diff options
Diffstat (limited to 'networkx/algorithms/approximation/tests/test_traveling_salesman.py')
| -rw-r--r-- | networkx/algorithms/approximation/tests/test_traveling_salesman.py | 11 |
1 files changed, 11 insertions, 0 deletions
diff --git a/networkx/algorithms/approximation/tests/test_traveling_salesman.py b/networkx/algorithms/approximation/tests/test_traveling_salesman.py index 5b8ff39e..56574f63 100644 --- a/networkx/algorithms/approximation/tests/test_traveling_salesman.py +++ b/networkx/algorithms/approximation/tests/test_traveling_salesman.py @@ -391,6 +391,7 @@ def test_held_karp_ascent(): import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") + pytest.importorskip("scipy") # Adjacency matrix from page 1153 of the 1970 Held and Karp paper # which have been edited to be directional, but also symmetric @@ -427,6 +428,7 @@ def test_ascent_fractional_solution(): import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") + pytest.importorskip("scipy") # This version of Figure 2 has all of the edge weights multiplied by 100 # and is a complete directed graph with infinite edge weights for the @@ -482,6 +484,7 @@ def test_ascent_method_asymmetric(): import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") + pytest.importorskip("scipy") G_array = np.array( [ @@ -516,6 +519,7 @@ def test_ascent_method_asymmetric_2(): import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") + pytest.importorskip("scipy") G_array = np.array( [ @@ -555,6 +559,7 @@ def test_held_karp_ascent_asymmetric_3(): import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") + pytest.importorskip("scipy") G_array = np.array( [ @@ -593,6 +598,7 @@ def test_held_karp_ascent_fractional_asymmetric(): import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") + pytest.importorskip("scipy") G_array = np.array( [ @@ -651,6 +657,7 @@ def test_spanning_tree_distribution(): import networkx.algorithms.approximation.traveling_salesman as tsp pytest.importorskip("numpy") + pytest.importorskip("scipy") z_star = { (0, 1): 5 / 12, @@ -705,6 +712,7 @@ def test_sample_spanning_tree(): from math import exp pytest.importorskip("numpy") + pytest.importorskip("scipy") gamma = { (0, 1): -0.6383, @@ -832,6 +840,7 @@ def test_asadpour_tsp(): # This version of Figure 2 has all of the edge weights multiplied by 100 # and the 0 weight edges have a weight of 1. pytest.importorskip("numpy") + pytest.importorskip("scipy") edge_list = [ (0, 1, 100), @@ -900,6 +909,7 @@ def test_asadpour_real_world(): always starts at city 0. """ np = pytest.importorskip("numpy") + pytest.importorskip("scipy") G_array = np.array( [ @@ -947,6 +957,7 @@ def test_asadpour_real_world_path(): nonstop flight. The brute force solution found the optimal tour to cost $872 """ np = pytest.importorskip("numpy") + pytest.importorskip("scipy") G_array = np.array( [ |