print('Using data from ' + start_calibration + ' until ' + end_calibration +
'\n')
print('1) Particle swarm optimization\n')
# define dataset
data = [df_sciensano['H_in'][start_calibration:end_calibration]]
states = ["H_in"]
weights = [1]
# set PSO optimisation settings
pars = ['warmup', 'beta', 'l', 'effectivity']
bounds = ((20, 40), (0.01, 0.09), (0.1, 20), (0.03, 0.97))
# run PSO optimisation
theta = pso.fit_pso(model,
data,
pars,
states,
bounds,
maxiter=maxiter,
popsize=popsize,
start_date=start_calibration)
theta = np.array([37.45480627, 0.04796753, 11, 0.14]) #-75918.16606140955
# Assign estimate
warmup, model.parameters = assign_PSO(model.parameters, pars, theta)
# Perform simulation
out = model.sim(end_calibration, start_date=start_calibration, warmup=warmup)
# Visualize fit
ax = plot_PSO(out, theta, pars, data, states, start_calibration,
end_calibration)
#plt.show()
#plt.close()
# run MCMC sampler
weights = [1]
# -----------
# Perform PSO
# -----------
# set optimisation settings
pars = ['warmup', 'beta', 'da']
bounds = ((10, 80), (0.020, 0.06), (3.0, 9.0))
# run optimisation
theta = pso.fit_pso(model,
data,
pars,
states,
bounds,
weights=weights,
maxiter=maxiter,
popsize=popsize,
dist='poisson',
poisson_offset=poisson_offset,
start_date=start_calibration,
processes=processes)
# Assign estimate
warmup, model.parameters = assign_PSO(model.parameters, pars, theta)
# Perform simulation
out = model.sim(end_calibration,
start_date=start_calibration,
warmup=warmup)
# Visualize fit
ax = plot_PSO(out, theta, pars, data, states, start_calibration,
end_calibration)
plt.show()