def frank_wolfe_on_I210():
'''
Frank-Wolfe on I210
'''
graph = np.loadtxt('data/I210_attack_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/I210_od.csv', delimiter=',', skiprows=1)
demand[:, 2] = 1. * demand[:, 2] / 4000
# run solver
f = solver_3(graph, demand, max_iter=1000, q=50, display=1, stop=1e-2)
# display cost
for a, b in zip(cost(f, graph), f * 4000):
print a, b
# visualization
node = np.loadtxt('data/I210_node.csv', delimiter=',', skiprows=1)
# extract features: 'capacity', 'length', 'fftt'
feat = extract_features('data/I210_attack_Sketch_net.csv')
ratio = cost_ratio(f, graph)
# merge features with the cost ratios
features = np.zeros((feat.shape[0], 4))
features[:, :3] = feat
features[:, 3] = ratio
# join features with (lat1,lon1,lat2,lon2)
links = process_links(graph, node, features)
color = features[:, 3] # we choose the costs
names = ['capacity', 'length', 'fftt', 'tt_over_fftt']
geojson_link(links, names, color)
def test_solver_sioux_falls_3(self):
print 'test Frank-Wolfe on Sioux Falls 3'
graph = np.loadtxt('data/SiouxFalls_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/SiouxFalls_od.csv', delimiter=',', skiprows=1)
demand[:,2] = demand[:,2] / 4000
f = solver_3(graph, demand, max_iter=1000)
results = np.loadtxt('data/SiouxFalls_results.csv')
self.check(f*4000, results, 1e-3)
def test_solver_sioux_falls_3(self):
print 'test Frank-Wolfe on Sioux Falls 3'
graph = np.loadtxt('data/SiouxFalls_net.csv',
delimiter=',',
skiprows=1)
demand = np.loadtxt('data/SiouxFalls_od.csv',
delimiter=',',
skiprows=1)
demand[:, 2] = demand[:, 2] / 4000
f = solver_3(graph, demand, max_iter=1000)
results = np.loadtxt('data/SiouxFalls_results.csv')
self.check(f * 4000, results, 1e-3)
def test_solver_3(self):
print 'test_solver_3'
graph = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/braess_od.csv', delimiter=',', skiprows=1)
demand = np.reshape(demand, (1, 3))
f = solver_3(graph, demand, max_iter=100)
#print f
self.check(f, np.array([1., 1., 0., 1., 1.]), 1e-1)
# modify demand
demand[0, 2] = 0.5
f = solver_2(graph, demand)
self.check(f, np.array([.5, .0, .5, .0, .5]), 1e-8)
def test_solver_3(self):
print 'test_solver_3'
graph = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/braess_od.csv', delimiter=',', skiprows=1)
demand=np.reshape(demand, (1,3))
f = solver_3(graph, demand, max_iter=100)
#print f
self.check(f, np.array([1.,1.,0.,1.,1.]), 1e-1)
# modify demand
demand[0,2] = 0.5
f = solver_2(graph, demand)
self.check(f, np.array([.5,.0,.5,.0,.5]), 1e-8)
def Cython_Func_LA():
#graph = np.loadtxt('data/SiouxFalls_net.csv', delimiter=',', skiprows=1)
#demand = np.loadtxt('data/SiouxFalls_od.csv', delimiter=',', skiprows=1)
graph = np.loadtxt('data/LA_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/LA_od_2.csv', delimiter=',', skiprows=1)
graph[10787, -1] = graph[10787, -1] / (1.5**4)
graph[3348, -1] = graph[3348, -1] / (1.2**4)
demand[:, 2] = 0.5 * demand[:, 2] / 4000
#import pdb; pdb.set_trace()
f = solver_3(graph, demand, max_iter=1000)
#results = np.loadtxt('data/SiouxFalls_results.csv')
np.savetxt('data/la/LA_Cython.csv', f, delimiter=',')
