def check__LA_connectivity(): graph, demand, node = load_LA() print np.min(graph[:, 1:3]) print np.max(graph[:, 1:3]) print np.min(demand[:, :2]) print np.max(demand[:, :2]) # od = construct_od(demand) # g = construct_igraph(graph) f = np.zeros((graph.shape[0], )) print average_cost_all_or_nothing(f, graph, demand)
def check__LA_connectivity(): graph, demand, node = load_LA() print np.min(graph[:,1:3]) print np.max(graph[:,1:3]) print np.min(demand[:,:2]) print np.max(demand[:,:2]) od = construct_od(demand) g = construct_igraph(graph) f = np.zeros((graph.shape[0],)) print average_cost_all_or_nothing(f, graph, demand)
def test_average_cost_all_or_nothing(self): net = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1) flow = np.array([.5,.5,.0,.5,.5]) demand = np.loadtxt('data/braess_od.csv', delimiter=',', skiprows=1) demand=np.reshape(demand, (1,3)) c = average_cost_all_or_nothing(flow, net, demand) self.assertTrue(abs(c - 1.0) < 1e-8)
def test_average_cost_all_or_nothing(self): net = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1) flow = np.array([.5, .5, .0, .5, .5]) demand = np.loadtxt('data/braess_od.csv', delimiter=',', skiprows=1) demand = np.reshape(demand, (1, 3)) c = average_cost_all_or_nothing(flow, net, demand) self.assertTrue(abs(c - 1.0) < 1e-8)
def compute_metrics_beta(alpha, beta, f, net, d, feat, subset, out, row, fs=None, net2=None, \ length_unit='Mile', time_unit='Minute'): ''' Save in the numpy array 'out' at the specific 'row' the following metrics - average cost for non-routed - average cost for routed - average cost - average cost on a subset (e.g. local routes) - average cost outside of a subset (e.g. non-local routes) - total gas emissions - total gas emissions on a subset (e.g. local routes) - total gas emissions outside of a subset (e.g. non-local routes) - total flow in the network - total flow in the network on a subset (e.g. local routes) - total flow in the network outside of a subset (e.g. non-local routes) ''' if length_unit == 'Meter': lengths = feat[:, 1] / 1609.34 # convert into miles elif length_unit == 'Mile': lengths = feat[:, 1] if time_unit == 'Minute': a = 60.0 elif time_unit == 'Second': a = 3600. b = 60. / a speed = a * np.divide(lengths, np.maximum(cost(f, net), 10e-8)) co2 = np.multiply(gas_emission(speed), lengths) out[row, 0] = alpha out[row, 1] = beta out[row, 4] = b * average_cost(f, net, d) out[row, 5] = b * average_cost_subset(f, net, d, subset) out[row, 6] = out[row, 3] - out[row, 4] out[row, 7] = co2.dot(f) / f.dot(lengths) out[row, 8] = np.multiply(co2, subset).dot(f) / f.dot(lengths) out[row, 9] = out[row, 6] - out[row, 7] out[row, 10] = np.sum(np.multiply(f, lengths)) * 4000. out[row, 11] = np.sum(np.multiply(np.multiply(f, lengths), subset)) * 4000. out[row, 12] = out[row, 9] - out[row, 10] if alpha == 0.0: out[row, 2] = b * average_cost(f, net, d) out[row, 3] = b * average_cost_all_or_nothing(f, net, d) return if alpha == 1.0: L = all_or_nothing_assignment(cost(f, net2), net, d) out[row, 2] = b * cost(f, net).dot(L) / np.sum(d[:, 2]) out[row, 3] = b * average_cost(f, net, d) return out[row, 2] = b * cost(f, net).dot(fs[:, 0]) / np.sum( (1 - alpha) * d[:, 2]) out[row, 3] = b * cost(f, net).dot(fs[:, 1]) / np.sum(alpha * d[:, 2])
def compute_metrics_beta(alpha, beta, f, net, d, feat, subset, out, row, fs=None, net2=None, \ length_unit='Mile', time_unit='Minute'): ''' Save in the numpy array 'out' at the specific 'row' the following metrics - average cost for non-routed - average cost for routed - average cost - average cost on a subset (e.g. local routes) - average cost outside of a subset (e.g. non-local routes) - total gas emissions - total gas emissions on a subset (e.g. local routes) - total gas emissions outside of a subset (e.g. non-local routes) - total flow in the network - total flow in the network on a subset (e.g. local routes) - total flow in the network outside of a subset (e.g. non-local routes) ''' if length_unit == 'Meter': lengths = feat[:,1] / 1609.34 # convert into miles elif length_unit == 'Mile': lengths = feat[:,1] if time_unit == 'Minute': a = 60.0 elif time_unit == 'Second': a = 3600. b = 60./a speed = a * np.divide(lengths, np.maximum(cost(f, net), 10e-8)) co2 = np.multiply(gas_emission(speed), lengths) out[row,0] = alpha out[row,1] = beta out[row,4] = b * average_cost(f, net, d) out[row,5] = b * average_cost_subset(f, net, d, subset) out[row,6] = out[row,3] - out[row,4] out[row,7] = co2.dot(f) / f.dot(lengths) out[row,8] = np.multiply(co2, subset).dot(f) / f.dot(lengths) out[row,9] = out[row,6] - out[row,7] out[row,10] = np.sum(np.multiply(f, lengths)) * 4000. out[row,11] = np.sum(np.multiply(np.multiply(f, lengths), subset)) * 4000. out[row,12] = out[row,9] - out[row,10] if alpha == 0.0: out[row,2] = b * average_cost(f, net, d) out[row,3] = b * average_cost_all_or_nothing(f, net, d) return if alpha == 1.0: L = all_or_nothing_assignment(cost(f, net2), net, d) out[row,2] = b * cost(f, net).dot(L) / np.sum(d[:,2]) out[row,3] = b * average_cost(f, net, d) return out[row,2] = b * cost(f, net).dot(fs[:,0]) / np.sum((1-alpha)*d[:,2]) out[row,3] = b * cost(f, net).dot(fs[:,1]) / np.sum(alpha*d[:,2])