def save_simulation_result_to_mat():
output = {}
loss_set = SimulationRecord.objects.filter(estimation_method=probe_based_name)\
.distinct('loss_rate').values_list('loss_rate', flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(
estimation_method=probe_based_name,
loss_rate__range=(l - 0.001, l + 0.001))
err.append(mean_relative_error(data))
output['probe_loss_set'] = list(loss_set)
output['probe_rel_err'] = err
loss_set = SimulationRecord.objects.filter(estimation_method=distance_based_name)\
.distinct('loss_rate').values_list('loss_rate', flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(
estimation_method=distance_based_name,
loss_rate__range=(l - 0.001, l + 0.001))
err.append(mean_relative_error(data))
output['distance_loss_set'] = list(loss_set)
output['distance_rel_err'] = err
loss_set = SimulationRecord.objects.filter(estimation_method="mobsampling")\
.distinct("loss_rate").values_list("loss_rate", flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(estimation_method="mobsampling",
loss_rate__range=(l - 0.001,
l + 0.001))
err.append(mean_relative_error(data))
output['mobsampling_loss_set'] = list(loss_set)
output['mobsampling_rel_err'] = list(err)
loss_set = SimulationRecord.objects.filter(estimation_method="exploiting...")\
.distinct("loss_rate").values_list("loss_rate", flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(
estimation_method="exploiting...",
loss_rate__range=(l - 0.001, l + 0.001))
err.append(mean_relative_error(data))
output['exploiting_loss_set'] = list(loss_set)
output['exploiting_rel_err'] = list(err)
print "writing to .mat file"
savemat('simulator/data/data2.mat', output, appendmat=False)
def save_simulation_result_to_mat():
output = {}
loss_set = SimulationRecord.objects.filter(estimation_method=probe_based_name)\
.distinct('loss_rate').values_list('loss_rate', flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(estimation_method=probe_based_name,
loss_rate__range=(l-0.001, l+0.001))
err.append(mean_relative_error(data))
output['probe_loss_set'] = list(loss_set)
output['probe_rel_err'] = err
loss_set = SimulationRecord.objects.filter(estimation_method=distance_based_name)\
.distinct('loss_rate').values_list('loss_rate', flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(estimation_method=distance_based_name,
loss_rate__range=(l-0.001, l+0.001))
err.append(mean_relative_error(data))
output['distance_loss_set'] = list(loss_set)
output['distance_rel_err'] = err
loss_set = SimulationRecord.objects.filter(estimation_method="mobsampling")\
.distinct("loss_rate").values_list("loss_rate", flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(estimation_method="mobsampling",
loss_rate__range=(l-0.001, l+0.001))
err.append(mean_relative_error(data))
output['mobsampling_loss_set'] = list(loss_set)
output['mobsampling_rel_err'] = list(err)
loss_set = SimulationRecord.objects.filter(estimation_method="exploiting...")\
.distinct("loss_rate").values_list("loss_rate", flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(estimation_method="exploiting...",
loss_rate__range=(l-0.001, l+0.001))
err.append(mean_relative_error(data))
output['exploiting_loss_set'] = list(loss_set)
output['exploiting_rel_err'] = list(err)
print "writing to .mat file"
savemat('simulator/data/data2.mat', output, appendmat=False)
def mob_sampling_plotter():
loss_set = SimulationRecord.objects.filter(estimation_method="mobsampling")\
.distinct("loss_rate").values_list("loss_rate", flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(estimation_method="mobsampling",
loss_rate__range=(l-0.001, l+0.001))
err.append(mean_relative_error(data))
plt.plot(loss_set, err, color='red')
plt.show()
def distance_based_plotter():
loss_set = SimulationRecord.objects.filter(estimation_method=distance_based_name)\
.distinct('loss_rate').values_list('loss_rate', flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(estimation_method=distance_based_name,
loss_rate__range=(l-0.001, l+0.001))
err.append(mean_relative_error(data))
plt.plot(loss_set, err, color='red')
plt.show()
def mob_sampling_plotter():
loss_set = SimulationRecord.objects.filter(estimation_method="mobsampling")\
.distinct("loss_rate").values_list("loss_rate", flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(estimation_method="mobsampling",
loss_rate__range=(l - 0.001,
l + 0.001))
err.append(mean_relative_error(data))
plt.plot(loss_set, err, color='red')
plt.show()
def distance_based_plotter():
loss_set = SimulationRecord.objects.filter(estimation_method=distance_based_name)\
.distinct('loss_rate').values_list('loss_rate', flat=True)
err = []
for l in loss_set:
data = SimulationRecord.objects.filter(
estimation_method=distance_based_name,
loss_rate__range=(l - 0.001, l + 0.001))
err.append(mean_relative_error(data))
plt.plot(loss_set, err, color='red')
plt.show()
