def frank_wolfe_on_chicago_2():
'''
Frank-Wolfe on Chicago with the inputs processed from:
http://www.bgu.ac.il/~bargera/tntp/
but we multiply the demand by 2 to have more congestion
'''
graph, demand, node, features = load_chicago()
results = np.loadtxt('data/Chicago_results_2.csv', delimiter=',')
demand[:, 2] = demand[:, 2] / 2000 # technically, it's 2*demand/4000
# f = solver(graph, demand, max_iter=1000, display=1, stop=1e-2)
# f = solver_2(graph, demand, max_iter=1000, q=100, display=1, stop=1e-2)
f = solver_3(graph, demand, max_iter=1000, q=50, display=1, stop=1e-2)
print np.linalg.norm(f * 4000 - results) / np.linalg.norm(results)
print average_cost(f, graph, demand)
def frank_wolfe_on_chicago_2():
"""
Frank-Wolfe on Chicago with the inputs processed from:
http://www.bgu.ac.il/~bargera/tntp/
but we multiply the demand by 2 to have more congestion
"""
graph, demand, node, features = load_chicago()
results = np.loadtxt("data/Chicago_results_2.csv", delimiter=",")
demand[:, 2] = demand[:, 2] / 2000 # technically, it's 2*demand/4000
# f = solver(graph, demand, max_iter=1000, display=1, stop=1e-2)
# f = solver_2(graph, demand, max_iter=1000, q=100, display=1, stop=1e-2)
f = solver_3(graph, demand, max_iter=1000, q=50, display=1, stop=1e-2)
print np.linalg.norm(f * 4000 - results) / np.linalg.norm(results)
print average_cost(f, graph, demand)
def visualize_equilibrium_in_chicago():
"""
visualize costs in the network of Chicago
"""
net, demand, node, f = load_chicago()
flow = np.loadtxt("data/Chicago_results_2.csv", delimiter=",")
# flow = np.loadtxt('data/Chicago_results.csv', delimiter=',', skiprows=1)[:,2]
print average_cost(flow / 4000.0, net)
costs = cost_ratio(flow / 4000.0, net)
features = np.zeros((f.shape[0], 4))
features[:, :3] = f
features[:, 3] = costs
links = process_links(net, node, features)
color = features[:, 3] - 1.0 # we choose the costs
geojson_link(links, ["capacity", "length", "fftt", "tt_over_fftt"], color)
def visualize_equilibrium_in_chicago():
'''
visualize costs in the network of Chicago
'''
net, demand, node, f = load_chicago()
flow = np.loadtxt('data/Chicago_results_2.csv', delimiter=',')
# flow = np.loadtxt('data/Chicago_results.csv', delimiter=',', skiprows=1)[:,2]
print average_cost(flow/4000., net, demand)
costs = cost_ratio(flow/4000., net)
features = np.zeros((f.shape[0],4))
features[:,:3] = f
features[:,3] = costs
links = process_links(net, node, features)
color = features[:,3] - 1.0 # we choose the costs
geojson_link(links, ['capacity', 'length', 'fftt', 'tt_over_fftt'], color)
def test_average_cost(self):
net = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1)
net[:, 5] = np.array([2.] * 5)
flow = np.array([0., 1., 2., 3., 4.])
od = np.array([[0, 0, 0.], [0, 0, 1.], [0, 0, 2.], [0, 0, 3.],
[0, 0, 4.]])
self.assertTrue(
np.linalg.norm(average_cost(flow, net, od) - 22.) < 1e-8)
def test_average_cost(self):
net = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1)
net[:,5] = np.array([2.]*5)
flow = np.array([0., 1., 2., 3., 4.])
od = np.array([[0,0,0.],[0,0,1.],[0,0,2.],[0,0,3.],[0,0,4.]])
self.assertTrue(np.linalg.norm(average_cost(flow, net, od) - 22.) < 1e-8)
