baffle | tiny javascript library for obfuscating and revealing text

You've already added canvas to the pane, try to apply .setScaleX/Y before pane.getChildren().add(canvas).

The cooldown rate is automatically derived from the timeGradient, which is simply put 0.0 at the start, 0.5 at half the spentTime and 1.0 at all of the spentTime.

But yes, the classic Simulated Annealing method has 2 parameters (starting temperature and cooldown rate). One could implement such an SA pretty easily by copy-pasting the SimulatedAnnealingAcceptor and configuring it in the AcceptorConfig.

That being said, tuning 2 parameters is a pain for users. That's why OptaPlanner default SA only has 1 parameter that - together with the termination - is translated into the 2 parameters that SA needs.

A*A^(-1) = I and A^(-1)*A = I

should both be true.

I get somethin like this for the first multiplication:

It only happens if you don’t have an AAD B2C application registration created in the directory.

Follow this https://docs.microsoft.com/en-us/azure/active-directory-b2c/tutorial-register-applications?tabs=app-reg-ga

The key step is this

Under Supported account types, select Accounts in any identity provider or organizational directory (for authenticating users with user flows).

You missread this - it says 32 BYTE aligned, not BIT.

So you have to do 32-byte alignment instead of 4-byte alignment.

To align any stack variable you can use alignas(32) T var;, where T can be any type for example std::array.

To align std::vector's memory or any other heap-based structure alignas(...) is not enough, you have to write special aligning allocator (see Test() function for example of usage):

Try it online!

First & foremost, beyond a certain size of data, & especially when working with raw text or tokenized text, you probably don't want to be using Pandas dataframes for every interim result.

They add extra overhead & complication that isn't fully 'Pythonic'. This is particularly the case for:

• Python list objects where each word is a separate string: once you've tokenized raw strings into this format, as for example to feed such texts to Gensim's Word2Vec model, trying to put those into Pandas just leads to confusing list-representation issues (as with your columns where the same text might be shown as either ['yessir', 'shit', 'is', 'real'] – which is a true Python list literal – or [yessir, shit, is, real] – which is some other mess likely to break if any tokens have challenging characters).
• the raw word-vectors (or later, text-vectors): these are more compact & natural/efficient to work with in raw Numpy arrays than Dataframes

So, by all means, if Pandas helps for loading or other non-text fields, use it there. But then use more fundamntal Python or Numpy datatypes for tokenized text & vectors - perhaps using some field (like a unique ID) in your Dataframe to correlate the two.

Especially for large text corpuses, it's more typical to get away from CSV and instead use large text files, with one text per newline-separated line, and any each line being pre-tokenized so that spaces can be fully trusted as token-separated.

That is: even if your initial text data has more complicated punctuation-sensative tokenization, or other preprocessing that combines/changes/splits other tokens, try to do that just once (especially if it involves costly regexes), writing the results to a single simple text file which then fits the simple rules: read one text per line, split each line only by spaces.

Lots of algorithms, like Gensim's Word2Vec or FastText, can either stream such files directly or via very low-overhead iterable-wrappers - so the text is never completely in memory, only read as needed, repeatedly, for multiple training iterations.

For more details on this efficient way to work with large bodies of text, see this artice: https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/

The compiler is warning you that the default assignment of your string property (which is null) doesn't match its stated type (which is non-null string).

This is emitted when nullable reference types are switched on, which changes all reference types to be non-null, unless stated otherwise with a ?.

For example, your code could be changed to

i would prefer to use it like the referred method in the mongoose docs

https://mongoosejs.com/docs/api.html#schema_Schema-method

where mentioning the name is an argument for the method function.Not like what you have done here eg. const schema = kittySchema = new Schema(..);

Currently, you are not parallelizing much. You can start by parallelizing the f function since it looks computational demanding: