Hi all,
I'm currently modifying our tree code so that it runs on both fortran and c
continuous arrays. After some benchmarking I got aware of the following
numpy behavior that was contrary to what I was expecting::
>>> X = # some feature matrix
>>> X = np.asfortranarray(X)
>>> X.flags.f_contiguous
True
>>> # so far so good
>>> X_train = X[:1000]
>>> X_train.flags.f_contiguous
False
>>> X_train.flags.c_contiguous
False
>>> # damn - seems like a view is neither c nor fortran continuous
>>> X_train = X_train.copy() # lets materialize the view
>>> X_train.flags.f_contiguous
False
>>> X_train.flags.c_contiguous
True
In the tree code, I check if an array is continuous - if not, I call
``np.asarray`` and set the ``order`` according to ``flags.f_contiguous`` or
``flags.c_contiguous``, however, in the case of views that does not work.
How would you handle this case?
thanks,
Peter
--
Peter Prettenhofer
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