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[kgryte]

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[DivitJain26]

While working on feat: add blas/base/cgbmv #10492, I ran into a few points I’d like to confirm.

1. I want to confirm whether my band matrix packing is correct for M = 4, N = 4, KL = 1, KU = 2, (no-transpose).

For Column Major

"A_mat": [
  [ 1.0, 1.0, 3.0, 3.0, 6.0, 6.0, 0.0, 0.0 ],
  [ 2.0, 2.0, 4.0, 4.0, 7.0, 7.0, 10.0, 10.0 ],
  [ 0.0, 0.0, 5.0, 5.0, 8.0, 8.0, 11.0, 11.0 ],
  [ 0.0, 0.0, 0.0, 0.0, 9.0, 9.0, 12.0, 12.0 ]
],
"A_compact": [
  [ 0.0, 0.0, 0.0, 0.0, 6.0, 6.0, 10.0, 10.0 ],
  [ 0.0, 0.0, 3.0, 3.0, 7.0, 7.0, 11.0, 11.0 ],
  [ 1.0, 1.0, 4.0, 4.0, 8.0, 8.0, 12.0, 12.0 ],
  [ 2.0, 2.0, 5.0, 5.0, 9.0, 9.0, 0.0, 0.0 ]
],
"A": [ 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 0.0, 0.0, 3.0, 3.0, 4.0, 4.0, 5.0, 5.0, 6.0, 6.0, 7.0, 7.0, 8.0, 8.0, 9.0, 9.0, 10.0, 10.0, 11.0, 11.0, 12.0, 12.0, 0.0, 0.0, 0.0, 0.0 ]

For Row Major

"A_mat": [
  [ 1.0, 1.0, 3.0, 3.0, 6.0, 6.0, 0.0, 0.0 ],
  [ 2.0, 2.0, 4.0, 4.0, 7.0, 7.0, 10.0, 10.0 ],
  [ 0.0, 0.0, 5.0, 5.0, 8.0, 8.0, 11.0, 11.0 ],
  [ 0.0, 0.0, 0.0, 0.0, 9.0, 9.0, 12.0, 12.0 ]
],
"A_compact": [
  [ 0.0, 0.0, 1.0, 1.0, 3.0, 3.0, 6.0, 6.0 ],
  [ 2.0, 2.0, 4.0, 4.0, 7.0, 7.0, 10.0, 10.0 ],
  [ 5.0, 5.0, 8.0, 8.0, 11.0, 11.0, 0.0, 0.0 ],
  [ 9.0, 9.0, 12.0, 12.0, 0.0, 0.0, 0.0, 0.0 ]
],
"A": [ 0.0, 0.0, 1.0, 1.0, 3.0, 3.0, 6.0, 6.0, 2.0, 2.0, 4.0, 4.0, 7.0, 7.0, 10.0, 10.0, 5.0, 5.0, 8.0, 8.0, 11.0, 11.0, 0.0, 0.0, 9.0, 9.0, 12.0, 12.0, 0.0, 0.0, 0.0, 0.0 ]

2. I noticed in feat: add blas/base/sgbmv #5928 and feat: add blas/base/dgbmv #6121 that KL is not used in the same way as described in the intel/onemkl band storage reference for column-major and row-major layouts.

3. The current feat: add blas/base/sgbmv #5928 and feat: add blas/base/dgbmv #6121 tests only use KL = 1, KU = 1, and square matrices. Would it make sense to add tests with different KL and KU values and rectangular matrices to improve coverage?

4. While going through the complex BLAS routines, I noticed that there don’t appear to be explicit test cases covering the conjugate-transpose case. Although the implementation seems to handle this case, I couldn’t find dedicated tests that directly validate its correctness. Would you recommend adding specific test cases for the conjugate-transpose scenario?

5. how to properly generate a random complex banded matrix (e.g., for cgbmv) for examples/index.js. I saw examples for real banded matrices and complex dense matrices, but I’m unsure what the recommended approach is for complex banded storage. Should I use filled2dBy with Complex64 or construct the compact band array manually?

[kgryte]

@DivitJain26 Have you already explored using our banded storage utilities for generating nested matrices: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/banded/to-compact?

[kgryte]

@DivitJain26 Re: 4. Yes, you need to add cases for the conjugate-transpose. That doesn't exist in the other packages because they are real-valued.

Re: 3. Sure. You'll likely want to follow the same JSON fixture based approach.

Re: 2. How does it differ? Can you show the difference in code and generated results? And does it differ than our banded/to-compact utility?

Re: 5. Yes, you can use with filled2by with Complex64. Likely no need to construct manually.

[nakul-krishnakumar]

I would like to discuss about:

1. Scope of a GSoC Proposal to add Batch ML algorithms (@stdlib/ml/*). I am planning 1-2 algorithms per week as it can get quite huge (atleast that is what I understood in my initial trial of implementing dkmeansld). I would like to also discuss if it would be too less work from what is expected.
2. Possibility of implementing @stdlib/ml/dkmeans before writing proposal so that I could plan the scope of the proposal accordingly. (Getting this package merged will give me an exact idea on the expected API Design for rest of the packages)
3. What all implementations to be given more priority? Should I consider only the clustering algorithms for the proposal or should I consider some algorithms from each subsection (eg: clustering, regression, classification, dimensionality reduction)?

[shubham220420]

I would like to discuss. :

1. About integrating stdlib inside jstat, I have some initial idea which I want to discuss and want to know whether it aligns with the scope of project or not.

[DivitJain26]

@DivitJain26 Re: 4. Yes, you need to add cases for the conjugate-transpose. That doesn't exist in the other packages because they are real-valued.

Re: 3. Sure. You'll likely want to follow the same JSON fixture based approach.

Re: 2. How does it differ? Can you show the difference in code and generated results? And does it differ than our banded/to-compact utility?

Re: 5. Yes, you can use with filled2by with Complex64. Likely no need to construct manually.

For Point 2.
Please see the changes in the second branch in
feat: add blas/base/cgbmv #10492 commit (5d4e1c8f73af76e93f643bcd5d8888cbbef97832)

Before

	for ( i1 = 0; i1 < ylen; i1++ ) {
		retmp = 0.0;
		imtmp = 0.0;
		oa2 = oa + ( i1 * sa1 );
		i0start = max( 0, i1 - KU );
		i0end = min( xlen, i1 + KL + 1 );
		for ( i0 = i0start; i0 < i0end; i0++ ) {
			kup1 = i0 - i1;
			ia = oa2 + ( ( kup1 + KU ) * sa0 );
			if ( i0 < xlen && ia >= 0 ) {
				ix = ox + ( i0 * sx );
				rea = viewA[ ia ];
				ima = sign * viewA[ ia + 1 ];
				rex = viewX[ ix ];
				imx = viewX[ ix + 1 ];
				retmp = f32( retmp + f32( ( rea * rex ) - ( ima * imx ) ) );
				imtmp = f32( imtmp + f32( ( rea * imx ) + ( ima * rex ) ) );
			}
		}
		iy = oy + ( i1 * sy );
		muladd( realpha, imalpha, retmp, imtmp, viewY[ iy ], viewY[ iy + 1 ], viewY, 1, iy ); // eslint-disable-line max-len
	}
	return y;

Now

	for ( i1 = 0; i1 < ylen; i1++ ) {
		retmp = 0.0;
		imtmp = 0.0;
		oa2 = oa + ( i1 * sa1 );
		i0start = max( 0, i1 - KL );                           // updated
		i0end = min( xlen, i1 + KU + 1 );                // updated
		for ( i0 = i0start; i0 < i0end; i0++ ) {
			kup1 = i0 - i1;
			ia = oa2 + ( ( kup1 + KL ) * sa0 );       // updated
			if ( i0 < xlen && ia >= 0 ) {
				ix = ox + ( i0 * sx );
				rea = viewA[ ia ];
				ima = sign * viewA[ ia + 1 ];
				rex = viewX[ ix ];
				imx = viewX[ ix + 1 ];
				retmp = f32( retmp + f32( ( rea * rex ) - ( ima * imx ) ) );
				imtmp = f32( imtmp + f32( ( rea * imx ) + ( ima * rex ) ) );
			}
		}
		iy = oy + ( i1 * sy );
		muladd( realpha, imalpha, retmp, imtmp, viewY[ iy ], viewY[ iy + 1 ], viewY, 1, iy ); // eslint-disable-line max-len
	}
	return y;

here the inner loop starts at max( 0, i1 - KL ), end at min( xlen, i1 + KU + 1 ) and ia = oa + ( i1 * sa1 ) ( ( i0 - i1 + KL ) * sa0 )
which is overlooked when KU = KL but it becomes critical when KU > KL., the previous bounds result in incorrect element access.

In the same PR the test cgbmv/test/fixtures/row_major_xpyp.json only passes after the mentioned changes. The test fails if the previous bounds and index access technique is used because here KU > KL (eg. KU = 2, KL = 1).

feat: add blas/base/sgbmv #5928 and feat: add blas/base/dgbmv #6121 uses the old pattern.

[prajjwalbajpai]

Review on my PR implementing dgetc2 LAPACK routine. I have made changes that were suggested for the other PRs I have made so I hope it should be in a good state.

[AyushiJain18270]

I would like to ask about xorshift32 PRNG
Since it is single internal state :
why a separate seedLength property is exposed? whether this is mainly for API consistency with multi-state generators(MINSTD,MT)

[DivitJain26]

@DivitJain26 Have you already explored using our banded storage utilities for generating nested matrices: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/banded/to-compact?

Should I use this as a reference for making the fixtures?
if so then what cahges should i make in base implementation of feat: add blas/base/cgbmv #10492
It is working for my current packing pattern.

[navyansh007]

Review on PR #8187 - related to implementation of math/base/special/sici
Review on PR #10657 - related to implementation of math/base/special/expm1f

[manit2004]

Do we have I1 Modified Bessel function of first kind in stdlib?

[Witty-31-06]

• I have questions regarding implementation dnancumin.

• Need clarification on the C doctest GSoC issue. Does eslint doctesting applies to README.md files too? I took a look at the eslint/doctest, can you guide me to the code where README files examples are also tested?

  • For C code inside README, I mainly found two kinds of code snippet, one is annotation on the expected output and one is example.c. Can we execute them in a child process to check the output? Is this feasible?

[nirmaljb]

I am working on my proposal notes for math/base/special, and I've mentioned some questions inside, can you please look into it?
https://befitting-tuberose-75b.notion.site/Base-Special-Proposal-Notes-301f09ba3c548095a4c4fd2a76ca67c3

[bhargava-d16]

Review on PR stdlib-js/stdlib#10620 - related to implementation of stats/base/dists/anglit/cdf
Review on PR stdlib-js/stdlib#10596 related to implementation of stats/base/dists/anglit/entropy
Review on PR stdlib-js/stdlib#9983 - related to implementation of stats/base/dists/bradford/logpdf

I also wanted to ask about the documentations to take reference from along with this https://github.com/scipy/scipy/blob/main/scipy/stats/_continuous_distns.py