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feat: add C ndarray interface and refactor implementation for `stats/…
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…base/dstdevyc`

PR-URL: #4628
Co-authored-by: Athan Reines <[email protected]>
Reviewed-by: Athan Reines <[email protected]>
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166 changes: 135 additions & 31 deletions lib/node_modules/@stdlib/stats/base/dstdevyc/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -98,17 +98,16 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
var dstdevyc = require( '@stdlib/stats/base/dstdevyc' );
```

#### dstdevyc( N, correction, x, stride )
#### dstdevyc( N, correction, x, strideX )

Computes the [standard deviation][standard-deviation] of a double-precision floating-point strided array `x` using a one-pass algorithm proposed by Youngs and Cramer.

```javascript
var Float64Array = require( '@stdlib/array/float64' );

var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;

var v = dstdevyc( N, 1, x, 1 );
var v = dstdevyc( x.length, 1.0, x, 1 );
// returns ~2.0817
```

Expand All @@ -117,18 +116,16 @@ 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 [standard deviation][standard-deviation] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [standard deviation][standard-deviation] 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 corrected sample [standard deviation][standard-deviation], 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 [`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 [standard deviation][standard-deviation] 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 [standard deviation][standard-deviation] 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, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var N = floor( x.length / 2 );

var v = dstdevyc( N, 1, x, 2 );
var v = dstdevyc( 4, 1.0, x, 2 );
// returns 2.5
```

Expand All @@ -138,45 +135,39 @@ 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, -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 = dstdevyc( N, 1, x1, 2 );
var v = dstdevyc( 4, 1.0, x1, 2 );
// returns 2.5
```

#### dstdevyc.ndarray( N, correction, x, stride, offset )
#### dstdevyc.ndarray( N, correction, x, strideX, offsetX )

Computes the [standard deviation][standard-deviation] of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer and alternative indexing semantics.

```javascript
var Float64Array = require( '@stdlib/array/float64' );

var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;

var v = dstdevyc.ndarray( N, 1, x, 1, 0 );
var v = dstdevyc.ndarray( x.length, 1.0, x, 1, 0 );
// returns ~2.0817
```

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 [standard deviation][standard-deviation] 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 [standard deviation][standard-deviation] 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, -2.0, 2.0, 3.0, 4.0 ] );
var N = floor( x.length / 2 );

var v = dstdevyc.ndarray( N, 1, x, 2, 1 );
var v = dstdevyc.ndarray( 4, 1.0, x, 2, 1 );
// returns 2.5
```

Expand All @@ -202,28 +193,141 @@ var v = dstdevyc.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 discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
var dstdevyc = require( '@stdlib/stats/base/dstdevyc' );

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 = discreteUniform( 10, -50, 50, {
'dtype': 'float64'
});
console.log( x );

var v = dstdevyc( x.length, 1, x, 1 );
var v = dstdevyc( x.length, 1.0, x, 1 );
console.log( v );
```

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

### Usage

```c
#include "stdlib/stats/base/dstdevyc.h"
```

#### stdlib_strided_dstdevyc( N, correction, \*X, strideX )

Computes the [standard deviation][standard-deviation] of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.

```c
const double x[] = { 1.0, -2.0, 2.0 };

double v = stdlib_strided_dstdevyc( 3, 1.0, x, 1 );
// returns ~2.0817
```
The function accepts the following arguments:
- **N**: `[in] CBLAS_INT` number of indexed elements.
- **correction**: `[in] double` 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 [standard deviation][standard-deviation] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [standard deviation][standard-deviation] 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 corrected sample [standard deviation][standard-deviation], 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**: `[in] double*` input array.
- **strideX**: `[in] CBLAS_INT` stride length for `X`.
```c
double stdlib_strided_dstdevyc( const CBLAS_INT N, const double correction, const double *X, const CBLAS_INT strideX );
```

#### stdlib_strided_dstdevyc_ndarray( N, correction, \*X, strideX, offsetX )

Computes the [standard deviation][standard-deviation] of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer and alternative indexing semantics.

```c
const double x[] = { 1.0, -2.0, 2.0 };

double v = stdlib_strided_dstdevyc_ndarray( 3, 1.0, x, 1, 0 );
// returns ~2.0817
```
The function accepts the following arguments:
- **N**: `[in] CBLAS_INT` number of indexed elements.
- **correction**: `[in] double` 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 [standard deviation][standard-deviation] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [standard deviation][standard-deviation] 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 corrected sample [standard deviation][standard-deviation], 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**: `[in] double*` input array.
- **strideX**: `[in] CBLAS_INT` stride length for `X`.
- **offsetX**: `[in] CBLAS_INT` starting index for `X`.
```c
double stdlib_strided_dstdevyc_ndarray( const CBLAS_INT N, const double correction, 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/dstdevyc.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 };

// Specify the number of elements:
const int N = 4;

// Specify the stride length:
const int strideX = 2;

// Compute the variance:
double v = stdlib_strided_dstdevyc( N, 1.0, x, strideX );

// Print the result:
printf( "sample standard deviation: %lf\n", v );
}
```
</section>
<!-- /.examples -->
</section>
<!-- /.c -->
* * *
<section class="references">
Expand Down
20 changes: 10 additions & 10 deletions lib/node_modules/@stdlib/stats/base/dstdevyc/benchmark/benchmark.js
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Expand Up @@ -21,14 +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 Float64Array = require( '@stdlib/array/float64' );
var pkg = require( './../package.json' ).name;
var dstdevyc = require( './../lib/dstdevyc.js' );


// VARIABLES //

var options = {
'dtype': 'float64'
};


// FUNCTIONS //

/**
Expand All @@ -39,13 +45,7 @@ var dstdevyc = require( './../lib/dstdevyc.js' );
* @returns {Function} benchmark function
*/
function createBenchmark( len ) {
var x;
var i;

x = new Float64Array( len );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = ( randu()*20.0 ) - 10.0;
}
var x = uniform( len, -10.0, 10.0, options );
return benchmark;

function benchmark( b ) {
Expand All @@ -54,7 +54,7 @@ function createBenchmark( len ) {

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
v = dstdevyc( x.length, 1, x, 1 );
v = dstdevyc( x.length, 1.0, x, 1 );
if ( isnan( v ) ) {
b.fail( 'should not return NaN' );
}
Expand Down
16 changes: 6 additions & 10 deletions lib/node_modules/@stdlib/stats/base/dstdevyc/benchmark/benchmark.native.js
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Expand Up @@ -22,10 +22,9 @@

var resolve = require( 'path' ).resolve;
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 Float64Array = require( '@stdlib/array/float64' );
var tryRequire = require( '@stdlib/utils/try-require' );
var pkg = require( './../package.json' ).name;

Expand All @@ -36,6 +35,9 @@ var dstdevyc = tryRequire( resolve( __dirname, './../lib/dstdevyc.native.js' ) )
var opts = {
'skip': ( dstdevyc instanceof Error )
};
var options = {
'dtype': 'float64'
};


// FUNCTIONS //
Expand All @@ -48,13 +50,7 @@ var opts = {
* @returns {Function} benchmark function
*/
function createBenchmark( len ) {
var x;
var i;

x = new Float64Array( len );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = ( randu()*20.0 ) - 10.0;
}
var x = uniform( len, -10.0, 10.0, options );
return benchmark;

function benchmark( b ) {
Expand All @@ -63,7 +59,7 @@ function createBenchmark( len ) {

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
v = dstdevyc( x.length, 1, x, 1 );
v = dstdevyc( x.length, 1.0, x, 1 );
if ( isnan( v ) ) {
b.fail( 'should not return NaN' );
}
Expand Down
20 changes: 10 additions & 10 deletions lib/node_modules/@stdlib/stats/base/dstdevyc/benchmark/benchmark.ndarray.js
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Original file line number Diff line number Diff line change
Expand Up @@ -21,14 +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 Float64Array = require( '@stdlib/array/float64' );
var pkg = require( './../package.json' ).name;
var dstdevyc = require( './../lib/ndarray.js' );


// VARIABLES //

var options = {
'dtype': 'float64'
};


// FUNCTIONS //

/**
Expand All @@ -39,13 +45,7 @@ var dstdevyc = 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++ ) {
x[ i ] = ( randu()*20.0 ) - 10.0;
}
var x = uniform( len, -10.0, 10.0, options );
return benchmark;

function benchmark( b ) {
Expand All @@ -54,7 +54,7 @@ function createBenchmark( len ) {

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
v = dstdevyc( x.length, 1, x, 1, 0 );
v = dstdevyc( x.length, 1.0, x, 1, 0 );
if ( isnan( v ) ) {
b.fail( 'should not return NaN' );
}
Expand Down
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Coverage Report

Package Statements Branches Functions Lines
stats/base/dstdevyc $\color{green}358/358$
$\color{green}+100.00\%$ 		$\color{green}13/13$
$\color{green}+100.00\%$ 		$\color{green}4/4$
$\color{green}+100.00\%$ 		$\color{green}358/358$
$\color{green}+100.00\%$

The above coverage report was generated for the changes in this push.

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