time-to-botec

Benchmark sampling in different programming languages
Log | Files | Refs | README

dsnanmeanors.js (1719B)


      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 // MAIN //
     22 
     23 /**
     24 * Computes the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
     25 *
     26 * @param {PositiveInteger} N - number of indexed elements
     27 * @param {Float32Array} x - input array
     28 * @param {integer} stride - stride length
     29 * @returns {number} arithmetic mean
     30 *
     31 * @example
     32 * var Float32Array = require( '@stdlib/array/float32' );
     33 *
     34 * var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
     35 * var N = x.length;
     36 *
     37 * var v = dsnanmeanors( N, x, 1 );
     38 * // returns ~0.3333
     39 */
     40 function dsnanmeanors( N, x, stride ) {
     41 	var sum;
     42 	var ix;
     43 	var v;
     44 	var n;
     45 	var i;
     46 
     47 	if ( N <= 0 ) {
     48 		return NaN;
     49 	}
     50 	if ( N === 1 || stride === 0 ) {
     51 		return x[ 0 ];
     52 	}
     53 	if ( stride < 0 ) {
     54 		ix = (1-N) * stride;
     55 	} else {
     56 		ix = 0;
     57 	}
     58 	sum = 0.0;
     59 	n = 0;
     60 	for ( i = 0; i < N; i++ ) {
     61 		v = x[ ix ];
     62 		if ( v === v ) {
     63 			sum += v;
     64 			n += 1;
     65 		}
     66 		ix += stride;
     67 	}
     68 	if ( n === 0 ) {
     69 		return NaN;
     70 	}
     71 	return sum / n;
     72 }
     73 
     74 
     75 // EXPORTS //
     76 
     77 module.exports = dsnanmeanors;