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
"""
# Building time frames
time_frames.append(range(int(track.track_time[i][0]), int(track.track_time[i][-1] + cfg_exp.time_step),
cfg_exp.time_step))
# not valid prediction are saved as 100
ap_timed_pred[i], ap_timed_sd[i] = fn_exp.timed_predictions(ap_pred[i], ap_pred_times[i], time_frames[i])
# remove NaNs
ap_timed_pred[i] = fn.remove_nan(ap_timed_pred[i])
# Holt's filtering algorithm
holt = np.zeros(ap_timed_pred[i].shape)
holt[0, :] = ap_timed_pred[i][0, :]
holt_trend = np.zeros(ap_timed_pred[i].shape)
for j in range(1, ap_timed_pred[i].shape[0]):
holt[j, :] = (1 - cfg_exp.alpha) * (holt[j-1, :] + holt_trend[j-1, :]) + cfg_exp.alpha * ap_timed_pred[i][j, :]
holt_trend[j, :] = cfg_exp.trend * (holt[j, :] - holt[j-1, :]) + (1 - cfg_exp.trend) * holt_trend[j-1, :]
ap_timed_holt[i] = holt
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
"""
# Building time frames
time_frames.append(np.array(range(int(track.track_time[i][0]),
int(track.track_time[i][-1] + cfg_exp.time_step),
cfg_exp.time_step)))
# not valid prediction are saved as 100
ap_timed_pred_raw[i], ap_timed_sd[i] = fn_exp.timed_predictions(ap_pred[i], ap_pred_times[i], track.track_time_int[i])
timeout_matrix = np.zeros(ap_timed_pred_raw[i].shape).astype('int')
timeout_matrix[0, :] = np.isnan(ap_timed_pred_raw[i][0, :]) * (cfg_exp.timeout + 1)
for j in range(1, ap_timed_pred_raw[i].shape[0]):
for k in range(ap_timed_pred_raw[i].shape[1]):
if np.isnan(ap_timed_pred_raw[i][j, k]):
timeout_matrix[j, k] = timeout_matrix[j-1, k] + 1
else:
timeout_matrix[j, k] = 0
ap_timeout[i] = timeout_matrix
# Kalman's filtering algorithm
kalman_x_p = np.zeros(ap_timed_pred_raw[i].shape)
kalman_x_m = np.zeros(ap_timed_pred_raw[i].shape)
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
"""
# Building time frames
time_frames.append(range(int(track.track_time[i][0]), int(track.track_time[i][-1] + cfg_exp.time_step),
cfg_exp.time_step))
# not valid prediction are saved as 100
ap_timed_pred[i], ap_timed_sd[i] = fn_exp.timed_predictions(ap_pred[i], ap_pred_times[i], time_frames[i])
# remove NaNs
ap_timed_pred[i] = fn.remove_nan(ap_timed_pred[i])
# Kalman's filtering algorithm
kalman_x_p = np.zeros(ap_timed_pred[i].shape)
kalman_x_p[0, :] = ap_timed_pred[i][0, :]
kalman_p_m = np.zeros(ap_timed_pred[i].shape)
kalman_p_p = np.zeros(ap_timed_pred[i].shape)
kalman_p_p[0, :] = np.ones((1, 4)) * cfg_exp.kalman_q
for j in range(1, ap_timed_pred[i].shape[0]):
kalman_p_m[j, :] = kalman_p_p[j-1, :] + cfg_exp.kalman_q
kalman_k_j = kalman_p_m[j, :] / (kalman_p_m[j, :] + cfg_exp.kalman_r)
kalman_p_p[j, :] = (1 - kalman_k_j) * kalman_p_m[j, :]