samples | Windows 10 IoT Core Samples

I don't know if it is critical for your problem but modifying this

tic/toc should be fine, but it looks like the timing is being skewed by memory pre-allocation.

I can reproduce similar timings to your MATLAB example, however

• On first run (clear workspace)

  • Loop approach takes 2.08 sec
  • Vectorised approach takes 1.04 sec
  • Vectorisation saves 50% execution time

• On second run (workspace not cleared)

  • Loop approach takes 2.55 sec
  • Vectorised approach takes 0.065 sec
  • Vectorisation "saves" 97.5% execution time

My guess would be that since the loop approach explicitly creates a new matrix via zeros, the memory is reallocated from scratch on every run and you don't see the speed improvement on subsequent runs.

However, when HH remains in memory and the HH=___ line outputs a matrix of the same size, I suspect MATLAB is doing some clever memory allocation to speed up the operation.

We can prove this theory with the following test:

I cannot reproduce your issue using:

1. openjdk:8-jre-alpine docker image
2. JMeter 5.4.1
3. Test plan Test.jmx from extras folder of JMeter

Demo:

If you cannot reproduce the above behaviour I think you made some changes either to Results File Configuration or to Reporting Configuration or both so you need to inspect all the JMeter Properties which differ from the defaults and restore their values to the original ones.

If you need further support you need to share at least first 2 lines of your outFilePath.jtl results file. Better if possible the full file and all the .properties files from JMeter's "bin" folder.

You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01

Turns out that Windows 10 Update Assistant incorrectly reported it upgraded my OS to 21H2 on my laptop. Checking Windows version by running winver reports that my OS is still 21H1. Of course CUDA in WSL2 will not work in Windows 10 without 21H2.

After successfully installing 21H2 I can confirm CUDA works with WSL2 even for laptops with Optimus NVIDIA cards.

As mentioned in the comments above, it was resolved after moved the service to run on a separate process (by specifying service with android:process in manifest)

The version 202.7660.26.42.7322048 is

As the order is the same one way is to simply search for adjacent key names & parse out whats in-between:

When the function DataLongGenerator() creates Z, it's a random uniform data in a matrix. The actual coding is