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()