time-to-botec

Benchmark sampling in different programming languages
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nanvariancetk.js (1740B)


      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 variance of a strided array ignoring `NaN` values and using a one-pass textbook algorithm.
     25 *
     26 * @param {PositiveInteger} N - number of indexed elements
     27 * @param {number} correction - degrees of freedom adjustment
     28 * @param {NumericArray} x - input array
     29 * @param {integer} stride - stride length
     30 * @returns {number} variance
     31 *
     32 * @example
     33 * var x = [ 1.0, -2.0, NaN, 2.0 ];
     34 *
     35 * var v = nanvariancetk( x.length, 1, x, 1 );
     36 * // returns ~4.3333
     37 */
     38 function nanvariancetk( N, correction, x, stride ) {
     39 	var S2;
     40 	var ix;
     41 	var nc;
     42 	var S;
     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 		v = x[ 0 ];
     52 		if ( v === v && N-correction > 0.0 ) {
     53 			return 0.0;
     54 		}
     55 		return NaN;
     56 	}
     57 	if ( stride < 0 ) {
     58 		ix = (1-N) * stride;
     59 	} else {
     60 		ix = 0;
     61 	}
     62 	S2 = 0.0;
     63 	S = 0.0;
     64 	n = 0;
     65 	for ( i = 0; i < N; i++ ) {
     66 		v = x[ ix ];
     67 		if ( v === v ) {
     68 			S2 += v * v;
     69 			S += v;
     70 			n += 1;
     71 		}
     72 		ix += stride;
     73 	}
     74 	nc = n - correction;
     75 	if ( nc <= 0.0 ) {
     76 		return NaN;
     77 	}
     78 	return (S2 - ((S/n)*S)) / nc;
     79 }
     80 
     81 
     82 // EXPORTS //
     83 
     84 module.exports = nanvariancetk;