optima | New container scheduler for Docker | Continuous Deployment library

As i shows in attached images, my grid row is square shaped space before connecting to server and load the images, but when images are loaded it changes it's height and become rectangle shape. how can i avoid it ? i want to keep the square shaped space.

my photos are also squared.

before and after after connecting to server [1]: https://i.stack.imgur.com/lZj6F.png [2]: https://i.stack.imgur.com/ZaKQd.png

Code :

I was trying to run a command from PHP to my Linux server but I'm having some trouble with one command: This is the script:

Need new variable to contain a 1 if the factor variable (chargeback_date) has a record and a 0 if it is missing a record. The factor variable is chargeback_date and the new variable I am trying to create is chargeback. chargeback_rate only has records when there is a chargeback and the rest of the records are not null, but missing and the variable is of type factor. The code I put returns a 0 for every record in my new variable chargeback. Here is the code: mutate(chargeback=as.numeric(ifelse(!is.missing(challenge$chargeback_date), 0, 1), levels = c(0,1)))

I'm struggling with getting the desired output of Array of objects. So let's I have this Array of object:

Hi all I am newbie in python and I want to know how to search for data in this txt file in the link where the user will input the name of the car and the app should search the file and print the line back here is my code I tried to input Kia Optima LX 4dr for search but it not work

I have a data which is having more than 1500 rows. Each row has a sentence. I am trying to find out the best method to find the most similar sentences among all. I have tried this example but the processing is so much slow that it took around 20 minutes for 1500 rows data.

I have used the code from my previous question and tried many types to improve the speed but it doesn't affect much. I came across universal sentence encoder using tensorflow which seems fast and having good accuracy. I am working on colab you can check it here

Suggestions / refer links /codes are appreciated.

I have a data which is having more than 1500 rows. Each row has a sentence. I am trying to find out the best method to find the most similar sentences among all.

What I have tried

1. I have tried K-mean algorithm which groups similar sentences in a cluster. But I found a drawback in which I have to pass K to create a cluster. It is hard to guess K. I tried elbo method to guess the clusters but grouping all together isn't sufficient. In this approach I am getting all the data grouped. I am looking for data which is similar above 0.90% data should be returned with ID.

2. I tried cosine similarity in which I used TfidfVectorizer to create matrix and then passed in cosine similarity. Even this approach didn't worked properly.

What I am looking for

I want an approach where I can pass a threshold example 0.90 data in all rows which are similar to each other above 0.90% should be returned as a result.

I'm looking for a regex pattern that matches the following string:

Some example text (SET) that demonstrates what I'm looking for. Energy system models (ESM) are used to find specific optima (SCO). Some say computer systems (CUST) are cool. In the summer playing outside (OUTS) should be preferred.

My goal is to match the following:

I know this would seem similar to a lot of questions asked previously on the same topic. I have surveyed most of them but they don't quite answer my question. My problem is that my gradient is not converging to optima, it is rather diverging and oscillating at even very low values of alpha.

My data generation function is below