lda | Extracting Hidden Topics from Texts using LDA Model | Topic Modeling library

loop through a 16 bit subtraction until the result is negative

"Branch" to Label if result is >0,

Do you see that these descriptions contradict each other?
The 1st one continues on 0, the 2nd one stops on 0.
Only you can decide which one is correct!

From a comment:

This code is part of a Bin to Ascii conversion, made by power of ten subtraction. The bin value could be >$8000, so it is 'negative' but this does not matter. In the first iteration I sub 10000 each cycle until the result is 'below 0', then I restore the previous value and continue with the remainder. The problem is how to detect the 'below 0' condition as said in the post

Next example subtracts 10000 ($2710) from the unsigned word stored at zero page address $90. The low byte is at $90, the high byte is at $91 (little endian).

I am compiling with: g++ -lblas -llapack -llapacke -I /usr/include main.cpp

That command line is wrong. Do this instead:

The latest major Gensim release, 4.0, removed the wrappers of other library algorithms. Per the "Migrating from Gensim 3.x to 4" wiki page:

15. Removed third party wrappers

These wrappers of 3rd party libraries required too much effort. There were no volunteers to maintain and support them properly in Gensim.

If your work depends on any of the modules below, feel free to copy it out of Gensim 3.8.3 (the last release where they appear), and extend & maintain the wrapper yourself.

The removed submodules are:

It looks like some of your documents are empty, in the sense that they contain no counts of any feature.

You can remove them with:

We can initialize a dataset and then rbind instead of printing

The 1000 iteration setting is designed to be a safe number for most collection sizes, and also to communicate "this is a large, round number, so don't think it's very precise". It's likely that smaller numbers will be fine. I once ran a model for 1000000 iterations, and fully half the token assignments never changed from the 1000 iteration model.

Could you be more specific about the cross validation results? Was it that different folds had different MRRs, which were individually stable over iteration counts? Or that individual fold MRRs varied by iteration count, but they balanced out in the overall mean? It's not unusual for different folds to have different "difficulty". Fixing the random seed also wouldn't make a difference if the data is different.

What you can do is first calculate all the unique values over your host_listings_count column and exclude the ones you don't want. In your case that's only filtering for more than 1 property. You can then sort this list and use it as index on your value_counts output like so:

It uses the fixed point iteration. The frequency histograms method is just an efficient way to calculate it. They provide an algebraically equivalent way to do the exact same computation. The update function consists of a sum over a large number of Digamma functions. This function by itself is difficult to compute, but the difference between two Digamma functions (where the arguments differ by an integer) is relatively easy to compute, and even better, it "telescopes" so that the answer to Digamma(a + n) - Digamma(a) is one operation away from the answer to Digamma(a + n + 1) - Digamma(a). If you work through the histogram of counts from 1 to the max, adding up the number of times you saw a count of n at each step, the calculation becomes extremely fast. Initially, we were worried that hyperparameter optimization would take so long that no one would do it. With this trick it's so fast it's not really significant compared to the Gibbs sampling.