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feat: refactored and added protocol support to stats/base/meanwd
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‎lib/node_modules/@stdlib/stats/base/meanwd/README.md‎

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@@ -51,7 +51,7 @@ The [arithmetic mean][arithmetic-mean] is defined as
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var meanwd = require( '@stdlib/stats/base/meanwd' );
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```
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54-
#### meanwd( N, x, stride )
54+
#### meanwd( N, x, strideX )
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Computes the [arithmetic mean][arithmetic-mean] of a strided array `x` using Welford's algorithm.
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@@ -67,17 +67,14 @@ The function has the following parameters:
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- **N**: number of indexed elements.
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- **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
70-
- **stride**: index increment for `x`.
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- **strideX**: stride length for `x`.
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The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in `x`,
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The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in `x`,
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```javascript
75-
var floor = require( '@stdlib/math/base/special/floor' );
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var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
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var N = floor( x.length / 2 );
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var v = meanwd( N, x, 2 );
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var v = meanwd( 4, x, 2 );
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// returns 1.25
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```
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@@ -87,42 +84,37 @@ Note that indexing is relative to the first index. To introduce an offset, use [
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var floor = require( '@stdlib/math/base/special/floor' );
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var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
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var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
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var N = floor( x0.length / 2 );
96-
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var v = meanwd( N, x1, 2 );
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var v = meanwd( 4, x1, 2 );
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// returns 1.25
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```
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101-
#### meanwd.ndarray( N, x, stride, offset )
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#### meanwd.ndarray( N, x, strideX, offsetX )
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Computes the [arithmetic mean][arithmetic-mean] of a strided array using Welford's algorithm and alternative indexing semantics.
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```javascript
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var x = [ 1.0, -2.0, 2.0 ];
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var N = x.length;
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var v = meanwd.ndarray( N, x, 1, 0 );
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var v = meanwd.ndarray( 3, x, 1, 0 );
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// returns ~0.33333
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```
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The function has the following additional parameters:
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- **offset**: starting index for `x`.
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- **offsetX**: starting index for `x`.
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While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [arithmetic mean][arithmetic-mean] for every other value in `x` starting from the second value
110+
While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the [arithmetic mean][arithmetic-mean] for every other element in the strided array starting from the second element
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```javascript
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var floor = require( '@stdlib/math/base/special/floor' );
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var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
123-
var N = floor( x.length / 2 );
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125-
var v = meanwd.ndarray( N, x, 2, 1 );
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var v = meanwd.ndarray( 4, x, 2, 1 );
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// returns 1.25
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```
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@@ -135,6 +127,7 @@ var v = meanwd.ndarray( N, x, 2, 1 );
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## Notes
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- If `N <= 0`, both functions return `NaN`.
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- Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]).
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- Depending on the environment, the typed versions ([`dmeanwd`][@stdlib/stats/strided/dmeanwd], [`smeanwd`][@stdlib/stats/base/smeanwd], etc.) are likely to be significantly more performant.
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</section>
@@ -148,18 +141,12 @@ var v = meanwd.ndarray( N, x, 2, 1 );
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<!-- eslint no-undef: "error" -->
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```javascript
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var randu = require( '@stdlib/random/base/randu' );
152-
var round = require( '@stdlib/math/base/special/round' );
153-
var Float64Array = require( '@stdlib/array/float64' );
144+
var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
154145
var meanwd = require( '@stdlib/stats/base/meanwd' );
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156-
var x;
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var i;
158-
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x = new Float64Array( 10 );
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for ( i = 0; i < x.length; i++ ) {
161-
x[ i ] = round( (randu()*100.0) - 50.0 );
162-
}
147+
var x = discreteUniform( 10, -50, 50, {
148+
'dtype': 'float64'
149+
});
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console.log( x );
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var v = meanwd( x.length, x, 1 );
@@ -210,6 +197,8 @@ console.log( v );
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[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
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[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
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[@welford:1962a]: https://doi.org/10.1080/00401706.1962.10490022
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[@vanreeken:1968a]: https://doi.org/10.1145/362929.362961

‎lib/node_modules/@stdlib/stats/base/meanwd/benchmark/benchmark.js‎

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@@ -21,11 +21,18 @@
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
24+
var uniform = require( '@stdlib/random/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var pkg = require( './../package.json' ).name;
28-
var meanwd = require( './../lib/meanwd.js' );
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var meanwd = require( './../lib/main.js' );
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// VARIABLES //
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var options = {
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'dtype': 'generic'
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};
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// FUNCTIONS //
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var x;
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var i;
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x = [];
45-
for ( i = 0; i < len; i++ ) {
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x.push( ( randu()*20.0 ) - 10.0 );
47-
}
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var x = uniform( len, -10, 10, options );
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return benchmark;
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function benchmark( b ) {

‎lib/node_modules/@stdlib/stats/base/meanwd/benchmark/benchmark.ndarray.js‎

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@@ -21,13 +21,20 @@
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
24+
var uniform = require( '@stdlib/random/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var pkg = require( './../package.json' ).name;
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var meanwd = require( './../lib/ndarray.js' );
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// VARIABLES //
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var options = {
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'dtype': 'generic'
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};
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// FUNCTIONS //
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/**
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var x;
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var i;
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x = [];
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for ( i = 0; i < len; i++ ) {
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x.push( ( randu()*20.0 ) - 10.0 );
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}
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var x = uniform( len, -10, 10, options );
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return benchmark;
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function benchmark( b ) {

‎lib/node_modules/@stdlib/stats/base/meanwd/docs/repl.txt‎

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{{alias}}( N, x, stride )
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{{alias}}( N, x, strideX )
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Computes the arithmetic mean of a strided array using Welford's algorithm.
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The `N` and `stride` parameters determine which elements in `x` are accessed
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at runtime.
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The `N` and stride parameters determine which elements in the strided array
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are accessed at runtime.
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Indexing is relative to the first index. To introduce an offset, use a typed
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array view.
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x: Array<number>|TypedArray
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Input array.
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stride: integer
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Index increment.
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strideX: integer
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Stride length.
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Returns
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-------
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> {{alias}}( x.length, x, 1 )
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~0.3333
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36-
// Using `N` and `stride` parameters:
36+
// Using `N` and stride parameters:
3737
> x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];
38-
> var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
39-
> var stride = 2;
40-
> {{alias}}( N, x, stride )
38+
> {{alias}}( 3, x, 2 )
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~0.3333
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// Using view offsets:
4442
> var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );
4543
> var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
46-
> N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
47-
> stride = 2;
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> {{alias}}( N, x1, stride )
44+
> {{alias}}( 3, x1, 2 )
4945
~-0.3333
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51-
{{alias}}.ndarray( N, x, stride, offset )
47+
48+
{{alias}}.ndarray( N, x, strideX, offsetX )
5249
Computes the arithmetic mean of a strided array using Welford's algorithm
5350
and alternative indexing semantics.
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@@ -64,10 +61,10 @@
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x: Array<number>|TypedArray
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Input array.
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stride: integer
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Index increment.
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strideX: integer
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Stride length.
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offset: integer
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offsetX: integer
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Starting index.
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Returns
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// Using offset parameter:
8683
> var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];
87-
> var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
88-
> {{alias}}.ndarray( N, x, 2, 1 )
84+
> {{alias}}.ndarray( 3, x, 2, 1 )
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~-0.3333
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See Also

‎lib/node_modules/@stdlib/stats/base/meanwd/docs/types/index.d.ts‎

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/// <reference types="@stdlib/types"/>
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import { NumericArray } from '@stdlib/types/array';
23+
import { NumericArray, Collection, AccessorArrayLike } from '@stdlib/types/array';
24+
25+
/**
26+
* Input array.
27+
*/
28+
type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
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2530
/**
2631
* Interface describing `meanwd`.
@@ -31,7 +36,7 @@ interface Routine {
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*
3237
* @param N - number of indexed elements
3338
* @param x - input array
34-
* @param stride - stride length
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* @param strideX - stride length
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* @returns arithmetic mean
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*
3742
* @example
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* var v = meanwd( x.length, x, 1 );
4146
* // returns ~0.3333
4247
*/
43-
( N: number, x: NumericArray, stride: number ): number;
48+
( N: number, x: InputArray, strideX: number ): number;
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/**
4651
* Computes the arithmetic mean of a strided array using Welford's algorithm and alternative indexing semantics.
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5762
* var v = meanwd.ndarray( x.length, x, 1, 0 );
5863
* // returns ~0.3333
5964
*/
60-
ndarray( N: number, x: NumericArray, stride: number, offset: number ): number;
65+
ndarray( N: number, x: InputArray, strideX: number, offset: number ): number;
6166
}
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6368
/**
6469
* Computes the arithmetic mean of a strided array using Welford's algorithm.
6570
*
6671
* @param N - number of indexed elements
6772
* @param x - input array
68-
* @param stride - stride length
73+
* @param strideX - stride length
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* @returns arithmetic mean
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*
7176
* @example

‎lib/node_modules/@stdlib/stats/base/meanwd/docs/types/test.ts‎

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1616
* limitations under the License.
1717
*/
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import AccessorArray = require( '@stdlib/array/base/accessor' );
1920
import meanwd = require( './index' );
2021

2122

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2627
const x = new Float64Array( 10 );
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meanwd( x.length, x, 1 ); // $ExpectType number
30+
meanwd( x.length, new AccessorArray( x ), 1 ); // $ExpectType number
2931
}
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// The compiler throws an error if the function is provided a first argument which is not a number...
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8587
const x = new Float64Array( 10 );
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8789
meanwd.ndarray( x.length, x, 1, 0 ); // $ExpectType number
90+
meanwd.ndarray( x.length, new AccessorArray( x ), 1, 0 ); // $ExpectType number
8891
}
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// The compiler throws an error if the `ndarray` method is provided a first argument which is not a number...

‎lib/node_modules/@stdlib/stats/base/meanwd/examples/index.js‎

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'use strict';
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21-
var randu = require( '@stdlib/random/base/randu' );
22-
var round = require( '@stdlib/math/base/special/round' );
23-
var Float64Array = require( '@stdlib/array/float64' );
21+
var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
2422
var meanwd = require( './../lib' );
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26-
var x;
27-
var i;
28-
29-
x = new Float64Array( 10 );
30-
for ( i = 0; i < x.length; i++ ) {
31-
x[ i ] = round( (randu()*100.0) - 50.0 );
32-
}
24+
var x = discreteUniform( 10, -50, 50, {
25+
'dtype': 'float64'
26+
});
3327
console.log( x );
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3529
var v = meanwd( x.length, x, 1 );

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