def test_leaders_single(self): # Tests leaders using a flat clustering generated by single linkage. X = hierarchy_test_data.Q_X Y = pdist(X) Z = linkage(Y) T = fcluster(Z, criterion='maxclust', t=3) Lright = (np.array([53, 55, 56]), np.array([2, 3, 1])) L = leaders(Z, T) assert_equal(L, Lright)
def check_fcluster_maxclust_monocrit(self, t): expectedT = hierarchy_test_data.fcluster_maxclust[t] Z = single(hierarchy_test_data.Q_X) T = fcluster(Z, t, criterion='maxclust_monocrit', monocrit=maxdists(Z)) assert_(is_isomorphic(T, expectedT))
def check_fcluster(self, t, criterion): # Tests fcluster(Z, criterion=criterion, t=t) on a random 3-cluster data set. expectedT = getattr(hierarchy_test_data, 'fcluster_' + criterion)[t] Z = single(hierarchy_test_data.Q_X) T = fcluster(Z, criterion=criterion, t=t) assert_(is_isomorphic(T, expectedT))