feat: add C ndarray interface and refactor implementation for stats/base/dnanmin

[aayush0325]

Resolves None

Description

What is the purpose of this pull request?

This pull request:

• adds C ndarray intreface for stats/base/dnamin
• refactors JavaScript implementation
• refactors tests, docs, benchmarks and examples.

Related Issues

Does this pull request have any related issues?

This pull request:

• resolves None

Questions

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No.

Other

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Please ensure the following tasks are completed before submitting this pull request.

• Read, understood, and followed the contributing guidelines.

---

@stdlib-js/reviewers

[stdlib-bot]

Coverage Report

Package	Statements	Branches	Functions	Lines
stats/base/dnanmin	$\color{green}381/381$ $\color{green}+100.00\%$	$\color{green}31/31$ $\color{green}+100.00\%$	$\color{green}4/4$ $\color{green}+100.00\%$	$\color{green}381/381$ $\color{green}+100.00\%$

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

[aayush0325]

Same question here as #4206, What to use for generating random arrays with NaN values, left the JS examples and benchmarks untouched for now

[stdlib-bot]

PR Commit Message

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

PR-URL: https://github.com/stdlib-js/stdlib/pull/4235

Co-authored-by: aayush0325 <[email protected]>
Reviewed-by: Philipp Burckhardt <[email protected]>

Please review the above commit message and make any necessary adjustments.

[Planeshifter]

@aayush0325

We have done things like this:

var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
var bernoulli = require( '@stdlib/random/base/bernoulli' );
var filledarrayBy = require( '@stdlib/array/filled-by' );

function rand() {
        if ( bernoulli( 0.2 ) ) {
                return NaN;
        }
        return discreteUniform( 0, 100 );
}

var x = filledarrayBy( 10, 'float64', rand );

[aayush0325]

@aayush0325

We have done things like this:

var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
var bernoulli = require( '@stdlib/random/base/bernoulli' );
var filledarrayBy = require( '@stdlib/array/filled-by' );

function rand() {
        if ( bernoulli( 0.2 ) ) {
                return NaN;
        }
        return discreteUniform( 0, 100 );
}

var x = filledarrayBy( 10, 'float64', rand );

I'll push the required changes here and everywhere else, Thanks @Planeshifter!

[aayush0325]

I've pushed a fix, let me know if there's anything to be changed here @Planeshifter. I'll make similar changes in all my other PRs after i get the green light here

[Planeshifter]

@aayush0325 To preserve the existing behavior and range of generated values, might be better to use the uniform instead of discrete uniform, as it looks like we didn't just benchmark for integer values here. I only meant the code snippet as an example of how we have done it elsewhere, namely in some of the extended BLAS base packages, not that we should change to discrete uniform or draw from a different range than was previously done.

Otherwise looks good to me.

[aayush0325]

ah, my bad i'll revert the commit just a sec

[aayush0325]

please give this a final look @Planeshifter! sorry for the misunderstanding.

[Planeshifter]

@aayush0325 Just pushed a commit with what I envisioned: Basically updating the benchmarks to avoid the for-loops while not changing the distribution from which the values are drawn. Does that make sense?

Thanks for the PR and your proactive approach in updating open ones, which makes life as a reviewer much easier!

I can merge this PR shortly after CI once all checks are green.

[stdlib-bot]

PR Commit Message

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

PR-URL: https://github.com/stdlib-js/stdlib/pull/4235

Co-authored-by: Philipp Burckhardt <[email protected]>
Co-authored-by: aayush0325 <[email protected]>
Reviewed-by: Philipp Burckhardt <[email protected]>

Please review the above commit message and make any necessary adjustments.

[aayush0325]

Basically updating the benchmarks to avoid the for-loops while not changing the distribution from which the values are drawn. Does that make sense?

got it, updating all other similar PRs like this soon, thanks for all your help!