feat: add ndarray/find

[gururaj1512]

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Resolves a part of #2656.

Description

What is the purpose of this pull request?

This pull request:

• Adds implementation for ndarray/find

Related Issues

Does this pull request have any related issues?

This pull request:

• Resolves a part of [RFC]: achieve ndarray API parity with built-in JavaScript arrays  #2656.

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No.

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• Read, understood, and followed the contributing guidelines.

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[stdlib-bot]

Coverage Report

Package	Statements	Branches	Functions	Lines
ndarray/find	$\color{red}206/211$ $\color{green}+97.63\%$	$\color{red}28/29$ $\color{green}+96.55\%$	$\color{green}1/1$ $\color{green}+100.00\%$	$\color{red}206/211$ $\color{green}+97.63\%$

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

[kgryte]

@gururaj1512 Before you start doing too many of things in #2656 and us needing to go back and fix things, I'd put these on pause. As mentioned in #2656, the issue is that find (and every) are reductions, and thus should support specifying axes over which to perform the reductions. And furthermore, the implementation proposed in this PR may work for the flattened case; however, it won't work for the axes case. And lastly, we should always return ndarrays, not scalars. In short, there is a bit of R&D which needs to be done in order for these to move forward.

[kgryte]

One other thing: each top-level ndarray implementation should be a wrapper around a "base" implementation. Notice how in base/for-each, we write kernels for efficient iteration: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/for-each/lib. I would expect something similar for reductions.

[kgryte]

So before top-level implementations, we need to create the "base" implementations.

[gururaj1512]

@gururaj1512 Before you start doing too many of things in #2656 and us needing to go back and fix things, I'd put these on pause. As mentioned in #2656, the issue is that find (and every) are reductions, and thus should support specifying axes over which to perform the reductions. And furthermore, the implementation proposed in this PR may work for the flattened case; however, it won't work for the axes case. And lastly, we should always return ndarrays, not scalars. In short, there is a bit of R&D which needs to be done in order for these to move forward.

Yaa I had similar doubts while creating this PR. Just created this quick PR to discuss about it.

[gururaj1512]

@kgryte, Should we pause this, or is there something I can work on? I would love to work on issues related to this.

[kgryte]

@gururaj1512 Yes, let's pause. I pinged you on the tracking issue.