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var alpha = 5.0;
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var stride = 2;
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gscal( N, alpha, x, stride );
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// x => [ -10.0, 1.0, 15.0, -5.0, 20.0, 0.0, -5.0, -3.0 ]
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```
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Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var floor = require( '@stdlib/math/base/special/floor' );
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// Initial array...
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var x0 = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );
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// Create an offset view...
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var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
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var N = floor( x0.length / 2 );
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var alpha = 5.0;
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var stride = 2;
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// Scale every other value...
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gscal( N, alpha, x1, stride );
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// x0 => <Float64Array>[ 1.0, -10.0, 3.0, -20.0, 5.0, -30.0 ]
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```
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#### gscal.ndarray( N, alpha, x, stride, offset )
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Multiplies a vector `x` by a constant `alpha`, with alternative indexing semantics.
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```javascript
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var x = [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ];
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gscal.ndarray( x.length, 5.0, x, 1, 0 );
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// x => [ -10.0, 5.0, 15.0, -25.0, 20.0, 0.0, -5.0, -15.0 ]
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```
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The function accepts the following additional parameters:
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- **offset**: starting index.
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While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to multiply the last three elements of `x` by a constant
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```javascript
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var x = [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ];
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var alpha = 5.0;
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gscal.ndarray( 3, alpha, x, 1, x.length-3 );
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// x => [ 1.0, -2.0, 3.0, -20.0, 25.0, -30.0 ]
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```
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- If `N <= 0` or `stride <= 0`, both functions return `x` unchanged.
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- `gscal()` corresponds to the [BLAS][blas] level 1 function [`dscal`][dscal] with the exception that this implementation works with any array type, not just Float64Arrays. Depending on the environment, the typed versions ([`dscal`][@stdlib/blas/base/dscal], [`sscal`][@stdlib/blas/base/sscal], etc.) are likely to be significantly more performant.
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var round = require( '@stdlib/math/base/special/round' );
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var randu = require( '@stdlib/random/base/randu' );
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var Float64Array = require( '@stdlib/array/float64' );
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var gscal = require( '@stdlib/blas/base/gscal' );
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var rand;
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var sign;
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var x;
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var i;
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x = new Float64Array( 100 );
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for ( i = 0; i < x.length; i++ ) {
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rand = round( randu()*100.0 );
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sign = randu();
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if ( sign < 0.5 ) {
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sign = -1.0;
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} else {
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sign = 1.0;
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}
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x[ i ] = sign * rand;
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}
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console.log( x );
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gscal( x.length, 5.0, x, 1 );
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console.log( x );
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```
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</section>
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<!-- /.examples -->
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<section class="links">
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[blas]: http://www.netlib.org/blas
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[dscal]: http://www.netlib.org/lapack/explore-html/df/d28/group__double__blas__level1.html
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[mdn-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array
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[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
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[@stdlib/blas/base/dscal]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/dscal
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[@stdlib/blas/base/sscal]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/sscal
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/blas/base/gscal/benchmark/benchmark.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2020 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var Float64Array = require( '@stdlib/array/float64' );
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var pkg = require( './../package.json' ).name;
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var gscal = require( './../lib/main.js' );
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// FUNCTIONS //
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/**
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* Create a benchmark function.
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*
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* @private
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* @param {PositiveInteger} len - array length
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var x;
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var i;
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x = new Float64Array( len );
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for ( i = 0; i < len; i++ ) {
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x[ i ] = ( randu()*10.0 ) - 20.0;
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}
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return benchmark;
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function benchmark( b ) {
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var y;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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y = gscal( x.length, 5.0, x, 1 );
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if ( isnan( y ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan(y) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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function main() {
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var len;
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var min;
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var max;
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var f;
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var i;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( i = min; i <= max; i++ ) {
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len = pow( 10, i );
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f = createBenchmark( len );
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bench( pkg+':len='+len, f );
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}
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}
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main();

‎lib/node_modules/@stdlib/blas/base/gscal/benchmark/benchmark.ndarray.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2020 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var Float64Array = require( '@stdlib/array/float64' );
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var pkg = require( './../package.json' ).name;
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var gscal = require( './../lib/main.js' ).ndarray;
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// FUNCTIONS //
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/**
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* Create a benchmark function.
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*
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* @private
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* @param {PositiveInteger} len - array length
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var x;
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var i;
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x = new Float64Array( len );
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for ( i = 0; i < len; i++ ) {
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x[ i ] = ( randu()*10.0 ) - 20.0;
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}
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return benchmark;
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function benchmark( b ) {
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var y;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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y = gscal( x.length, 5.0, x, 1, 0 );
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if ( isnan( y ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan(y) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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function main() {
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var len;
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var min;
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var max;
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var f;
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var i;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( i = min; i <= max; i++ ) {
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len = pow( 10, i );
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f = createBenchmark( len );
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bench( pkg+':ndarray:len='+len, f );
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}
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}
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main();

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