dtm | implements topics that change over time | Topic Modeling library

I am trying to make predicitions with my LDA model. But when i pass a string to it it gives an error about mismatching input features. Now my question is how can i make my model accept any input and still predict the right topic. Right now it takes 54777 as input.

model:

I am using BERTopic to perform the topic modelling, everything works perfectly fine. However, since I am forcing the algorithm to give me 10 topics using nr_topics=10 as output, and when I visualize the topics overtime using topic_model.visualize_topics_over_time(topics_over_time, top_n_topics=10, width=1250, height=450), some colors are repeated for topics as there are only 7 colors mentioned in the function visualize_topics_over_time. I tried executing the same function in my python notebook with additional color values, but it gives me the following error:

Can someone please help me update the function with additional four colors?

I have the shiny app below in which I create a wordcloud. This wordcloud is based on the shiny widgets in the sidebar. The selectInput() subsets it by label, the Maximum Number of Words: is supposed to show the maximum count of words that will be displayed in the wordcloud and the Minimun Frequency the minimum frequency that a word needs to be displayed. Those widgets are reactive and are based on the df() function which creates the dataframe needed for the wordcloud. The proble is that when I subset using input$freq the dataframe has fewer rows than needed to subset with input$max as well so nothing is displayed.

I am looking to return a specific group in the previous row via regex.

Suppose I have the following information and the target is to extract the value 90 on the basis of the differentiation in the following line.

I have webservice using websockets, and need to implement zero-downtime deployment. Because I don't want drop existing connections on deploy, I've decided to implement blue/green deploy. My actual solution looks like:

1. I've created two identical services in portainer, listening on different ports. Every service has set in node environments some identifier, for example alfa and beta
2. Both services are hidden behind load balancer, and balancer is periodically checking status of each service. If service responds on specific route (/balancer-keepalive-check) with string "OK", this service is active and balancer can routing to this service. If service is responding with string "STOP", balancer mark this service as inaccessible, but active connections will be preserved
3. which service is active and which is stopped is synced over redis. In redis there are keys lb.service.alfa and lb.service.beta which can contains values 1 for active and 0 for inactive. Example of implementation /balancer-keepalive-check route in nestjs:

The lidR package has a neat way to work with huge (pointcloud) datasets: The catalog function (doc here) avoids loading the dataset to memory and can treat mosaics [datasets that are spread across multiple (non-overlapping) tiles] as single dataset. It loads required tiles on-the-fly during computations in an intelligent way. It is great to avoid working with huge files (multiple GBs) and keep memory requirements lean if only working with small parts of the dataset.

Is there a similar convenient/memory-efficient/"lidR-catalog-way" to process large raster mosaics in R? Or more put in a more general way: Is there a way to work with mosaic raster datasets in R without merging them first?

I am aware of the mosaic (doc) and merge functions, which allow me to merge my tiled raster mosaic into a single raster dataset. I also found that gdal will do so a lot faster and memory efficient. Here is a R-snippet for this:

I have tried several times, but it seems like I cannot create a unit test for a very basic Guard in Angular 12 which has

• canActivate
• canActivateChild

as its main methods. Please find the following code:

Why doesn't this code work? I don't know how to describe what happens but here's the code.

I have tried to compile one probabilistic model posted on github (https://github.com/gerowam/influence), but for the person who is not familiar with C language and compiling, it is really hard to know how to install relevant libraries and do compiling with "Makefile" on ubuntu 20.04.