def test_all_or_nothing(self):
# load the data
graph = np.loadtxt("data/braess_graph.csv", delimiter=",", skiprows=1)
od = np.loadtxt("data/braess_od.csv", delimiter=",", skiprows=1)
od = np.reshape(od, (1, 3))
# all or nothing assignment
L = all_or_nothing(graph, od)
self.assertTrue(np.linalg.norm(L - np.array([2.0, 0.0, 2.0, 0.0, 2.0])) < eps)
# modify graph
graph[2, 3] = 1000.0
graph[0, 3] = 1.0
L = all_or_nothing(graph, od)
self.assertTrue(np.linalg.norm(L - np.array([0.0, 2.0, 0.0, 0.0, 2.0])) < eps)
def test_all_or_nothing_2(self):
# load the data
graph = np.loadtxt('data/graph_test.csv', delimiter=',')
# print graph
od = np.loadtxt('data/od_test.csv', delimiter=',')
od = np.reshape(od, (1, 3))
L = all_or_nothing(graph, od)
def test_all_or_nothing(self):
# load the data
graph = np.loadtxt('data/braess_graph.csv', delimiter=',', skiprows=1)
od = np.loadtxt('data/braess_od.csv', delimiter=',', skiprows=1)
od = np.reshape(od, (1, 3))
# all or nothing assignment
L = all_or_nothing(graph, od)
self.assertTrue(
np.linalg.norm(L - np.array([2., 0., 2., 0., 2.])) < eps)
# modify graph
graph[2, 3] = 1000.
graph[0, 3] = 1.
L = all_or_nothing(graph, od)
self.assertTrue(
np.linalg.norm(L - np.array([0., 2., 0., 0., 2.])) < eps)
def test_all_or_nothing_2(self):
# load the data
graph = np.loadtxt("data/graph_test.csv", delimiter=",")
# print graph
od = np.loadtxt("data/od_test.csv", delimiter=",")
od = np.reshape(od, (1, 3))
L = all_or_nothing(graph, od)
def search_direction(f, graph, g, demand):
# computes the Frank-Wolfe step
# g is just a canvas containing the link information and to be updated with
# the most recent edge costs
x = np.power(f.reshape((f.shape[0],1)), np.array([0,1,2,3,4]))
grad = np.einsum('ij,ij->i', x, graph[:,3:])
g[:,3] = grad
return all_or_nothing(g, demand), grad
def search_direction(f, graph, g, demand):
# computes the Frank-Wolfe step
# g is just a canvas containing the link information and to be updated with
# the most recent edge costs
x = np.power(f.reshape((f.shape[0], 1)), np.array([0, 1, 2, 3, 4]))
grad = np.einsum('ij,ij->i', x, graph[:, 3:])
g[:, 3] = grad
return all_or_nothing(g, demand), grad
def residual(graphs, demands, fs):
links = int(np.max(graphs[0][:,0])+1)
f = np.sum(fs, axis=1)
x = np.power(f.reshape((links,1)), np.array([0,1,2,3,4]))
r = 0.
for i, (graph, demand) in enumerate(zip(graphs, demands)):
g = np.copy(graph[:,:4])
grad = np.einsum('ij,ij->i', x, graph[:,3:])
g[:,3] = grad
L = all_or_nothing(g, demand)
r = r + grad.dot(fs[:,i] - L)
return r
def residual(graphs, demands, fs):
links = int(np.max(graphs[0][:, 0]) + 1)
f = np.sum(fs, axis=1)
x = np.power(f.reshape((links, 1)), np.array([0, 1, 2, 3, 4]))
r = 0.
for i, (graph, demand) in enumerate(zip(graphs, demands)):
g = np.copy(graph[:, :4])
grad = np.einsum('ij,ij->i', x, graph[:, 3:])
g[:, 3] = grad
L = all_or_nothing(g, demand)
r = r + grad.dot(fs[:, i] - L)
return r
def search_direction(f, graph, g, od):
# computes the Frank-Wolfe step
# g is just a canvas containing the link information and to be updated with
# the most recent edge costs
x = np.power(f.reshape((f.shape[0], 1)), np.array([0, 1, 2, 3, 4]))
grad = np.einsum('ij,ij->i', x, graph[:, 3:])
g.es["weight"] = grad.tolist()
#start timer
#start_time1 = timeit.default_timer()
L = all_or_nothing(g, od)
#end of timer
#elapsed1 = timeit.default_timer() - start_time1
#print ("all_or_nothing took %s seconds" % elapsed1)
return L, grad
def total_free_flow_cost(g, demand):
# computes the total cost under free flow travel times
L = all_or_nothing(g, demand)
return g[:,3].dot(L)
def total_free_flow_cost(g, demand):
# computes the total cost under free flow travel times
L = all_or_nothing(g, demand)
return g[:,3].dot(L)
def total_free_flow_cost(g, od):
return np.array(g.es["weight"]).dot(all_or_nothing(g, od))
def total_ff_costs_heterogeneous(graphs, demands):
return np.sum([g[:,3].dot(all_or_nothing(g[:,:4], d)) for g,d in zip(graphs, demands)])
def total_ff_costs_heterogeneous(graphs, demands):
return np.sum([
g[:, 3].dot(all_or_nothing(g[:, :4], d))
for g, d in zip(graphs, demands)
])