Question: So I need to classify a spiral dataset. I have been experimenting with a bunch of algorithms like KNN, Kernel SVM, etc. I would like to try to improve the performance of Logistic Regression using feature engineering, preprocessing, etc.

I am also using scikit learn to do all of the classifications.

I fully understand Logistic Regression is not the proper algorithm to do this sort of problem. This is more of a learning excerise for Pre processing and other feature engineering/extraction methods to see how much I can improve this specific model.

Here is an example dataset I would use for the classification. Any suggestions of how I can manipulate the dataset to use in the Logistic Regression algorithm would be helpful.

[![Example Dataset](https://i.stack.imgur.com/biy1H.png)](https://i.stack.imgur.com/biy1H.png)

I also have datasets with multiple spirals as well. some datasets have 2 classes or sometimes up to 5. This means up to 5 spirals.

Answer: 
The C++20 way is

```
template <typename T, size_t S> requires (S > 0)
class Example
{
    // ...
};

```
`T values[0];` is alread ill-formed. Thus, if you configure the compiler to not use language extensions, then it won't compile.

Or, you can use a `static_assert`.