def test_l2_in_simple_case(self):
"""Tests of L2 metrics."""
test_data = np.array([[0, 1, 3, 12, 12, 11, 13, 1055], [-1, -1, -1, 0, 0, 0, 0, 1]])
test_data = test_data.transpose()
reference = sklearn.cluster.AgglomerativeClustering(n_clusters=3, affinity="l2", linkage="average")
reference_ans = reference.fit_predict(test_data)
test = hierarchical.HierarchicalClustering(n_clusters=3, affinity="l2", linkage="average")
test_ans = test.fit_predict(test_data)
reference_ans = maping(n=3, ours=test_ans, default=reference_ans)
ans = np.array_equal(test_ans, reference_ans)
msg = "get: " + str(test_ans) + "instead of: " + str(reference_ans)
self.assertEqual(ans, True, msg)
def test_argument_matching(self):
"""Tests if our interface is compatible with sklearn."""
test_data = np.array([[0, 1, 3, 12, 12, 11, 13, 1055], [-1, -1, -1, 0, 0, 0, 0, 1]])
test_data = test_data.transpose()
reference = sklearn.cluster.AgglomerativeClustering(3, "euclidean")
reference_ans = reference.fit_predict(test_data)
test = hierarchical.HierarchicalClustering(3, "euclidean")
test_ans = test.fit_predict(test_data)
reference_ans = maping(n=3, ours=test_ans, default=reference_ans)
ans = np.array_equal(test_ans, reference_ans)
msg = "get: " + str(test_ans) + "instead of: " + str(reference_ans)
self.assertEqual(ans, True, msg)