… and documentation. Fixes #6862 (#6907)

* Make KernelCenterer a _pairwise operation

Replicate solution to 9a52077 except that `_pairwise` should always be `True` for `KernelCenterer` because it's supposed to receive a Gram matrix. This should make `KernelCenterer` usable in `Pipeline`s.

Happy to add tests, just tell me what should be covered.

* Adding test for PR #6900

* Simplifying imports and test

* updating changelog links on homepage (#6901)

* first commit

* changed binary average back to macro

* changed binomialNB to multinomialNB

* emphasis on "higher return values are better..." (#6909)

* fix typo in comment of hierarchical clustering (#6912)

* [MRG] Allows KMeans/MiniBatchKMeans to use float32 internally by using cython fused types (#6846)

* Fix sklearn.base.clone for all scipy.sparse formats (#6910)

* DOC If git is not installed, need to catch OSError

Fixes #6860

* DOC add what's new for clone fix

* fix a typo in ridge.py (#6917)

* pep8

* TST: Speed up: cv=2

This is a smoke test. Hence there is no point having cv=4

* Added support for sample_weight in linearSVR, including tests and documentation

* Changed assert to assert_allclose and assert_almost_equal, reduced the test tolerance

* Fixed pep8 violations and sampleweight format

* rebased with upstream

… and documentation. Fixes scikit-learn#6862 (scikit-learn#6907)

* Make KernelCenterer a _pairwise operation

Replicate solution to scikit-learn@9a52077 except that `_pairwise` should always be `True` for `KernelCenterer` because it's supposed to receive a Gram matrix. This should make `KernelCenterer` usable in `Pipeline`s.

Happy to add tests, just tell me what should be covered.

* Adding test for PR scikit-learn#6900

* Simplifying imports and test

* updating changelog links on homepage (scikit-learn#6901)

* first commit

* changed binary average back to macro

* changed binomialNB to multinomialNB

* emphasis on "higher return values are better..." (scikit-learn#6909)

* fix typo in comment of hierarchical clustering (scikit-learn#6912)

* [MRG] Allows KMeans/MiniBatchKMeans to use float32 internally by using cython fused types (scikit-learn#6846)

* Fix sklearn.base.clone for all scipy.sparse formats (scikit-learn#6910)

* DOC If git is not installed, need to catch OSError

Fixes scikit-learn#6860

* DOC add what's new for clone fix

* fix a typo in ridge.py (scikit-learn#6917)

* pep8

* TST: Speed up: cv=2

This is a smoke test. Hence there is no point having cv=4

* Added support for sample_weight in linearSVR, including tests and documentation

* Changed assert to assert_allclose and assert_almost_equal, reduced the test tolerance

* Fixed pep8 violations and sampleweight format

* rebased with upstream

… and documentation. Fixes scikit-learn#6862 (scikit-learn#6907)

* Make KernelCenterer a _pairwise operation

Replicate solution to scikit-learn@9a52077 except that `_pairwise` should always be `True` for `KernelCenterer` because it's supposed to receive a Gram matrix. This should make `KernelCenterer` usable in `Pipeline`s.

Happy to add tests, just tell me what should be covered.

* Adding test for PR scikit-learn#6900

* Simplifying imports and test

* updating changelog links on homepage (scikit-learn#6901)

* first commit

* changed binary average back to macro

* changed binomialNB to multinomialNB

* emphasis on "higher return values are better..." (scikit-learn#6909)

* fix typo in comment of hierarchical clustering (scikit-learn#6912)

* [MRG] Allows KMeans/MiniBatchKMeans to use float32 internally by using cython fused types (scikit-learn#6846)

* Fix sklearn.base.clone for all scipy.sparse formats (scikit-learn#6910)

* DOC If git is not installed, need to catch OSError

Fixes scikit-learn#6860

* DOC add what's new for clone fix

* fix a typo in ridge.py (scikit-learn#6917)

* pep8

* TST: Speed up: cv=2

This is a smoke test. Hence there is no point having cv=4

* Added support for sample_weight in linearSVR, including tests and documentation

* Changed assert to assert_allclose and assert_almost_equal, reduced the test tolerance

* Fixed pep8 violations and sampleweight format

* rebased with upstream