def I210_parametric_study(alphas):
# load the network and its properties
g_r, d, node, feat = load_I210_modified()
# modify the costs on non routed network
g_nr, small_capacity = multiply_cognitive_cost(g_r, feat, 3000., 100.)
#divide the demand by 4000 to computationally optimize
d[:,2] = d[:,2] / 4000.
for alpha in alphas:
if alpha == 0.0:
print 'non-routed = 1.0, routed = 0.0'
f_nr = solver_3(g_nr, d, max_iter=1000, stop=1e-3)
fs=np.zeros((f_nr.shape[0],2))
fs[:,0]=f_nr
elif alpha == 1.0:
print 'non-routed = 0.0, routed = 1.0'
f_r = solver_3(g_r, d, max_iter=1000, stop=1e-3)
fs=np.zeros((f_r.shape[0],2))
fs[:,1]=f_r
else:
print 'non-routed = {}, routed = {}'.format(1-alpha, alpha)
d_nr, d_r = heterogeneous_demand(d, alpha)
fs = gauss_seidel([g_nr,g_r], [d_nr,d_r], solver_3, max_iter=1000, \
stop=1e-3, stop_cycle=1e-3, q=50, past=20)
np.savetxt('data/I210_modified/test_{}.csv'.format(int(alpha*100)), fs, \
delimiter=',', header='f_nr,f_r')
def parametric_study(alphas, g, d, node, geometry, thres, cog_cost, output, \
stop=1e-2, stop_cycle=1e-2):
g_nr, small_capacity = multiply_cognitive_cost(g, geometry, thres, cog_cost)
if (type(alphas) is float) or (type(alphas) is int):
alphas = [alphas]
for alpha in alphas:
#special case where in fact homogeneous game
if alpha == 0.0:
print 'non-routed = 1.0, routed = 0.0'
f_nr = solver_3(g_nr, d, max_iter=1000, display=1, stop=stop)
fs=np.zeros((f_nr.shape[0],2))
fs[:,0]=f_nr
elif alpha == 1.0:
print 'non-routed = 0.0, routed = 1.0'
f_r = solver_3(g, d, max_iter=1000, display=1, stop=stop)
fs=np.zeros((f_r.shape[0],2))
fs[:,1]=f_r
#run solver
else:
print 'non-routed = {}, routed = {}'.format(1-alpha, alpha)
d_nr, d_r = heterogeneous_demand(d, alpha)
fs = gauss_seidel([g_nr,g], [d_nr,d_r], solver_3, max_iter=1000, \
display=1, stop=stop, stop_cycle=stop_cycle, q=50, past=20)
np.savetxt(output.format(int(alpha*100)), fs, \
delimiter=',', header='f_nr,f_r')
def I210_parametric_study(alphas):
# load the network and its properties
g_r, d, node, feat = load_I210_modified()
# modify the costs on non routed network
g_nr, small_capacity = multiply_cognitive_cost(g_r, feat, 3000., 100.)
#divide the demand by 4000 to computationally optimize
d[:, 2] = d[:, 2] / 4000.
for alpha in alphas:
if alpha == 0.0:
print 'non-routed = 1.0, routed = 0.0'
f_nr = solver_3(g_nr, d, max_iter=1000, stop=1e-3)
fs = np.zeros((f_nr.shape[0], 2))
fs[:, 0] = f_nr
elif alpha == 1.0:
print 'non-routed = 0.0, routed = 1.0'
f_r = solver_3(g_r, d, max_iter=1000, stop=1e-3)
fs = np.zeros((f_r.shape[0], 2))
fs[:, 1] = f_r
else:
print 'non-routed = {}, routed = {}'.format(1 - alpha, alpha)
d_nr, d_r = heterogeneous_demand(d, alpha)
fs = gauss_seidel([g_nr,g_r], [d_nr,d_r], solver_3, max_iter=1000, \
stop=1e-3, stop_cycle=1e-3, q=50, past=20)
np.savetxt('data/I210_modified/test_{}.csv'.format(int(alpha*100)), fs, \
delimiter=',', header='f_nr,f_r')
def main():
for alpha in [.75]:
# for alpha in np.linspace(0, .49, 50):
# for alpha in np.linspace(.5, .99, 50):
print "ALPHA:", alpha
start_time2 = timeit.default_timer()
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)
node = np.loadtxt('data/LA_node.csv', delimiter=',')
features = extract_features('data/LA_net.txt')
# graph = np.loadtxt('data/Chicago_net.csv', delimiter=',', skiprows=1)
# demand = np.loadtxt('data/Chicago_od.csv', delimiter=',', skiprows=1)
# node = np.loadtxt('data/Chicago_node.csv', delimiter=',', skiprows=1)
# features = extract_features('data/ChicagoSketch_net.txt')
# features = table in the format [[capacity, length, FreeFlowTime]]
# alpha = .2 # also known as r
thres = 1000.
cog_cost = 3000.
demand[:, 2] = 0.5 * demand[:, 2] / 4000
g_nr, small_capacity = multiply_cognitive_cost(graph, features, thres,
cog_cost)
d_nr, d_r = heterogeneous_demand(demand, alpha)
fs, hs, n_d = fw_heterogeneous_1([graph, g_nr], [d_r, d_nr],
alpha,
max_iter=30,
display=1)
print n_d
output = {'f': fs, 'h': hs, 'n_d': n_d}
with open('graph_stuff/LA_net_od_2_alpha_{}.txt'.format(alpha),
'w') as outfile:
# with open('graph_stuff/Chicago_net_od_2_alpha_{}.txt'.format(alpha), 'w') as outfile:
outfile.write(pickle.dumps(output))
#end of timer
elapsed2 = timeit.default_timer() - start_time2
print("Execution took %s seconds" % elapsed2)
def main():
start_time2 = timeit.default_timer()
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)
alpha = .2
demand[:, 2] = 0.5 * demand[:, 2] / 4000
d_nr, d_r = heterogeneous_demand(demand, alpha)
f, h = solver_3(graph, d_nr, d_r, max_iter=10, display=1)
#end of timer
elapsed2 = timeit.default_timer() - start_time2
print("Execution took %s seconds" % elapsed2)
visualize_LA()
# graph = np.loadtxt('data/Chicago_net.csv', delimiter=',', skiprows=1)
# demand = np.loadtxt('data/Chicago_od.csv', delimiter=',', skiprows=1)
# node = np.loadtxt('data/Chicago_node.csv', delimiter=',', skiprows=1)
# features = extract_features('data/ChicagoSketch_net.txt')
# features = table in the format [[capacity, length, FreeFlowTime]]
# alpha = .2 # also known as r
thres = 1000.
cog_cost = 3000.
demand[:, 2] = 0.5 * demand[:, 2] / 4000
g_nr, small_capacity = multiply_cognitive_cost(graph, features, thres,
cog_cost)
d_nr, d_r = heterogeneous_demand(demand, alpha)
fs, hs, n_d = fw_heterogeneous_1([graph, g_nr], [d_r, d_nr],
alpha,
max_iter=30,
display=1)
# print n_d
output = {'f': fs, 'h': hs, 'n_d': n_d}
with open('graph_stuff/LA_net_od_2_alpha_{}.txt'.format(alpha),
'w') as outfile:
# with open('graph_stuff/Chicago_net_od_2_alpha_{}.txt'.format(alpha), 'w') as outfile:
outfile.write(pickle.dumps(output))
#end of timer
elapsed = timeit.default_timer() - start_time
