File Content: test_mds.py import numpy as np from numpy.testing import assert_array_almost_equal from sklearn.manifold import mds from sklearn.utils.testing import assert_raises def test_smacof(): # test metric smacof using the data of "Modern Multidimensional Scaling", # Borg & Groenen, p 154 sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) Z = np.array([[-.266, -.539], [.451, .252], [.016, -.238], [-.200, .524]]) X, _ = mds.smacof(sim, init=Z, n_components=2, max_iter=1, n_init=1) X_true = np.array([[-1.415, -2.471], [1.633, 1.107], [.249, -.067], [-.468, 1.431]]) assert_array_almost_equal(X, X_true, decimal=3) def test_smacof_error(): # Not symmetric similarity matrix: sim = np.array([[0, 5, 9, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) assert_raises(ValueError, mds.smacof, sim) # Not squared similarity matrix: sim = np.array([[0, 5, 9, 4], [5, 0, 2, 2], [4, 2, 1, 0]]) assert_raises(ValueError, mds.smacof, sim) # init not None and not correct format: sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) Z = np.array([[-.266, -.539], [.016, -.238], [-.200, .524]]) assert_raises(ValueError, mds.smacof, sim, init=Z, n_init=1) def test_MDS(): 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=3, dissimilarity="precomputed") mds_clf.fit(sim) Submit