Add check for array contiguity

[dpaetzel]

Fixes #198 for now by throwing an error.

For me, this works:

>>> import xcsf
>>> import numpy as np
>>> xcsf.XCS(x_dim=2, max_trials=10).fit(np.array([[1.1, 1.3], [1.2, 2.2]], order='F'), np.array([0.0, 1.0], order='F'))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: X and Y must be C-contiguous
>>> xcsf.XCS(x_dim=2, max_trials=10).fit(np.array([[1.1, 1.3], [1.2, 2.2]]), np.array([0.0, 1.0], order='F'))
2025-07-16T16:04:29.780 trials=10 train=0.52924 pset=2000.0 mset=105.6 mfrac=0.00
>>> 

[dpaetzel]

Note that this is still missing the predict part for now.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 70.58%. Comparing base (6d476e3) to head (ca15acd).
Report is 1 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #199   +/-   ##
=======================================
  Coverage   70.58%   70.58%           
=======================================
  Files          61       61           
  Lines        8939     8939           
  Branches      933      933           
=======================================
  Hits         6310     6310           
  Misses       2629     2629           

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[dpaetzel]

I added the same code to predict as well.

>>> m = xcsf.XCS(x_dim=2, max_trials=10).fit(np.array([[1.1, 1.3], [1.2, 2.2]]), np.array([0.0, 1.0], order='F')).predict(np.array([[1.0, 2.0], [2.3, 3.3]]))
2025-07-16T16:11:27.428 trials=10 train=0.10000 pset=2000.0 mset=17.9 mfrac=0.00
>>> m = xcsf.XCS(x_dim=2, max_trials=10).fit(np.array([[1.1, 1.3], [1.2, 2.2]]), np.array([0.0, 1.0], order='F')).predict(np.array([[1.0, 2.0], [2.3, 3.3]], order='F'))
2025-07-16T16:11:33.610 trials=10 train=0.45355 pset=2000.0 mset=74.5 mfrac=0.00
>>>

[dpaetzel]

This could be cleaned up a bit (e.g. storing NPY_ARRAY_C_CONTIGUOUS_ as a global constant) but I wanted to keep all the changes in the same place for now.