kf | Runs on Kubernetes | BPM library

In my application, I called one API for getting some of data and from that I am downloading files. After downloading callback called. In this case table view updated well. But suppose user visit the screen and before downloading content back to previous screen and again visit same screen. In that case table view not updated. I am facing this issue only when user back to previous controller without completing download content and come again on same screen.

WarrantyVC.Swift

I have this dataset with target LULUS, it's an imbalance dataset. I'm trying to print roc auc score if I could for each fold of my data but in every fold somehow it's always raise error saying ValueError: y should be a 1d array, got an array of shape (15, 2) instead.. I'm kind of confused which part I did wrong because I do it exactly like in the documentation. And in several fold, I get it that It won't print the score if there's only one label but then it will return the second type of error about 1d array.

I have not found any similar question to this...

I have this python script to generate a count matrix from files containing only sequences, but it takes eternity to run but I know awk will do a faster job. i am not so good with awk but hoping someone might be able to help. the python script is as follows:

I am trying to calibrate a model using pykalman and the scipy optimiser. For some reasons scipy seem to think that my input is a masked array, but it is not. I have added the code below:

I have a simple dataset. First I tried to split the dataset using train_test_split(). Then I tried to use KFold(). The code is given below

I have two similar columns of json in a dataframe:

Column A's values look like this:

{"categoryCode": "ZZ","yesNoCode": "Y","conditionIndicator1": "ST"}

Column B's values look like this: {"categoryCode": "ZZ","yesNoCode": "Y","conditionIndicator1": "ST", "conditionIndicator2": "RN"}

I have other columns as well, whose values are similar, but have 3 condition indicators, 4 condition indicators or 5 condition indicators. Each column's values always have the same number of condition indicators.

I'd like to do something easy like val resultDf = df.select(array($"col1", $"col2", ...)) but this fails because the array function won't allow you to select columns of different types. So I need to call some function f to do a transformation on each column's json, so that all of them have five condition indicators.

val resultDf = df.select(array(f($"col1"), f($"col2"), ...)).alias("normalizedData")

This would yield:

Each tuple in this list should consist of a train_indices list and a test_indices list containing the training/testing data point indices for that particular K th split.

Below is what we want to achieve with the dataset:

I have dataframe coords_pa with two columns(x=longitude, y = latitude). For example:

coords_pa <-data.frame(x=c(35.75004,41.41671,67.41672,49.58338),y=c(63.58333,63.41666,38.75004, 54.58338))

How could I create new dataframe coords_pa_new with two columns (x,y), but it will contain only coordinates with condition: x>40 and y>64?

I tried to find and succeed this task, but I do not know how to convert it in one dataframe:

kf = ifelse(coords_pa$y>64,1,0)

newdata = cbind(kf,coords_pa)

ff = ifelse(coords_pa$x>40,1,0)

newdata1 = cbind(ff,coords_pa)

I need to give some logic when I'm taping on items in UICollectionView but my didSelectItemAt method doesn't work

Here is my code of ViewController