max_rssi = np.max(row)
if cfg.min_rssi <= max_rssi <= cfg.max_rssi:
new_dataset.append(row + [angle])
csvfile.close()
dataset[cal_index] = np.array(new_dataset)
dataset_angle[cal_index] = dataset[cal_index][:, -1]
"""
Create test data, one for each AP
"""
test_data[cal_index] = fn.arrange_data(dataset[cal_index][:, :-1])
dataseta_for_sd = pd.DataFrame(dataset[cal_index][:, :-1], columns=['V0', 'V1', 'V2', 'V3', 'H0', 'H1',
'H2', 'H3'])
# filter noise below -10 dB
test_data[cal_index] = fn.noise_filter(np.array(test_data[cal_index]))
# read database from file
test_data[cal_index] = pd.DataFrame(test_data[cal_index], columns=['V0', 'V1', 'V2', 'V3', 'H0', 'H1',
'H2', 'H3', 'V_m_H'])
dataset_angle[cal_index] = pd.DataFrame(dataset_angle[cal_index], columns=['Angle'])
# filter angles out of range
valid_angle = (cfg.min_angle <= dataset_angle[cal_index]['Angle']) & \
(cfg.max_angle >= dataset_angle[cal_index]['Angle'])
test_data[cal_index] = test_data[cal_index].loc[valid_angle]
dataset_angle[cal_index] = dataset_angle[cal_index].loc[valid_angle]
dataset_angle[cal_index] = np.array(dataset_angle[cal_index]).ravel()
def minimize_time_delay(dt):
"""
Get experiment data
"""
ap = pd.read_csv(fname, index_col=0, names=['MAC', 'Time', 'RSSIs', 'channel'])
ap = ap.loc[ap.index == cfg_exp.mac]
ap['Time'] += dt
# ap = ap.loc[ap['Time'] >= track.track_time[i][0]]
# ap = ap.loc[ap['Time'] <= track.track_time[i][-1]]
ap_rssis = list(ap['RSSIs'])
for k in range(len(ap_rssis)):
ap_rssis[k] = parse_rssi(ap_rssis[k])
del ap['RSSIs']
if ap_rssis:
ap_rssis = pd.DataFrame(ap_rssis, columns=['V0', 'V1', 'V2', 'V3', 'H0', 'H1', 'H2', 'H3'],
index=ap.index)
ap = pd.concat([ap, ap_rssis], axis=1)
ap_rssis = np.array(ap_rssis)
ap_arranged = fn.arrange_data(ap_rssis)
# Arranging model data
ap_max = np.apply_along_axis(np.max, 1, ap_rssis)
# conditions
not_sat_power = ap_max <= config.max_rssi
not_low_power = ap_max >= config.min_rssi
not_erroneous = ap_arranged[:, 8] > -10
ap_arranged = pd.DataFrame(ap_arranged, columns=['V0', 'V1', 'V2', 'V3', 'H0', 'H1', 'H2',
'H3', 'VmH'], index=ap.index)
ap_arranged_filtered = ap_arranged.loc[not_sat_power & not_low_power & not_erroneous]
ap_arranged_filtered = np.array(ap_arranged_filtered)
ap_arranged_filtered = fn.noise_filter(ap_arranged_filtered)
# Filtered
ap_pred_times[i][j] = np.array(ap['Time'].loc[not_sat_power & not_low_power & not_erroneous])
# Predicting basic model result
if ap_arranged_filtered.shape[0]:
ap_pred[i][j] = cal.rfc[cur_ap_i].predict(ap_arranged_filtered)
ap_pred[i][j] = ap_pred[i][j].reshape((ap_pred[i][j].shape[0], 1))
# # plot predictions
# plt.plot(ap_pred_times[i][j], ap_pred[i][j], 'go')
# plt.show()
"""
amalgamating predictions for each time frame
"""
# not valid prediction are saved as 100
ap_timed_pred, ap_timed_sd = timed_predictions(ap_pred[i][j], ap_pred_times[i][j],
track.track_time_int[i])
# remove NaNs
ap_timed_pred = fn.remove_nan(ap_timed_pred)
# plt.plot(track.track_time_int[i], track.doa_true[0][:, j], 'r',
# track.track_time_int[i], ap_timed_pred, 'go')
# plt.show()
rsme = np.sqrt(np.sum((track.doa_true[0][:, j].reshape(ap_timed_pred.shape) - ap_timed_pred) ** 2) /
ap_timed_pred.shape[0])
# print 'AP: ', j, 'RSME is: ', rsme
return rsme
ap_arranged = fn.arrange_data(ap_rssis)
# Arranging model data
ap_max = np.apply_along_axis(np.max, 1, ap_rssis)
# conditions
not_sat_power = ap_max <= config.max_rssi
not_low_power = ap_max >= config.min_rssi
not_erroneous = ap_arranged[:, 8] > -10
ap_arranged = pd.DataFrame(ap_arranged, columns=['V0', 'V1', 'V2', 'V3', 'H0', 'H1', 'H2',
'H3', 'VmH'], index=ap.index)
ap_arranged_filtered = ap_arranged.loc[not_sat_power & not_low_power & not_erroneous]
ap_arranged_filtered = np.array(ap_arranged_filtered)
ap_arranged_filtered = fn.noise_filter(ap_arranged_filtered)
# Filtered
ap_pred_times[i][j] = np.array(ap['Time'].loc[not_sat_power & not_low_power & not_erroneous])
# Predicting basic model result
if ap_arranged_filtered.shape[0]:
ap_pred[i][j] = cal.rfc[cur_ap_i].predict(ap_arranged_filtered)
ap_pred[i][j] = ap_pred[i][j].reshape((ap_pred[i][j].shape[0], 1))
# # plot predictions
# plt.plot(ap_pred_times[i][j], ap_pred[i][j], 'go')
# plt.show()
"""
amalgamating predictions for each time frame
"""
max_rssi = np.max(row)
if cfg.min_rssi <= max_rssi <= cfg.max_rssi:
new_dataset.append(row + [angle])
csvfile.close()
dataset[cal_index] = np.array(new_dataset)
dataset_angle[cal_index] = dataset[cal_index][:, -1]
"""
Create training data, one for each AP
"""
arranged_data[cal_index] = fn.arrange_data(dataset[cal_index][:, :-1])
dataseta_for_sd = pd.DataFrame(dataset[cal_index][:, :-1], columns=['V0', 'V1', 'V2', 'V3', 'H0', 'H1',
'H2', 'H3'])
# filter noise below -10 dB
arranged_data[cal_index] = fn.noise_filter(np.array(arranged_data[cal_index]))
# read database from file
arranged_data[cal_index] = pd.DataFrame(arranged_data[cal_index], columns=['V0', 'V1', 'V2', 'V3', 'H0', 'H1',
'H2', 'H3', 'V_m_H'])
dataset_angle[cal_index] = pd.DataFrame(dataset_angle[cal_index], columns=['Angle'])
# filter angles out of range
valid_angle = (cfg.min_angle <= dataset_angle[cal_index]['Angle']) & \
(cfg.max_angle >= dataset_angle[cal_index]['Angle'])
arranged_data[cal_index] = arranged_data[cal_index].loc[valid_angle]
dataset_angle[cal_index] = dataset_angle[cal_index].loc[valid_angle]
dataset_angle[cal_index] = np.array(dataset_angle[cal_index]).ravel()
predictions = np.zeros(())
ap_timed_pred = np.zeros((len(track.time_frames), len(track.valid_ants)))
ap_timed_sd = np.zeros((len(track.time_frames), len(track.valid_ants)))
for i in track.valid_ants:
"""
Create training data
"""
# read database from file
dataset = pd.read_csv('dataset_ap' + str(i) + '.csv',
names=['V0', 'V1', 'V2', 'V3', 'H0', 'H1', 'H2', 'H3', 'V_m_H'])
dataset_time = np.zeros((dataset.shape[0], 1))
dataset = np.array(dataset)
dataset = np.hstack((dataset_time, dataset))
# filter noise below -10 dB
dataset = fn.noise_filter(np.array(dataset))
dataset_angle = pd.read_csv('dataset_angle_ap' + str(i) + '.csv', names=['Angle'])
# filter angles out of range
valid_angle = (cfg_exp.min_angle <= dataset_angle['Angle']) & (cfg_exp.max_angle >= dataset_angle['Angle'])
dataset = fn.filter_rows(dataset, valid_angle)
dataset_angle = dataset_angle.loc[valid_angle]
dataset_angle = np.array(dataset_angle).ravel()
# Fitting to RF
clf = RandomForestClassifier()
clf.fit(np.array(dataset[:, 1:]), dataset_angle)
# creating predicted test set angles