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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 isFloat32ndarrayLike = require( '@stdlib/assert/is-float32ndarray-like' );
var isNegativeInteger = require( '@stdlib/assert/is-negative-integer' ).isPrimitive;
var without = require( '@stdlib/array/base/without' );
var numel = require( '@stdlib/ndarray/base/numel' );
var normalizeIndex = require( '@stdlib/ndarray/base/normalize-index' );
var ndarraylike2ndarray = require( '@stdlib/ndarray/base/ndarraylike2ndarray' );
var nditerStacks = require( '@stdlib/ndarray/iter/stacks' );
var empty = require( '@stdlib/ndarray/empty' );
var base = require( '@stdlib/blas/base/snrm2' ).ndarray;
var format = require( '@stdlib/string/format' );
// MAIN //
/**
* Computes the L2-norm of a single-precision floating-point vector.
*
* @param {ndarrayLike} x - input array
* @param {NegativeInteger} [dim=-1] - dimension for which to compute the L2-norm
* @throws {TypeError} first argument must be a ndarray containing single-precision floating-point numbers
* @throws {TypeError} first argument must have at least one dimension
* @throws {TypeError} second argument must be a negative integer
* @throws {RangeError} second argument is out-of-bounds
* @returns {ndarray} ndarray containing the L2-norm
*
* @example
* var Float32Array = require( '@stdlib/array/float32' );
* var array = require( '@stdlib/ndarray/array' );
*
* var x = array( new Float32Array( [ 1.0, -2.0, 2.0 ] ) );
*
* var y = snrm2( x );
* // returns <ndarray>[ 3.0 ]
*/
function snrm2( x ) {
var dim;
var xsh;
var osh;
var xit;
var out;
var xc;
var vx;
var dm;
var S;
var v;
var i;
if ( !isFloat32ndarrayLike( x ) ) {
throw new TypeError( format( 'invalid argument. First argument must be an ndarray containing single-precision floating-point numbers. Value: `%s`.', x ) );
}
// Convert the input array to a "base" ndarray:
xc = ndarraylike2ndarray( x );
// Resolve the input array shape:
xsh = xc.shape;
// Validate that we've been provided a non-zero-dimensional array...
if ( xsh.length < 1 ) {
throw new TypeError( format( 'invalid argument. First argument must have at least one dimension.' ) );
}
// Validate that the dimension argument is a negative integer...
if ( arguments.length > 1 ) {
dim = arguments[ 1 ];
if ( !isNegativeInteger( dim ) ) {
throw new TypeError( format( 'invalid argument. Second argument must be a negative integer. Value: `%s`.', dim ) );
}
} else {
dim = -1;
}
// Validate that a provided dimension index is within bounds...
dm = xsh.length - 1;
dim = normalizeIndex( dim, dm );
if ( dim === -1 ) {
throw new RangeError( format( 'invalid argument. Second argument must be a value on the interval: [%d,%d]. Value: `%d`.', -( dm+1 ), -1, arguments[ 1 ] ) );
}
// Resolve the size of the dimension over which to compute the L2-norm:
S = xsh[ dim ];
// Resolve the output array shape by excluding the reduced dimension:
osh = without( xc.shape, dim );
// Allocate an empty output array:
out = empty( osh, {
'dtype': xc.dtype,
'order': xc.order
});
// If we are only provided a one-dimensional input array, we can skip iterating over stacks...
if ( osh.length === 0 ) {
v = base( S, xc.data, xc.strides[0], xc.offset );
out.iset( v );
return out;
}
// Create an iterator for iterating over stacks of vectors:
xit = nditerStacks( xc, [ dim ] );
// Compute the L2-norm for each vector...
for ( i = 0; i < numel( osh ); i++ ) {
vx = xit.next().value;
v = base( S, vx.data, vx.strides[0], vx.offset );
out.iset( i, v );
}
return out;
}
// EXPORTS //
module.exports = snrm2;
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