Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

Hi, it is a nice work to learning machine learning and I like it very much. I am new to HTML and JS, can you give a step-by-step guide to install, train and play this game?

The code in the documentation should be updated so it can be used with a newer version of Neataptic. E.g. Architect -> architect. The JSFiddle's should also be updated accordingly.

The generation of the diagrams for the user manual requires a lot of manual work: trigger the generation and copying the graphs into the right directory. This generation process needs to be unified and automated. Ideally one script call, e.g. generate_graphs.sh, should trigger the generation process and copy the results into the correct directory.

The place where the diagram generation code i

Description of the bug

The run() method of the SimulatedAnnealing class has a bug when the annealing method does not find a better state than the initial one.

When does it happens

The bug happens when the annealing algorithm fails to find a better state than the initial one. This can happen when the maxim

I am entering the subject as a non mathematician and non data scientist. I'm a potential user of your code who is looking to solve actual down to earth practical problems. While your code apparently beautifully includes a multitude of problems and algorithms, they are really wasted on me as I simply don't get the math, the lingo or the examples.

I therefore suggest you provide a few practical e