alpha = 0.602
gamma = 0.101
c = 1.419123356
a = 4.83338664027225
A = 30.0
C = 0 # added as an experiment - to control the behaviour of ck - (0 = no impact)
# Order : In-vehicle time, Access time, Egress time, Transfer Walk time, Origin wait time, Transfer wait time
##Applicable to mumford1 InvehicleTime, Origin Wait Time, Transfer Wait time
initial_guess = [
5.0, 5.0, 5.0
] # [2.0, 2.8, 3.0, 1.0, 1.5, 2.0] # far [5.0, 5.0, 5.0, 5.0, 5.0, 5.0]
initial_cost = sg.runAssignmentCalculateErrorRMSN(
Visum,
initial_guess,
obsStopPoints=observedStopPointDf,
obsLineRoutes=observedRouteListDf)
initial_cost = sg.runAssignmentCalculateErrorRMSN(
Visum,
initial_guess,
obsStopPoints=observedStopPointDf,
obsLineRoutes=observedRouteListDf)
print initial_guess, initial_cost
plot_dict = OrderedDict()
plot_dict = {0: [initial_cost, initial_guess]}
u = np.copy(initial_guess)
# print df_rmsn.head(10) - values are assigned correctly
# Save simulated rmsn values to the dataframe
estimateList = [1.0, 2.0, 2.0, 1.5, 2.0, 3.0]
for estimate in range(len(estimateList)):
estimates = copy.copy(estimateList)
# print estimates
for i in range(len(parameterValueList)):
# print i
estimates[estimate] = parameterValueList[i]
print estimates
rmsnValue = sg.runAssignmentCalculateErrorRMSN(Visum, estimates, obsStopPoints=observedStopPointDf,
obsLineRoutes=observedRouteListDf)
df_rmsn.at[i, df_rmsn_columns[estimate + 1]] = rmsnValue
df_rmsn.to_csv(
"C:\\Users\\thenuwan.jayasinghe\\OneDrive - tum.de\\Thesis\\1_Coding\\Experiments\\28012020_evaluate_spsa_varients\\results\\sensitivity_network_2_2.csv")
# creating subplot
titleList = ["In-Vehicle Time", "Access Time", "Egress Time", "Transfer Walk Time", "Origin Wait Time",
"Transfer Wait Time"]
df_rmsn_para = df_rmsn[['inVeh', 'access', 'egress', 'traWalk', 'oriWait', 'traWait']]
fig, axes = plt.subplots(nrows=2, ncols=3)
xlim = (0, 10)
ylim = (0, 15)
fig.suptitle('Sensitivity Analysis of the parameters')
max_iterations = 300
alpha = 0.602
gamma = 0.101
c = 0.6
a = 3.7
A = 30.0
C = 0 # added as an experiment - to control the behaviour of ck - (0 = no impact)
# Order : In-vehicle time, Access time, Egress time, Walk time, Origin wait time, Transfer wait time
initial_guess = [
5.0, 5.0, 5.0, 5.0, 5.0, 5.0
] # [2.0, 2.8, 3.0, 1.0, 1.5, 2.0] # far [5.0, 5.0, 5.0, 5.0, 5.0, 5.0] #exact [1.0, 2.0, 2.0, 1.5, 2.0, 3.0]
initial_cost = sg.runAssignmentCalculateErrorRMSN(Visum, initial_guess,
observedStopPointDf,
observedTransferWalkTimeDf)
print initial_guess, initial_cost
plot_dict = OrderedDict()
plot_dict = {0: [initial_cost, initial_guess]}
u = np.copy(initial_guess)
# measure time - start
t_start = timeit.default_timer()
for k in range(max_iterations):
ak = a / ((A + k + 1)**alpha)
ck = c / ((C + k + 1)**gamma)
plot_dict = OrderedDict()
parameterValueList = np.arange(0.0, 9.9, 0.1)
print type(parameterValueList)
# Order : In-vehicle time, Access time, Egress time, Walk time, Origin wait time, Transfer wait time
estimateList = [1.0, 2.0, 2.0, 1.5, 2.0, 3.0]
titleList = ["In-Vehicle Time", "Access Time", "Egress Time", "Transfer Walk Time", "Origin Wait Time",
"Transfer Wait Time"]
for i in range(len(parameterValueList)):
# print i
estimateList[5] = parameterValueList[i]
print estimateList
rmsnValue = sg.runAssignmentCalculateErrorRMSN(Visum, estimateList, observedStopPointDf, observedTransferWalkTimeDf)
plot_dict[i] = rmsnValue
# creation of the plot
coefficient_values = parameterValueList.tolist()
rmsn_values = []
for key, value in plot_dict.items():
rmsn_values.append(value)
# customize the grid
fig, ax = plt.subplots()
plt.plot(coefficient_values, rmsn_values)
