Improve test for MDS parallel processing case - requires update in MDS

Description

There is a test that MDS does not create an error when performing parallel processing (n_jobs > 1) https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/tests/test_mds.py#L55-L61

That test should also assert that the result is comparable to with n_jobs = 1.

Steps/Code to Reproduce

Proposed test:

def test_MDS_parallel():
    sim = np.array([[0, 5, 3, 4],
                    [5, 0, 2, 2],
                    [3, 2, 0, 1],
                    [4, 2, 1, 0]])
    mds_clf = mds.MDS(metric=False, n_jobs=1, n_init=4, dissimilarity="precomputed")
    result1 = mds_clf.fit(sim)

    # TODO: if sim is modified my MDS.fit then set it back before the next test

    mds_clf = mds.MDS(metric=False, n_jobs=4, n_init=4, dissimilarity="precomputed")
    result2 = mds_clf.fit(sim)

    assert_array_almost_equal(result1, result2, decimal=3)

... as a replacement for https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/tests/test_mds.py#L55-L61

The smacof algorithm involves an iterative search from n_init random initial solutions. In the parallel case each job searches from a new defined seed. In the non-parallel case, random_state is used to continue searching from a single starting seed. These varying approaches don't produce the same results. The non-parallel case must be modified to search from increasing seeds in order to be consistent with the parallel case. That will allow for consistent output when switching between the two parameter options and also allow for this new test to pass.

See https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/mds.py#L248-L271

Expected Results

Actual Results

Versions