# 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 = []
for th in threshold:
ordinary_errors.append(
pfn.compute_errors(true_theta.ordinary, estimated_theta.ordinary, th))
hermite_errors.append(
x = allx[0:parvalue, 0::10, :] # picking every 10th term to get a total of 101 time points
t = allt[0:parvalue, 0::10]
data_param = prm.data(theta = 0.5 * np.random.rand(prm.dof, prm.dim), gvec = sim_param.gvec)
print("Data shape:", x.shape)
print("Theta shape:", data_param.theta.shape)
print("Theta:", data_param.theta)
em_param = prm.em(tol=0.01, 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 = []
for th in threshold:
ordinary_errors.append(pfn.compute_errors(true_theta.ordinary, estimated_theta.ordinary, th))
hermite_errors.append(pfn.compute_errors(true_theta.hermite, estimated_theta.hermite, th))
print("\n")
# save to file
