feat: ndarray-dnanminabs

Author: Neerajpathak07

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

@@ -36,36 +36,33 @@ limitations under the License.
 var dnanminabs = require( '@stdlib/stats/base/dnanminabs' );
 ```
 
-#### dnanminabs( N, x, stride )
+#### dnanminabs( N, x, strideX )
 
 Computes the minimum absolute value of a double-precision floating-point strided array `x`, ignoring `NaN` values.
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
-var N = x.length;
 
-var v = dnanminabs( N, x, 1 );
+var v = dnanminabs( x.length, x, 1 );
 // returns 1.0
 ```
 
 The function has the following parameters:
 
 -   **N**: number of indexed elements.
 -   **x**: input [`Float64Array`][@stdlib/array/float64].
--   **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 minimum absolute value 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 minimum absolute value of every other element in `x`,
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 1.0, 2.0, -7.0, -2.0, 4.0, 3.0, NaN, NaN ] );
-var N = floor( x.length / 2 );
 
-var v = dnanminabs( N, x, 2 );
+var v = dnanminabs( 5, x, 2 );
 // returns 1.0
 ```
 
@@ -75,45 +72,39 @@ 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, 3.0, 4.0, NaN, NaN ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var N = floor( x0.length / 2 );
-
-var v = dnanminabs( N, x1, 2 );
+var v = dnanminabs( 5, x1, 2 );
 // returns 1.0
 ```
 
-#### dnanminabs.ndarray( N, x, stride, offset )
+#### dnanminabs.ndarray( N, x, strideX, offsetX )
 
 Computes the minimum absolute value of a double-precision floating-point strided array, ignoring `NaN` values and using alternative indexing semantics.
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
-var N = x.length;
 
-var v = dnanminabs.ndarray( N, x, 1, 0 );
+var v = dnanminabs.ndarray( x.length, x, 1, 0 );
 // returns 1.0
 ```
 
 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 minimum absolute 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 parameter supports indexing semantics based on a starting index. For example, to calculate the minimum absolute value for every other element in `x` starting from the second element
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, NaN, NaN ] );
-var N = floor( x.length / 2 );
 
-var v = dnanminabs.ndarray( N, x, 2, 1 );
+var v = dnanminabs.ndarray( 5, x, 2, 1 );
 // returns 1.0
 ```
 
@@ -164,6 +155,107 @@ console.log( v );
 
 <!-- /.examples -->
 
+<!-- C usage documentation. -->
+
+<section class="usage">
+
+### Usage
+
+```c
+#include "stdlib/stats/base/dnanminabs.h"
+```
+
+#### stdlib_strided_dnanminabs( N, \*X, strideX )
+
+Computes the minimum absolute value of a double-precision floating-point strided array, ignoring `NaN` values.
+
+```c
+const double x[] = { 1.0, -2.0, -3.0, 4.0, -5.0, -6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };
+
+double v = stdlib_strided_dnanminabs( 5, x, 2 );
+// returns 1.0
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **X**: `[in] double*` input array.
+-   **strideX**: `[in] CBLAS_INT` stride length for `X`.
+
+```c
+double stdlib_strided_dnanminabs( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );
+```
+
+#### stdlib_strided_dnanminabs_ndarray( N, \*X, strideX, offsetX )
+
+Computes the minimum absolute value of a double-precision floating-point strided array, ignoring `NaN` values and using alternative indexing semantics.
+
+```c
+const double x[] = { 1.0, -2.0, -3.0, 4.0, -5.0, -6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };
+
+double v = stdlib_strided_dnanminabs_ndarray( 5, x, 2, 0 );
+// returns 1.0
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **X**: `[in] double*` input array.
+-   **strideX**: `[in] CBLAS_INT` stride length for `X`.
+-   **offsetX**: `[in] CBLAS_INT` starting index for `X`.
+
+```c
+double stdlib_strided_dnanminabs_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
+```
+
+</section>
+
+<!-- /.usage -->
+
+<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="notes">
+
+</section>
+
+<!-- /.notes -->
+
+<!-- C API usage examples. -->
+
+<section class="examples">
+
+### Examples
+
+```c
+#include "stdlib/stats/base/dnanminabs.h"
+#include <stdio.h>
+
+int main( void ) {
+    // Create a strided array:
+    const double x[] = { 1.0, -2.0, -3.0, 4.0, -5.0, -6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };
+
+    // Specify the number of elements:
+    const int N = 5;
+
+    // Specify the stride length:
+    const int strideX = 2;
+
+    // Compute the minimum absolute value:
+    double v = stdlib_strided_dnanminabs( N, x, strideX );
+
+    // Print the result:
+    printf( "minabs: %lf\n", v );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
 <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
 
 <section class="related">

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

@@ -21,16 +21,29 @@
 // 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 Float64Array = require( '@stdlib/array/float64' );
 var pkg = require( './../package.json' ).name;
 var dnanminabs = require( './../lib/dnanminabs.js' );
 
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @returns {number} Random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.2 ) ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -39,17 +52,7 @@ var dnanminabs = require( './../lib/dnanminabs.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		if ( randu() < 0.2 ) {
-			x[ i ] = NaN;
-		} else {
-			x[ i ] = ( randu()*20.0 ) - 10.0;
-		}
-	}
+	var x = filledarrayBy( len, 'float64', rand );
 	return benchmark;
 
 	function benchmark( b ) {

lib/node_modules/@stdlib/stats/base/dnanminabs/benchmark/benchmark.native.js

@@ -22,10 +22,11 @@
 
 var resolve = require( 'path' ).resolve;
 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 Float64Array = require( '@stdlib/array/float64' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var pkg = require( './../package.json' ).name;
 
@@ -40,6 +41,18 @@ var opts = {
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @returns {number} Random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.2 ) ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -48,17 +61,7 @@ var opts = {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		if ( randu() < 0.2 ) {
-			x[ i ] = NaN;
-		} else {
-			x[ i ] = ( randu()*20.0 ) - 10.0;
-		}
-	}
+	var x = filledarrayBy( len, 'float64', rand );
 	return benchmark;
 
 	function benchmark( b ) {

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

@@ -21,16 +21,29 @@
 // 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 Float64Array = require( '@stdlib/array/float64' );
 var pkg = require( './../package.json' ).name;
 var dnanminabs = require( './../lib/ndarray.js' );
 
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @returns {number} Random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.2 ) ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -39,17 +52,7 @@ var dnanminabs = require( './../lib/ndarray.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		if ( randu() < 0.2 ) {
-			x[ i ] = NaN;
-		} else {
-			x[ i ] = ( randu()*20.0 ) - 10.0;
-		}
-	}
+	var x = filledarrayBy( len, 'float64', rand );
 	return benchmark;
 
 	function benchmark( b ) {