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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x 6x | /** * @license Apache-2.0 * * Copyright (c) 2025 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'; /** * WebAssembly routine to compute the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring `NaN` values and using ordinary recursive summation. * * @module @stdlib/blas/ext/base/wasm/dnanasumors * * @example * var Float64Array = require( '@stdlib/array/float64' ); * var dnanasumors = require( '@stdlib/blas/ext/base/wasm/dnanasumors' ); * * // Define a strided array: * var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] ); * * // Perform operation: * var v = dnanasumors.main( x.length, x, 1 ); * // returns 5.0 * * @example * var Float64Array = require( '@stdlib/array/float64' ); * var dnanasumors = require( '@stdlib/blas/ext/base/wasm/dnanasumors' ); * * // Define a strided array: * var x = new Float64Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); * * // Perform operation: * var v = dnanasumors.ndarray( 4, x, 2, 1 ); * // returns 9.0 * * @example * var Memory = require( '@stdlib/wasm/memory' ); * var oneTo = require( '@stdlib/array/one-to' ); * var zeros = require( '@stdlib/array/zeros' ); * var dnanasumors = require( '@stdlib/blas/ext/base/wasm/dnanasumors' ); * * // Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB): * var mem = new Memory({ * 'initial': 10, * 'maximum': 100 * }); * * // Create a BLAS routine: * var mod = new dnanasumors.Module( mem ); * // returns <Module> * * // Initialize the routine: * mod.initializeSync(); * * // Define a vector data type: * var dtype = 'float64'; * * // Specify a vector length: * var N = 3; * * // Define a pointer (i.e., byte offset) for storing the input vector: * var xptr = 0; * * // Write vector values to module memory: * mod.write( xptr, oneTo( N, dtype ) ); * * // Perform computation: * var v = mod.main( N, xptr, 1 ); * // returns 6.0 */ // MODULES // var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' ); var main = require( './main.js' ); var Module = require( './module.js' ); // MAIN // setReadOnly( main, 'Module', Module ); // EXPORTS // module.exports = main; // exports: { "Module": "main.Module" } |