time-to-botec

Benchmark sampling in different programming languages
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README.md (3512B)

      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 # incrmae
     22 
     23 > Compute the [mean absolute error][mean-absolute-error] (MAE) incrementally.
     24 
     25 <section class="intro">
     26 
     27 The [mean absolute error][mean-absolute-error] is defined as
     28 
     29 <!-- <equation class="equation" label="eq:mean_absolute_error" align="center" raw="\operatorname{MAE}  = \frac{\sum_{i=0}^{n-1} |y_i - x_i|}{n}" alt="Equation for the mean absolute error."> -->
     30 
     31 <div class="equation" align="center" data-raw-text="\operatorname{MAE}  = \frac{\sum_{i=0}^{n-1} |y_i - x_i|}{n}" data-equation="eq:mean_absolute_error">
     32     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/mae/docs/img/equation_mean_absolute_error.svg" alt="Equation for the mean absolute 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 incrmae = require( '@stdlib/stats/incr/mae' );
     48 ```
     49 
     50 #### incrmae()
     51 
     52 Returns an accumulator `function` which incrementally computes the [mean absolute error][mean-absolute-error].
     53 
     54 ```javascript
     55 var accumulator = incrmae();
     56 ```
     57 
     58 #### accumulator( \[x, y] )
     59 
     60 If provided input values `x` and `y`, the accumulator function returns an updated [mean absolute error][mean-absolute-error]. If not provided input values `x` and `y`, the accumulator function returns the current [mean absolute error][mean-absolute-error].
     61 
     62 ```javascript
     63 var accumulator = incrmae();
     64 
     65 var m = accumulator( 2.0, 3.0 );
     66 // returns 1.0
     67 
     68 m = accumulator( -1.0, -4.0 );
     69 // returns 2.0
     70 
     71 m = accumulator( -3.0, 5.0 );
     72 // returns 4.0
     73 
     74 m = accumulator();
     75 // returns 4.0
     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 -   **Warning**: the [mean absolute error][mean-absolute-error] is scale-dependent and, thus, the measure should **not** be used to make comparisons between datasets having different scales.
     88 
     89 </section>
     90 
     91 <!-- /.notes -->
     92 
     93 <section class="examples">
     94 
     95 ## Examples
     96 
     97 <!-- eslint no-undef: "error" -->
     98 
     99 ```javascript
    100 var randu = require( '@stdlib/random/base/randu' );
    101 var incrmae = require( '@stdlib/stats/incr/mae' );
    102 
    103 var accumulator;
    104 var v1;
    105 var v2;
    106 var i;
    107 
    108 // Initialize an accumulator:
    109 accumulator = incrmae();
    110 
    111 // For each simulated datum, update the mean absolute error...
    112 for ( i = 0; i < 100; i++ ) {
    113     v1 = ( randu()*100.0 ) - 50.0;
    114     v2 = ( randu()*100.0 ) - 50.0;
    115     accumulator( v1, v2 );
    116 }
    117 console.log( accumulator() );
    118 ```
    119 
    120 </section>
    121 
    122 <!-- /.examples -->
    123 
    124 <section class="links">
    125 
    126 [mean-absolute-error]: https://en.wikipedia.org/wiki/Mean_absolute_error
    127 
    128 </section>
    129 
    130 <!-- /.links -->