time-to-botec

Benchmark sampling in different programming languages
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README.md (5315B)


      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
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     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.
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     19 -->
     20 
     21 # incrmpcorrdist
     22 
     23 > Compute a moving [sample Pearson product-moment correlation distance][pearson-correlation] incrementally.
     24 
     25 <section class="intro">
     26 
     27 The [sample Pearson product-moment correlation distance][pearson-correlation] is defined as
     28 
     29 <!-- <equation class="equation" label="eq:pearson_distance" align="center" raw="d_{x,y} = 1 - r_{x,y} = 1 - \frac{\operatorname{cov_n(x,y)}}{\sigma_x \sigma_y}" alt="Equation for the Pearson product-moment correlation distance."> -->
     30 
     31 <div class="equation" align="center" data-raw-text="d_{x,y} = 1 - r_{x,y} = 1 - \frac{\operatorname{cov_n(x,y)}}{\sigma_x \sigma_y}" data-equation="eq:pearson_distance">
     32     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/mpcorrdist/docs/img/equation_pearson_distance.svg" alt="Equation for the Pearson product-moment correlation distance.">
     33     <br>
     34 </div>
     35 
     36 <!-- </equation> -->
     37 
     38 where `r` is the [sample Pearson product-moment correlation coefficient][pearson-correlation], `cov(x,y)` is the sample covariance, and `σ` corresponds to the sample standard deviation. As `r` resides on the interval `[-1,1]`, `d` resides on the interval `[0,2]`.
     39 
     40 </section>
     41 
     42 <!-- /.intro -->
     43 
     44 <section class="usage">
     45 
     46 ## Usage
     47 
     48 ```javascript
     49 var incrmpcorrdist = require( '@stdlib/stats/incr/mpcorrdist' );
     50 ```
     51 
     52 #### incrmpcorrdist( window\[, mx, my] )
     53 
     54 Returns an accumulator `function` which incrementally computes a moving [sample Pearson product-moment correlation distance][pearson-correlation]. The `window` parameter defines the number of values over which to compute the moving [sample Pearson product-moment correlation distance][pearson-correlation].
     55 
     56 ```javascript
     57 var accumulator = incrmpcorrdist( 3 );
     58 ```
     59 
     60 If means are already known, provide `mx` and `my` arguments.
     61 
     62 ```javascript
     63 var accumulator = incrmpcorrdist( 3, 5.0, -3.14 );
     64 ```
     65 
     66 #### accumulator( \[x, y] )
     67 
     68 If provided input values `x` and `y`, the accumulator function returns an updated [sample Pearson product-moment correlation distance][pearson-correlation]. If not provided input values `x` and `y`, the accumulator function returns the current [sample Pearson product-moment correlation distance][pearson-correlation].
     69 
     70 ```javascript
     71 var accumulator = incrmpcorrdist( 3 );
     72 
     73 var r = accumulator();
     74 // returns null
     75 
     76 // Fill the window...
     77 r = accumulator( 2.0, 1.0 ); // [(2.0, 1.0)]
     78 // returns 1.0
     79 
     80 r = accumulator( -5.0, 3.14 ); // [(2.0, 1.0), (-5.0, 3.14)]
     81 // returns ~2.0
     82 
     83 r = accumulator( 3.0, -1.0 ); // [(2.0, 1.0), (-5.0, 3.14), (3.0, -1.0)]
     84 // returns ~1.925
     85 
     86 // Window begins sliding...
     87 r = accumulator( 5.0, -9.5 ); // [(-5.0, 3.14), (3.0, -1.0), (5.0, -9.5)]
     88 // returns ~1.863
     89 
     90 r = accumulator( -5.0, 1.5 ); // [(3.0, -1.0), (5.0, -9.5), (-5.0, 1.5)]
     91 // returns ~1.803
     92 
     93 r = accumulator();
     94 // returns ~1.803
     95 ```
     96 
     97 </section>
     98 
     99 <!-- /.usage -->
    100 
    101 <section class="notes">
    102 
    103 ## Notes
    104 
    105 -   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.
    106 -   As `W` (x,y) 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.
    107 -   Due to limitations inherent in representing numeric values using floating-point format (i.e., the inability to represent numeric values with infinite precision), the [sample correlation distance][pearson-correlation] between perfectly correlated random variables may **not** be `0` or `2`. In fact, the [sample correlation distance][pearson-correlation] is **not** guaranteed to be strictly on the interval `[0,2]`. Any computed distance should, however, be within floating-point roundoff error.
    108 
    109 </section>
    110 
    111 <!-- /.notes -->
    112 
    113 <section class="examples">
    114 
    115 ## Examples
    116 
    117 <!-- eslint no-undef: "error" -->
    118 
    119 ```javascript
    120 var randu = require( '@stdlib/random/base/randu' );
    121 var incrmpcorrdist = require( '@stdlib/stats/incr/mpcorrdist' );
    122 
    123 var accumulator;
    124 var x;
    125 var y;
    126 var i;
    127 
    128 // Initialize an accumulator:
    129 accumulator = incrmpcorrdist( 5 );
    130 
    131 // For each simulated datum, update the moving sample correlation distance...
    132 for ( i = 0; i < 100; i++ ) {
    133     x = randu() * 100.0;
    134     y = randu() * 100.0;
    135     accumulator( x, y );
    136 }
    137 console.log( accumulator() );
    138 ```
    139 
    140 </section>
    141 
    142 <!-- /.examples -->
    143 
    144 <section class="links">
    145 
    146 [pearson-correlation]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
    147 
    148 </section>
    149 
    150 <!-- /.links -->