def generate_augmented_matrix(graph, routes, sensors, flow_portions, flow_from_each_node=1.0):
route_indices_by_origin = flows.get_route_indices_by_origin(routes)
f = []
where_did_f_come_from = []
for node in graph.nodes():
for (p, n) in graph.node[node]['2nd_flow']:
f.append(graph.node[node]['2nd_flow'][(p, n)][0])
where_did_f_come_from.append((graph.node[node]['2nd_flow'][(p, n)][1]));
f = np.array(f);
phis = []
print ":begin"
for node in graph.nodes():
route_indices_from_node = route_indices_by_origin[node]
edges_in_route = [set(zip(routes[i], routes[i][1:])) for i in route_indices_from_node]
phi = np.zeros(shape=(len(f), len(route_indices_from_node)))
for j in xrange(len(route_indices_from_node)):
for i in xrange(len(f)):
info = where_did_f_come_from[i]
if route_indices_from_node[j] in info:
phi[i, j] = flow_from_each_node
phis.append(phi)
print ":end"
print where_did_f_come_from
return np.hstack(phis), f
def generate_random_matrix(graph, routes, sensors, flow_portions,
flow_from_each_node=1.0):
# All route indices are with respect to _routes_.
route_indices_by_origin = flows.get_route_indices_by_origin(routes)
# f = np.array([graph[u][v]['flow'] for (u,v) in sensors])
alphas = []
mus = []
num_routes = []
for node in graph.nodes():
route_indices_from_node = route_indices_by_origin[node]
edges_in_route = [collections.Counter(zip(routes[i], routes[i][1:])) for i in \
route_indices_from_node]
alpha = np.zeros(shape=len(route_indices_from_node))
mu = np.zeros(shape=len(route_indices_from_node))
for j in xrange(len(route_indices_from_node)):
alpha[j] = flow_portions[route_indices_from_node[j]]
route = routes[route_indices_from_node[j]]
mu[j] = sum(1./graph[de[0]][de[1]]['weight'] for de in zip(route,
route[1:]))
num_routes.append(len(route_indices_from_node))
mus.append(mu)
alphas.append(alpha)
alpha = np.concatenate(alphas)
phi = np.random.normal(size=(len(sensors), len(routes)))
f = np.dot(phi, alpha)
return phi, np.concatenate(alphas), np.concatenate(mus), f, \
np.array(num_routes)
