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This is a very basic question relating to setting attributes that I don't find a definitive answer to. I have a python class as follows

I have a Networkx graph like the following image (image source)

I perform edge attacks and observe the change in values at the node of the resulting subgraph.

Example, If I attack edge (a,2): edge (a, 2) and (2, 1) will be removed. To explain a bit, when edge (a, 2) is attacked the node 2 will have a degree < 2. So the edge that's connected to node 2 is also removed.

The above attack results in a subgraph

Each time an edge is attacked, the value of the terminal node labelled e observed over time changes. Let's say I perform 5 (attack = 5) attacks, I have a time x attack matrix (time=25, attack=5) that stores the time-series data of node e.

I would like to ask for suggestions on how to visualize the effect of these attacks on the value of node e changing over time. EDIT:

What information do you want to be able to see or identify from your visualizations?

I want to see the attack on which edge has the maximum effect on the time course value observed at e. We could imagine this to be a transportation network and the values at node reflect the amount of a product that has reached the location/node. From the source node b, the goods are transported to target node e. The observation made is the change in node values after an edge is attacked and no observation of the edge value is available.

Please find the code that is used to attack edges

Is it possible to overload arbitrary operators in Python? Or is one restricted to the list of operators which have associated magic methods as listed here: https://www.python-course.eu/python3_magic_methods.php ?

I'm asking because I noticed that Numpy uses the @ operator to perform matrix multiplication e.g. C=A@B where A,B are Numpy arrays, and I was wondering how they did it.

Edit: The @ operator is not in the list I linked to.

Could someone point me to the Numpy source where this is done?

I'm refreshing my memory about some python features that I didn't get yet, I'm learning from this python tutorial and there's an example that I don't fully understand. It's about a decorator counting calls to a function, here's the code:

MAIN TROUBLE

As an example, say I would like to decorate split() method of str class (the example is representative of what I try to do, except I would like to decorate agg method of pandas DataFrame class).

At least, I get the same error.

I prefer not to use @ nor to create a child of str class to keep things simple (and maybe it is why it is not working...)

I encounter following trouble.

In this example below, the method m on class A is called just once.

I understand this is a feature, this is the Pythonic way to solve the issue where A's m method would be called twice (if it was implemented in the naive way) in this diamond-like inheritance scenario.

This is all described here:
https://www.python-course.eu/python3_multiple_inheritance.php

(1) But under the hood... how did they achieve this behavior i.e. that class A's m method is called ONLY once?!
Asked simplistically: which line is being "skipped" during execution - is it line #1 or line # 2 ?

Could someone shed more light on this?
I have never used multiple inheritance seriously because I mostly program in Java. So I am really curious about this scenario here and more specifically to the inner-workings behind it.

Note: I just want to get the general idea of how this works in Python, not really understand every tiny detail here.

(2) What if I want (in this same scenario and for some reason) A's m method to be called twice (or N times depending on how many base classes of D we have), while still going through using super(). Is this possible? Does super() support such mode of operation?

(3) Is this just some tree or DAG visiting algorithm where they keep track which class's m method has already been visited and just don't visit it (call it) twice? If so then simplistically speaking I guess '# 2' is the line which is skipped.

First post on Overstack.

I'm new to Python and coding in general. I'm now learning about functions and presently looking at some examples that describe global variables on Python-Course.eu:

I am trying to write a class that allows me to record the number of zip files in a location and store that list as an attribute so I can reference it repeatedly. I don't want the check for zip files to be run automatically when the class is initialized because it takes a long time (it does more than what's written here). I have written that but now I would like to expand it with new features. I would like to add two features to it. First, I would also like the ability to recheck the path after setting the attribute (imagine I've created the class then moved more files into the directory). And secondly, I would like the ability to change the directory I look for the zip files in. Here is my original class:

I have seen other posts that say just add extra dimensions as it expects. Firstly, I don't know how to do that exactly, but most importantly, I want to know why my dimensions are changing so I can figure this out on my own for future models.

Note, I am restricted to only MLP for training. This means only Fully Connected layers. No Convolutional Layers nor feedbacks are allowed (LSTM or any RNN architecture). No pre-trained models are allowed such as (resnet, densenet, ...). I can use other operations in-between layers, like Dropout, Batch Normalization or other types of layer input/output manipulation. I expect I will have to supply my entire code to get the help I need. Please forgive all my comments in my code, which I have as a reminder to me as to what everything does. I do know I will need data augmentation, but need this to work first.