time-to-botec

Benchmark sampling in different programming languages
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README.md (3488B)

      1 <!--
      2 
      3 @license Apache-2.0
      4 
      5 Copyright (c) 2018 The Stdlib Authors.
      6 
      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
     10 
     11    http://www.apache.org/licenses/LICENSE-2.0
     12 
     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
     17 limitations under the License.
     18 
     19 -->
     20 
     21 # incrvariance
     22 
     23 > Compute an [unbiased sample variance][sample-variance] incrementally.
     24 
     25 <section class="intro">
     26 
     27 The [unbiased sample variance][sample-variance] is defined as
     28 
     29 <!-- <equation class="equation" label="eq:unbiased_sample_variance" align="center" raw="s^2 = \frac{1}{n-1} \sum_{i=0}^{n-1} ( x_i - \bar{x} )^2" alt="Equation for the unbiased sample variance."> -->
     30 
     31 <div class="equation" align="center" data-raw-text="s^2 = \frac{1}{n-1} \sum_{i=0}^{n-1} ( x_i - \bar{x} )^2" data-equation="eq:unbiased_sample_variance">
     32     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/variance/docs/img/equation_unbiased_sample_variance.svg" alt="Equation for the unbiased sample variance.">
     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 incrvariance = require( '@stdlib/stats/incr/variance' );
     48 ```
     49 
     50 #### incrvariance( \[mean] )
     51 
     52 Returns an accumulator `function` which incrementally computes an [unbiased sample variance][sample-variance].
     53 
     54 ```javascript
     55 var accumulator = incrvariance();
     56 ```
     57 
     58 If the mean is already known, provide a `mean` argument.
     59 
     60 ```javascript
     61 var accumulator = incrvariance( 3.0 );
     62 ```
     63 
     64 #### accumulator( \[x] )
     65 
     66 If provided an input value `x`, the accumulator function returns an updated [unbiased sample variance][sample-variance]. If not provided an input value `x`, the accumulator function returns the current [unbiased sample variance][sample-variance].
     67 
     68 ```javascript
     69 var accumulator = incrvariance();
     70 
     71 var s2 = accumulator( 2.0 );
     72 // returns 0.0
     73 
     74 s2 = accumulator( 1.0 ); // => ((2-1.5)^2+(1-1.5)^2) / (2-1)
     75 // returns 0.5
     76 
     77 s2 = accumulator( 3.0 ); // => ((2-2)^2+(1-2)^2+(3-2)^2) / (3-1)
     78 // returns 1.0
     79 
     80 s2 = accumulator();
     81 // returns 1.0
     82 ```
     83 
     84 </section>
     85 
     86 <!-- /.usage -->
     87 
     88 <section class="notes">
     89 
     90 ## Notes
     91 
     92 -   Input values are **not** type checked. If provided `NaN` or a value which, when used in computations, results in `NaN`, the accumulated value is `NaN` for **all** future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.
     93 
     94 </section>
     95 
     96 <!-- /.notes -->
     97 
     98 <section class="examples">
     99 
    100 ## Examples
    101 
    102 <!-- eslint no-undef: "error" -->
    103 
    104 ```javascript
    105 var randu = require( '@stdlib/random/base/randu' );
    106 var incrvariance = require( '@stdlib/stats/incr/variance' );
    107 
    108 var accumulator;
    109 var v;
    110 var i;
    111 
    112 // Initialize an accumulator:
    113 accumulator = incrvariance();
    114 
    115 // For each simulated datum, update the unbiased sample variance...
    116 for ( i = 0; i < 100; i++ ) {
    117     v = randu() * 100.0;
    118     accumulator( v );
    119 }
    120 console.log( accumulator() );
    121 ```
    122 
    123 </section>
    124 
    125 <!-- /.examples -->
    126 
    127 <section class="links">
    128 
    129 [sample-variance]: https://en.wikipedia.org/wiki/Variance
    130 
    131 </section>
    132 
    133 <!-- /.links -->