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I have two numpy arrays. One numpy array is 2D of shape (200,x) where x can be positive integer. And another array is 1D array of shape (x,) where x is same as the 2D numpy array. I want to randomly drop columns in both arrays in unison if the second dimension of 2D array is greater than 1000 to make it of size (200,1000) and the corresponding 1D array will also be (1000,). I know I can use np.delete but I don't how to ensure that columns are randomly dropped such that second dimension of 2D array is 1000 and the length of 1D array is also 1000. Insights will be appreciated.

I have the following problem I'm unable to resolve :

I have two version controlled directories on my local machine. One is dirA which holds a lot of my stuff and is a private repo I don't want to share. The other is dirB, a small public repo with files I do want to share, it's mainly for others to clone and pull, the changes all come from me.

The thing is that dirB holds files which are all coming from dirA, it's basically a subset of dirA though it has another structure.

Anytime I make changes to the files in dirA that are also in dirB, I'd like to update dirB and then push the updated files to the public repo.

I tried using rsync and unison but I have to do it by hand anytime I update the specific files I mentioned, it's not very efficient. I gathered git submodules or hooks could help me but I don't understand what tool I should use.

In substance, I have :

Given a dataframe df as follows:

I recently started working with Ruby after writing mostly very functional/compositional JS and some point free clojure.

I've learned that Ruby is very "open" to modification or extension. Neato.

I set myself a challenge, and promptly failed to make any progress towards it. Likely this is because I'm still in the "Don't know what terms to search" stage of learning Ruby.

Write a module such that when used it does two things...

1. Causes all methods defined on the module/class to become static
2. Causes all methods defined on the module/class to return a lambda of themselves instead

For rule 1 this would look something like

Trying to set up instance of Facebook CTF on an AWS EC2 instance running Ubuntu 16.04 and am running into the following error when doing the quick setup.

I have a numpy 2d-array of shape (N, N) representing a NxN correlation matrix. This matrix is symmetric. Say N=5, then an example of this 2d-array would be:

Just try to learn some MLP basics. I've found this code on Github. But it returs an error. Can somebody help me please to make it work? It seems there is some error in test part, Or may be you can give me some working samples of multylevel perceptrons in Python

This is the error -AssertionError: Training data and labels of different dimensions

I have 3 blocks of text. I want block 1 to fade in and show for 3 seconds and then to fade out. As soon as its opacity reaches 0, I want text block 2 to fade in and be visible for 3 seconds... same with text block 3. I thought I could achieve this by assigning the am animation to all 3 and different animation-delay for each, but they all seem to eventually display in unison:

The title pretty much says it.

I installed NixOS 20.03 in a virtual machine to play a little with it and, if all goes well, install it on my work laptop.

I use Unison's gui, unison-gtk, to keep the laptop and my main production machine in sync. I found the unison package on the search page, but this package does not install unison-gtk, and there seems to be no package for it either. How can I install it?

Thanks in advance.