Heterogeneous

My goal is to store elements of different data types in one vector. A ball as well as an cuboid is an object.

I am fitting a heterogeneous linear mixed effect model which is in the lcmm package in R. Currently, I am only getting the class-specific and weighted subject-specific prediction from the predictY function. But, I want a subject-specific prediction. Is there any way to construct a subject-specific prediction from this package? Any help is appreciated.

I need to scrape job postings from Indeed. I managed to scrape the titles and links for each job post, and now am struggling to scrape the full job descriptions of each job posting (I don't want the summary - I want each job post's full job description).

My code looks like this:

I am trying to run a Linear Regression in python sklearn on this dataset. I want to impute the NaN values, and in three specific columns I want to replace zeros with NaN so that I can impute those values too. Note: there are other columns with zeros that do not need to be imputed.

For the imputation I am trying to use ColumnTransformer and then use pipeline to run the linear regression. I used this article for reference. However, I am getting an error: ValueError: Specifying the columns using strings is only supported for pandas DataFrames

Can someone verify my process, and let me know if I am doing something wrong?

I have some requirements for the heterogeneous generic implementation for different types of Java objects. I can see warnings related to unchecked conversion. Any thought on the below implementation to avoid any runtime exception. Please find the below sample code.

According to the Kubernetes docs a cluster can have up to 5000 nodes. Imagine I have then node0 with 1CPU, node1 with 2CPU, etc.

Let's say I have a single-core, synchronous process which I want to run instances of. Am I then limited by the smallest node in a heterogeneous cluster of nodes? If not, can I enforce choosing the node largest CPU (which would then limit me to the single node) or smallest CPU (which would then allow seamless and consistent parallel execution) or will Kubernetes do it in the background?

I am getting an error in R when running the cv.glmnet function that I can't trace. Research only hinted me to the approx() function that throws the error when searching for lambda but I am not familiar enough with glmnet to figure out how it connects.

The error is: "need at least two non-NA values to interpolate"

While the solution seems obvious, I am working with simulated data, so definitely no NAs in there. I double-checked multiple times. I also dropped columns with constant variables in one attempt but didn't help either.

Reproducible example is a bit messy due to the complexity of the data (and also not minimal since the error doesn't occur in small example data) but please see below:

As you can see below, I downloaded an array of structures containing heterogeneous objects that were decoded into enums containing nested objects.

I would now like to put said objects into a generic Model structure, but the compiler won't allow this - the error is described below in the code comment. I am relatively new to programming in Swift, I would appreciate your help.

An example of the response from the server is below.

The list consists of elements that have heterogeneous substructures in the info fields. Each of them contains 3 fields with the same types, but they have different keys.

I don't know how to decode this, I haven't encountered such a problem so far. I can't find an example on the Internet that fits this case.

I wanted to decode the enum type at the beginning and select the appropriate info structure based on it, but it doesn't work.

I would very much appreciate your help.