time-to-botec

Benchmark sampling in different programming languages
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ndarray.js (1989B)


      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 dapxsum = require( '@stdlib/blas/ext/base/dapxsum' ).ndarray;
     24 
     25 
     26 // MAIN //
     27 
     28 /**
     29 * Computes the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm.
     30 *
     31 * ## References
     32 *
     33 * -   Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154](https://doi.org/10.2307/2286154).
     34 *
     35 * @param {PositiveInteger} N - number of indexed elements
     36 * @param {Float64Array} x - input array
     37 * @param {integer} stride - stride length
     38 * @param {NonNegativeInteger} offset - starting index
     39 * @returns {number} arithmetic mean
     40 *
     41 * @example
     42 * var Float64Array = require( '@stdlib/array/float64' );
     43 * var floor = require( '@stdlib/math/base/special/floor' );
     44 *
     45 * var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
     46 * var N = floor( x.length / 2 );
     47 *
     48 * var v = dmeanli( N, x, 2, 1 );
     49 * // returns 1.25
     50 */
     51 function dmeanli( N, x, stride, offset ) {
     52 	if ( N <= 0 ) {
     53 		return NaN;
     54 	}
     55 	if ( N === 1 || stride === 0 ) {
     56 		return x[ offset ];
     57 	}
     58 	return x[ offset ] + ( dapxsum( N-1, -x[ offset ], x, stride, offset+stride ) / N ); // eslint-disable-line max-len
     59 }
     60 
     61 
     62 // EXPORTS //
     63 
     64 module.exports = dmeanli;