Python sklearn.utils.check_symmetric() Examples

The following are 3 code examples of sklearn.utils.check_symmetric(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.utils , or try the search function .
Example #1
Source File: test_validation.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_check_symmetric():
    arr_sym = np.array([[0, 1], [1, 2]])
    arr_bad = np.ones(2)
    arr_asym = np.array([[0, 2], [0, 2]])

    test_arrays = {'dense': arr_asym,
                   'dok': sp.dok_matrix(arr_asym),
                   'csr': sp.csr_matrix(arr_asym),
                   'csc': sp.csc_matrix(arr_asym),
                   'coo': sp.coo_matrix(arr_asym),
                   'lil': sp.lil_matrix(arr_asym),
                   'bsr': sp.bsr_matrix(arr_asym)}

    # check error for bad inputs
    assert_raises(ValueError, check_symmetric, arr_bad)

    # check that asymmetric arrays are properly symmetrized
    for arr_format, arr in test_arrays.items():
        # Check for warnings and errors
        assert_warns(UserWarning, check_symmetric, arr)
        assert_raises(ValueError, check_symmetric, arr, raise_exception=True)

        output = check_symmetric(arr, raise_warning=False)
        if sp.issparse(output):
            assert_equal(output.format, arr_format)
            assert_array_equal(output.toarray(), arr_sym)
        else:
            assert_array_equal(output, arr_sym)
Example #2
Source File: selfrepresentation.py    From subspace-clustering with MIT License 5 votes vote down vote up 
def _spectral_clustering(self):
        affinity_matrix_ = check_symmetric(self.affinity_matrix_)
        random_state = check_random_state(self.random_state)
        
        laplacian = sparse.csgraph.laplacian(affinity_matrix_, normed=True)
        _, vec = sparse.linalg.eigsh(sparse.identity(laplacian.shape[0]) - laplacian, 
                                     k=self.n_clusters, sigma=None, which='LA')
        embedding = normalize(vec)
        _, self.labels_, _ = cluster.k_means(embedding, self.n_clusters, 
                                             random_state=random_state, n_init=self.n_init)
Example #3
Source File: test_validation.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_check_symmetric():
    arr_sym = np.array([[0, 1], [1, 2]])
    arr_bad = np.ones(2)
    arr_asym = np.array([[0, 2], [0, 2]])

    test_arrays = {'dense': arr_asym,
                   'dok': sp.dok_matrix(arr_asym),
                   'csr': sp.csr_matrix(arr_asym),
                   'csc': sp.csc_matrix(arr_asym),
                   'coo': sp.coo_matrix(arr_asym),
                   'lil': sp.lil_matrix(arr_asym),
                   'bsr': sp.bsr_matrix(arr_asym)}

    # check error for bad inputs
    assert_raises(ValueError, check_symmetric, arr_bad)

    # check that asymmetric arrays are properly symmetrized
    for arr_format, arr in test_arrays.items():
        # Check for warnings and errors
        assert_warns(UserWarning, check_symmetric, arr)
        assert_raises(ValueError, check_symmetric, arr, raise_exception=True)

        output = check_symmetric(arr, raise_warning=False)
        if sp.issparse(output):
            assert_equal(output.format, arr_format)
            assert_array_equal(output.toarray(), arr_sym)
        else:
            assert_array_equal(output, arr_sym)