* MAINT Remove -Wcpp warnings when compiling sklearn.svm._liblinear
* Convert the required data in X to bytes
* Add NULL check for class_weight_label
* Add NULL check for class_weight
* Add sklearn.svm._liblinear in setup.py
* Use intermediate memoryviews for static reinterpretation of dtypes
* * Remove the usage of tobytes()
* Use separate memory views for float64 and float32 to handle the possibly dtypes of X
* Separate the functionality to get the bytes of X in functions for sparse and normal ndarray
* Remove the use of functions and implement the functionality directly in train_wrap
* Minor refactor
* Refactor variables names involving X to use capital X
* Define y, class_weight and sample_weight as const memory views
* Use const with X_indices and X_indptr memory view declarations
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Jérémie du Boisberranger