92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
#!/usr/bin/python
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import numpy as np
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from scipy.optimize import curve_fit
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from libs.models import log_gamma_loc
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def modelfit_log_gamma_func(input_data, *target_data):
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# unpack input_data
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rx_locs = input_data
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# unpack target_data
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if len(target_data) < 4:
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print("missing arguments, should have loc_x, loc_y, pwr, gamma at least")
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return
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tx_loc_x = target_data[0]
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tx_loc_y = target_data[1]
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tx_power = target_data[2]
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env_gamma = target_data[3]
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tx_loc_z = None if len(target_data) == 4 else target_data[4]
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tx_loc = np.array([tx_loc_x, tx_loc_y])
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if tx_loc_z is not None:
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tx_loc = np.array([tx_loc_x, tx_loc_y, tx_loc_z])
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return log_gamma_loc(rx_locs, tx_loc, tx_power, env_gamma)
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def modelfit_log_gamma(
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rx_locs,
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rx_rsses,
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bounds_pwr=(-60, 0),
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bounds_gamma=(2, 6),
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bounds_loc_x=(0, 6.2),
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bounds_loc_y=(0, 6.2),
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bounds_loc_z=None,
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monte_carlo_sampling=False,
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monte_carlo_sampling_rate=0.8
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):
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# initial seeds
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seed_pwr = np.random.uniform(bounds_pwr[0], bounds_pwr[1])
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seed_gamma = np.random.uniform(bounds_gamma[0], bounds_gamma[1])
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seed_loc_x = np.random.uniform(bounds_loc_x[0], bounds_loc_x[1])
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seed_loc_y = np.random.uniform(bounds_loc_y[0], bounds_loc_y[1])
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if bounds_loc_z is not None:
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seed_loc_z = np.random.uniform(bounds_loc_z[0], bounds_loc_z[1])
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seeds = [
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seed_loc_x, seed_loc_y,
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seed_pwr, seed_gamma,
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seed_loc_z
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]
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bounds = list(zip(*[
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bounds_loc_x, bounds_loc_y,
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bounds_pwr, bounds_gamma,
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bounds_loc_z
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]))
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else:
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seeds = [
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seed_loc_x, seed_loc_y,
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seed_pwr, seed_gamma
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]
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bounds = list(zip(*[
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bounds_loc_x, bounds_loc_y,
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bounds_pwr, bounds_gamma
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]))
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if monte_carlo_sampling and rx_rsses.shape[0] > 10:
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logistics = np.random.choice(
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np.arange(rx_rsses.shape[0]),
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size=int(monte_carlo_sampling_rate * rx_rsses.shape[0]),
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replace=False
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)
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rx_locs = rx_locs[logistics, :]
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rx_rsses = rx_rsses[logistics, :]
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# fit
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popt, pcov = curve_fit(
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modelfit_log_gamma_func, rx_locs, rx_rsses,
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p0=seeds, bounds=bounds
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)
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# unpack popt
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if bounds_loc_z is None:
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est_loc_x, est_loc_y, est_tx_pwr, est_env_gamma = popt
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est_tx_loc = np.array([est_loc_x, est_loc_y])
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else:
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est_loc_x, est_loc_y, est_tx_pwr, est_env_gamma, est_loc_z = popt
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est_tx_loc = np.array([est_loc_x, est_loc_y, est_loc_z])
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est_rsses = log_gamma_loc(rx_locs, est_tx_loc, est_tx_pwr, est_env_gamma)
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est_errors = est_rsses - rx_rsses
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pmse = 1.0 * np.nansum(est_errors * est_errors) / est_errors.shape[0]
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return pmse, est_tx_loc, est_tx_pwr, est_env_gamma, est_rsses
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