Mallet | based package for statistical natural language processing | Natural Language Processing library

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.

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.

If you've installed the latest Gensim, 4.0.0 (as of late March, 2021), the LdaMallet model has been removed, along with a number of other tools which simply wrapped external tools/APIs.

You can see the note in the Gensim migration guide at:

https://github.com/RaRe-Technologies/gensim/wiki/Migrating-from-Gensim-3.x-to-4#15-removed-third-party-wrappers

If the use of that tool is essential to your project, you may be able to:

• install an older version of Gensim, such as 3.8.3 - though of course you'd then be missing the latest fixes & optimizations on any other Gensim models you're using

• extract the ldamallet.py source code from that older version & update/move it to your own code for private use - dealing with whatever issues arise

This looks like a classpath issue. I'm not sure how Java on Windows handles classpath now. Try setting %MALLET_HOME% to C:\Mallet-2.0.8, not the bin directory? The classes would be in %MALLET_HOME%\class also, perhaps try adding that to %PATH% or %CLASSPATH%?

Pseudo-code:

Just use display flex on your logo's container:

This is in the optimize_interval argument. From the wrapper documentation:

optimize_interval (int, optional) – Optimize hyperparameters every optimize_interval iterations

So although alpha is originally set (or left as the default), if you set optimize_interval then every n iterations, the alpha and beta will be optimised automatically.

Is the position var, which you take from the touch location, used to set the position of the mallet? If it is, then if you want the mallet above the touch, why not do something like position.y += 50 immediately after position = location to move it up by 50 points?

Alternatively, you might find it more logical to set the mallet's anchorPoint property (https://developer.apple.com/documentation/spritekit/skspritenode/1519877-anchorpoint and https://developer.apple.com/documentation/spritekit/skspritenode/using_the_anchor_point_to_move_a_sprite) to be somewhere other than the default poisition (the centre of the sprite) e.g. the point that corresponds to the part of the handle of the mallet where one would normally hold it.

So, there's 2 types of objects you need to be aware of.

dict: when you load JSON it's stored as a dictionary object. Dict objects let you access the values through keys, like you're doing here -> info['data']['featured']

list: print(info) is showing that 'info' is a list of things. You can tell by the square brackets [], or just by calling print(type(info)). A list is ordered so to access the name for the first object in your list you would say info[0]['name'].

To get all the objects in your list in a row you can use a for loop:

for x in info: print(x['name']) You can name 'x' whatever you want. That loop is just saying " for every object in this list, perform the following action"