time-to-botec

Benchmark sampling in different programming languages
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README.md (6010B)


      1 <!--
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      3 @license Apache-2.0
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      5 Copyright (c) 2020 The Stdlib Authors.
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      7 Licensed under the Apache License, Version 2.0 (the "License");
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     11    http://www.apache.org/licenses/LICENSE-2.0
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     15 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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     19 -->
     20 
     21 # meanpn
     22 
     23 > Calculate the [arithmetic mean][arithmetic-mean] of a strided array using a two-pass error correction algorithm.
     24 
     25 <section class="intro">
     26 
     27 The [arithmetic mean][arithmetic-mean] is defined as
     28 
     29 <!-- <equation class="equation" label="eq:arithmetic_mean" align="center" raw="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" alt="Equation for the arithmetic mean."> -->
     30 
     31 <div class="equation" align="center" data-raw-text="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:arithmetic_mean">
     32     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@7158616c9399e313d462e91b1a5c0f82d090c372/lib/node_modules/@stdlib/stats/base/meanpn/docs/img/equation_arithmetic_mean.svg" alt="Equation for the arithmetic mean.">
     33     <br>
     34 </div>
     35 
     36 <!-- </equation> -->
     37 
     38 </section>
     39 
     40 <!-- /.intro -->
     41 
     42 <section class="usage">
     43 
     44 ## Usage
     45 
     46 ```javascript
     47 var meanpn = require( '@stdlib/stats/base/meanpn' );
     48 ```
     49 
     50 #### meanpn( N, x, stride )
     51 
     52 Computes the [arithmetic mean][arithmetic-mean] of a strided array `x` using a two-pass error correction algorithm.
     53 
     54 ```javascript
     55 var x = [ 1.0, -2.0, 2.0 ];
     56 var N = x.length;
     57 
     58 var v = meanpn( N, x, 1 );
     59 // returns ~0.3333
     60 ```
     61 
     62 The function has the following parameters:
     63 
     64 -   **N**: number of indexed elements.
     65 -   **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
     66 -   **stride**: index increment for `x`.
     67 
     68 The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in `x`,
     69 
     70 ```javascript
     71 var floor = require( '@stdlib/math/base/special/floor' );
     72 
     73 var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
     74 var N = floor( x.length / 2 );
     75 
     76 var v = meanpn( N, x, 2 );
     77 // returns 1.25
     78 ```
     79 
     80 Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
     81 
     82 <!-- eslint-disable stdlib/capitalized-comments -->
     83 
     84 ```javascript
     85 var Float64Array = require( '@stdlib/array/float64' );
     86 var floor = require( '@stdlib/math/base/special/floor' );
     87 
     88 var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
     89 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
     90 
     91 var N = floor( x0.length / 2 );
     92 
     93 var v = meanpn( N, x1, 2 );
     94 // returns 1.25
     95 ```
     96 
     97 #### meanpn.ndarray( N, x, stride, offset )
     98 
     99 Computes the [arithmetic mean][arithmetic-mean] of a strided array using a two-pass error correction algorithm and alternative indexing semantics.
    100 
    101 ```javascript
    102 var x = [ 1.0, -2.0, 2.0 ];
    103 var N = x.length;
    104 
    105 var v = meanpn.ndarray( N, x, 1, 0 );
    106 // returns ~0.33333
    107 ```
    108 
    109 The function has the following additional parameters:
    110 
    111 -   **offset**: starting index for `x`.
    112 
    113 While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [arithmetic mean][arithmetic-mean] for every other value in `x` starting from the second value
    114 
    115 ```javascript
    116 var floor = require( '@stdlib/math/base/special/floor' );
    117 
    118 var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
    119 var N = floor( x.length / 2 );
    120 
    121 var v = meanpn.ndarray( N, x, 2, 1 );
    122 // returns 1.25
    123 ```
    124 
    125 </section>
    126 
    127 <!-- /.usage -->
    128 
    129 <section class="notes">
    130 
    131 ## Notes
    132 
    133 -   If `N <= 0`, both functions return `NaN`.
    134 -   Depending on the environment, the typed versions ([`dmeanpn`][@stdlib/stats/base/dmeanpn], [`smeanpn`][@stdlib/stats/base/smeanpn], etc.) are likely to be significantly more performant.
    135 
    136 </section>
    137 
    138 <!-- /.notes -->
    139 
    140 <section class="examples">
    141 
    142 ## Examples
    143 
    144 <!-- eslint no-undef: "error" -->
    145 
    146 ```javascript
    147 var randu = require( '@stdlib/random/base/randu' );
    148 var round = require( '@stdlib/math/base/special/round' );
    149 var Float64Array = require( '@stdlib/array/float64' );
    150 var meanpn = require( '@stdlib/stats/base/meanpn' );
    151 
    152 var x;
    153 var i;
    154 
    155 x = new Float64Array( 10 );
    156 for ( i = 0; i < x.length; i++ ) {
    157     x[ i ] = round( (randu()*100.0) - 50.0 );
    158 }
    159 console.log( x );
    160 
    161 var v = meanpn( x.length, x, 1 );
    162 console.log( v );
    163 ```
    164 
    165 </section>
    166 
    167 <!-- /.examples -->
    168 
    169 * * *
    170 
    171 <section class="references">
    172 
    173 ## References
    174 
    175 -   Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958][@neely:1966a].
    176 -   Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036][@schubert:2018a].
    177 
    178 </section>
    179 
    180 <!-- /.references -->
    181 
    182 <section class="links">
    183 
    184 [arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean
    185 
    186 [mdn-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array
    187 
    188 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
    189 
    190 [@stdlib/stats/base/dmeanpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanpn
    191 
    192 [@stdlib/stats/base/smeanpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/smeanpn
    193 
    194 [@neely:1966a]: https://doi.org/10.1145/365719.365958
    195 
    196 [@schubert:2018a]: https://doi.org/10.1145/3221269.3223036
    197 
    198 </section>
    199 
    200 <!-- /.links -->