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feat: add support for accessor arrays and refactor stats/base/nanmean
PR-URL: #7326 Closes: #5655 Co-authored-by: Athan Reines <[email protected]> Reviewed-by: Athan Reines <[email protected]>
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‎lib/node_modules/@stdlib/stats/base/nanmean/README.md

Lines changed: 27 additions & 38 deletions
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@@ -51,78 +51,67 @@ The [arithmetic mean][arithmetic-mean] is defined as
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var nanmean = require( '@stdlib/stats/base/nanmean' );
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```
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54-
#### nanmean( N, x, stride )
54+
#### nanmean( N, x, strideX )
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56-
Computes the [arithmetic mean][arithmetic-mean] of a strided array `x`, ignoring `NaN` values.
56+
Computes the [arithmetic mean][arithmetic-mean] of a strided array, ignoring `NaN` values.
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```javascript
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var x = [ 1.0, -2.0, NaN, 2.0 ];
60-
var N = x.length;
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62-
var v = nanmean( N, x, 1 );
61+
var v = nanmean( x.length, x, 1 );
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// returns ~0.3333
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```
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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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72-
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' );
76-
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var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ];
78-
var N = floor( x.length / 2 );
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80-
var v = nanmean( N, x, 2 );
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var v = nanmean( 4, x, 2 );
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// returns 1.25
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```
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Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
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86-
<!-- eslint-disable stdlib/capitalized-comments -->
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<!-- eslint-disable stdlib/capitalized-comments, max-len -->
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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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92-
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
87+
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] );
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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 = nanmean( N, x1, 2 );
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var v = nanmean( 5, x1, 2 );
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// returns 1.25
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```
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101-
#### nanmean.ndarray( N, x, stride, offset )
94+
#### nanmean.ndarray( N, x, strideX, offsetX )
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Computes the [arithmetic mean][arithmetic-mean] of a strided array, ignoring `NaN` values and using alternative indexing semantics.
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```javascript
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var x = [ 1.0, -2.0, NaN, 2.0 ];
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var N = x.length;
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109-
var v = nanmean.ndarray( N, x, 1, 0 );
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var v = nanmean.ndarray( x.length, x, 1, 0 );
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// returns ~0.33333
111103
```
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The function has the following additional parameters:
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- **offset**: starting index for `x`.
107+
- **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
109+
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 `x` 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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122-
var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ];
123-
var N = floor( x.length / 2 );
112+
var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];
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125-
var v = nanmean.ndarray( N, x, 2, 1 );
114+
var v = nanmean.ndarray( 5, x, 2, 1 );
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// returns 1.25
127116
```
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@@ -136,6 +125,7 @@ var v = nanmean.ndarray( N, x, 2, 1 );
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- If `N <= 0`, both functions return `NaN`.
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- If every indexed element is `NaN`, both functions return `NaN`.
128+
- 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 ([`dnanmean`][@stdlib/stats/strided/dnanmean], [`snanmean`][@stdlib/stats/base/snanmean], etc.) are likely to be significantly more performant.
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</section>
@@ -149,22 +139,19 @@ var v = nanmean.ndarray( N, x, 2, 1 );
149139
<!-- eslint no-undef: "error" -->
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```javascript
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var randu = require( '@stdlib/random/base/randu' );
153-
var round = require( '@stdlib/math/base/special/round' );
154-
var Float64Array = require( '@stdlib/array/float64' );
142+
var uniform = require( '@stdlib/random/base/uniform' );
143+
var filledarrayBy = require( '@stdlib/array/filled-by' );
144+
var bernoulli = require( '@stdlib/random/base/bernoulli' );
155145
var nanmean = require( '@stdlib/stats/base/nanmean' );
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157-
var x;
158-
var i;
159-
160-
x = new Float64Array( 10 );
161-
for ( i = 0; i < x.length; i++ ) {
162-
if ( randu() < 0.2 ) {
163-
x[ i ] = NaN;
164-
} else {
165-
x[ i ] = round( (randu()*100.0) - 50.0 );
147+
function rand() {
148+
if ( bernoulli( 0.8 ) < 1 ) {
149+
return NaN;
166150
}
151+
return uniform( -50.0, 50.0 );
167152
}
153+
154+
var x = filledarrayBy( 10, 'float64', rand );
168155
console.log( x );
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170157
var v = nanmean( x.length, x, 1 );
@@ -201,6 +188,8 @@ console.log( v );
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202189
[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
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191+
[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
192+
204193
<!-- <related-links> -->
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[@stdlib/stats/strided/dnanmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmean

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

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@@ -21,15 +21,30 @@
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// MODULES //
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var bench = require( '@stdlib/bench' );
24-
var randu = require( '@stdlib/random/base/randu' );
24+
var uniform = require( '@stdlib/random/base/uniform' );
25+
var bernoulli = require( '@stdlib/random/base/bernoulli' );
26+
var filledarrayBy = require( '@stdlib/array/filled-by' );
2527
var isnan = require( '@stdlib/math/base/assert/is-nan' );
2628
var pow = require( '@stdlib/math/base/special/pow' );
2729
var pkg = require( './../package.json' ).name;
28-
var nanmean = require( './../lib/nanmean.js' );
30+
var nanmean = require( './../lib/main.js' );
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3133
// FUNCTIONS //
3234

35+
/**
36+
* Returns a random number.
37+
*
38+
* @private
39+
* @returns {number} random number
40+
*/
41+
function rand() {
42+
if ( bernoulli( 0.8 ) < 1 ) {
43+
return NaN;
44+
}
45+
return uniform( -10.0, 10.0 );
46+
}
47+
3348
/**
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* Creates a benchmark function.
3550
*
@@ -38,17 +53,7 @@ var nanmean = require( './../lib/nanmean.js' );
3853
* @returns {Function} benchmark function
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*/
4055
function createBenchmark( len ) {
41-
var x;
42-
var i;
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44-
x = [];
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for ( i = 0; i < len; i++ ) {
46-
if ( randu() < 0.2 ) {
47-
x.push( NaN );
48-
} else {
49-
x.push( ( randu()*20.0 ) - 10.0 );
50-
}
51-
}
56+
var x = filledarrayBy( len, 'generic', rand );
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return benchmark;
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function benchmark( b ) {

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

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@@ -21,7 +21,9 @@
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// MODULES //
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var bench = require( '@stdlib/bench' );
24-
var randu = require( '@stdlib/random/base/randu' );
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var uniform = require( '@stdlib/random/base/uniform' );
25+
var bernoulli = require( '@stdlib/random/base/bernoulli' );
26+
var filledarrayBy = require( '@stdlib/array/filled-by' );
2527
var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
2729
var pkg = require( './../package.json' ).name;
@@ -30,6 +32,19 @@ var nanmean = require( './../lib/ndarray.js' );
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// FUNCTIONS //
3234

35+
/**
36+
* Returns a random number.
37+
*
38+
* @private
39+
* @returns {number} random number
40+
*/
41+
function rand() {
42+
if ( bernoulli( 0.8 ) < 1 ) {
43+
return NaN;
44+
}
45+
return uniform( -10.0, 10.0 );
46+
}
47+
3348
/**
3449
* Creates a benchmark function.
3550
*
@@ -38,17 +53,7 @@ var nanmean = require( './../lib/ndarray.js' );
3853
* @returns {Function} benchmark function
3954
*/
4055
function createBenchmark( len ) {
41-
var x;
42-
var i;
43-
44-
x = [];
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for ( i = 0; i < len; i++ ) {
46-
if ( randu() < 0.2 ) {
47-
x.push( NaN );
48-
} else {
49-
x.push( ( randu()*20.0 ) - 10.0 );
50-
}
51-
}
56+
var x = filledarrayBy( len, 'generic', rand );
5257
return benchmark;
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5459
function benchmark( b ) {

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

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Original file line numberDiff line numberDiff line change
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2-
{{alias}}( N, x, stride )
2+
{{alias}}( N, x, strideX )
33
Computes the arithmetic mean of a strided array, ignoring `NaN` values.
44

5-
The `N` and `stride` parameters determine which elements in `x` are accessed
6-
at runtime.
5+
The `N` and stride parameters determine which elements in the strided array
6+
are accessed at runtime.
77

88
Indexing is relative to the first index. To introduce an offset, use a typed
99
array view.
@@ -20,8 +20,8 @@
2020
x: Array<number>|TypedArray
2121
Input array.
2222

23-
stride: integer
24-
Index increment.
23+
strideX: integer
24+
Stride length.
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2626
Returns
2727
-------
@@ -35,22 +35,19 @@
3535
> {{alias}}( x.length, x, 1 )
3636
~0.3333
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38-
// Using `N` and `stride` parameters:
38+
// Using `N` and stride parameters:
3939
> x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];
40-
> var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
41-
> var stride = 2;
42-
> {{alias}}( N, x, stride )
40+
> {{alias}}( 4, x, 2 )
4341
~0.3333
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4543
// Using view offsets:
46-
> var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN ] );
44+
> var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );
4745
> var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
48-
> N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
49-
> stride = 2;
50-
> {{alias}}( N, x1, stride )
46+
> {{alias}}( 4, x1, 2 )
5147
~-0.3333
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53-
{{alias}}.ndarray( N, x, stride, offset )
49+
50+
{{alias}}.ndarray( N, x, strideX, offsetX )
5451
Computes the arithmetic mean of a strided array, ignoring `NaN` values and
5552
using alternative indexing semantics.
5653

@@ -66,10 +63,10 @@
6663
x: Array<number>|TypedArray
6764
Input array.
6865

69-
stride: integer
70-
Index increment.
66+
strideX: integer
67+
Stride length.
7168

72-
offset: integer
69+
offsetX: integer
7370
Starting index.
7471

7572
Returns
@@ -85,9 +82,8 @@
8582
~0.3333
8683

8784
// Using offset parameter:
88-
> var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN ];
89-
> var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
90-
> {{alias}}.ndarray( N, x, 2, 1 )
85+
> x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ];
86+
> {{alias}}.ndarray( 4, x, 2, 1 )
9187
~-0.3333
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9389
See Also

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

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2121
/// <reference types="@stdlib/types"/>
2222

23-
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>;
2429

2530
/**
2631
* Interface describing `nanmean`.
@@ -31,7 +36,7 @@ interface Routine {
3136
*
3237
* @param N - number of indexed elements
3338
* @param x - input array
34-
* @param stride - stride length
39+
* @param strideX - stride length
3540
* @returns arithmetic mean
3641
*
3742
* @example
@@ -40,15 +45,15 @@ interface Routine {
4045
* var v = nanmean( 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;
4449

4550
/**
4651
* Computes the arithmetic mean of a strided array, ignoring `NaN` values and using alternative indexing semantics.
4752
*
4853
* @param N - number of indexed elements
4954
* @param x - input array
50-
* @param stride - stride length
51-
* @param offset - starting index
55+
* @param strideX - stride length
56+
* @param offsetX - starting index
5257
* @returns arithmetic mean
5358
*
5459
* @example
@@ -57,15 +62,15 @@ interface Routine {
5762
* var v = nanmean.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, offsetX: number ): number;
6166
}
6267

6368
/**
6469
* Computes the arithmetic mean of a strided array, ignoring `NaN` values.
6570
*
6671
* @param N - number of indexed elements
6772
* @param x - input array
68-
* @param stride - stride length
73+
* @param strideX - stride length
6974
* @returns arithmetic mean
7075
*
7176
* @example

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