print("Data shape:", x.shape)
print("Theta shape:", data_param.theta.shape)
print("Theta:", data_param.theta)
# parvalue number of sub intervals
# Note : numsubinterval = 1 => only observed data points, no intermediate brownian bridges
em_param = prm.em(tol=0.01 * prm.dim * prm.dof,
burninpaths=10,
mcmcpaths=100,
numsubintervals=5,
niter=100,
dt=(allt[0, 1] - allt[0, 0]))
# call to EM which returns the final error and estimated theta value
error_list, theta_list, gammavec_list = em(x, t, em_param, data_param)
estimated_theta = prm.theta_transformations(theta=theta_list[-1],
theta_type='hermite')
true_theta = prm.theta_transformations(theta=dc.true_theta(sim_param),
theta_type='ordinary')
print("\n Estimated ordinary: ", np.transpose(estimated_theta.ordinary),
"\n True ordinary: ", np.transpose(true_theta.ordinary))
print("\n Estimated hermite: ", np.transpose(estimated_theta.hermite),
"\n True hermite: ", np.transpose(true_theta.hermite))
threshold = np.array([0., 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5])
ordinary_errors = []
hermite_errors = []
print("Data shape:", x.shape)
print("Theta shape:", data_param.theta.shape)
print("Theta:", data_param.theta)
# parvalue number of sub intervals
# Note : numsubinterval = 1 => only observed data points, no intermediate brownian bridges
em_param = prm.em(tol=0.1,
burninpaths=10,
mcmcpaths=100,
numsubintervals=5,
niter=100,
dt=(allt[0, 1] - allt[0, 0]))
# call to EM which returns the final error and estimated theta value
start_time = time.time()
error_list, theta_list, gammavec_list, avg_ar_burnin, avg_ar_sampling = em(
x, t, em_param, data_param)
time_taken = time.time() - start_time
print("Time taken for 4D EM: ", time_taken)
print("Average AR burnin: ", avg_ar_burnin, ", average AR sampling: ",
avg_ar_sampling)
estimated_theta = prm.theta_transformations(theta=theta_list[-1],
theta_type='hermite')
true_theta = prm.theta_transformations(theta=dc.true_theta(sim_param),
theta_type='ordinary')
print("\n Estimated ordinary: ", np.transpose(estimated_theta.ordinary),
"\n True ordinary: ", np.transpose(true_theta.ordinary))
print("\n Estimated hermite: ", np.transpose(estimated_theta.hermite),
"\n True hermite: ", np.transpose(true_theta.hermite))
