time-to-botec

Benchmark sampling in different programming languages
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README.md (6109B)


      1 <!--
      2 
      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");
      8 you may not use this file except in compliance with the License.
      9 You may obtain a copy of the License at
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     11    http://www.apache.org/licenses/LICENSE-2.0
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     13 Unless required by applicable law or agreed to in writing, software
     14 distributed under the License is distributed on an "AS IS" BASIS,
     15 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     16 See the License for the specific language governing permissions and
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     19 -->
     20 
     21 # dsnanmeanors
     22 
     23 > Calculate the [arithmetic mean][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.
     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@23c6ac239685f96addd36b5dc7ac2e76839922d7/lib/node_modules/@stdlib/stats/base/dsnanmeanors/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 dsnanmeanors = require( '@stdlib/stats/base/dsnanmeanors' );
     48 ```
     49 
     50 #### dsnanmeanors( N, x, stride )
     51 
     52 Computes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array `x`, ignoring `NaN` values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
     53 
     54 ```javascript
     55 var Float32Array = require( '@stdlib/array/float32' );
     56 
     57 var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
     58 var N = x.length;
     59 
     60 var v = dsnanmeanors( N, x, 1 );
     61 // returns ~0.3333
     62 ```
     63 
     64 The function has the following parameters:
     65 
     66 -   **N**: number of indexed elements.
     67 -   **x**: input [`Float32Array`][@stdlib/array/float32].
     68 -   **stride**: index increment for `x`.
     69 
     70 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`,
     71 
     72 ```javascript
     73 var Float32Array = require( '@stdlib/array/float32' );
     74 var floor = require( '@stdlib/math/base/special/floor' );
     75 
     76 var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ] );
     77 var N = floor( x.length / 2 );
     78 
     79 var v = dsnanmeanors( N, x, 2 );
     80 // returns 1.25
     81 ```
     82 
     83 Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
     84 
     85 <!-- eslint-disable stdlib/capitalized-comments -->
     86 
     87 ```javascript
     88 var Float32Array = require( '@stdlib/array/float32' );
     89 var floor = require( '@stdlib/math/base/special/floor' );
     90 
     91 var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
     92 var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
     93 
     94 var N = floor( x0.length / 2 );
     95 
     96 var v = dsnanmeanors( N, x1, 2 );
     97 // returns 1.25
     98 ```
     99 
    100 #### dsnanmeanors.ndarray( N, x, stride, offset )
    101 
    102 Computes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation with extended accumulation and alternative indexing semantics.
    103 
    104 ```javascript
    105 var Float32Array = require( '@stdlib/array/float32' );
    106 
    107 var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
    108 var N = x.length;
    109 
    110 var v = dsnanmeanors.ndarray( N, x, 1, 0 );
    111 // returns ~0.33333
    112 ```
    113 
    114 The function has the following additional parameters:
    115 
    116 -   **offset**: starting index for `x`.
    117 
    118 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
    119 
    120 ```javascript
    121 var Float32Array = require( '@stdlib/array/float32' );
    122 var floor = require( '@stdlib/math/base/special/floor' );
    123 
    124 var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
    125 var N = floor( x.length / 2 );
    126 
    127 var v = dsnanmeanors.ndarray( N, x, 2, 1 );
    128 // returns 1.25
    129 ```
    130 
    131 </section>
    132 
    133 <!-- /.usage -->
    134 
    135 <section class="notes">
    136 
    137 ## Notes
    138 
    139 -   If `N <= 0`, both functions return `NaN`.
    140 -   If every indexed element is `NaN`, both functions return `NaN`.
    141 -   Accumulated intermediate values are stored as double-precision floating-point numbers.
    142 -   Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation to compute an arithmetic mean is acceptable; in all other cases, exercise due caution.
    143 
    144 </section>
    145 
    146 <!-- /.notes -->
    147 
    148 <section class="examples">
    149 
    150 ## Examples
    151 
    152 <!-- eslint no-undef: "error" -->
    153 
    154 ```javascript
    155 var randu = require( '@stdlib/random/base/randu' );
    156 var round = require( '@stdlib/math/base/special/round' );
    157 var Float32Array = require( '@stdlib/array/float32' );
    158 var dsnanmeanors = require( '@stdlib/stats/base/dsnanmeanors' );
    159 
    160 var x;
    161 var i;
    162 
    163 x = new Float32Array( 10 );
    164 for ( i = 0; i < x.length; i++ ) {
    165     if ( randu() < 0.2 ) {
    166         x[ i ] = NaN;
    167     } else {
    168         x[ i ] = round( (randu()*100.0) - 50.0 );
    169     }
    170 }
    171 console.log( x );
    172 
    173 var v = dsnanmeanors( x.length, x, 1 );
    174 console.log( v );
    175 ```
    176 
    177 </section>
    178 
    179 <!-- /.examples -->
    180 
    181 <section class="references">
    182 
    183 </section>
    184 
    185 <!-- /.references -->
    186 
    187 <section class="links">
    188 
    189 [arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean
    190 
    191 [@stdlib/array/float32]: https://www.npmjs.com/package/@stdlib/array-float32
    192 
    193 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
    194 
    195 </section>
    196 
    197 <!-- /.links -->