feat: refactor and add protocol support to stats/base/nanmskmin

PR-URL: #6103
Closes: #5663
Co-authored-by: Athan Reines <[email protected]>
Reviewed-by: Athan Reines <[email protected]> 
Co-authored-by: Gururaj Gurram <[email protected]>
Co-authored-by: stdlib-bot <[email protected]>
Reviewed-by: Gururaj Gurram <[email protected]>

Author: 4 people

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

@@ -38,7 +38,7 @@ var nanmskmin = require( '@stdlib/stats/base/nanmskmin' );
 
 #### nanmskmin( N, x, strideX, mask, strideMask )
 
-Computes the minimum value of a strided array `x` according to a `mask`, ignoring `NaN` values.
+Computes the minimum value of a strided array according to a `mask`, ignoring `NaN` values.
 
 ```javascript
 var x = [ 1.0, -2.0, -4.0, 2.0, NaN ];
@@ -52,41 +52,35 @@ The function has the following parameters:
 
 -   **N**: number of indexed elements.
 -   **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
--   **strideX**: index increment for `x`.
+-   **strideX**: stride length for `x`.
 -   **mask**: mask [`Array`][mdn-array] or [`typed array`][mdn-typed-array]. If a `mask` array element is `0`, the corresponding element in `x` is considered valid and **included** in computation. If a `mask` array element is `1`, the corresponding element in `x` is considered invalid/missing and **excluded** from computation.
--   **strideMask**: index increment for `mask`.
+-   **strideMask**: stride length for `mask`.
 
-The `N` and `stride` parameters determine which elements are accessed at runtime. For example, to compute the minimum value of every other element in `x`,
+The `N` and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to compute the minimum value of every other element in `x`,
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
+var x = [ 1.0, 2.0, -7.0, -2.0, 4.0, 3.0, -5.0, -6.0, NaN, NaN ];
+var mask = [ 0, 0, 0, 0, 0, 0, 1, 1, 0, 0 ];
 
-var x = [ 1.0, 2.0, -7.0, -2.0, 4.0, 3.0, -5.0, -6.0 ];
-var mask = [ 0, 0, 0, 0, 0, 0, 1, 1 ];
-var N = floor( x.length / 2 );
-
-var v = nanmskmin( N, x, 2, mask, 2 );
+var v = nanmskmin( 5, x, 2, mask, 2 );
 // returns -7.0
 ```
 
 Note that indexing is relative to the first index. To introduce offsets, use [`typed array`][mdn-typed-array] views.
 
-<!-- eslint-disable stdlib/capitalized-comments -->
+<!-- eslint-disable stdlib/capitalized-comments, max-len -->
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var Uint8Array = require( '@stdlib/array/uint8' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
-var x0 = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, 5.0, 6.0 ] );
+var x0 = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, 5.0, 6.0, NaN, NaN ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var mask0 = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 1, 1 ] );
+var mask0 = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 1, 1, 0, 0 ] );
 var mask1 = new Uint8Array( mask0.buffer, mask0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var N = floor( x0.length / 2 );
-
-var v = nanmskmin( N, x1, 2, mask1, 2 );
+var v = nanmskmin( 5, x1, 2, mask1, 2 );
 // returns -2.0
 ```
 
@@ -107,16 +101,13 @@ The function has the following additional parameters:
 -   **offsetX**: starting index for `x`.
 -   **offsetMask**: starting index for `mask`.
 
-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 minimum value 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 parameters support indexing semantics based on starting indices. For example, to calculate the minimum value 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, 3.0, 4.0, -5.0, -6.0 ];
-var mask = [ 0, 0, 0, 0, 0, 0, 1, 1 ];
-var N = floor( x.length / 2 );
+var x = [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, -5.0, -6.0, NaN, NaN ];
+var mask = [ 0, 0, 0, 0, 0, 0, 1, 1, 0, 0 ];
 
-var v = nanmskmin.ndarray( N, x, 2, 1, mask, 2, 1 );
+var v = nanmskmin.ndarray( 5, x, 2, 1, mask, 2, 1 );
 // returns -2.0
 ```
 
@@ -130,6 +121,7 @@ var v = nanmskmin.ndarray( N, x, 2, 1, mask, 2, 1 );
 
 -   If `N <= 0`, both functions return `NaN`.
 -   Depending on the environment, the typed versions ([`dnanmskmin`][@stdlib/stats/strided/dnanmskmin], [`snanmskmin`][@stdlib/stats/strided/snanmskmin], 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>
 
@@ -142,31 +134,22 @@ var v = nanmskmin.ndarray( N, x, 2, 1, mask, 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 Uint8Array = require( '@stdlib/array/uint8' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
 var nanmskmin = require( '@stdlib/stats/base/nanmskmin' );
 
-var mask;
-var x;
-var i;
-
-x = new Float64Array( 10 );
-mask = new Uint8Array( x.length );
-for ( i = 0; i < x.length; i++ ) {
-    if ( randu() < 0.2 ) {
-        mask[ i ] = 1;
-    } else {
-        mask[ i ] = 0;
-    }
-    if ( randu() < 0.1 ) {
-        x[ i ] = NaN;
-    } else {
-        x[ i ] = round( (randu()*100.0) - 50.0 );
+function rand() {
+    if ( bernoulli( 0.8 ) < 1 ) {
+        return NaN;
     }
+    return uniform( -50.0, 50.0 );
 }
+
+var x = filledarrayBy( 10, 'float64', rand );
 console.log( x );
+
+var mask = filledarrayBy( x.length, 'uint8', bernoulli.factory( 0.2 ) );
 console.log( mask );
 
 var v = nanmskmin( x.length, x, 1, mask, 1 );
@@ -203,6 +186,8 @@ 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
+
 <!-- <related-links> -->
 
 [@stdlib/stats/strided/dnanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanmskmin

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

@@ -21,15 +21,30 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
 var pkg = require( './../package.json' ).name;
-var nanmskmin = require( './../lib/nanmskmin.js' );
+var nanmskmin = require( './../lib/main.js' );
 
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @private
+* @returns {number} random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.8 ) < 1 ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -40,18 +55,9 @@ var nanmskmin = require( './../lib/nanmskmin.js' );
 function createBenchmark( len ) {
 	var mask;
 	var x;
-	var i;
 
-	x = [];
-	mask = [];
-	for ( i = 0; i < len; i++ ) {
-		if ( randu() < 0.2 ) {
-			mask.push( 1 );
-		} else {
-			mask.push( 0 );
-		}
-		x.push( ( randu()*20.0 ) - 10.0 );
-	}
+	x = filledarrayBy( len, 'generic', rand );
+	mask = filledarrayBy( len, 'uint8', bernoulli.factory( 0.2 ) );
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -21,7 +21,9 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
 var pkg = require( './../package.json' ).name;
@@ -30,6 +32,19 @@ var nanmskmin = require( './../lib/ndarray.js' );
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @private
+* @returns {number} random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.8 ) < 1 ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -40,18 +55,9 @@ var nanmskmin = require( './../lib/ndarray.js' );
 function createBenchmark( len ) {
 	var mask;
 	var x;
-	var i;
 
-	x = [];
-	mask = [];
-	for ( i = 0; i < len; i++ ) {
-		if ( randu() < 0.2 ) {
-			mask.push( 1 );
-		} else {
-			mask.push( 0 );
-		}
-		x.push( ( randu()*20.0 ) - 10.0 );
-	}
+	x = filledarrayBy( len, 'generic', rand );
+	mask = filledarrayBy( len, 'uint8', bernoulli.factory( 0.2 ) );
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -3,8 +3,8 @@
     Computes the minimum value of a strided array according to a mask and
     ignoring `NaN` values.
 
-    The `N` and `stride` parameters determine which elements 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 offsets, use a typed
     array views.
@@ -26,13 +26,13 @@
         Input array.
 
     strideX: integer
-        Index increment for `x`.
+        Stride length for `x`.
 
     mask: Array<number>|TypedArray
         Mask array.
 
     strideMask: integer
-        Index increment for `mask`.
+        Stride length for `mask`.
 
     Returns
     -------
@@ -47,22 +47,21 @@
     > {{alias}}( x.length, x, 1, mask, 1 )
     -2.0
 
-    // Using `N` and `stride` parameters:
-    > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, -4.0 ];
+    // Using `N` and stride parameters:
+    > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN ];
     > mask = [ 0, 0, 0, 0, 0, 0, 1 ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > {{alias}}( N, x, 2, mask, 2 )
+    > {{alias}}( 4, x, 2, mask, 2 )
     -2.0
 
     // Using view offsets:
-    > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, -4.0 ] );
+    > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] );
     > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
-    > var mask0 = new {{alias:@stdlib/array/uint8}}( [ 0, 0, 0, 0, 0, 0, 1 ] );
+    > var mask0 = new {{alias:@stdlib/array/uint8}}( [ 0, 0, 0, 0, 0, 0, 1, 1 ] );
     > var mask1 = new {{alias:@stdlib/array/uint8}}( mask0.buffer, mask0.BYTES_PER_ELEMENT*1 );
-    > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
-    > {{alias}}( N, x1, 2, mask1, 2 )
+    > {{alias}}( 4, x1, 2, mask1, 2 )
     -2.0
 
+
 {{alias}}.ndarray( N, x, strideX, offsetX, mask, strideMask, offsetMask )
     Computes the minimum value of a strided array according to a mask, ignoring
     `NaN` values and using alternative indexing semantics.
@@ -80,7 +79,7 @@
         Input array.
 
     strideX: integer
-        Index increment for `x`.
+        Stride length for `x`.
 
     offsetX: integer
         Starting index for `x`.
@@ -89,7 +88,7 @@
         Mask array.
 
     strideMask: integer
-        Index increment for `mask`.
+        Stride length for `mask`.
 
     offsetMask: integer
         Starting index for `mask`.
@@ -108,10 +107,9 @@
     -2.0
 
     // Using offset parameter:
-    > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, -4.0 ];
-    > mask = [ 0, 0, 0, 0, 0, 0, 1 ];
-    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
-    > {{alias}}.ndarray( N, x, 2, 1, mask, 2, 1 )
+    > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, -4.0, NaN, NaN ];
+    > mask = [ 0, 0, 0, 0, 0, 0, 1, 0, 0 ];
+    > {{alias}}.ndarray( 4, x, 2, 1, mask, 2, 1 )
     -2.0
 
     See Also