bach | Bach Testing Framework | Unit Testing library

I have a csv that I want to use to search an api for data, but the row which stores the data used for the api search can contain a second value separated by ; like this:

I am trying to reproduce this simple plotly graph that shows images on datapoint hover. The code is available here.

However, I am failing to reproduce it. This is what I have so far:

I have a question about the input baches (if understand it correctly). (i refer this for the classification)

There is a train_images.shape of fashion dataset is (60000, 28, 28). if understand it correctly, it is 60000 images with 28*28 shape. train_labels is array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)

In my case, I have a list which has images in it. The size of the list is 2000 and each image have the shape of 250*250. I convert my labels using np.array(y_train).

problem is-: i cant not use my "train_images" in model.fit(train_images, train_labels, epochs=50) because it is not in a shape model needs.

I think, I need to convert the list of data into (2000,250,250).

can someone help me with how to do that? i directly convert into List to Numpy. which it did not work.

also suggest me what could be the change if i use (2000,250,250).

i did like this, but i think it is not optimal way to do it

I have a dataset that is basically formatted backwards from how I need it to perform a specific analysis. It represents entities and the articles they are found in, represented by id numbers (see below. Column headings [article 1, 2, 3, etc.] are just the 1st, 2nd, 3rd articles they appear in. The index in the cell is the substantive part). What I'd like to get is a count of how many entities appear in each article, which I think I could do with something like dplyr's group_by and summarise, but I can't find anywhere where you can apply it to a range of columns (there are actually 97 article columns in the dataset).

Two specific transformations that would be useful for me are

1. The number of entities in each article calculated as the count of the times each unique ID appears in an entity row. eg:

1. The entities in each article. eg:

All this should be possible from this dataset, I just can't figure out how to get it into a workable format. Thanks for your help!

i'm trying to understand object-relational technology and created parent type:

I'm trying to learn Python and for some reason print summary returns all records, 7 times per record instead of returning all records. I'm running Python3.10, what would be the cause of this? My code is down below:

It creates a bar graph that extends horizontally.
CodeSandBox

The font size of labels such as Bach and Morzart is small and I want to increase it. Could you please tell me how to change the font size of labels?

I'm creating my first Flutter app. The app should show among other things on some places on a map. For the map I use google_maps_flutter (https://pub.dev/packages/google_maps_flutter).

Everything works fine on Android. As soon as I start the app on IOS and click on "Map" in my app, the app crashes without stacktrace. Android Studio just says "Lost connection to Device".

I have the SDK for Android and IOS active on the Google Cloud Platform and have added the key in the appDelegate.swift in my project.

I dont get it. Does anyone know what the problem could be? Thanks a lot for your help <3

This is my Class, which returns the Scaffold with Google Maps Widget:

I have a bunch of names from the web (first name, last name, of people in different countries). Some of the countries have statistics on how many people have each last name, as shown in some places like here.

Well, that Japanese surname list only lists the top 100. I have other lists like for Vietnamese listing the top 20, and other lists the top 50 or 1000 even in some places. But I have real name lists that are up to the 1000+ count. So I might have 2000 Japanese surnames, with only 100 that have listed the actual count of people with that surname.

What I would like to do is built a "faker" sort of library, that generates realistic names based on these statistics. I know how to pick a random element from a weighted array in JavaScript, so once the "weights" (number of people with that name) are included for each name, it is just a matter of plugging it into that algorithm.

My question is, how can I "complete the curve" on the names that don't have a weight on them? That is, say we have an exponential-like curve sort of, from the 20 or 100 names that have weights on them. I would then like to randomly pick names from the remaining unweighted list, and give them a value that places them somewhat realistically in the remaining tail of the curve. How can that be done?

For example, here is a list of Vietnamese names with weights: