Documentation for core/normalise could specify the type of normalization

[shark8me]

The documentation for the core/normalise method could be more explicit, since there are multiple types of normalization used (in the context of machine learning). The current docstring says "Normalises a numerical vector (scales to unit length). Returns a new normalised vector.".

However there are multiple notions of normalization. Here are examples of normalization in other (similar) libraries:

• One example is numpy norm, which has multiple ways of normalization.
• Scikit-learn normalization provides options for L1 or L2 normalization (a subset of those provided by numpy.linalg.norm).
• Feature scaling in machine learning is also called normalization.

Would you consider (for the sake of a PR) feature scaling methods such as those described here as being within the scope of core.matrix?

--
Thanks

[mikera]

Would probably need to be considered separately. Here's my initial thoughts:

• Computing a L1 or L2 on Lx norm is in scope. These are common vector operations and benefit from potential implementation-specific optimisation (via core.matrix protocols)
• Rescaling by means / minimum / maximum sounds a bit domain specific and is easy to do with a couple of core.matrix operations. Probably ot in scope.