time-to-botec

Benchmark sampling in different programming languages
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README.md (3613B)

      1 <!--
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      3 @license Apache-2.0
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      5 Copyright (c) 2018 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
     17 limitations under the License.
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     19 -->
     20 
     21 # incrme
     22 
     23 > Compute the [mean error][mean-absolute-error] (ME) incrementally.
     24 
     25 <section class="intro">
     26 
     27 The [mean error][mean-absolute-error] is defined as
     28 
     29 <!-- <equation class="equation" label="eq:mean_error" align="center" raw="\operatorname{ME} = \frac{1}{n} \sum_{i=0}^{n-1} (y_i - x_i)" alt="Equation for the mean error."> -->
     30 
     31 <div class="equation" align="center" data-raw-text="\operatorname{ME} = \frac{1}{n} \sum_{i=0}^{n-1} (y_i - x_i)" data-equation="eq:mean_error">
     32     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@7d6e6319f451be0997d35a6cf491b08e1f2cb5cf/lib/node_modules/@stdlib/stats/incr/me/docs/img/equation_mean_error.svg" alt="Equation for the mean error.">
     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 incrme = require( '@stdlib/stats/incr/me' );
     48 ```
     49 
     50 #### incrme()
     51 
     52 Returns an accumulator `function` which incrementally computes the [mean error][mean-absolute-error].
     53 
     54 ```javascript
     55 var accumulator = incrme();
     56 ```
     57 
     58 #### accumulator( \[x, y] )
     59 
     60 If provided input values `x` and `y`, the accumulator function returns an updated [mean error][mean-absolute-error]. If not provided input values `x` and `y`, the accumulator function returns the current [mean error][mean-absolute-error].
     61 
     62 ```javascript
     63 var accumulator = incrme();
     64 
     65 var m = accumulator( 2.0, 3.0 );
     66 // returns 1.0
     67 
     68 m = accumulator( -1.0, -4.0 );
     69 // returns -1.0
     70 
     71 m = accumulator( -3.0, 5.0 );
     72 // returns 2.0
     73 
     74 m = accumulator();
     75 // returns 2.0
     76 ```
     77 
     78 </section>
     79 
     80 <!-- /.usage -->
     81 
     82 <section class="notes">
     83 
     84 ## Notes
     85 
     86 -   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.
     87 -   Be careful when interpreting the [mean error][mean-absolute-error] as errors can cancel. This stated, that errors can cancel makes the [mean error][mean-absolute-error] suitable for measuring the bias in forecasts.
     88 -   **Warning**: the [mean error][mean-absolute-error] is scale-dependent and, thus, the measure should **not** be used to make comparisons between datasets having different scales.
     89 
     90 </section>
     91 
     92 <!-- /.notes -->
     93 
     94 <section class="examples">
     95 
     96 ## Examples
     97 
     98 <!-- eslint no-undef: "error" -->
     99 
    100 ```javascript
    101 var randu = require( '@stdlib/random/base/randu' );
    102 var incrme = require( '@stdlib/stats/incr/me' );
    103 
    104 var accumulator;
    105 var v1;
    106 var v2;
    107 var i;
    108 
    109 // Initialize an accumulator:
    110 accumulator = incrme();
    111 
    112 // For each simulated datum, update the mean error...
    113 for ( i = 0; i < 100; i++ ) {
    114     v1 = ( randu()*100.0 ) - 50.0;
    115     v2 = ( randu()*100.0 ) - 50.0;
    116     accumulator( v1, v2 );
    117 }
    118 console.log( accumulator() );
    119 ```
    120 
    121 </section>
    122 
    123 <!-- /.examples -->
    124 
    125 <section class="links">
    126 
    127 [mean-absolute-error]: https://en.wikipedia.org/wiki/Mean_absolute_error
    128 
    129 </section>
    130 
    131 <!-- /.links -->