time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (1656B)


      1 /**
      2 * @license Apache-2.0
      3 *
      4 * Copyright (c) 2020 The Stdlib Authors.
      5 *
      6 * Licensed under the Apache License, Version 2.0 (the "License");
      7 * you may not use this file except in compliance with the License.
      8 * You may obtain a copy of the License at
      9 *
     10 *    http://www.apache.org/licenses/LICENSE-2.0
     11 *
     12 * Unless required by applicable law or agreed to in writing, software
     13 * distributed under the License is distributed on an "AS IS" BASIS,
     14 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     15 * See the License for the specific language governing permissions and
     16 * limitations under the License.
     17 */
     18 
     19 'use strict';
     20 
     21 // MODULES //
     22 
     23 var dnansumpw = require( './dnansumpw.js' );
     24 
     25 
     26 // VARIABLES //
     27 
     28 var WORKSPACE = [ 0.0, 0 ];
     29 
     30 
     31 // MAIN //
     32 
     33 /**
     34 * Computes the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using pairwise summation.
     35 *
     36 * @param {PositiveInteger} N - number of indexed elements
     37 * @param {Float64Array} x - input array
     38 * @param {integer} stride - stride length
     39 * @param {NonNegativeInteger} offset - starting index
     40 * @returns {number} arithmetic mean
     41 *
     42 * @example
     43 * var Float64Array = require( '@stdlib/array/float64' );
     44 * var floor = require( '@stdlib/math/base/special/floor' );
     45 *
     46 * var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
     47 * var N = floor( x.length / 2 );
     48 *
     49 * var v = dnanmeanpw( N, x, 2, 1 );
     50 * // returns 1.25
     51 */
     52 function dnanmeanpw( N, x, stride, offset ) {
     53 	WORKSPACE[ 0 ] = 0.0;
     54 	WORKSPACE[ 1 ] = 0;
     55 	dnansumpw( N, WORKSPACE, x, stride, offset );
     56 	return WORKSPACE[ 0 ] / WORKSPACE[ 1 ];
     57 }
     58 
     59 
     60 // EXPORTS //
     61 
     62 module.exports = dnanmeanpw;