def test_load_and_save_H(): print "\n-- 'load_H', 'save_H'" H, _, _ = load_H(join(data_directory, 'Torus_H.csv'), zeroindexing=False) print "Loaded H: \n", H print "Shape: ", H.shape filename2 = 'Torus_H2.csv' print "Saving and loading can lead to different precision" save_H(join(data_directory, filename2), H) H, _, _ = load_H(join(data_directory, filename2)) print H print "Shape: ", H.shape save_H(join(data_directory, 'Torus_H3.csv'), H) print "\nLoading always leads to float" H = np.array([[1, 2, 3], [4, 5, 6]]) # change 6 to 6.5 print "Original H:\n", H print "Shape: ", H.shape filename3 = 'test_load_and_save_H.csv' save_H(join(data_directory, filename3), H, delimiter=' ') H, _, _ = load_H(join(data_directory, filename3), delimiter=None) print "Loaded H:\n", H print "Shape: ", H.shape
def test_eps_convergence_linbp_Torus(): print "\n-- 'eps_convergence_linbp' for Torus ---" W, n = load_W(join(data_directory, 'Torus_W.csv'), zeroindexing=False) print 'W dense:\n', W.todense() print 'W:\n', W Hc, k, _ = load_H(join(data_directory, 'Torus_H.csv'), zeroindexing=False) print "H\n", Hc print # Simple spectral = 0.658 start = time.time() eps = eps_convergence_linbp(Hc, W) end = time.time()-start print "Eps:", eps print "Time needed:", end # Echo spectral = 0.488 start = time.time() eps = eps_convergence_linbp(Hc, W, echo=True) end = time.time()-start print "Eps:", eps print "Time needed:", end
def test_eps_convergence_linbp_Torus(): print "\n-- 'eps_convergence_linbp' for Torus ---" W, n = load_W(join(data_directory, 'Torus_W.csv'), zeroindexing=False) print 'W dense:\n', W.todense() print 'W:\n', W Hc, k, _ = load_H(join(data_directory, 'Torus_H.csv'), zeroindexing=False) print "H\n", Hc print # Simple spectral = 0.658 start = time.time() eps = eps_convergence_linbp(Hc, W) end = time.time() - start print "Eps:", eps print "Time needed:", end # Echo spectral = 0.488 start = time.time() eps = eps_convergence_linbp(Hc, W, echo=True) end = time.time() - start print "Eps:", eps print "Time needed:", end