time-to-botec

Benchmark sampling in different programming languages
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README.md (4886B)


      1 <!--
      2 
      3 @license Apache-2.0
      4 
      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.
     18 
     19 -->
     20 
     21 # incrmmda
     22 
     23 > Compute a moving [mean directional accuracy][mean-directional-accuracy] (MDA) incrementally.
     24 
     25 <section class="intro">
     26 
     27 For a window of size `W`, the [mean directional accuracy][mean-directional-accuracy] is defined as
     28 
     29 <!-- <equation class="equation" label="eq:mean_directional_accuracy" align="center" raw="\operatorname{MDA} = \begin{cases} 1 & \textrm{if}\ W = 1 \\ \frac{1}{W} \sum_{i=1}^{W} \delta_{\operatorname{sgn}(\Delta f_{i,i-1}),\ \operatorname{sgn}(\Delta a_{i,i-1})} & \textrm{if}\ W > 1 \end{cases}" alt="Equation for the mean directional accuracy."> -->
     30 
     31 <div class="equation" align="center" data-raw-text="\operatorname{MDA} = \begin{cases} 1 & \textrm{if}\ W = 1 \\\frac{1}{W} \sum_{i=1}^{W} \delta_{\operatorname{sgn}(\Delta f_{i,i-1}),\ \operatorname{sgn}(\Delta a_{i,i-1})} & \textrm{if}\ W > 1 \end{cases}" data-equation="eq:mean_directional_accuracy">
     32     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@99730afbace8256ce53cfbc0714c7f3cac92466a/lib/node_modules/@stdlib/stats/incr/mmda/docs/img/equation_mean_directional_accuracy.svg" alt="Equation for the mean directional accuracy.">
     33     <br>
     34 </div>
     35 
     36 <!-- </equation> -->
     37 
     38 where `f_i` is the forecast value, `a_i` is the actual value, `sgn(x)` is the [signum][@stdlib/math/base/special/signum] function, and `δ` is the [Kronecker delta][@stdlib/math/base/special/kronecker-delta]. 
     39 
     40 </section>
     41 
     42 <!-- /.intro -->
     43 
     44 <section class="usage">
     45 
     46 ## Usage
     47 
     48 ```javascript
     49 var incrmmda = require( '@stdlib/stats/incr/mmda' );
     50 ```
     51 
     52 #### incrmmda( window )
     53 
     54 Returns an accumulator `function` which incrementally computes a moving [mean directional accuracy][mean-directional-accuracy]. The `window` parameter defines the number of values over which to compute the moving [mean directional accuracy][mean-directional-accuracy].
     55 
     56 ```javascript
     57 var accumulator = incrmmda( 3 );
     58 ```
     59 
     60 #### accumulator( \[f, a] )
     61 
     62 If provided input values `f` and `a`, the accumulator function returns an updated [mean directional accuracy][mean-directional-accuracy]. If not provided input values `f` and `a`, the accumulator function returns the current [mean directional accuracy][mean-directional-accuracy].
     63 
     64 ```javascript
     65 var accumulator = incrmmda( 3 );
     66 
     67 var m = accumulator();
     68 // returns null
     69 
     70 // Fill the window...
     71 m = accumulator( 2.0, 3.0 ); // [(+,+)]
     72 // returns 1.0
     73 
     74 m = accumulator( 1.0, 4.0 ); // [(+,+), (-,+)]
     75 // returns 0.5
     76 
     77 m = accumulator( 3.0, 9.0 ); // [(+,+), (-,+), (+,+)]
     78 // returns ~0.67
     79 
     80 // Window begins sliding...
     81 m = accumulator( 7.0, 3.0 ); // [(-,+), (+,+), (+,-)]
     82 // returns ~0.33
     83 
     84 m = accumulator( 5.0, 3.0 ); // [(+,+), (+,-), (-,0)]
     85 // returns ~0.33
     86 
     87 m = accumulator();
     88 // returns ~0.33
     89 ```
     90 
     91 </section>
     92 
     93 <!-- /.usage -->
     94 
     95 <section class="notes">
     96 
     97 ## Notes
     98 
     99 -   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 **at least** `W-1` 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.
    100 -   As `W` (f,a) pairs are needed to fill the window buffer, the first `W-1` returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values. 
    101 
    102 </section>
    103 
    104 <!-- /.notes -->
    105 
    106 <section class="examples">
    107 
    108 ## Examples
    109 
    110 <!-- eslint no-undef: "error" -->
    111 
    112 ```javascript
    113 var randu = require( '@stdlib/random/base/randu' );
    114 var incrmmda = require( '@stdlib/stats/incr/mmda' );
    115 
    116 var accumulator;
    117 var v1;
    118 var v2;
    119 var i;
    120 
    121 // Initialize an accumulator:
    122 accumulator = incrmmda( 5 );
    123 
    124 // For each simulated datum, update the moving mean directional accuracy...
    125 for ( i = 0; i < 100; i++ ) {
    126     v1 = ( randu()*100.0 ) - 50.0;
    127     v2 = ( randu()*100.0 ) - 50.0;
    128     accumulator( v1, v2 );
    129 }
    130 console.log( accumulator() );
    131 ```
    132 
    133 </section>
    134 
    135 <!-- /.examples -->
    136 
    137 <section class="links">
    138 
    139 [mean-directional-accuracy]: https://en.wikipedia.org/wiki/Mean_Directional_Accuracy_%28MDA%29
    140 
    141 [@stdlib/math/base/special/signum]: https://www.npmjs.com/package/@stdlib/math-base-special-signum
    142 
    143 [@stdlib/math/base/special/kronecker-delta]: https://www.npmjs.com/package/@stdlib/math-base-special-kronecker-delta
    144 
    145 </section>
    146 
    147 <!-- /.links -->