Comments by @baptischmi

Copied and adapted from Slack
thank you and well done! I especially like the addition of the data quality in terms of number of data points used for the main values of flight,  dbd, and sd_vvp, and the 'vcp'.
i have few minor comments - hope some are helpful:

[x] To me it seems unnecessary to include both the u and v component, and the combined ground speed and direction. Why not simply having u, v, and w? it it because of back compatibility for other function that need 'ff' or 'dd'?
[x] What is the angular data gap? is this information not included in the definition of NA or NaN for 'dens' or any other quantity?
[ ] 'radar_wavelength': Do you mean by "unknown" radar wavelength that vol2bird used a default setting, which is 5.3 and 10.6 for S and C-band, respectively?
[x] 'radar_wavelength' inform on the processing of the data. i think information on rain filter may be also important, or which products fed vol2bird (DBZH, TH, VRADH, CCORH, RHOHV, PHIDP, ZDR). See #35
[x] instead of having a column 'rcs', why not defining 'dens' as a multiplication of 'dbz' with a fixed rcs of 11 cm^2? same for sd_vvp_thredhold. 'dens' can be easily recalculated if these two must be modified.
[x] define the value below/above the minimum/maximum allowed, e.g. 'u', 'v', 'dens'.
[x] i like the proposed order of the variable. I particular, i like having 'sd_vpp' close to 'eta'. it make sense to first have all data related to the speed and direction, finish with sd_vvp, and then display the density/reflectivity values.

@baptischmi what do you mean with:

define the value below/above the minimum/maximum allowed, e.g. 'u', 'v', 'dens'.

A minimum and maximum are defined for those three variables.

u,v have historically been part of the vol2bird profile data, but I agree there is duplication with ff,dd. Currently only ff,dd are required, and u,v are optional
angular data gap refers to field gap of vol2bird output - it's a boolean that states if there are too many angular sectors missing to do a velocity fit - not contained in the other quantities

inform on processing of the data

vpts format in its current form unfortunately doesn't support substantial amounts of metadata, which I agree is a pity and one of the largest limitations of this table format. In the past we have discussed adding a field that contains a reference to a separate table containing metadata values, but our decision was to leave that for a future iteration, as it involves a lot of extra data prep work. @peterdesmet should we track this feature with a separate issue?
since eta = rcs * dens if sd_vpp>sd_vvp_threshold, there is duplication here again. It is more a matter of convenience to have this value present as a column, and since there is some (simple) algebra involved to obtain this often used quantity, I'm inclined to keep it. rcs as a column I think is useful, for when people want to include seasonal trends of rcs (potentially same for sd_vvp_threshold).

@peterdesmet any thoughts on whether duplication of data is something we want to avoid at all cost?

any thoughts on whether duplication of data is something we want to avoid at all cost?

I don't think we have to avoid it at all costs. If many people use ff and dd, it's a welcome convenience. We could opt to make u and v the required terms. We could opt to remove ff and dd (but in that case we should list how to calculate ff and dd).
Created a separate issue for the metadata #35.

I prefer to keep ff and dd, as they are easier to interpret

@baptischmi what do you mean with:

define the value below/above the minimum/maximum allowed, e.g. 'u', 'v', 'dens'.

A minimum and maximum are defined for those three variables.
@peterdesmet
For instance, speed 'ff' above 100 m/s are already far from being reliable, but which value is set, if for some processing artefacts, you get higher speed values higher than 100 m/s? would you set the value as NA, or as the maximum value allowed (i.e. 100 m/s).

PS: This comment not valid for 'dens' (as stated in my original message), for which the maximum is 'Inf', but rather only for 'u', 'v', 'w', 'ff'.

I just scanned the format definition. As the goal is stated quite general:
VPTS is a community developed data exchange format for biological signals detected by weather radars.
It seems it should be able to also possibly describe results from other possible identification algorithms. If that is less the goal this comment can be ignored otherwise I feel some of the required field are quite specific to vol2bird. For example sd_vvp and possibly dd and ff. It is not given those metrics are a result of each algorithm.

Do you mean by "unknown" radar wavelength that vol2bird used a default setting, which is 5.3 and 10.6 for S and C-band, respectively?

@baptischmi: No, I mean that if you do not know the wavelength, but know it is a C-band radar, to then set radar_wavelength values to 5.3.
@adokter are wavelength values used by vol2bird when processing? Would they always be known in produced then?

Yes, wavelength is needed to calculate eta from reflectivity dBZ. It reads it from the raw file if available as metadata. If not, it assumes it's C-band with wavelength 5.3 cm

@bart1 👍  updated in 1c9da40.

Constant for all records from the same radar.

@adokter any way you would rephrase this then (without making it too complicated)?

@peterdesmet slight tweak:

Wavelength of the radar in cm. Most C-band radars operate at approximately 5.3 cm wavelength, and most S-band at 10.6 cm

Thanks, updated. And with that we have addressed all comments by @baptischmi listed at the start of this issue.


only first element in the bypass-list is applied

only the first element in the bypass-list is being applied
my code
         winhttp_proxy { 'proxy':
           ensure       => present,
           proxy_server => $proxyserver,
           bypass_list  => ['asdds.jkhsa', '*.dsadasdsa.com', 'sadasdas']
         }

Ive tested using a string semicolon and comma separated but that errors out
I believe the issue is in the set command as its enumerated all the elements eg
Debug: Prefetching netsh resources for winhttp_proxy
Debug: Executing: 'cmd.exe /c C:\Windows\System32\netsh.exe winhttp dump'
Notice: /Stage[main]/Winconfig::Proxy/Winhttp_proxy[proxy]/bypass_list: bypass_list changed ['asdds.jkhsa'] to 'asdds.jkhsa *.dsadasdsa.com sadasdas'
Debug: Executing: 'cmd.exe /c C:\Windows\System32\netsh.exe winhttp set proxy proxy-server="proxy:3128" bypass-list="asdds.jkhsa"'
Debug: /Stage[main]/Winconfig::Proxy/Winhttp_proxy[proxy]: The container Class[Winconfig::Proxy] will propagate my refresh event

the result of the netsh winhttp dump
netsh.exe winhttp dump

# -----------------------------------------
# WinHTTP Proxy Configuration
# -----------------------------------------
pushd winhttp

set proxy proxy-server="proxy:3128" bypass-list="asdds.jkhsa"

popd

# End of WinHTTP Proxy Configuration

its was missing a join
  def flush
    cmd = [ 'cmd.exe', '/c', command(:netsh), 'winhttp', 'set', 'proxy', 'proxy-server="%s"' % @property_hash[:proxy_server], 'bypass-list="%s"' % @property_hash[:bypass_list].join(";") ]

Can you send a PR?

Actually, it's #3


Training on different sampling rates

Would be nice if you provided clear instructions on how this model can be trained on different sampling rates.

There are so many ways...
First, check a preprocessing method for  your Mel-spectrogram
Second, change the initial frequency value for resampling
https://github.com/sh-lee-prml/BigVGAN/blob/main/models_bigvgan.py#L104
Calculate this according to your sampling, hop size, and upsampling rate
I'll show you an example (hop size 256 and upsampling rate =[8,8,2,2])
16000 sr --> initial_freq = [500, 4000, 8000, 16000]
22050sr --> initial_freq = [690, 5513, 11025, 22050]
24000sr --> initial_freq = [750, 6000, 12000, 24000]

thanks for the work, how about for different hop size, say 20ms with 24000sr ?

Hi. What is the relationship between hop size, upsampling rates and initial_freq values? Thank you in advance.

Is there any suggestion if I use it at 48khz?

For different sampling rates, do you need to modify the parameters of MPD, similar to https://github.com/jik876/hifi-gan/issues/58.


bug: Log is spammed with unknown field "status.conditions[0].lastTransitionTime"

Current Behavior
After discussing it with Jintao Zhang on slack i am creating this issue here :
our logs are being spammed with
unknown field "status.conditions[0].lastTransitionTime"
It's not a new issue, been happening for a while with various versions of APISIX.
I always make sure to upgrade the CRDs when upgrading apisix so i don't think that's the issue.
Currently we're running 3.4.1 and apisix-ingress-controller version 1.6.1-no-git-module-go1.19.8
before upgrading CRDs were upgraded via
helm show crds apisix/apisix-ingress-controller | kubectl apply -f -
Expected Behavior
not seeing this message.
Error Logs
unknown field "status.conditions[0].lastTransitionTime"
Steps to Reproduce
basically run apisix with ingress-controller, create some routes and watch the logs.
Environment

APISIX Ingress controller version (run apisix-ingress-controller version --long)

/ingress-apisix # ./apisix-ingress-controller  version --long
Version: 1.6.1
Git SHA: no-git-module
Go Version: go1.19.8
Building OS/Arch: linux/aarch64
Running OS/Arch: linux/arm64

Kubernetes cluster version (run kubectl version)

Client Version: version.Info{Major:"1", Minor:"25", GitVersion:"v1.25.2", GitCommit:"5835544ca568b757a8ecae5c153f317e5736700e", GitTreeState:"clean", BuildDate:"2022-09-21T14:25:45Z", GoVersion:"go1.19.1", Compiler:"gc", Platform:"darwin/arm64"}
Kustomize Version: v4.5.7
Server Version: version.Info{Major:"1", Minor:"27+", GitVersion:"v1.27.4-eks-2d98532", GitCommit:"3d90c097c72493c2f1a9dd641e4a22d24d15be68", GitTreeState:"clean", BuildDate:"2023-07-28T16:51:44Z", GoVersion:"go1.20.6", Compiler:"gc", Platform:"linux/amd64"}

could you please try to use https://github.com/apache/apisix-ingress-controller/tree/master/samples/deploy/crd/v1 ?


How to install manually?

I don't like using cocoa pods so I would like to install this manually onto my project.
I manually dragged over all the files within SwiftCharts including subfolders, but I'm getting some errors:

Is there any other files I need to drag in?
Thanks!

Fixed..I had to include import UIKit in all the problematic classes
This issue is caused with using Xcode 8.3.3 for those not using cocoa pods and want to install manually.


Generated files have incorrect permissions

Problem
(foobar) pkg> resolve
ERROR: SystemError: opening file "/Users/mattbr/Desktop/temp/foobar/Manifest.toml": Permission denied

So it's not lost to the Julia Slack, seems to be an issue w/ my machine.


fix(translations): complete Chinese theme translations

Completed untranslated portion

Pre-flight checklist

[x] I have read the Contributing Guidelines on pull requests.
[ ] If this is a code change: I have written unit tests and/or added dogfooding pages to fully verify the new behavior.
[ ] If this is a new API or substantial change: the PR has an accompanying issue (closes #0000) and the maintainers have approved on my working plan.

Motivation

Test Plan

Test links

Deploy preview: https://deploy-preview-_____--docusaurus-2.netlify.app/
Related issues/PRs

Thanks
@Josh-Cena let me know if you can review

(we need the CLA signature to merge this PR in case you didn't already sign it - the bot can take some time)

(we need the CLA signature to merge this PR in case you didn't already sign it - the bot can take some time)

Three little things ago I have signed the CLA, but the robot did not pass


The test is not being used

The test \yiiunit\framework\ar\ActiveRecordTestTrait::testFindScalar() is not being used
There are other tests of a trait are not used.
https://github.com/LAV45/yii2/commit/8201dee0abbd997b109d8a3bc495ace915629020
https://travis-ci.org/LAV45/yii2/builds/172776879

thanks for sharing. Proper test management will be more and more important. Worst case scenario is that you have all kinds of unnecessary 'ghost tests' in place which prevent you from doing updates.
It would be nice to have a workflow & criteria in place to deal with the removal of tests.

The matter is that none of the tests which is a Trait not executed
Here is an example from the documentation how to connect Trait for phpunit tests
Mocking Traits and Abstract Classes

nope, tests in traits are executed fine. However, in this particular case, testFindScalar was overrided and never executed.

https://github.com/yiisoft/yii2/pull/12909/commits/757bca59d56fdf30a709cfbdc31bcb699d148170

Please check also these tests, they just are not used
https://github.com/LAV45/yii2/commit/a0f7628b8350d8b0b8a68abd5cc73518d353e7f2
https://travis-ci.org/LAV45/yii2/builds/172993859

didnt see your first link, thought it was 1.

see https://github.com/yiisoft/yii2/pull/12909#discussion_r86779365


Compare BLAS and non-BLAS performance

Now that we've made BLAS support optional on several linfa crates, we should compare the performance of those crates with and without BLAS. Doing this requires those crates to have a complete set of benchmarks that represent realistic workloads. If BLAS turns out to have no performance improvements, we can even remove BLAS support, improving code quality.
Benchmark status for each crate that supports BLAS:

[x] linfa-clustering:

[x] Benchmarks (pretty much complete, might want to add more workloads)
[x] Compare BLAS and non-BLAS perf
[x] Issue: #255

[ ] linfa-ica:

[x] Benchmarks
[x] Compare BLAS and non-BLAS perf
[ ] Issue: #262

[ ] linfa-reduction:

[ ] Benchmarks
[ ] Compare BLAS and non-BLAS perf

[ ] linfa-preprocessing:

[x] Benchmarks
[x] Compare BLAS and non-BLAS perf
[ ] Issue: #254

[ ] linfa-pls:

[x] Benchmarks
[ ] Compare BLAS and non-BLAS perf

[ ] linfa-linear:

[x] Benchmarks
[ ] Compare BLAS and non-BLAS perf

[ ] linfa-elasticnet:

[ ] Benchmarks
[ ] Compare BLAS and non-BLAS perf

I'm not 100% sure about removing the optional BLAS support, because that's technically a breaking change. That type of change would be unacceptable on a crate like serde, even on a breaking release, but we might be able to get away with it.

Intel MKL libraries have their own ASM routines with architecture-specific optimizations, so it's not viable to completely reproduce in pure Rust. There's definitely space of improvement in the Rust library though.

Thank you, that's what I thought. Perhaps someday we can get Intel itself to provide a Rust + assembly version.

with guidance I am willing to do a blas vs non blas comparison for linfa-clustering.

with guidance I am willing to do a blas vs non blas comparison for linfa-clustering.

Since benchmarks already exist for linfa-clustering, run the benchmarks with and without the BLAS feature flags and post the results. Also post your commands and the specs of your machine.

@YuhanLiin Linfa Clustering results attached as zip.
Commands:
1st run -  cargo bench -p linfa-clustering -F intel-mkl-static
2md run - cargo bench -p linfa-clustering
System specs:
hardware:
Processor	12th Gen Intel(R) Core(TM) i5-1235U   1.30 GHz
Installed RAM	8.00 GB (7.72 GB usable)
Device ID	5F0BF67B-026D-4E93-8F42-D89620D51451
Product ID	00342-20909-03914-AAOEM
System type	64-bit operating system, x64-based processor
Pen and touch	Pen and touch support with 10 touch points

OS:
Edition	Windows 11 Home
Version	22H2
Installed on	9/29/2022
OS build	22621.674
Experience	Windows Feature Experience Pack 1000.22634.1000.0

criterion.zip

Linfa_preprocessing results below. These benchmarks use iai instead of criterion thus it does not automatically let you know if the difference is statistically different.  The last set of results here has the percentage delta between the two commands.
cargo bench -p linfa-preprocessing -F intel-mkl-static -q
iai_standard_scaler_bench
  Instructions:           247707707 (No change)
  L1 Accesses:            313682891 (No change)
  L2 Accesses:              6242917 (No change)
  RAM Accesses:             1076129 (No change)
  Estimated Cycles:       382561991 (No change)

iai_min_max_scaler_bench
  Instructions:           183232614 (No change)
  L1 Accesses:            216728794 (No change)
  L2 Accesses:              7319612 (No change)
  RAM Accesses:             1124133 (No change)
  Estimated Cycles:       292671509 (No change)

iai_max_abs_scaler_bench
  Instructions:           184320331 (No change)
  L1 Accesses:            194024307 (No change)
  L2 Accesses:              9669054 (No change)
  RAM Accesses:             1024959 (No change)
  Estimated Cycles:       278243142 (No change)

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

iai_fit_vectorizer
  Instructions:<PHONE_NUMBER>
  L1 Accesses:<PHONE_NUMBER>
  L2 Accesses:             31377942
  RAM Accesses:             1427414
  Estimated Cycles:<PHONE_NUMBER>

iai_fit_transform_vectorizer
  Instructions:<PHONE_NUMBER>
  L1 Accesses:<PHONE_NUMBER>
  L2 Accesses:             33558959
  RAM Accesses:             1488231
  Estimated Cycles:<PHONE_NUMBER>

iai_fit_tf_idf
  Instructions:<PHONE_NUMBER>
  L1 Accesses:<PHONE_NUMBER>
  L2 Accesses:             31485644
  RAM Accesses:             1426626
  Estimated Cycles:<PHONE_NUMBER>

iai_fit_transform_tf_idf
  Instructions:           869383776
  L1 Accesses:<PHONE_NUMBER>
  L2 Accesses:              2996272
  RAM Accesses:              147146
  Estimated Cycles:<PHONE_NUMBER>

iai_pca_bench
  Instructions:<PHONE_NUMBER>
  L1 Accesses:<PHONE_NUMBER>
  L2 Accesses:            606301143
  RAM Accesses:             4366308
  Estimated Cycles:<PHONE_NUMBER>

iai_zca_bench
  Instructions:           593669460
  L1 Accesses:            745426778
  L2 Accesses:             20078499
  RAM Accesses:             3345546
  Estimated Cycles:       962913383

iai_cholesky_bench
  Instructions:           517218067
  L1 Accesses:            641365471
  L2 Accesses:             19722717
  RAM Accesses:             3300250
  Estimated Cycles:       855487806

cargo bench -p linfa-preprocessing -q
running 53 tests
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
test result: ok. 0 passed; 0 failed; 53 ignored; 0 measured; 0 filtered out; finished in 0.01s

iai_standard_scaler_bench
  Instructions:           247707707 (+0.024685%)
  L1 Accesses:            313683265 (+0.025857%)
  L2 Accesses:              6242496 (-0.004485%)
  RAM Accesses:             1076176 (+0.117498%)
  Estimated Cycles:       382561905 (+0.032395%)

iai_min_max_scaler_bench
  Instructions:           183232614 (+0.033374%)
  L1 Accesses:            216728467 (+0.037104%)
  L2 Accesses:              7320099 (+0.008580%)
  RAM Accesses:             1123973 (+0.094041%)
  Estimated Cycles:       292668017 (+0.041185%)

iai_max_abs_scaler_bench
  Instructions:           184320331 (+0.033177%)
  L1 Accesses:            194024734 (+0.041837%)
  L2 Accesses:              9668647 (-0.002751%)
  RAM Accesses:             1024939 (+0.116826%)
  Estimated Cycles:       278240834 (+0.043747%)

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

iai_fit_vectorizer
  Instructions:<PHONE_NUMBER> (-0.301018%)
  L1 Accesses:<PHONE_NUMBER> (-0.265049%)
  L2 Accesses:             30834301 (-1.785174%)
  RAM Accesses:             1429386 (-1.773712%)
  Estimated Cycles:<PHONE_NUMBER> (-0.314589%)

iai_fit_transform_vectorizer
  Instructions:<PHONE_NUMBER> (-0.211742%)
  L1 Accesses:<PHONE_NUMBER> (-0.211798%)
  L2 Accesses:             32930389 (-1.922160%)
  RAM Accesses:             1478958 (-2.444305%)
  Estimated Cycles:<PHONE_NUMBER> (-0.258297%)

iai_fit_tf_idf
  Instructions:<PHONE_NUMBER> (-0.189165%)
  L1 Accesses:<PHONE_NUMBER> (-0.200605%)
  L2 Accesses:             30837261 (-2.111559%)
  RAM Accesses:             1428415 (-1.787255%)
  Estimated Cycles:<PHONE_NUMBER> (-0.260586%)

iai_fit_transform_tf_idf
  Instructions:<PHONE_NUMBER> (+578.7082%)
  L1 Accesses:<PHONE_NUMBER> (+598.1243%)
  L2 Accesses:             33019866 (+995.8834%)
  RAM Accesses:             1502558 (+758.9531%)
  Estimated Cycles:<PHONE_NUMBER> (+603.7655%)

iai_pca_bench
  Instructions:<PHONE_NUMBER> (+2.836558%)
  L1 Accesses:<PHONE_NUMBER> (-3.532371%)
  L2 Accesses:            612619759 (+1.044968%)
  RAM Accesses:             4296011 (-1.009489%)
  Estimated Cycles:<PHONE_NUMBER> (-1.973983%)

iai_zca_bench
  Instructions:           608404933 (+4.710235%)
  L1 Accesses:            741514513 (+1.982326%)
  L2 Accesses:             25116941 (+25.19884%)
  RAM Accesses:             3292475 (-0.800950%)
  Estimated Cycles:       982335843 (+4.107730%)

iai_cholesky_bench
  Instructions:           536186203 (+6.262738%)
  L1 Accesses:            646261524 (+3.727183%)
  L2 Accesses:             24747630 (+25.58514%)
  RAM Accesses:             3277475 (+0.113386%)
  Estimated Cycles:       884711299 (+5.807638%)

Also for linfa_linear is the expectation that the written benchmark just use the diabetes dataset (n=1599)? Or do we want to do something akin to a small,mediu, large benchmark say something like n=:1_000, 10_000, 100_000?
I imagine the latter would allow us to know if the blas non-blas tradeoff is also a function of sample size.

We should go with the latter.

We should go with the latter.
BTW, for the linfa-clustering benchmarks, the "previous benchmark" is the one with BLAS enabled right?

That is correct.

linfa-ica benchmarks
Context

Laptop mid charge level with wall charger power plugged in
Laptop not in use during test
Laptop not over heating

Run: cargo bench -p linfa-ica
     Running benches\fast_ica.rs (target\release\deps\fast_ica-d0c3b5752e019610.exe)
Fast ICA/GFunc_Cube/1000
                        time:   [63.292 µs 63.701 µs 64.270 µs]
Found 24 outliers among 100 measurements (24.00%)
  22 (22.00%) high mild
  2 (2.00%) high severe
Fast ICA/GFunc_Cube/10000
                        time:   [594.96 µs 599.06 µs 603.51 µs]
Found 6 outliers among 100 measurements (6.00%)
  5 (5.00%) high mild
  1 (1.00%) high severe
Fast ICA/GFunc_Cube/100000
                        time:   [10.097 ms 10.163 ms 10.236 ms]
Found 8 outliers among 100 measurements (8.00%)
  4 (4.00%) high mild
  4 (4.00%) high severe

Fast ICA/GFunc_Logcosh/1000
                        time:   [80.528 µs 80.737 µs 80.965 µs]
Found 8 outliers among 100 measurements (8.00%)
  1 (1.00%) low severe
  5 (5.00%) low mild
  1 (1.00%) high mild
  1 (1.00%) high severe
Fast ICA/GFunc_Logcosh/10000
                        time:   [790.09 µs 792.99 µs 796.68 µs]
Found 13 outliers among 100 measurements (13.00%)
  1 (1.00%) low mild
  7 (7.00%) high mild
  5 (5.00%) high severe
Fast ICA/GFunc_Logcosh/100000
                        time:   [11.175 ms 11.241 ms 11.311 ms]
Found 3 outliers among 100 measurements (3.00%)
  2 (2.00%) high mild
  1 (1.00%) high severe

Fast ICA/Exp/1000       time:   [95.226 µs 95.656 µs 96.168 µs]
Found 11 outliers among 100 measurements (11.00%)
  6 (6.00%) high mild
  5 (5.00%) high severe
Fast ICA/Exp/10000      time:   [930.76 µs 935.38 µs 941.12 µs]
Found 12 outliers among 100 measurements (12.00%)
  1 (1.00%) low severe
  1 (1.00%) low mild
  7 (7.00%) high mild
  3 (3.00%) high severe
Fast ICA/Exp/100000     time:   [18.221 ms 18.381 ms 18.559 ms]
Found 9 outliers among 100 measurements (9.00%)
  2 (2.00%) high mild
  7 (7.00%) high severe

Run cargo bench -p linfa-ica -F intel-mkl-static
     Running benches\fast_ica.rs (target\release\deps\fast_ica-2e03c80ee39d2cab.exe)
Fast ICA/GFunc_Cube/1000
                        time:   [51.978 µs 52.182 µs 52.431 µs]
                        change: [-35.893% -31.248% -26.694%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 13 outliers among 100 measurements (13.00%)
  9 (9.00%) high mild
  4 (4.00%) high severe
Fast ICA/GFunc_Cube/10000
                        time:   [466.80 µs 469.63 µs 473.14 µs]
                        change: [-21.665% -20.949% -20.232%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 9 outliers among 100 measurements (9.00%)
  1 (1.00%) low mild
  6 (6.00%) high mild
  2 (2.00%) high severe
Fast ICA/GFunc_Cube/100000
                        time:   [9.2229 ms 9.3005 ms 9.3897 ms]
                        change: [-9.4661% -8.4831% -7.3959%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 11 outliers among 100 measurements (11.00%)
  5 (5.00%) high mild
  6 (6.00%) high severe

Fast ICA/GFunc_Logcosh/1000
                        time:   [69.136 µs 69.490 µs 69.884 µs]
                        change: [-13.958% -13.385% -12.795%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 8 outliers among 100 measurements (8.00%)
  3 (3.00%) low mild
  4 (4.00%) high mild
  1 (1.00%) high severe
Fast ICA/GFunc_Logcosh/10000
                        time:   [672.37 µs 675.86 µs 680.20 µs]
                        change: [-15.858% -14.933% -13.922%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 20 outliers among 100 measurements (20.00%)
  2 (2.00%) low mild
  4 (4.00%) high mild
  14 (14.00%) high severe
Fast ICA/GFunc_Logcosh/100000
                        time:   [10.314 ms 10.412 ms 10.527 ms]
                        change: [-8.3714% -7.3728% -6.1955%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 8 outliers among 100 measurements (8.00%)
  3 (3.00%) high mild
  5 (5.00%) high severe

Fast ICA/Exp/1000       time:   [76.481 µs 76.798 µs 77.180 µs]
                        change: [-20.431% -19.929% -19.446%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 13 outliers among 100 measurements (13.00%)
  4 (4.00%) high mild
  9 (9.00%) high severe
Fast ICA/Exp/10000      time:   [708.61 µs 714.43 µs 721.13 µs]
                        change: [-23.322% -22.474% -21.608%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 5 outliers among 100 measurements (5.00%)
  1 (1.00%) low mild
  4 (4.00%) high mild
Fast ICA/Exp/100000     time:   [16.429 ms 16.604 ms 16.795 ms]
                        change: [-10.997% -9.6641% -8.3325%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 9 outliers among 100 measurements (9.00%)
  6 (6.00%) high mild
  3 (3.00%) high severe

criterion.zip

The zip file is also attached and has flamegraphs. Will dump in #262.
criterion.zip
Context

Laptop fully charged
Laptop not in use during test
Laptop not over heating
Laptop plugged in

System specs:
hardware:
Processor	12th Gen Intel(R) Core(TM) i5-1235U   1.30 GHz
Installed RAM	8.00 GB (7.72 GB usable)
Device ID	5F0BF67B-026D-4E93-8F42-D89620D51451
Product ID	00342-20909-03914-AAOEM
System type	64-bit operating system, x64-based processor
Pen and touch	Pen and touch support with 10 touch points

OS:
Edition	Windows 11 Home
Version	22H2
Installed on	9/29/2022
OS build	22621.674
Experience	Windows Feature Experience Pack 1000.22634.1000.0

cargo bench -p linfa-ica -q
oojo12@femi-device:/mnt/c/Users/femio/Documents/linfa$ cargo bench -p linfa-ica -q
running 6 tests
iiiiii
test result: ok. 0 passed; 0 failed; 6 ignored; 0 measured; 0 filtered out; finished in 0.00s

Fast ICA/GFunc_Cube/1000
                        time:   [63.201 µs 63.223 µs 63.246 µs]
Found 4 outliers among 100 measurements (4.00%)
  2 (2.00%) high mild
  2 (2.00%) high severe
Fast ICA/GFunc_Cube/10000
                        time:   [556.67 µs 557.19 µs 557.86 µs]
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high severe
Fast ICA/GFunc_Cube/100000
                        time:   [10.315 ms 10.340 ms 10.370 ms]
Found 7 outliers among 100 measurements (7.00%)
  5 (5.00%) high mild
  2 (2.00%) high severe

Fast ICA/GFunc_Logcosh/1000
                        time:   [93.606 µs 94.643 µs 95.681 µs]
Found 14 outliers among 100 measurements (14.00%)
  12 (12.00%) low severe
  1 (1.00%) low mild
  1 (1.00%) high severe
Fast ICA/GFunc_Logcosh/10000
                        time:   [932.63 µs 932.98 µs 933.41 µs]
Found 8 outliers among 100 measurements (8.00%)
  5 (5.00%) high mild
  3 (3.00%) high severe
Fast ICA/GFunc_Logcosh/100000
                        time:   [12.501 ms 12.526 ms 12.555 ms]
Found 10 outliers among 100 measurements (10.00%)
  7 (7.00%) high mild
  3 (3.00%) high severe

Fast ICA/Exp/1000       time:   [94.891 µs 94.935 µs 94.982 µs]
Found 7 outliers among 100 measurements (7.00%)
  1 (1.00%) low mild
  3 (3.00%) high mild
  3 (3.00%) high severe
Fast ICA/Exp/10000      time:   [809.34 µs 809.89 µs 810.59 µs]
Found 14 outliers among 100 measurements (14.00%)
  3 (3.00%) low mild
  2 (2.00%) high mild
  9 (9.00%) high severe
Fast ICA/Exp/100000     time:   [17.182 ms 17.236 ms 17.294 ms]
Found 11 outliers among 100 measurements (11.00%)
  6 (6.00%) high mild
  5 (5.00%) high severe

cargo bench -p linfa-ica -q -F intel-mkl-static
oojo12@femi-device:/mnt/c/Users/femio/Documents/linfa$ cargo bench -p linfa-ica -q -F intel-mkl-static
running 6 tests
iiiiii
test result: ok. 0 passed; 0 failed; 6 ignored; 0 measured; 0 filtered out; finished in 0.00s

Fast ICA/GFunc_Cube/1000
                        time:   [54.565 µs 54.610 µs 54.665 µs]
                        change: [-13.726% -13.591% -13.443%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 10 outliers among 100 measurements (10.00%)
  3 (3.00%) high mild
  7 (7.00%) high severe
Fast ICA/GFunc_Cube/10000
                        time:   [469.52 µs 473.98 µs 478.95 µs]
                        change: [-16.749% -16.201% -15.667%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 17 outliers among 100 measurements (17.00%)
  4 (4.00%) high mild
  13 (13.00%) high severe
Fast ICA/GFunc_Cube/100000
                        time:   [10.505 ms 10.600 ms 10.699 ms]
                        change: [+1.6209% +2.5106% +3.6220%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 13 outliers among 100 measurements (13.00%)
  13 (13.00%) high mild

Fast ICA/GFunc_Logcosh/1000
                        time:   [91.057 µs 92.249 µs 93.431 µs]
                        change: [-6.2060% -5.4401% -4.7246%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 22 outliers among 100 measurements (22.00%)
  1 (1.00%) low mild
  4 (4.00%) high mild
  17 (17.00%) high severe
Fast ICA/GFunc_Logcosh/10000
                        time:   [916.11 µs 919.42 µs 922.88 µs]
                        change: [-1.3843% -1.0460% -0.6588%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Fast ICA/GFunc_Logcosh/100000
                        time:   [13.872 ms 14.020 ms 14.171 ms]
                        change: [+10.756% +11.932% +13.114%] (p = 0.00 < 0.05)
                        Performance has regressed.

Fast ICA/Exp/1000       time:   [84.897 µs 85.525 µs 86.097 µs]
                        change: [-9.5933% -9.2310% -8.8506%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 9 outliers among 100 measurements (9.00%)
  5 (5.00%) low severe
  2 (2.00%) low mild
  2 (2.00%) high mild
Fast ICA/Exp/10000      time:   [713.15 µs 715.78 µs 718.45 µs]
                        change: [-11.707% -11.404% -11.114%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 3 outliers among 100 measurements (3.00%)
  1 (1.00%) low mild
  2 (2.00%) high mild
Fast ICA/Exp/100000     time:   [19.594 ms 19.828 ms 20.067 ms]
                        change: [+13.723% +15.042% +16.547%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 6 outliers among 100 measurements (6.00%)
  3 (3.00%) low mild
  3 (3.00%) high mild

cargo bench -p linfa-linear -q
oojo12@femi-device:/mnt/c/Users/femio/Documents/linfa$ cargo bench -p linfa-linear -q
running 69 tests
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
test result: ok. 0 passed; 0 failed; 69 ignored; 0 measured; 0 filtered out; finished in 0.00s

Linfa_linear/OLS-5Feats/1000
                        time:   [15.159 µs 15.213 µs 15.279 µs]
Linfa_linear/GLM-5Feats/1000
                        time:   [352.49 µs 352.81 µs 353.17 µs]
Found 3 outliers among 100 measurements (3.00%)
  1 (1.00%) low mild
  1 (1.00%) high mild
  1 (1.00%) high severe
Linfa_linear/OLS-5Feats/10000
                        time:   [180.66 µs 181.26 µs 181.95 µs]
Linfa_linear/GLM-5Feats/10000
                        time:   [3.3059 ms 3.3098 ms 3.3143 ms]
Found 9 outliers among 100 measurements (9.00%)
  6 (6.00%) high mild
  3 (3.00%) high severe
Linfa_linear/OLS-5Feats/100000
                        time:   [3.2894 ms 3.3143 ms 3.3397 ms]
Found 9 outliers among 100 measurements (9.00%)
  4 (4.00%) low mild
  4 (4.00%) high mild
  1 (1.00%) high severe
Benchmarking Linfa_linear/GLM-5Feats/100000: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 10.2s, or reduce sample count to 40.
Linfa_linear/GLM-5Feats/100000
                        time:   [101.81 ms 102.74 ms 103.75 ms]
Found 4 outliers among 100 measurements (4.00%)
  4 (4.00%) high mild
Linfa_linear/OLS-10Feats/100000
                        time:   [11.356 ms 11.464 ms 11.571 ms]
Benchmarking Linfa_linear/GLM-10Feats/100000: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 9.6s, or reduce sample count to 50.
Linfa_linear/GLM-10Feats/100000
                        time:   [95.747 ms 96.903 ms 98.005 ms]
Found 3 outliers among 100 measurements (3.00%)
  3 (3.00%) low mild

cargo bench -p linfa-linear -q -F intel-mkl-static
oojo12@femi-device:/mnt/c/Users/femio/Documents/linfa$ cargo bench -p linfa-linear -q -F intel-mkl-static
running 69 tests
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
test result: ok. 0 passed; 0 failed; 69 ignored; 0 measured; 0 filtered out; finished in 0.00s

Linfa_linear/OLS-5Feats/1000
                        time:   [13.778 µs 13.828 µs 13.864 µs]
                        change: [-13.505% -12.651% -11.689%] (p = 0.00 < 0.05)
                        Performance has improved.
Linfa_linear/GLM-5Feats/1000
                        time:   [295.62 µs 296.73 µs 298.11 µs]
                        change: [-14.818% -14.391% -13.879%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high severe
Linfa_linear/OLS-5Feats/10000
                        time:   [168.71 µs 168.78 µs 168.87 µs]
                        change: [-7.9615% -7.6014% -7.2511%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 5 outliers among 100 measurements (5.00%)
  4 (4.00%) high mild
  1 (1.00%) high severe
Linfa_linear/GLM-5Feats/10000
                        time:   [2.2417 ms 2.2426 ms 2.2436 ms]
                        change: [-32.339% -32.244% -32.159%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 6 outliers among 100 measurements (6.00%)
  6 (6.00%) high mild
Linfa_linear/OLS-5Feats/100000
                        time:   [2.7646 ms 2.7759 ms 2.7892 ms]
                        change: [-16.990% -16.246% -15.524%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 5 outliers among 100 measurements (5.00%)
  3 (3.00%) high mild
  2 (2.00%) high severe
Benchmarking Linfa_linear/GLM-5Feats/100000: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 15.8s, or reduce sample count to 30.
Linfa_linear/GLM-5Feats/100000
                        time:   [154.41 ms 155.21 ms 156.07 ms]
                        change: [+49.403% +51.072% +52.729%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 3 outliers among 100 measurements (3.00%)
  3 (3.00%) high mild
Linfa_linear/OLS-10Feats/100000
                        time:   [9.6127 ms 9.6585 ms 9.7072 ms]
                        change: [-16.607% -15.750% -14.808%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 2 outliers among 100 measurements (2.00%)
  2 (2.00%) high mild
Benchmarking Linfa_linear/GLM-10Feats/100000: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 10.5s, or reduce sample count to 40.
Linfa_linear/GLM-10Feats/100000
                        time:   [104.48 ms 105.18 ms 105.95 ms]
                        change: [+7.1473% +8.5397% +10.042%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 12 outliers among 100 measurements (12.00%)
  3 (3.00%) high mild
  9 (9.00%) high severe

cargo bench -p linfa-pls -q
oojo12@femi-device:/mnt/c/Users/femio/Documents/linfa$ cargo bench -p linfa-pls -q
running 25 tests
iiiiiiiiiiiiiiiiiiiiiiiii
test result: ok. 0 passed; 0 failed; 25 ignored; 0 measured; 0 filtered out; finished in 0.00s

Linfa_pls/Regression-Nipals-5Feats/1000
                        time:   [208.09 µs 208.19 µs 208.33 µs]
Found 7 outliers among 100 measurements (7.00%)
  4 (4.00%) high mild
  3 (3.00%) high severe
Linfa_pls/Canonical-Nipals-5Feats/1000
                        time:   [7.9120 ns 7.9683 ns 8.0416 ns]
Linfa_pls/Cca-Nipals-5Feats/1000
                        time:   [8.1832 ns 8.3129 ns 8.4271 ns]
Found 17 outliers among 100 measurements (17.00%)
  2 (2.00%) high mild
  15 (15.00%) high severe
Linfa_pls/Regression-Nipals-5Feats/10000
                        time:   [2.0402 ms 2.0407 ms 2.0413 ms]
Found 9 outliers among 100 measurements (9.00%)
  4 (4.00%) high mild
  5 (5.00%) high severe
Linfa_pls/Canonical-Nipals-5Feats/10000
                        time:   [7.8653 ns 7.8872 ns 7.9119 ns]
Found 15 outliers among 100 measurements (15.00%)
  9 (9.00%) high mild
  6 (6.00%) high severe
Linfa_pls/Cca-Nipals-5Feats/10000
                        time:   [7.8686 ns 7.8951 ns 7.9242 ns]
Found 10 outliers among 100 measurements (10.00%)
  2 (2.00%) high mild
  8 (8.00%) high severe
Linfa_pls/Regression-Nipals-5Feats/100000
                        time:   [25.019 ms 25.074 ms 25.137 ms]
Found 8 outliers among 100 measurements (8.00%)
  5 (5.00%) high mild
  3 (3.00%) high severe
Linfa_pls/Canonical-Nipals-5Feats/100000
                        time:   [8.3477 ns 8.4512 ns 8.5605 ns]
Found 23 outliers among 100 measurements (23.00%)
  19 (19.00%) low severe
  4 (4.00%) low mild
Linfa_pls/Cca-Nipals-5Feats/100000
                        time:   [8.8812 ns 8.9391 ns 9.0124 ns]
Found 22 outliers among 100 measurements (22.00%)
  1 (1.00%) low mild
  5 (5.00%) high mild
  16 (16.00%) high severe
Linfa_pls/Regression-Nipals-10Feats/100000
                        time:   [44.065 ms 45.090 ms 46.250 ms]
Found 3 outliers among 100 measurements (3.00%)
  2 (2.00%) high mild
  1 (1.00%) high severe
Linfa_pls/Canonical-Nipals-10Feats/100000
                        time:   [8.9908 ns 9.0708 ns 9.1734 ns]
Linfa_pls/Cca-Nipals-10Feats/100000
                        time:   [9.5099 ns 9.6530 ns 9.8343 ns]
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high severe
Linfa_pls/Regression-Svd-5Feats/1000
                        time:   [213.69 µs 215.88 µs 218.25 µs]
Found 2 outliers among 100 measurements (2.00%)
  1 (1.00%) high mild
  1 (1.00%) high severe
Linfa_pls/Canonical-Svd-5Feats/1000
                        time:   [9.7439 ns 10.166 ns 10.639 ns]
Found 15 outliers among 100 measurements (15.00%)
  2 (2.00%) high mild
  13 (13.00%) high severe
Linfa_pls/Cca-Svd-5Feats/1000
                        time:   [10.236 ns 10.311 ns 10.392 ns]
Found 5 outliers among 100 measurements (5.00%)
  3 (3.00%) high mild
  2 (2.00%) high severe
Linfa_pls/Regression-Svd-5Feats/10000
                        time:   [2.1973 ms 2.2315 ms 2.2708 ms]
Found 7 outliers among 100 measurements (7.00%)
  4 (4.00%) high mild
  3 (3.00%) high severe
Linfa_pls/Canonical-Svd-5Feats/10000
                        time:   [8.8178 ns 8.8215 ns 8.8253 ns]
Found 8 outliers among 100 measurements (8.00%)
  3 (3.00%) low mild
  2 (2.00%) high mild
  3 (3.00%) high severe
Linfa_pls/Cca-Svd-5Feats/10000
                        time:   [8.8147 ns 8.8184 ns 8.8233 ns]
Found 10 outliers among 100 measurements (10.00%)
  2 (2.00%) low severe
  4 (4.00%) low mild
  2 (2.00%) high mild
  2 (2.00%) high severe
Linfa_pls/Regression-Svd-5Feats/100000
                        time:   [24.624 ms 24.814 ms 24.990 ms]
Found 4 outliers among 100 measurements (4.00%)
  2 (2.00%) low severe
  2 (2.00%) low mild
Linfa_pls/Canonical-Svd-5Feats/100000
                        time:   [9.1704 ns 9.2239 ns 9.2828 ns]
Linfa_pls/Cca-Svd-5Feats/100000
                        time:   [9.1081 ns 9.1352 ns 9.1682 ns]
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high mild
Linfa_pls/Regression-Svd-10Feats/100000
                        time:   [36.395 ms 36.598 ms 36.812 ms]
Found 3 outliers among 100 measurements (3.00%)
  3 (3.00%) high mild
Linfa_pls/Canonical-Svd-10Feats/100000
                        time:   [9.2100 ns 9.2693 ns 9.3374 ns]
Linfa_pls/Cca-Svd-10Feats/100000
                        time:   [8.8536 ns 8.8783 ns 8.9081 ns]

cargo bench -p linfa-pls -q -F intel-mkl-static
oojo12@femi-device:/mnt/c/Users/femio/Documents/linfa$ cargo bench -p linfa-pls -q -F intel-mkl-static
running 25 tests
iiiiiiiiiiiiiiiiiiiiiiiii
test result: ok. 0 passed; 0 failed; 25 ignored; 0 measured; 0 filtered out; finished in 0.00s

Linfa_pls/Regression-Nipals-5Feats/1000
                        time:   [218.90 µs 221.13 µs 223.47 µs]
                        change: [+1.3774% +2.6556% +3.9910%] (p = 0.00 < 0.05)
                        Performance has regressed.
Linfa_pls/Canonical-Nipals-5Feats/1000
                        time:   [8.6595 ns 8.6959 ns 8.7395 ns]
                        change: [+5.9673% +7.1422% +8.3531%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 3 outliers among 100 measurements (3.00%)
  1 (1.00%) high mild
  2 (2.00%) high severe
Linfa_pls/Cca-Nipals-5Feats/1000
                        time:   [8.7229 ns 8.7770 ns 8.8314 ns]
                        change: [+7.1095% +8.1816% +9.2140%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 2 outliers among 100 measurements (2.00%)
  2 (2.00%) high mild
Linfa_pls/Regression-Nipals-5Feats/10000
                        time:   [2.1112 ms 2.1290 ms 2.1477 ms]
                        change: [+3.4751% +4.3265% +5.2747%] (p = 0.00 < 0.05)
                        Performance has regressed.
Linfa_pls/Canonical-Nipals-5Feats/10000
                        time:   [8.5796 ns 8.6079 ns 8.6421 ns]
                        change: [+8.3916% +8.8811% +9.3770%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 17 outliers among 100 measurements (17.00%)
  2 (2.00%) low severe
  3 (3.00%) low mild
  1 (1.00%) high mild
  11 (11.00%) high severe
Linfa_pls/Cca-Nipals-5Feats/10000
                        time:   [7.7817 ns 7.8567 ns 7.9529 ns]
                        change: [+2.0146% +3.2795% +4.5430%] (p = 0.00 < 0.05)
                        Performance has regressed.
Linfa_pls/Regression-Nipals-5Feats/100000
                        time:   [23.306 ms 23.380 ms 23.462 ms]
                        change: [-7.1240% -6.7554% -6.3878%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 13 outliers among 100 measurements (13.00%)
  9 (9.00%) high mild
  4 (4.00%) high severe
Linfa_pls/Canonical-Nipals-5Feats/100000
                        time:   [7.6522 ns 7.6820 ns 7.7210 ns]
                        change: [-9.0386% -7.7480% -6.4691%] (p = 0.00 < 0.05)
                        Performance has improved.
Linfa_pls/Cca-Nipals-5Feats/100000
                        time:   [7.6062 ns 7.6240 ns 7.6486 ns]
                        change: [-14.799% -14.298% -13.788%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 16 outliers among 100 measurements (16.00%)
  2 (2.00%) low mild
  3 (3.00%) high mild
  11 (11.00%) high severe
Linfa_pls/Regression-Nipals-10Feats/100000
                        time:   [31.430 ms 31.724 ms 32.066 ms]
                        change: [-31.536% -29.642% -27.860%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 17 outliers among 100 measurements (17.00%)
  6 (6.00%) high mild
  11 (11.00%) high severe
Linfa_pls/Canonical-Nipals-10Feats/100000
                        time:   [8.5970 ns 8.7048 ns 8.8243 ns]
                        change: [-9.8506% -7.7853% -5.1734%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 21 outliers among 100 measurements (21.00%)
  3 (3.00%) low mild
  13 (13.00%) high mild
  5 (5.00%) high severe
Linfa_pls/Cca-Nipals-10Feats/100000
                        time:   [8.2053 ns 8.3359 ns 8.4771 ns]
                        change: [-26.655% -23.143% -19.891%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 17 outliers among 100 measurements (17.00%)
  14 (14.00%) low severe
  1 (1.00%) low mild
  1 (1.00%) high mild
  1 (1.00%) high severe
Linfa_pls/Regression-Svd-5Feats/1000
                        time:   [174.57 µs 174.64 µs 174.71 µs]
                        change: [-19.443% -18.597% -17.826%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 6 outliers among 100 measurements (6.00%)
  2 (2.00%) low mild
  3 (3.00%) high mild
  1 (1.00%) high severe
Linfa_pls/Canonical-Svd-5Feats/1000
                        time:   [7.6545 ns 7.6862 ns 7.7219 ns]
                        change: [-22.915% -20.565% -18.412%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 8 outliers among 100 measurements (8.00%)
  4 (4.00%) high mild
  4 (4.00%) high severe
Linfa_pls/Cca-Svd-5Feats/1000
                        time:   [7.6873 ns 7.7222 ns 7.7659 ns]
                        change: [-24.608% -23.624% -22.684%] (p = 0.00 < 0.05)
                        Performance has improved.
Benchmarking Linfa_pls/Regression-Svd-5Feats/10000: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 8.5s, enable flat sampling, or reduce sample count to 50.
Linfa_pls/Regression-Svd-5Feats/10000
                        time:   [1.6892 ms 1.6901 ms 1.6913 ms]
                        change: [-25.447% -24.119% -22.944%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 14 outliers among 100 measurements (14.00%)
  9 (9.00%) high mild
  5 (5.00%) high severe
Linfa_pls/Canonical-Svd-5Feats/10000
                        time:   [7.6419 ns 7.6752 ns 7.7131 ns]
                        change: [-13.385% -13.067% -12.712%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 17 outliers among 100 measurements (17.00%)
  5 (5.00%) high mild
  12 (12.00%) high severe
Linfa_pls/Cca-Svd-5Feats/10000
                        time:   [7.6413 ns 7.6644 ns 7.6900 ns]
                        change: [-13.127% -12.772% -12.346%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 12 outliers among 100 measurements (12.00%)
  7 (7.00%) high mild
  5 (5.00%) high severe
Linfa_pls/Regression-Svd-5Feats/100000
                        time:   [19.110 ms 19.183 ms 19.268 ms]
                        change: [-23.324% -22.693% -22.010%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 7 outliers among 100 measurements (7.00%)
  3 (3.00%) high mild
  4 (4.00%) high severe
Linfa_pls/Canonical-Svd-5Feats/100000
                        time:   [7.7722 ns 7.8342 ns 7.9111 ns]
                        change: [-13.952% -12.946% -11.929%] (p = 0.00 < 0.05)
                        Performance has improved.
Linfa_pls/Cca-Svd-5Feats/100000
                        time:   [7.6404 ns 7.6651 ns 7.6930 ns]
                        change: [-16.846% -16.515% -16.161%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 12 outliers among 100 measurements (12.00%)
  8 (8.00%) high mild
  4 (4.00%) high severe
Linfa_pls/Regression-Svd-10Feats/100000
                        time:   [28.623 ms 29.112 ms 29.658 ms]
                        change: [-21.816% -20.454% -18.806%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 13 outliers among 100 measurements (13.00%)
  1 (1.00%) high mild
  12 (12.00%) high severe
Linfa_pls/Canonical-Svd-10Feats/100000
                        time:   [7.6373 ns 7.6673 ns 7.7036 ns]
                        change: [-20.068% -19.433% -18.792%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 15 outliers among 100 measurements (15.00%)
  5 (5.00%) high mild
  10 (10.00%) high severe
Linfa_pls/Cca-Svd-10Feats/100000
                        time:   [7.7171 ns 7.7675 ns 7.8295 ns]
                        change: [-13.947% -13.029% -12.112%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 21 outliers among 100 measurements (21.00%)
  13 (13.00%) high mild
  8 (8.00%) high severe

Why are the ICA results you posted different from mine?

Some thoughts

Criterion docs that contain some insights
Not a compiler expert, but we are using different machines so is it possible that what mine compiled to was different?
One of them could have been a spurious result. We can raise the significance level of the tests to make it more robust to this.

Quick question in the non-blas case we are using linfa-linalg correct? If so, has that package itself been benchmarked against the blas alternative? I'm just wondering that is a more direct comparison getting to what we are interest in here.

So how about the below to increase confidence?

Up the confidence level to 97 (default 95) - yielding 97% confidence
that the true runtime lies within our estimated confidence interval
Up measurement time to 10s (default 3) - time allotted to finish a
sample however if it is to low Criterion will automatically increase it.
Up sample size to 200 (default 100) - number of samples in a run
Up warmup time to 10s (default 5s) - time given to comp to adjust to load
Increase noise threshold to 5% (changes less than 5% will be ignored.
The default is .01)
Decrease significance level to .02 (default.05) - for hypothesis testing
in criterions context the default means about 5% of identical benchmarks
will be considered different due to noise.

On Thu, Nov 10, 2022, 10:11 PM Yuhan Lin @.***> wrote:

The only difference between the first and second ICA benchmarks are the
charge levels (first one is half power and second one is full power). I
guess something like this can make a big difference. I'll update the ICA
issue with the new results. I think what you want is to reduce the
significance level, not increasing it, and I'm willing to accept that. You
can also increase the noise threshold. Instead of running the benchmarks
twice we can just increase the sample size and measurement time to run more
iterations and increase confidence in our results. Running benchmarks twice
just adds confusion when we get two different results.
—
Reply to this email directly, view it on GitHub
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You are receiving this because you commented.Message ID:
@.***>

Try running a few benchmarks with these changes. If they don't take too long, then we can accept them.
Regarding benchmarking linfa-linalg directly, if we were to actually do a general comparison between linfa-linalg and BLAS, then linfa-linalg will definitely be slower.  However, I'm only interested in the performance of the linfa ML algorithms, which only exercises a subset of the linalg algorithms with a subset of the possible inputs. Reaching performance parity on this subset of BLAS functionality is much easier than on the entirety of all BLAS usecases. As much as I'd like to say that all non-BLAS SVD algorithms will perform just as well as their BLAS counterparts, it's not going to happen with linfa-linalg.
New algorithms using linalg routines may perform worse without BLAS than with BLAS. Practically speaking, after BLAS removal of existing algorithms, we won't care about BLAS performance of new algorithms. Rather, we'll use benchmarks and profiling to improve the non-BLAS performance to an acceptable level. This is acceptable because most new algorithms don't use BLAS at all, so it's unlikely that we'll run into this problem in the first place.

Gotcha that makes sense. As it pertains to the new settings here are the results. Albeit I upped the measurement time to 15 and still got a warning message about either reducing sample size or increasing it slightly for one of the parameterized benchmarks.
Normal - 1min 39secs
Robust - 3min 38secs
That warning disappeared at 20 and doesn't appear if I drop sample size down to 150 and keep measurement time at 15.

I don't really care about that warning. We can keep the sample size and measurement time the same.
Can you make a PR for the benchmark changes so we don't need to discuss them here?

Context

Laptop fully charged
Laptop not in use during test
Laptop not over heating
Laptop plugged in

System specs:
hardware:
Processor	12th Gen Intel(R) Core(TM) i5-1235U   1.30 GHz
Installed RAM	8.00 GB (7.72 GB usable)
Device ID	5F0BF67B-026D-4E93-8F42-D89620D51451
Product ID	00342-20909-03914-AAOEM
System type	64-bit operating system, x64-based processor
Pen and touch	Pen and touch support with 10 touch points

OS:
Edition	Windows 11 Home
Version	22H2
Installed on	9/29/2022
OS build	22621.674
Experience	Windows Feature Experience Pack 1000.22634.1000.0

Algorithms
Linfa PLS
Linfa_pls/Regression-Nipals-5Feats/1000
                        time:   [344.24 µs 357.38 µs 373.74 µs]
                        change: [+36.794% +44.166% +53.327%] (p = 0.00 < 0.05)
                        Performance has regressed.
Linfa_pls/Canonical-Nipals-5Feats/1000
                        time:   [9.6842 ns 9.7007 ns 9.7215 ns]
                        change: [-5.3093% -4.9069% -4.5169%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 33 outliers among 200 measurements (16.50%)
  11 (5.50%) high mild
  22 (11.00%) high severe
Linfa_pls/Cca-Nipals-5Feats/1000
                        time:   [9.6890 ns 9.7024 ns 9.7189 ns]
                        change: [-5.7148% -4.8418% -3.7950%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 26 outliers among 200 measurements (13.00%)
  9 (4.50%) high mild
  17 (8.50%) high severe
Linfa_pls/Regression-Nipals-5Feats/10000
                        time:   [3.1733 ms 3.1816 ms 3.1909 ms]
                        change: [-7.0568% -6.7312% -6.4065%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 11 outliers among 200 measurements (5.50%)
  5 (2.50%) high mild
  6 (3.00%) high severe
Linfa_pls/Canonical-Nipals-5Feats/10000
                        time:   [12.144 ns 12.381 ns 12.578 ns]
                        change: [+6.0338% +8.4908% +11.165%] (p = 0.00 < 0.05)
                        Performance has regressed.
Linfa_pls/Cca-Nipals-5Feats/10000
                        time:   [10.821 ns 11.124 ns 11.472 ns]
                        change: [+17.739% +20.082% +22.441%] (p = 0.00 < 0.05)
                        Performance has regressed.
Linfa_pls/Regression-Nipals-5Feats/100000
                        time:   [44.548 ms 47.182 ms 49.851 ms]
                        change: [+20.101% +26.879% +33.073%] (p = 0.00 < 0.05)
                        Performance has regressed.
Linfa_pls/Canonical-Nipals-5Feats/100000
                        time:   [13.124 ns 13.139 ns 13.156 ns]
                        change: [+26.855% +27.495% +28.096%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 15 outliers among 200 measurements (7.50%)
  1 (0.50%) low severe
  1 (0.50%) low mild
  1 (0.50%) high mild
  12 (6.00%) high severe
Linfa_pls/Cca-Nipals-5Feats/100000
                        time:   [9.6890 ns 9.7102 ns 9.7384 ns]
                        change: [-5.7419% -5.2295% -4.7242%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 26 outliers among 200 measurements (13.00%)
  9 (4.50%) high mild
  17 (8.50%) high severe
Benchmarking Linfa_pls/Regression-Nipals-10Feats/100000: Warming up for 10.000 s
Warning: Unable to complete 200 samples in 10.0s. You may wish to increase target time to 12.1s, or reduce sample count to 160.
Linfa_pls/Regression-Nipals-10Feats/100000
                        time:   [82.851 ms 82.988 ms 83.140 ms]
                        change: [+66.261% +66.774% +67.292%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 8 outliers among 200 measurements (4.00%)
  4 (2.00%) high mild
  4 (2.00%) high severe
Linfa_pls/Canonical-Nipals-10Feats/100000
                        time:   [10.945 ns 11.265 ns 11.627 ns]
                        change: [+18.124% +20.740% +22.820%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 58 outliers among 200 measurements (29.00%)
  46 (23.00%) low severe
  1 (0.50%) low mild
  4 (2.00%) high mild
  7 (3.50%) high severe
Linfa_pls/Cca-Nipals-10Feats/100000
                        time:   [9.6863 ns 9.6991 ns 9.7153 ns]
                        change: [-5.5358% -4.9845% -4.4494%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 28 outliers among 200 measurements (14.00%)
  8 (4.00%) high mild
  20 (10.00%) high severe
Linfa_pls/Regression-Svd-5Feats/1000
                        time:   [292.50 µs 293.19 µs 294.09 µs]
                        change: [-9.4257% -8.4861% -7.6607%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 10 outliers among 200 measurements (5.00%)
  8 (4.00%) high mild
  2 (1.00%) high severe
Linfa_pls/Canonical-Svd-5Feats/1000
                        time:   [9.6979 ns 9.7179 ns 9.7438 ns]
                        change: [-10.038% -8.2813% -6.4957%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 24 outliers among 200 measurements (12.00%)
  8 (4.00%) high mild
  16 (8.00%) high severe
Linfa_pls/Cca-Svd-5Feats/1000
                        time:   [9.6933 ns 9.7073 ns 9.7240 ns]
                        change: [-13.856% -11.941% -10.039%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 25 outliers among 200 measurements (12.50%)
  12 (6.00%) high mild
  13 (6.50%) high severe
Linfa_pls/Regression-Svd-5Feats/10000
                        time:   [2.8835 ms 2.8900 ms 2.8975 ms]
                        change: [-37.503% -33.962% -30.066%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 11 outliers among 200 measurements (5.50%)
  4 (2.00%) high mild
  7 (3.50%) high severe
Linfa_pls/Canonical-Svd-5Feats/10000
                        time:   [9.6885 ns 9.7073 ns 9.7301 ns]
                        change: [-7.8209% -5.4202% -4.1597%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 32 outliers among 200 measurements (16.00%)
  8 (4.00%) high mild
  24 (12.00%) high severe
Linfa_pls/Cca-Svd-5Feats/10000
                        time:   [9.6913 ns 9.7106 ns 9.7336 ns]
                        change: [-4.8600% -4.3851% -3.9269%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 30 outliers among 200 measurements (15.00%)
  11 (5.50%) high mild
  19 (9.50%) high severe
Linfa_pls/Regression-Svd-5Feats/100000
                        time:   [32.291 ms 32.372 ms 32.466 ms]
                        change: [-5.3005% -4.7793% -4.2841%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 14 outliers among 200 measurements (7.00%)
  7 (3.50%) high mild
  7 (3.50%) high severe
Linfa_pls/Canonical-Svd-5Feats/100000
                        time:   [9.6894 ns 9.7054 ns 9.7258 ns]
                        change: [-4.9622% -4.4433% -3.9503%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 30 outliers among 200 measurements (15.00%)
  9 (4.50%) high mild
  21 (10.50%) high severe
Linfa_pls/Cca-Svd-5Feats/100000
                        time:   [9.6950 ns 9.7104 ns 9.7292 ns]
                        change: [-13.648% -11.669% -9.7167%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 23 outliers among 200 measurements (11.50%)
  3 (1.50%) high mild
  20 (10.00%) high severe
Linfa_pls/Regression-Svd-10Feats/100000
                        time:   [52.774 ms 55.614 ms 58.474 ms]
                        change: [-24.889% -19.201% -13.529%] (p = 0.00 < 0.05)
                        Performance has improved.
Linfa_pls/Canonical-Svd-10Feats/100000
                        time:   [9.6883 ns 9.7062 ns 9.7283 ns]
                        change: [-5.8813% -4.9856% -4.2806%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 27 outliers among 200 measurements (13.50%)
  6 (3.00%) high mild
  21 (10.50%) high severe
Linfa_pls/Cca-Svd-10Feats/100000
                        time:   [9.6895 ns 9.7063 ns 9.7268 ns]
                        change: [-5.8981% -4.8350% -3.8458%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 29 outliers among 200 measurements (14.50%)
  10 (5.00%) high mild
  19 (9.50%) high severe

Linfa-linear
Linfa_linear/OLS-5Feats/1000
                        time:   [18.425 µs 20.095 µs 22.118 µs]
                        change: [+81.182% +91.628% +104.40%] (p = 0.00 < 0.02)
                        Performance has regressed.
Linfa_linear/GLM-5Feats/1000
                        time:   [347.05 µs 375.86 µs 406.45 µs]
                        change: [+41.988% +47.201% +51.446%] (p = 0.00 < 0.02)
                        Performance has regressed.
Found 49 outliers among 200 measurements (24.50%)
  31 (15.50%) low severe
  7 (3.50%) high mild
  11 (5.50%) high severe
Linfa_linear/OLS-5Feats/10000
                        time:   [175.92 µs 182.05 µs 190.11 µs]
                        change: [-3.1880% -0.9899% +1.6559%] (p = 0.45 > 0.02)
                        No change in performance detected.
Found 7 outliers among 200 measurements (3.50%)
  2 (1.00%) high mild
  5 (2.50%) high severe
Linfa_linear/GLM-5Feats/10000
                        time:   [2.1292 ms 2.1361 ms 2.1462 ms]
                        change: [-23.912% -23.612% -23.237%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 14 outliers among 200 measurements (7.00%)
  10 (5.00%) high mild
  4 (2.00%) high severe
Linfa_linear/OLS-5Feats/100000
                        time:   [5.7795 ms 5.9988 ms 6.2028 ms]
                        change: [+66.304% +72.788% +79.367%] (p = 0.00 < 0.02)
                        Performance has regressed.
Found 50 outliers among 200 measurements (25.00%)
  48 (24.00%) low severe
  1 (0.50%) low mild
  1 (0.50%) high mild
Benchmarking Linfa_linear/GLM-5Feats/100000: Warming up for 10.000 s
Warning: Unable to complete 200 samples in 10.0s. You may wish to increase target time to 34.3s, or reduce sample count to 50.
Linfa_linear/GLM-5Feats/100000
                        time:   [124.87 ms 132.96 ms 141.01 ms]
                        change: [+120.60% +134.56% +148.17%] (p = 0.00 < 0.02)
                        Performance has regressed.
Linfa_linear/OLS-10Feats/100000
                        time:   [11.413 ms 11.437 ms 11.465 ms]
                        change: [+17.779% +18.446% +19.099%] (p = 0.00 < 0.02)
                        Performance has regressed.
Found 21 outliers among 200 measurements (10.50%)
  11 (5.50%) high mild
  10 (5.00%) high severe
Linfa_linear/GLM-10Feats/100000
                        time:   [33.518 ms 33.642 ms 33.771 ms]
                        change: [-93.703% -93.651% -93.603%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 5 outliers among 200 measurements (2.50%)
  5 (2.50%) high mild

These new benchmark results look strange. Some of the benchmarks have around a +40% runtime, which makes it seem like BLAS is a lot slower.

hmmm I reran it as a sanity check with the following changes to get the results below:

Disabled a few start-up applications
Switched mode to performance mode when on plug-in power source instead of balanced mode
Restarted my computer + waited a lil while for bootup process to finish
Every so often monitored the CPU/Disk/Memory during benchmarks. Nothing wild there, the usage was pretty consistent across all benches

New results
criterion.zip
Linfa-ica
Benchmarking Fast ICA/GFunc_Cube/1000: Collecting 200 samples in estimated 10.839 s (201k itFast ICA/GFunc_Cube/1000
                        time:   [53.628 µs 54.549 µs 55.637 µs]
                        change: [-18.316% -15.567% -12.326%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 30 outliers among 200 measurements (15.00%)
  13 (6.50%) high mild
  17 (8.50%) high severe
Benchmarking Fast ICA/GFunc_Cube/10000: Warming up for 10.000 s
Warning: Unable to complete 200 samples in 10.0s. You may wish to increase target time to 11.1s, enable flat sampling, or reduce sample count to 130.
Benchmarking Fast ICA/GFunc_Cube/10000: Collecting 200 samples in estimated 11.087 s (20k itFast ICA/GFunc_Cube/10000
                        time:   [544.05 µs 564.51 µs 588.85 µs]
                        change: [-21.221% -18.213% -15.100%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 27 outliers among 200 measurements (13.50%)
  12 (6.00%) high mild
  15 (7.50%) high severe
Benchmarking Fast ICA/GFunc_Cube/100000: Collecting 200 samples in estimated 11.160 s (1000 Fast ICA/GFunc_Cube/100000
                        time:   [10.846 ms 11.265 ms 11.692 ms]
                        change: [-18.176% -13.527% -8.7968%] (p = 0.00 < 0.02)
                        Performance has improved.

Benchmarking Fast ICA/GFunc_Logcosh/1000: Collecting 200 samples in estimated 10.117 s (141kFast ICA/GFunc_Logcosh/1000
                        time:   [71.111 µs 73.378 µs 76.084 µs]
                        change: [-25.449% -21.126% -16.787%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 31 outliers among 200 measurements (15.50%)
  10 (5.00%) high mild
  21 (10.50%) high severe
Benchmarking Fast ICA/GFunc_Logcosh/10000: Warming up for 10.000 s
Warning: Unable to complete 200 samples in 10.0s. You may wish to increase target time to 15.8s, enable flat sampling, or reduce sample count to 110.
Benchmarking Fast ICA/GFunc_Logcosh/10000: Collecting 200 samples in estimated 15.837 s (20kFast ICA/GFunc_Logcosh/10000
                        time:   [714.59 µs 739.90 µs 770.62 µs]
                        change: [-21.415% -17.674% -13.566%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 26 outliers among 200 measurements (13.00%)
  13 (6.50%) high mild
  13 (6.50%) high severe
Benchmarking Fast ICA/GFunc_Logcosh/100000: Collecting 200 samples in estimated 10.484 s (10Fast ICA/GFunc_Logcosh/100000
                        time:   [10.322 ms 10.499 ms 10.701 ms]
                        change: [-9.4825% -7.0584% -4.5651%] (p = 0.00 < 0.02)
                        Change within noise threshold.
Found 21 outliers among 200 measurements (10.50%)
  10 (5.00%) high mild
  11 (5.50%) high severe

Benchmarking Fast ICA/Exp/1000: Collecting 200 samples in estimated 11.258 s (141k iterationFast ICA/Exp/1000       time:   [81.798 µs 84.377 µs 87.337 µs]
                        change: [-27.709% -24.513% -21.232%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 34 outliers among 200 measurements (17.00%)
.7s, enable flat sampling, or reduce sample count to 110.
Benchmarking Fast ICA/Exp/10000: Collecting 200 samples in estimated 15.686 s (20k iterationFast ICA/Exp/10000      time:   [747.95 µs 775.90 µs 811.36 µs]
                        change: [-30.952% -27.691% -24.440%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 18 outliers among 200 measurements (9.00%)
  7 (3.50%) high mild
  11 (5.50%) high severe
Benchmarking Fast ICA/Exp/100000: Collecting 200 samples in estimated 10.445 s (600 iteratioFast ICA/Exp/100000     time:   [16.089 ms 16.508 ms 16.962 ms]
                        change: [-22.663% -18.994% -15.249%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 19 outliers among 200 measurements (9.50%)
  4 (2.00%) high mild
  15 (7.50%) high severe

running 0 tests

Linfa-linear

Benchmarking Linfa_linear/OLS-5Feats/1000: Collecting 200 samples in estimated 10.260 s (683Linfa_linear/OLS-5Feats/1000
                        time:   [16.100 µs 17.669 µs 19.373 µs]
                        change: [+1.4135% +9.4220% +18.296%] (p = 0.01 < 0.02)
                        Change within noise threshold.
Found 35 outliers among 200 measurements (17.50%)
  13 (6.50%) high mild
  22 (11.00%) high severe
Benchmarking Linfa_linear/GLM-5Feats/1000: Collecting 200 samples in estimated 10.588 s (40kLinfa_linear/GLM-5Feats/1000
                        time:   [264.29 µs 269.09 µs 274.75 µs]
                        change: [-32.986% -30.777% -28.649%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 17 outliers among 200 measurements (8.50%)
  4 (2.00%) high mild
  13 (6.50%) high severe
Benchmarking Linfa_linear/OLS-5Feats/10000: Collecting 200 samples in estimated 12.250 s (60Linfa_linear/OLS-5Feats/10000
                        time:   [181.90 µs 189.21 µs 197.10 µs]
                        change: [-7.4067% -1.4866% +4.9617%] (p = 0.61 > 0.02)
                        No change in performance detected.
Found 16 outliers among 200 measurements (8.00%)
  13 (6.50%) high mild
  3 (1.50%) high severe
Benchmarking Linfa_linear/GLM-5Feats/10000: Collecting 200 samples in estimated 10.137 s (32Linfa_linear/GLM-5Feats/10000
                        time:   [2.5267 ms 2.6327 ms 2.7492 ms]
                        change: [-23.462% -18.803% -13.714%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 30 outliers among 200 measurements (15.00%)
  7 (3.50%) high mild
  23 (11.50%) high severe
Benchmarking Linfa_linear/OLS-5Feats/100000: Collecting 200 samples in estimated 10.242 s (2Linfa_linear/OLS-5Feats/100000
                        time:   [3.5774 ms 3.6597 ms 3.7516 ms]
                        change: [-1.8761% +1.0010% +4.1621%] (p = 0.45 > 0.02)
                        No change in performance detected.
Found 24 outliers among 200 measurements (12.00%)
  7 (3.50%) high mild
  17 (8.50%) high severe
Benchmarking Linfa_linear/GLM-5Feats/100000: Collecting 200 samples in estimated 12.127 s (4Linfa_linear/GLM-5Feats/100000
                        time:   [30.049 ms 30.786 ms 31.600 ms]
                        change: [-28.585% -26.179% -23.484%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 26 outliers among 200 measurements (13.00%)
  6 (3.00%) high mild
  20 (10.00%) high severe
Benchmarking Linfa_linear/OLS-10Feats/100000: Collecting 200 samples in estimated 10.440 s (Linfa_linear/OLS-10Feats/100000
                        time:   [9.9221 ms 10.092 ms 10.279 ms]
                        change: [-15.483% -11.905% -8.0618%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 13 outliers among 200 measurements (6.50%)
  7 (3.50%) high mild
  6 (3.00%) high severe
Benchmarking Linfa_linear/GLM-10Feats/100000: Collecting 200 samples in estimated 17.521 s (Linfa_linear/GLM-10Feats/100000
                        time:   [42.114 ms 43.135 ms 44.247 ms]
                        change: [-42.517% -40.842% -39.047%] (p = 0.00 < 0.02)
                        Performance has improved.
Found 19 outliers among 200 measurements (9.50%)
  12 (6.00%) high mild
  7 (3.50%) high severe

Linfa-pls
Benchmarking Linfa_pls/Regression-Nipals-5Feats/1000: Collecting 200 samples in estimated 14Linfa_pls/Regression-Nipals-5Feats/1000
                        time:   [332.40 µs 339.32 µs 347.45 µs]
                        change: [-9.3788% -3.3968% +2.1272%] (p = 0.23 > 0.05)
                        No change in performance detected.
Found 32 outliers among 200 measurements (16.00%)
  6 (3.00%) high mild
  26 (13.00%) high severe
Benchmarking Linfa_pls/Canonical-Nipals-5Feats/1000: Collecting 200 samples in estimated 10.Linfa_pls/Canonical-Nipals-5Feats/1000
                        time:   [10.733 ns 10.837 ns 10.952 ns]
                        change: [-3.2810% -1.1013% +1.1901%] (p = 0.28 > 0.05)
                        No change in performance detected.
Found 11 outliers among 200 measurements (5.50%)
  9 (4.50%) high mild
  2 (1.00%) high severe
Benchmarking Linfa_pls/Cca-Nipals-5Feats/1000: Collecting 200 samples in estimated 10.000 s Linfa_pls/Cca-Nipals-5Feats/1000
                        time:   [11.561 ns 11.974 ns 12.400 ns]
                        change: [+2.5840% +5.3203% +7.9650%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 20 outliers among 200 measurements (10.00%)
  20 (10.00%) high mild
Benchmarking Linfa_pls/Regression-Nipals-5Feats/10000: Collecting 200 samples in estimated 1Linfa_pls/Regression-Nipals-5Feats/10000
                        time:   [3.4579 ms 3.5785 ms 3.7099 ms]
                        change: [-8.1025% -3.6560% +0.9395%] (p = 0.09 > 0.05)
                        No change in performance detected.
Found 33 outliers among 200 measurements (16.50%)
  9 (4.50%) high mild
  24 (12.00%) high severe
Benchmarking Linfa_pls/Canonical-Nipals-5Feats/10000: Collecting 200 samples in estimated 10Linfa_pls/Canonical-Nipals-5Feats/10000
                        time:   [10.836 ns 11.059 ns 11.316 ns]
                        change: [-5.2675% +2.0760% +7.2764%] (p = 0.56 > 0.05)
                        No change in performance detected.
Found 31 outliers among 200 measurements (15.50%)
  7 (3.50%) high mild
  24 (12.00%) high severe
Benchmarking Linfa_pls/Cca-Nipals-5Feats/10000: Collecting 200 samples in estimated 10.000 sLinfa_pls/Cca-Nipals-5Feats/10000
                        time:   [10.866 ns 11.006 ns 11.169 ns]
                        change: [+0.6067% +4.6806% +11.108%] (p = 0.04 < 0.05)
                        Change within noise threshold.
Found 14 outliers among 200 measurements (7.00%)
  9 (4.50%) high mild
  5 (2.50%) high severe
Benchmarking Linfa_pls/Regression-Nipals-5Feats/100000: Collecting 200 samples in estimated Linfa_pls/Regression-Nipals-5Feats/100000
                        time:   [36.568 ms 37.228 ms 37.971 ms]
                        change: [-11.704% -9.0450% -6.1640%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 23 outliers among 200 measurements (11.50%)
  13 (6.50%) high mild
  10 (5.00%) high severe
Benchmarking Linfa_pls/Canonical-Nipals-5Feats/100000: Collecting 200 samples in estimated 1Linfa_pls/Canonical-Nipals-5Feats/100000
                        time:   [10.766 ns 10.957 ns 11.190 ns]
                        change: [+1.1430% +3.1624% +5.2441%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 18 outliers among 200 measurements (9.00%)
  6 (3.00%) high mild
  12 (6.00%) high severe
Benchmarking Linfa_pls/Cca-Nipals-5Feats/100000: Collecting 200 samples in estimated 10.000 Linfa_pls/Cca-Nipals-5Feats/100000
                        time:   [10.813 ns 10.965 ns 11.136 ns]
                        change: [+1.3988% +2.8733% +4.5508%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 12 outliers among 200 measurements (6.00%)
  4 (2.00%) high mild
  8 (4.00%) high severe
Benchmarking Linfa_pls/Regression-Nipals-10Feats/100000: Collecting 200 samples in estimatedLinfa_pls/Regression-Nipals-10Feats/100000
                        time:   [48.517 ms 49.261 ms 50.071 ms]
                        change: [-9.5216% -7.2586% -4.9307%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 21 outliers among 200 measurements (10.50%)
  14 (7.00%) high mild
  7 (3.50%) high severe
Benchmarking Linfa_pls/Canonical-Nipals-10Feats/100000: Collecting 200 samples in estimated Linfa_pls/Canonical-Nipals-10Feats/100000
                        time:   [12.367 ns 12.652 ns 12.958 ns]
                        change: [+15.737% +18.169% +20.209%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 12 outliers among 200 measurements (6.00%)
  10 (5.00%) high mild
  2 (1.00%) high severe
Benchmarking Linfa_pls/Cca-Nipals-10Feats/100000: Collecting 200 samples in estimated 10.000Linfa_pls/Cca-Nipals-10Feats/100000
                        time:   [11.463 ns 11.820 ns 12.201 ns]
                        change: [+1.9071% +4.3553% +7.1001%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 15 outliers among 200 measurements (7.50%)
  15 (7.50%) high mild
Benchmarking Linfa_pls/Regression-Svd-5Feats/1000: Collecting 200 samples in estimated 13.55Linfa_pls/Regression-Svd-5Feats/1000
                        time:   [316.38 µs 325.86 µs 336.43 µs]
                        change: [-9.2796% -5.3762% -1.6286%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 24 outliers among 200 measurements (12.00%)
  6 (3.00%) high mild
  18 (9.00%) high severe
Benchmarking Linfa_pls/Canonical-Svd-5Feats/1000: Collecting 200 samples in estimated 10.000Linfa_pls/Canonical-Svd-5Feats/1000
                        time:   [10.764 ns 10.901 ns 11.069 ns]
                        change: [-0.1394% +2.1162% +4.6039%] (p = 0.06 > 0.05)
                        No change in performance detected.
Found 16 outliers among 200 measurements (8.00%)
  5 (2.50%) high mild
  11 (5.50%) high severe
Benchmarking Linfa_pls/Cca-Svd-5Feats/1000: Collecting 200 samples in estimated 10.000 s (96Linfa_pls/Cca-Svd-5Feats/1000
                        time:   [10.856 ns 11.018 ns 11.195 ns]
                        change: [+2.4001% +4.8511% +7.6410%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 27 outliers among 200 measurements (13.50%)
  10 (5.00%) high mild
  17 (8.50%) high severe
Benchmarking Linfa_pls/Regression-Svd-5Feats/10000: Collecting 200 samples in estimated 10.6Linfa_pls/Regression-Svd-5Feats/10000
                        time:   [3.2120 ms 3.3163 ms 3.4310 ms]
                        change: [+0.4988% +4.2011% +7.8310%] (p = 0.02 < 0.05)
                        Change within noise threshold.
Found 36 outliers among 200 measurements (18.00%)
  11 (5.50%) high mild
  25 (12.50%) high severe
Benchmarking Linfa_pls/Canonical-Svd-5Feats/10000: Collecting 200 samples in estimated 10.00Linfa_pls/Canonical-Svd-5Feats/10000
                        time:   [11.028 ns 11.299 ns 11.604 ns]
                        change: [-2.3799% -0.1380% +2.2186%] (p = 0.90 > 0.05)
                        No change in performance detected.
Found 18 outliers among 200 measurements (9.00%)
  11 (5.50%) high mild
  7 (3.50%) high severe
Benchmarking Linfa_pls/Cca-Svd-5Feats/10000: Collecting 200 samples in estimated 10.000 s (9Linfa_pls/Cca-Svd-5Feats/10000
                        time:   [11.117 ns 11.392 ns 11.688 ns]
                        change: [+5.1226% +8.1209% +11.584%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 23 outliers among 200 measurements (11.50%)
  12 (6.00%) high mild
  11 (5.50%) high severe
Benchmarking Linfa_pls/Regression-Svd-5Feats/100000: Collecting 200 samples in estimated 14.Linfa_pls/Regression-Svd-5Feats/100000
                        time:   [34.748 ms 35.355 ms 36.050 ms]
                        change: [-16.349% -13.904% -11.395%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 19 outliers among 200 measurements (9.50%)
  6 (3.00%) high mild
  13 (6.50%) high severe
Benchmarking Linfa_pls/Canonical-Svd-5Feats/100000: Collecting 200 samples in estimated 10.0Linfa_pls/Canonical-Svd-5Feats/100000
                        time:   [11.100 ns 11.332 ns 11.591 ns]
                        change: [+3.8935% +6.7934% +9.5674%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 23 outliers among 200 measurements (11.50%)
  22 (11.00%) high mild
  1 (0.50%) high severe
Benchmarking Linfa_pls/Cca-Svd-5Feats/100000: Collecting 200 samples in estimated 10.000 s (Linfa_pls/Cca-Svd-5Feats/100000
                        time:   [10.868 ns 11.004 ns 11.155 ns]
                        change: [+1.0660% +3.6581% +6.4764%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 20 outliers among 200 measurements (10.00%)
  4 (2.00%) high mild
  16 (8.00%) high severe
Benchmarking Linfa_pls/Regression-Svd-10Feats/100000: Collecting 200 samples in estimated 19Linfa_pls/Regression-Svd-10Feats/100000
                        time:   [46.033 ms 47.427 ms 48.927 ms]
                        change: [-22.650% -19.176% -15.678%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 13 outliers among 200 measurements (6.50%)
  13 (6.50%) high mild
Benchmarking Linfa_pls/Canonical-Svd-10Feats/100000: Collecting 200 samples in estimated 10.Linfa_pls/Canonical-Svd-10Feats/100000
                        time:   [10.891 ns 11.065 ns 11.271 ns]
                        change: [-21.951% -19.650% -17.248%] (p = 0.00 < 0.05)
                        Performance has improved.
Benchmarking Linfa_pls/Cca-Svd-10Feats/100000: Collecting 200 samples in estimated 10.000 s Linfa_pls/Cca-Svd-10Feats/100000
                        time:   [11.073 ns 11.306 ns 11.570 ns]
                        change: [-3.0381% -0.1359% +2.8312%] (p = 0.91 > 0.05)
                        No change in performance detected.
Found 25 outliers among 200 measurements (12.50%)
  7 (3.50%) high mild
  18 (9.00%) high severe


Fixing the absence of 'tag' in the version.

Can one of the admins verify this patch?

Can one of the admins verify this patch?


Please add com.Timenut.SongKey

SongKey is now happy to use your library.
com.Timenut.SongKey

Fixed. Sorry for the delay.


PlayOnEntry doesn't work

Hi there
I want to add a audio to one slide, and I found that PlayOnEntry property doesn't work for me. It doesn't report error, but It just not play on entry.
Private Sub Button1_Click(sender As Object, e As RibbonControlEventArgs) Handles Button1.Click
        Dim path = "c:\temp\test\slide1.wav"
        With Globals.ThisAddIn.Application.ActivePresentation.Slides(1).Shapes.AddMediaObject2(path).AnimationSettings
            .Animate = Microsoft.Office.Core.MsoTriState.msoTrue
            .AnimateBackground = Microsoft.Office.Core.MsoTriState.msoTrue
            With .PlaySettings
                .PlayOnEntry = Microsoft.Office.Core.MsoTriState.msoTrue
                .HideWhileNotPlaying = Microsoft.Office.Core.MsoTriState.msoTrue
            End With
        End With
    End Sub

@chenxizhang Thanks for your feedback. I suggest that you try the VBA developer community on Stack Overflow (https://stackoverflow.com/questions/tagged/vba) where you can get help from actual developers. If you find a bug in our documentation, please let us know. Thanks!


Can't upgrade to nuxt 3 stable

Version
@nuxtjs/tailwindcss: 6.1.3 / 6.2.0
nuxt: 3.0.0
yarn: 3.2.0
Steps to reproduce
Try to upgrade from nuxt rc14 to the stable release while using this package.
What is Expected?
Nuxt should start up without issues
What is actually happening?
On startup I'm getting errors like "Cannot find module 'tailwindcss/plugin'". It seems to happen because packages that @nuxtjs/tailwindcss depends on like tailwindcss, postcss & various postcss plugins no longer get installed at the root level of /node_modules, but instead get stored inside /node_modules/@nuxtjs/tailwindcss/node_modules. And so when Nuxt starts up, it can't find tailwindcss or if you add tailwindcss as a direct dependency to your package.json, then it's something else like postcss-custom-properties.
I'm not sure why this started happening, it's possible that something else in the Nuxt toolchain changed its dependency tree and this has changed how the package manager hoists all of the packages and stores them.
I think these issues might also be related to this issue:
https://github.com/nuxt-modules/tailwindcss/issues/586
https://github.com/nuxt-modules/tailwindcss/issues/576

Adding the following packages to my package.json as direct dependencies fixed the issue:
    "postcss": "^8.4.18",
    "postcss-custom-properties": "^12.1.9",
    "postcss-html": "^1.5.0",
    "postcss-nesting": "^10.2.0",
    "tailwindcss": "^3.2.0",

It's certainly not ideal, considering that you would usually want @nuxtjs/tailwindcss to manage the versions of these packages, but it works


Update Babel to ^7.7.0 and enable Flow enums parsing

The ability to parse Flow enums was added in Babel 7.7.0. It sits behind an option "enums" for the flow Babel plugin.
Update the Babel dependency to ^7.7.0, fix a small test issue, then enable enums: true.
I understand that we wouldn't need to do this with the 11.x series of babel-eslint because Babel is a peer dependency in 11.x, but that's still in beta and not officially released, so that's why I'm opening up a PR for the 10.x series.

The support for parsing enums should not be breaking, since enum is a "future reserved word" and its usage was previously a parse error.

Hi @kaicataldo, now that the holiday season has passed, I was wondering if you'd be willing to take another look at this.

@gkz Would you be willing to make this PR over in https://github.com/babel/babel/tree/master/eslint/babel-eslint-parser?  I'm working to get babel-eslint back to a sane state for the Babel v8 release, and doing all active development there now. Thanks!

Though, actually, this should already work in the latest version. Adding these tests could be helpful though.

We need to add the "flow" plugin to the parser options (to be able to parse Flow code), and add the enums: true flow plugin option to parse neums.
I'll take a look

@gkz The latest version reads your Babel config at parse time, so if Babel is able to parse it, ESLint should also.

@kaicataldo We are blocked on launching the enums feature because of lack of Eslint support. @babel/eslint-parser has not been released yet. I would appreciate it if you would consider merging this and releasing 10.0.4. Since the version of @babel/parser is specified as ^7.0.0, anyone installing babel-eslint will get the latest version of Babel parser already, so the only change in this PR is adding {enums: true}. Since enum is a reserved word, any usage would be a parse error.

I realized I need to add EnumDeclarations to the scope analysis - will update the PR soon

I have updated the test

Do we also need to update the peer dependency on @babel/core?

No, it would be a breaking change. If someone is using an old version of Babel, this PR is a noop.

Ah right, this is in v10. Got it :+1:

Published in<EMAIL_ADDRESS>


Fixed some issues

Nice red colors for errors. Works perfectly! But I noticed some things that could improve like the output message print newline so that the ouput afterwards starts on newline. I'll fix those


[Android] Update the mirrored workflow to publish the Android app to the Play Store with a Service Account

Why
As described here, we're waiting for a shared Service Account to be able to verify the CD workflow to publish the Android app to the Play Store. After getting the Service Account, we need to enable the app publishing step with r0adkll/upload-google-play@v1 in the mirrored workflow to ensure it would work with future template updates.
Who Benefits?
Developers

This ticket is pending due to missing necessary google-services JSON file for app publishing https://nimble-co.slack.com/archives/CFSQY8VK4/p1677646814091149.


Mobile Trading: Categories and Filters button is missing 'Categories &' + icon needs updating as looks old

Dev

Design
https://www.figma.com/file/Do4qnkCNM8P7lwBmCdYZCS4X/Market-List-Page?node-id=3829%3A1951

looks good


Number input Gray 90/100 unreadable

Summary
Cant use number input with gray 90 or 100, input is unreadable and buttons are invisible

Relevant information

cc @jnm2377 this should be fixed by your changes, right?


usb: device_next: report medium error on Mass Storage failed writes

End Write command with Medium Error / Write Error when underlying disk write or sync fails.
usb: device_next: accept padded Mass Storage CDBs
Universal Serial Bus Mass Storage Specification For Bootability requires that device shall accept Bulk-Only-Transport bCBWCBLength of 12 for all commands documented in the specification. Allow padding for commands documented in Bootability specification, but leave others intact.
This fixes Request Sense on Windows 11 which previously failed due to bCBWCBLength being 12 instead of 6.

checkpatch is failing on MACRO_WITH_FLOW_CONTROL because the ## inside the macro is outside the diff context. The macro itself is fine when whole macro is added in single commit. I can work around the issue by creating a brand new macro and not modifying the old one, but that feels wrong.

Completely avoided the macro warning by creating new function that returns unpadded CB length. This hopefully maintains the clear SCSI/USB separation (which will be useful in case someone wants to reuse the SCSI code outside USB context).


[Network] Fix Network Watcher commands

This checklist is used to make sure that common guidelines for a pull request are followed.
General Guidelines

[ ] The PR has modified HISTORY.rst describing any customer-facing, functional changes. Note that this does not include changes only to help content. (see Modifying change log).

Command Guidelines

[X] Each command and parameter has a meaningful description.
[X] Each new command has a test.

(see Authoring Command Modules)

View a preview at https://prompt.ws/r/Azure/azure-cli/5441
This is an experimental preview for @microsoft users.


Handle external IDs for mentions that use client-style paths rather than matrix.to-style

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Comparison is base (59d2a99) 89.40% compared to head (a670285) 89.39%.

Additional details and impacted files
@@            Coverage Diff             @@
##             main     #867      +/-   ##
==========================================
- Coverage   89.40%   89.39%   -0.02%
==========================================
  Files         118       86      -32
  Lines       16845    15324    -1521
  Branches      635        0     -635
==========================================
- Hits        15061    13699    -1362
+ Misses       1760     1625     -135
+ Partials       24        0      -24

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unittests-rust
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Fixed transfer learning architecture

This should fix #5. I also added a standalone T3D model so it could be trained after the transfer learning process is complete.

The paper uses 224x224 frames for both the 2D CNN and the 3D one, so I also made sure the input shapes match.

I will change the get_video.py file and then merge it to master