feat: add accessor protocol support to stats/base/varianceyc

lib/node_modules/@stdlib/stats/base/varianceyc/README.md

@@ -98,14 +98,14 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
 var varianceyc = require( '@stdlib/stats/base/varianceyc' );
 ```
 
-#### varianceyc( N, correction, x, stride )
+#### varianceyc( N, correction, x, strideX )
 
-Computes the [variance][variance] of a strided array `x` using a one-pass algorithm proposed by Youngs and Cramer.
+Computes the [variance][variance] of a strided array using a one-pass algorithm proposed by Youngs and Cramer.
 
 ```javascript
 var x = [ 1.0, -2.0, 2.0 ];
 
-var v = varianceyc( x.length, 1, x, 1 );
+var v = varianceyc( x.length, 1.0, x, 1 );
 // returns ~4.3333
 ```
 
@@ -114,17 +114,14 @@ The function has the following parameters:
 -   **N**: number of indexed elements.
 -   **correction**: degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [variance][variance] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
 -   **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
--   **stride**: index increment for `x`.
+-   **strideX**: stride length for `x`.
 
-The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
+The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
-
 var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
-var N = floor( x.length / 2 );
 
-var v = varianceyc( N, 1, x, 2 );
+var v = varianceyc( 4, 1.0, x, 2 );
 // returns 6.25
 ```
 
@@ -134,41 +131,35 @@ Note that indexing is relative to the first index. To introduce an offset, use [
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var N = floor( x0.length / 2 );
-
-var v = varianceyc( N, 1, x1, 2 );
+var v = varianceyc( 4, 1.0, x1, 2 );
 // returns 6.25
 ```
 
-#### varianceyc.ndarray( N, correction, x, stride, offset )
+#### varianceyc.ndarray( N, correction, x, strideX, offsetX )
 
 Computes the [variance][variance] of a strided array using a one-pass algorithm proposed by Youngs and Cramer and alternative indexing semantics.
 
 ```javascript
 var x = [ 1.0, -2.0, 2.0 ];
 
-var v = varianceyc.ndarray( x.length, 1, x, 1, 0 );
+var v = varianceyc.ndarray( x.length, 1.0, x, 1, 0 );
 // returns ~4.33333
 ```
 
 The function has the following additional parameters:
 
--   **offset**: starting index for `x`.
+-   **offsetX**: starting index for `x`.
 
-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 [variance][variance] for every other value in `x` starting from the second value
+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 [variance][variance] for every other element in `x` starting from the second element
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
-
 var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
-var N = floor( x.length / 2 );
 
-var v = varianceyc.ndarray( N, 1, x, 2, 1 );
+var v = varianceyc.ndarray( 4, 1.0, x, 2, 1 );
 // returns 6.25
 ```
 
@@ -183,6 +174,7 @@ var v = varianceyc.ndarray( N, 1, x, 2, 1 );
 -   If `N <= 0`, both functions return `NaN`.
 -   If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment), both functions return `NaN`.
 -   Depending on the environment, the typed versions ([`dvarianceyc`][@stdlib/stats/strided/dvarianceyc], [`svarianceyc`][@stdlib/stats/strided/svarianceyc], etc.) are likely to be significantly more performant.
+-   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]).
 
 </section>
 
@@ -195,21 +187,15 @@ var v = varianceyc.ndarray( N, 1, x, 2, 1 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var randu = require( '@stdlib/random/base/randu' );
-var round = require( '@stdlib/math/base/special/round' );
-var Float64Array = require( '@stdlib/array/float64' );
+var uniform = require( '@stdlib/random/array/uniform' );
 var varianceyc = require( '@stdlib/stats/base/varianceyc' );
 
-var x;
-var i;
-
-x = new Float64Array( 10 );
-for ( i = 0; i < x.length; i++ ) {
-    x[ i ] = round( (randu()*100.0) - 50.0 );
-}
+var x = uniform( 10, -50.0, 50.0, {
+    'dtype': 'generic'
+});
 console.log( x );
 
-var v = varianceyc( x.length, 1, x, 1 );
+var v = varianceyc( x.length, 1.0, x, 1 );
 console.log( v );
 ```
 
@@ -256,6 +242,8 @@ console.log( v );
 
 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
 
+[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
+
 [@stdlib/stats/strided/svarianceyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/svarianceyc
 
 [@youngs:1971a]: https://doi.org/10.1080/00401706.1971.10488826

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

@@ -21,11 +21,18 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var uniform = require( '@stdlib/random/array/uniform' );
 var pow = require( '@stdlib/math/base/special/pow' );
 var pkg = require( './../package.json' ).name;
-var varianceyc = require( './../lib/varianceyc.js' );
+var varianceyc = require( './../lib' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'generic'
+};
 
 
 // FUNCTIONS //
@@ -38,13 +45,7 @@ var varianceyc = require( './../lib/varianceyc.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = [];
-	for ( i = 0; i < len; i++ ) {
-		x.push( ( randu()*20.0 ) - 10.0 );
-	}
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {
@@ -53,7 +54,7 @@ function createBenchmark( len ) {
 
 		b.tic();
 		for ( i = 0; i < b.iterations; i++ ) {
-			v = varianceyc( x.length, 1, x, 1 );
+			v = varianceyc( x.length, 1.0, x, 1 );
 			if ( isnan( v ) ) {
 				b.fail( 'should not return NaN' );
 			}

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

@@ -21,13 +21,20 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/array/uniform' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
 var pkg = require( './../package.json' ).name;
 var varianceyc = require( './../lib/ndarray.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'generic'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -38,13 +45,7 @@ var varianceyc = require( './../lib/ndarray.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = [];
-	for ( i = 0; i < len; i++ ) {
-		x.push( ( randu()*20.0 ) - 10.0 );
-	}
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {
@@ -53,7 +54,7 @@ function createBenchmark( len ) {
 
 		b.tic();
 		for ( i = 0; i < b.iterations; i++ ) {
-			v = varianceyc( x.length, 1, x, 1, 0 );
+			v = varianceyc( x.length, 1.0, x, 1, 0 );
 			if ( isnan( v ) ) {
 				b.fail( 'should not return NaN' );
 			}

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

@@ -1,10 +1,10 @@
 
-{{alias}}( N, correction, x, stride )
+{{alias}}( N, correction, x, strideX )
     Computes the variance of a strided array using a one-pass algorithm proposed
     by Youngs and Cramer.
 
-    The `N` and `stride` parameters determine which elements in `x` are accessed
-    at runtime.
+    The `N` and stride parameters determine which elements in the strided array
+    are accessed at runtime.
 
     Indexing is relative to the first index. To introduce an offset, use a typed
     array view.
@@ -31,8 +31,8 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        Stride length for `x`.
 
     Returns
     -------
@@ -43,30 +43,27 @@
     --------
     // Standard Usage:
     > var x = [ 1.0, -2.0, 2.0 ];
-    > {{alias}}( x.length, 1, x, 1 )
+    > {{alias}}( x.length, 1.0, x, 1 )
     ~4.3333
 
-    // Using `N` and `stride` parameters:
+    // Using `N` and stride parameters:
     > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > var stride = 2;
-    > {{alias}}( N, 1, x, stride )
+    > {{alias}}( 3, 1.0, x, 2 )
     ~4.3333
 
     // Using view offsets:
     > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );
     > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
-    > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
-    > stride = 2;
-    > {{alias}}( N, 1, x1, stride )
+    > {{alias}}( 3, 1.0, x1, 2 )
     ~4.3333
 
-{{alias}}.ndarray( N, correction, x, stride, offset )
+
+{{alias}}.ndarray( N, correction, x, strideX, offsetX )
     Computes the variance of a strided array using a one-pass algorithm proposed
     by Youngs and Cramer and alternative indexing semantics.
 
     While typed array views mandate a view offset based on the underlying
-    buffer, the `offset` parameter supports indexing semantics based on a
+    buffer, the offset parameter supports indexing semantics based on a
     starting index.
 
     Parameters
@@ -89,11 +86,11 @@
     x: Array<number>|TypedArray
         Input array.
 
-    stride: integer
-        Index increment.
+    strideX: integer
+        Stride length for `x`.
 
-    offset: integer
-        Starting index.
+    offsetX: integer
+        Starting index for `x`.
 
     Returns
     -------
@@ -104,13 +101,12 @@
     --------
     // Standard Usage:
     > var x = [ 1.0, -2.0, 2.0 ];
-    > {{alias}}.ndarray( x.length, 1, x, 1, 0 )
+    > {{alias}}.ndarray( x.length, 1.0, x, 1, 0 )
     ~4.3333
 
     // Using offset parameter:
     > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > {{alias}}.ndarray( N, 1, x, 2, 1 )
+    > {{alias}}.ndarray( 3, 1.0, x, 2, 1 )
     ~4.3333
 
     See Also

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

@@ -20,7 +20,12 @@
 
 /// <reference types="@stdlib/types"/>
 
-import { NumericArray } from '@stdlib/types/array';
+import { NumericArray, Collection, AccessorArrayLike } from '@stdlib/types/array';
+
+/**
+* Input array.
+*/
+type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 
 /**
 * Interface describing `varianceyc`.
@@ -32,34 +37,34 @@ interface Routine {
 	* @param N - number of indexed elements
 	* @param correction - degrees of freedom adjustment
 	* @param x - input array
-	* @param stride - stride length
+	* @param strideX - stride length for `x`
 	* @returns variance
 	*
 	* @example
 	* var x = [ 1.0, -2.0, 2.0 ];
 	*
-	* var v = varianceyc( x.length, 1, x, 1 );
+	* var v = varianceyc( x.length, 1.0, x, 1 );
 	* // returns ~4.3333
 	*/
-	( N: number, correction: number, x: NumericArray, stride: number ): number;
+	( N: number, correction: number, x: InputArray, strideX: number ): number;
 
 	/**
 	* Computes the variance of a strided array using a one-pass algorithm proposed by Youngs and Cramer and alternative indexing semantics.
 	*
 	* @param N - number of indexed elements
 	* @param correction - degrees of freedom adjustment
 	* @param x - input array
-	* @param stride - stride length
-	* @param offset - starting index
+	* @param strideX - stride length for `x`
+	* @param offsetX - starting index for `x`
 	* @returns variance
 	*
 	* @example
 	* var x = [ 1.0, -2.0, 2.0 ];
 	*
-	* var v = varianceyc.ndarray( x.length, 1, x, 1, 0 );
+	* var v = varianceyc.ndarray( x.length, 1.0, x, 1, 0 );
 	* // returns ~4.3333
 	*/
-	ndarray( N: number, correction: number, x: NumericArray, stride: number, offset: number ): number;
+	ndarray( N: number, correction: number, x: InputArray, strideX: number, offsetX: number ): number;
 }
 
 /**
@@ -68,19 +73,19 @@ interface Routine {
 * @param N - number of indexed elements
 * @param correction - degrees of freedom adjustment
 * @param x - input array
-* @param stride - stride length
+* @param strideX - stride length for `x`
 * @returns variance
 *
 * @example
 * var x = [ 1.0, -2.0, 2.0 ];
 *
-* var v = varianceyc( x.length, 1, x, 1 );
+* var v = varianceyc( x.length, 1.0, x, 1 );
 * // returns ~4.3333
 *
 * @example
 * var x = [ 1.0, -2.0, 2.0 ];
 *
-* var v = varianceyc.ndarray( x.length, 1, x, 1, 0 );
+* var v = varianceyc.ndarray( x.length, 1.0, x, 1, 0 );
 * // returns ~4.3333
 */
 declare var varianceyc: Routine;