def test_optimize_1D(self):
# test on simple 1D example, plot the fit
steps = 100
dyn = UNGMTransition(GaussRV(1), GaussRV(1))
x, y = dyn.simulate_discrete(steps)
f = dyn.dyn_eval
dim_in, dim_out = dyn.dim_in, dyn.dim_state
par0 = 1 + np.random.rand(dim_out, dim_in + 1)
tf = MultiOutputGaussianProcessTransform(dim_in, dim_out, par0)
# use sampled system state trajectory to create training data
fy = np.zeros((dim_out, steps))
for k in range(steps):
fy[:, k] = f(x[:, k, 0], k)
b = [np.log((0.1, 1.0001))] + dim_in * [(None, None)]
opt = {'xtol': 1e-2, 'maxiter': 100}
log_par, res_list = tf.model.optimize(np.log(par0),
fy,
x[..., 0],
bounds=b,
method='L-BFGS-B',
options=opt)
print(np.exp(log_par))
self.assertTrue(False)
def setUpClass(cls):
cls.ssm = {}
# setup UNGM with Student RVs
x0 = StudentRV(1)
q = StudentRV(1, scale=np.array([[10.0]]))
r = StudentRV(1)
dyn = UNGMTransition(x0, q)
obs = UNGMMeasurement(r, dyn.dim_state)
x = dyn.simulate_discrete(100)
y = obs.simulate_measurements(x)
cls.ssm.update({'ungm': {'dyn': dyn, 'obs': obs, 'x': x, 'y': y}})
# setup CV with Student RVs
m_0 = np.array([10175, 295, 980, -35]).astype(np.float)
P_0 = np.diag([10000, 100, 10000, 100]).astype(np.float)
nu_0 = 1000.0
x0 = StudentRV(4, m_0, P_0, nu_0)
Q = np.diag([50, 5]).astype(np.float)
nu_q = 1000.0
q = StudentRV(2, scale=Q, dof=nu_q)
R = np.diag([50, 0.4e-6]).astype(np.float)
nu_r = 4.0
r = StudentRV(2, scale=R, dof=nu_r)
dyn = ConstantVelocity(x0, q, dt=0.5)
obs = Radar2DMeasurement(r, 4)
x = dyn.simulate_discrete(100)
y = obs.simulate_measurements(x)
cls.ssm.update({'cv': {'dyn': dyn, 'obs': obs, 'x': x, 'y': y}})
def hypers_demo(lscale=None):
print(f"Seed = {np.random.get_state()[1][0]}")
# set default lengthscales if unspecified
if lscale is None:
lscale = [1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1, 1, 3, 1e1, 3e1, 1e2, ]
steps, mc = 500, 100
# setup univariate non-stationary growth model
x0 = GaussRV(1, cov=np.atleast_2d(5.0))
q = GaussRV(1, cov=np.atleast_2d(10.0))
dyn = UNGMTransition(x0, q) # dynamics
r = GaussRV(1)
obs = UNGMMeasurement(r, 1) # observation model
x = dyn.simulate_discrete(steps, mc_sims=mc) # generate some data
z = obs.simulate_measurements(x)
num_el = len(lscale)
mean_f, cov_f = np.zeros((dyn.dim_in, steps, mc, num_el)), np.zeros((dyn.dim_in, dyn.dim_in, steps, mc, num_el))
for iel, el in enumerate(lscale):
# kernel parameters
ker_par = np.array([[1.0, el * dyn.dim_in]])
# initialize BHKF with current lenghtscale
f = GaussianProcessKalman(dyn, obs, ker_par, ker_par, points='ut', point_hyp={'kappa': 0.0})
# filtering
for s in range(mc):
mean_f[..., s, iel], cov_f[..., s, iel] = f.forward_pass(z[..., s])
# evaluate RMSE, NCI and NLL
rmseVsEl = squared_error(x[..., na], mean_f)
nciVsEl = rmseVsEl.copy()
nllVsEl = rmseVsEl.copy()
for k in range(steps):
for iel in range(num_el):
mse_mat = mse_matrix(x[:, k, :], mean_f[:, k, :, iel])
for s in range(mc):
nciVsEl[:, k, s, iel] = log_cred_ratio(x[:, k, s], mean_f[:, k, s, iel], cov_f[:, :, k, s, iel],
mse_mat)
nllVsEl[:, k, s, iel] = neg_log_likelihood(x[:, k, s], mean_f[:, k, s, iel], cov_f[:, :, k, s, iel])
# average out time and MC simulations
rmseVsEl = np.sqrt(np.mean(rmseVsEl, axis=1)).mean(axis=1)
nciVsEl = nciVsEl.mean(axis=(1, 2))
nllVsEl = nllVsEl.mean(axis=(1, 2))
# plot influence of changing lengthscale on the RMSE and NCI and NLL filter performance
plt.figure()
plt.semilogx(lscale, rmseVsEl.squeeze(), color='k', ls='-', lw=2, marker='o', label='RMSE')
plt.semilogx(lscale, nciVsEl.squeeze(), color='k', ls='--', lw=2, marker='o', label='NCI')
plt.semilogx(lscale, nllVsEl.squeeze(), color='k', ls='-.', lw=2, marker='o', label='NLL')
plt.grid(True)
plt.legend()
plt.show()
return {'el': lscale, 'rmse': rmseVsEl, 'nci': nciVsEl, 'neg_log_likelihood': nllVsEl}
def test_simulate(self):
time_steps = 50
# UNGM additive noise
dim = 1
init_dist = GaussRV(dim)
noise_dist = GaussRV(dim, cov=np.atleast_2d(10.0))
ungm_dyn = UNGMTransition(init_dist, noise_dist)
ungm_meas = UNGMMeasurement(GaussRV(dim), ungm_dyn.dim_state)
x = ungm_dyn.simulate_discrete(time_steps, mc_sims=20)
y = ungm_meas.simulate_measurements(x)
# UNGM non-additive noise
ungmna_dyn = UNGMNATransition(init_dist, noise_dist)
ungmna_meas = UNGMNAMeasurement(GaussRV(dim), ungm_dyn.dim_state)
x = ungmna_dyn.simulate_discrete(time_steps, mc_sims=20)
y = ungmna_meas.simulate_measurements(x)
def test_crash(self):
d = 1
tmc = MonteCarloTransform(d, n=1e4)
f = UNGMTransition(GaussRV(1, cov=1.0), GaussRV(1, cov=10.0)).dyn_eval
mean = np.zeros(d)
cov = np.eye(d)
# does it crash ?
tmc.apply(f, mean, cov, np.atleast_1d(1.0))
def setUpClass(cls):
# setup UNGM
x0 = GaussRV(1, cov=np.atleast_2d(1.0))
q = GaussRV(1, cov=np.atleast_2d(10.0))
cls.dyn_ungm = UNGMTransition(x0, q)
r = GaussRV(1, cov=np.atleast_2d(1.0))
cls.obs_ungm = UNGMMeasurement(r, 1)
# setup pendulum
dt = 0.01
x0 = GaussRV(2, np.array([1.5, 0]), 0.01 * np.eye(2))
q = GaussRV(2, cov=np.array([[dt**3 / 3, dt**2 / 2], [dt**2 / 2, dt]]))
cls.dyn_pend = Pendulum2DTransition(x0, q, dt)
r = GaussRV(1, cov=np.atleast_2d(0.1))
cls.obs_pend = Pendulum2DMeasurement(r, cls.dyn_pend.dim_state)
def test_crash(self):
dim = 1
mt = FullySymmetricStudentTransform(dim, degree=3)
f = UNGMTransition(StudentRV(1, scale=1.0), StudentRV(1, scale=10.0)).dyn_eval
mean = np.zeros(dim)
cov = np.eye(dim)
# does it crash ?
mt.apply(f, mean, cov, np.atleast_1d(1.0))
dim = 2
mt = FullySymmetricStudentTransform(dim, degree=5)
f = sum_of_squares
mean = np.zeros(dim)
cov = np.eye(dim)
# does it crash ?
mt.apply(f, mean, cov, np.atleast_1d(1.0))
def gpq_int_var_demo():
"""Compares integral variances of GPQ and GPQ+D by plotting."""
d = 1
f = UNGMTransition(GaussRV(d), GaussRV(d)).dyn_eval
mean = np.zeros(d)
cov = np.eye(d)
kpar = np.array([[10.0] + d * [0.7]])
gpq = GaussianProcessTransform(d,
1,
kern_par=kpar,
kern_str='rbf',
point_str='ut',
point_par={'kappa': 0.0})
gpqd = GaussianProcessDerTransform(d,
1,
kern_par=kpar,
point_str='ut',
point_par={'kappa': 0.0})
mct = MonteCarloTransform(d, n=1e4)
mean_gpq, cov_gpq, cc_gpq = gpq.apply(f, mean, cov, np.atleast_1d(1.0))
mean_gpqd, cov_gpqd, cc_gpqd = gpqd.apply(f, mean, cov, np.atleast_1d(1.0))
mean_mc, cov_mc, cc_mc = mct.apply(f, mean, cov, np.atleast_1d(1.0))
xmin_gpq = norm.ppf(0.0001, loc=mean_gpq, scale=gpq.model.integral_var)
xmax_gpq = norm.ppf(0.9999, loc=mean_gpq, scale=gpq.model.integral_var)
xmin_gpqd = norm.ppf(0.0001, loc=mean_gpqd, scale=gpqd.model.integral_var)
xmax_gpqd = norm.ppf(0.9999, loc=mean_gpqd, scale=gpqd.model.integral_var)
xgpq = np.linspace(xmin_gpq, xmax_gpq, 500)
ygpq = norm.pdf(xgpq, loc=mean_gpq, scale=gpq.model.integral_var)
xgpqd = np.linspace(xmin_gpqd, xmax_gpqd, 500)
ygpqd = norm.pdf(xgpqd, loc=mean_gpqd, scale=gpqd.model.integral_var)
plt.figure()
plt.plot(xgpq, ygpq, lw=2, label='gpq')
plt.plot(xgpqd, ygpqd, lw=2, label='gpq+d')
plt.gca().add_line(
Line2D([mean_mc, mean_mc], [0, 150], linewidth=2, color='k'))
plt.legend()
plt.show()
def setUpClass(cls):
cls.ssm = {}
# setup UNGM
x0 = GaussRV(1)
q = GaussRV(1, cov=np.array([[10.0]]))
r = GaussRV(1)
dyn = UNGMTransition(x0, q)
obs = UNGMMeasurement(r, 1)
x = dyn.simulate_discrete(100)
y = obs.simulate_measurements(x)
cls.ssm.update({'ungm': {'dyn': dyn, 'obs': obs, 'x': x, 'y': y}})
# setup UNGM with non-additive noise
x0 = GaussRV(1)
q = GaussRV(1, cov=np.array([[10.0]]))
r = GaussRV(1)
dyn = UNGMNATransition(x0, q)
obs = UNGMNAMeasurement(r, 1)
x = dyn.simulate_discrete(100)
y = obs.simulate_measurements(x)
cls.ssm.update({'ungmna': {'dyn': dyn, 'obs': obs, 'x': x, 'y': y}})
# setup 2D pendulum
x0 = GaussRV(2, mean=np.array([1.5, 0]), cov=0.01 * np.eye(2))
dt = 0.01
q = GaussRV(2,
cov=0.01 * np.array([[(dt**3) / 3,
(dt**2) / 2], [(dt**2) / 2, dt]]))
r = GaussRV(1, cov=np.array([[0.1]]))
dyn = Pendulum2DTransition(x0, q, dt=dt)
obs = Pendulum2DMeasurement(r, dyn.dim_state)
x = dyn.simulate_discrete(100)
y = obs.simulate_measurements(x)
cls.ssm.update({'pend': {'dyn': dyn, 'obs': obs, 'x': x, 'y': y}})
# setup reentry vehicle radar tracking
m0 = np.array([6500.4, 349.14, -1.8093, -6.7967, 0.6932])
P0 = np.diag([1e-6, 1e-6, 1e-6, 1e-6, 1])
x0 = GaussRV(5, m0, P0)
q = GaussRV(3, cov=np.diag([2.4064e-5, 2.4064e-5, 1e-6]))
r = GaussRV(2, cov=np.diag([1e-6, 0.17e-6]))
dyn = ReentryVehicle2DTransition(x0, q)
obs = Radar2DMeasurement(r, 5)
x = dyn.simulate_discrete(100)
y = obs.simulate_measurements(x)
cls.ssm.update({'rer': {'dyn': dyn, 'obs': obs, 'x': x, 'y': y}})
# setup coordinated turn bearing only tracking
m0 = np.array([1000, 300, 1000, 0, np.deg2rad(-3.0)])
P0 = np.diag([100, 10, 100, 10, 0.1])
x0 = GaussRV(5, m0, P0)
dt = 0.1
rho_1, rho_2 = 0.1, 1.75e-4
A = np.array([[dt**3 / 3, dt**2 / 2], [dt**2 / 2, dt]])
Q = np.zeros((5, 5))
Q[:2, :2], Q[2:4, 2:4], Q[4, 4] = rho_1 * A, rho_1 * A, rho_2 * dt
q = GaussRV(5, cov=Q)
r = GaussRV(4, cov=10e-3 * np.eye(4))
sen = np.vstack((1000 * np.eye(2), -1000 * np.eye(2))).astype(np.float)
dyn = CoordinatedTurnTransition(x0, q)
obs = BearingMeasurement(r, 5, state_index=[0, 2], sensor_pos=sen)
x = dyn.simulate_discrete(100)
y = obs.simulate_measurements(x)
cls.ssm.update({'ctb': {'dyn': dyn, 'obs': obs, 'x': x, 'y': y}})
# setup CTRS with radar measurements
x0 = GaussRV(5, cov=0.1 * np.eye(5))
q = GaussRV(2, cov=np.diag([0.1, 0.1 * np.pi]))
r = GaussRV(2, cov=np.diag([0.3, 0.03]))
dyn = ConstantTurnRateSpeed(x0, q)
obs = Radar2DMeasurement(r, 5)
x = dyn.simulate_discrete(100)
y = obs.simulate_measurements(x)
cls.ssm.update({'ctrs': {'dyn': dyn, 'obs': obs, 'x': x, 'y': y}})
def setUpClass(cls):
cls.models = []
cls.models.append(UNGMTransition(GaussRV(1), GaussRV(1)))
cls.models.append(Pendulum2DTransition(GaussRV(2), GaussRV(2)))
def lengthscale_filter_demo(lscale):
steps, mc = 500, 20
# initialize UNGM model
dyn = UNGMTransition(GaussRV(1, cov=5.0), GaussRV(1, cov=10.0))
obs = UNGMMeasurement(GaussRV(1, cov=1.0), 1)
# generate some data
x = dyn.simulate_discrete(steps, mc)
z = obs.simulate_measurements(x)
dim = dyn.dim_state
num_el = len(lscale)
# lscale = [1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1, 1, 3, 1e1, 3e1] # , 1e2, 3e2]
mean_f, cov_f = np.zeros((dim, steps, mc, num_el)), np.zeros(
(dim, dim, steps, mc, num_el))
for iel, el in enumerate(lscale):
# kernel parameters
ker_par = np.array([[1.0, el * dim]])
# initialize BHKF with current lenghtscale
f = GaussianProcessKalman(dyn,
obs,
ker_par,
ker_par,
kernel='rbf',
points='ut')
# filtering
for s in range(mc):
mean_f[..., s, iel], cov_f[..., s, iel] = f.forward_pass(z[..., s])
# evaluate RMSE, NCI and NLL
rmseVsEl = squared_error(x[..., na], mean_f)
nciVsEl = rmseVsEl.copy()
nllVsEl = rmseVsEl.copy()
for k in range(steps):
for iel in range(num_el):
mse_mat = mse_matrix(x[:, k, :], mean_f[:, k, :, iel])
for s in range(mc):
nciVsEl[:, k, s,
iel] = log_cred_ratio(x[:, k, s], mean_f[:, k, s, iel],
cov_f[:, :, k, s, iel], mse_mat)
nllVsEl[:, k, s,
iel] = neg_log_likelihood(x[:, k, s], mean_f[:, k, s,
iel],
cov_f[:, :, k, s, iel])
# average out time and MC simulations
rmseVsEl = np.sqrt(np.mean(rmseVsEl, axis=1)).mean(axis=1)
nciVsEl = nciVsEl.mean(axis=(1, 2))
nllVsEl = nllVsEl.mean(axis=(1, 2))
# plot influence of changing lengthscale on the RMSE and NCI and NLL filter performance
plt.figure()
plt.semilogx(lscale,
rmseVsEl.squeeze(),
color='k',
ls='-',
lw=2,
marker='o',
label='RMSE')
plt.semilogx(lscale,
nciVsEl.squeeze(),
color='k',
ls='--',
lw=2,
marker='o',
label='NCI')
plt.semilogx(lscale,
nllVsEl.squeeze(),
color='k',
ls='-.',
lw=2,
marker='o',
label='NLL')
plt.grid(True)
plt.legend()
plt.show()
plot_data = {
'el': lscale,
'rmse': rmseVsEl,
'nci': nciVsEl,
'neg_log_likelihood': nllVsEl
}
return plot_data
def tables():
steps, mc = 500, 100
# initialize UNGM model
dyn = UNGMTransition(GaussRV(1, cov=5.0), GaussRV(1, cov=10.0))
obs = UNGMMeasurement(GaussRV(1, cov=1.0), 1)
# generate some data
np.random.seed(0)
x = dyn.simulate_discrete(steps, mc)
z = obs.simulate_measurements(x)
par_ut = np.array([[3.0, 0.3]])
par_gh5 = np.array([[5.0, 0.6]])
par_gh7 = np.array([[3.0, 0.4]])
mulind_ut = np.array([[0, 1, 2]])
mulind_gh = lambda degree: np.atleast_2d(np.arange(degree))
# initialize filters/smoothers
algorithms = (
# Classical filters
UnscentedKalman(dyn, obs, alpha=1.0, beta=0.0),
GaussHermiteKalman(dyn, obs, deg=5),
GaussHermiteKalman(dyn, obs, deg=7),
# GPQ filters
GaussianProcessKalman(dyn,
obs,
par_ut,
par_ut,
kernel='rbf',
points='ut',
point_hyp={'alpha': 1.0}),
GaussianProcessKalman(dyn,
obs,
par_gh5,
par_gh5,
kernel='rbf',
points='gh',
point_hyp={'degree': 5}),
GaussianProcessKalman(dyn,
obs,
par_gh7,
par_gh7,
kernel='rbf',
points='gh',
point_hyp={'degree': 7}),
# BSQ filters
BayesSardKalman(dyn,
obs,
par_ut,
par_ut,
mulind_ut,
mulind_ut,
points='ut',
point_hyp={'alpha': 1.0}),
BayesSardKalman(dyn,
obs,
par_gh5,
par_gh5,
mulind_gh(5),
mulind_gh(5),
points='gh',
point_hyp={'degree': 5}),
BayesSardKalman(dyn,
obs,
par_gh7,
par_gh7,
mulind_gh(7),
mulind_gh(7),
points='gh',
point_hyp={'degree': 7}),
)
num_algs = len(algorithms)
# space for estimates
dim = dyn.dim_state
mean_f, cov_f = np.zeros((dim, steps, mc, num_algs)), np.zeros(
(dim, dim, steps, mc, num_algs))
mean_s, cov_s = np.zeros((dim, steps, mc, num_algs)), np.zeros(
(dim, dim, steps, mc, num_algs))
# do filtering/smoothing
t0 = time.time() # measure execution time
print('Running filters/smoothers ...')
for a, alg in enumerate(algorithms):
print('{}'.format(
alg.__class__.__name__)) # print filter/smoother name
for sim in range(mc):
mean_f[..., sim, a], cov_f[..., sim, a] = alg.forward_pass(z[...,
sim])
mean_s[..., sim, a], cov_s[..., sim, a] = alg.backward_pass()
alg.reset()
print('Done in {0:.4f} [sec]'.format(time.time() - t0))
# evaluate perfomance
scores = evaluate_performance(x, mean_f, cov_f, mean_s, cov_s)
rmseMean_f, nciMean_f, nllMean_f, rmseMean_s, nciMean_s, nllMean_s = scores[:
6]
rmseStd_f, nciStd_f, nllStd_f, rmseStd_s, nciStd_s, nllStd_s = scores[6:]
# put data into Pandas DataFrame for fancy printing and latex export
# row_labels = ['SR', 'UT', 'GH-5', 'GH-7', 'GH-10', 'GH-15', 'GH-20']
row_labels = ['UT', 'GH-5', 'GH-7']
num_labels = len(row_labels)
col_labels = [
'Classical', 'GPQ', 'BSQ', 'Classical (2std)', 'GPQ (2std)',
'BSQ (2std)'
]
rmse_table_f = pd.DataFrame(np.hstack(
(rmseMean_f.reshape(3,
num_labels).T, rmseStd_f.reshape(3,
num_labels).T)),
index=row_labels,
columns=col_labels)
nci_table_f = pd.DataFrame(np.hstack(
(nciMean_f.reshape(3, num_labels).T, nciStd_f.reshape(3,
num_labels).T)),
index=row_labels,
columns=col_labels)
nll_table_f = pd.DataFrame(np.hstack(
(nllMean_f.reshape(3, num_labels).T, nllStd_f.reshape(3,
num_labels).T)),
index=row_labels,
columns=col_labels)
rmse_table_s = pd.DataFrame(np.hstack(
(rmseMean_s.reshape(3,
num_labels).T, rmseStd_s.reshape(3,
num_labels).T)),
index=row_labels,
columns=col_labels)
nci_table_s = pd.DataFrame(np.hstack(
(nciMean_s.reshape(3, num_labels).T, nciStd_s.reshape(3,
num_labels).T)),
index=row_labels,
columns=col_labels)
nll_table_s = pd.DataFrame(np.hstack(
(nllMean_s.reshape(3, num_labels).T, nllStd_s.reshape(3,
num_labels).T)),
index=row_labels,
columns=col_labels)
# print kernel parameters
print('Kernel parameters')
print('{:5}: {}'.format('UT', par_ut))
print('{:5}: {}'.format('GH-5', par_gh5))
print('{:5}: {}'.format('GH-7', par_gh7))
print()
# print tables
pd.set_option('precision', 2, 'display.max_columns', 6)
print('Filter RMSE')
print(rmse_table_f)
print('Filter NCI')
print(nci_table_f)
print('Filter NLL')
print(nll_table_f)
print('Smoother RMSE')
print(rmse_table_s)
print('Smoother NCI')
print(nci_table_s)
print('Smoother NLL')
print(nll_table_s)
# return computed metrics for filters and smoothers
return {
'filter_RMSE': rmse_table_f,
'filter_NCI': nci_table_f,
'filter_NLL': nll_table_f,
'smoother_RMSE': rmse_table_s,
'smoother_NCI': nci_table_s,
'smoother_NLL': nll_table_s
}
import numpy as np import pandas as pd from research.gpq.icinco_demo import evaluate_performance from ssmtoybox.mtran import UnscentedTransform from ssmtoybox.ssinf import ExtendedKalman, ExtendedKalmanGPQD from ssmtoybox.ssmod import UNGMTransition, UNGMMeasurement from ssmtoybox.utils import GaussRV steps, mc = 50, 10 # time steps, mc simulations # setup univariate non-stationary growth model x0 = GaussRV(1, cov=np.atleast_2d(5.0)) q = GaussRV(1, cov=np.atleast_2d(10.0)) dyn = UNGMTransition(x0, q) # dynamics r = GaussRV(1) obs = UNGMMeasurement(r, 1) # observation model x = dyn.simulate_discrete(steps, mc) z = obs.simulate_measurements(x) # use only the central sigma-point usp_0 = np.zeros((dyn.dim_in, 1)) usp_ut = UnscentedTransform.unit_sigma_points(dyn.dim_in) # set the RBF kernel hyperparameters hyp_rbf = np.array([[1.0] + dyn.dim_in * [3.0]]) hyp_rbf_ut = np.array([[8.0] + dyn.dim_in * [0.5]]) # derivative observations only at the central point
def tables(steps=500, sims=100):
# setup univariate non-stationary growth model
x0 = GaussRV(1, cov=np.atleast_2d(5.0))
q = GaussRV(1, cov=np.atleast_2d(10.0))
dyn = UNGMTransition(x0, q) # dynamics
r = GaussRV(1)
obs = UNGMMeasurement(r, 1) # observation model
x = dyn.simulate_discrete(steps, mc_sims=sims) # generate some data
z = obs.simulate_measurements(x)
kern_par_sr = np.array([[1.0, 0.3 * dyn.dim_in]])
kern_par_ut = np.array([[1.0, 3.0 * dyn.dim_in]])
kern_par_gh = np.array([[1.0, 0.1 * dyn.dim_in]])
# initialize filters/smoothers
algorithms = (
CubatureKalman(dyn, obs),
UnscentedKalman(dyn, obs),
GaussHermiteKalman(dyn, obs),
GaussHermiteKalman(dyn, obs),
GaussHermiteKalman(dyn, obs),
GaussHermiteKalman(dyn, obs),
GaussHermiteKalman(dyn, obs),
GaussianProcessKalman(dyn, obs, kern_par_sr, kern_par_sr, points='sr'),
GaussianProcessKalman(dyn, obs, kern_par_ut, kern_par_ut, points='ut'),
GaussianProcessKalman(dyn, obs, kern_par_sr, kern_par_sr, points='gh', point_hyp={'degree': 5}),
GaussianProcessKalman(dyn, obs, kern_par_gh, kern_par_gh, points='gh', point_hyp={'degree': 7}),
GaussianProcessKalman(dyn, obs, kern_par_gh, kern_par_gh, points='gh', point_hyp={'degree': 10}),
GaussianProcessKalman(dyn, obs, kern_par_gh, kern_par_gh, points='gh', point_hyp={'degree': 15}),
GaussianProcessKalman(dyn, obs, kern_par_gh, kern_par_gh, points='gh', point_hyp={'degree': 20}),
)
num_algs = len(algorithms)
# space for estimates
mean_f, cov_f = np.zeros((dyn.dim_in, steps, sims, num_algs)), np.zeros((dyn.dim_in, dyn.dim_in, steps, sims, num_algs))
mean_s, cov_s = np.zeros((dyn.dim_in, steps, sims, num_algs)), np.zeros((dyn.dim_in, dyn.dim_in, steps, sims, num_algs))
# do filtering/smoothing
t0 = time.time() # measure execution time
print('Running filters/smoothers ...', flush=True)
for a, alg in enumerate(algorithms):
for sim in trange(sims, desc='{:25}'.format(alg.__class__.__name__), file=sys.stdout):
mean_f[..., sim, a], cov_f[..., sim, a] = alg.forward_pass(z[..., sim])
mean_s[..., sim, a], cov_s[..., sim, a] = alg.backward_pass()
alg.reset()
print('Done in {0:.4f} [sec]'.format(time.time() - t0))
# evaluate perfomance
scores = evaluate_performance(x, mean_f, cov_f, mean_s, cov_s)
rmseMean_f, nciMean_f, nllMean_f, rmseMean_s, nciMean_s, nllMean_s = scores[:6]
rmseStd_f, nciStd_f, nllStd_f, rmseStd_s, nciStd_s, nllStd_s = scores[6:]
# put data into Pandas DataFrame for fancy printing and latex export
row_labels = ['SR', 'UT', 'GH-5', 'GH-7', 'GH-10', 'GH-15',
'GH-20'] # [alg.__class__.__name__ for alg in algorithms]
col_labels = ['Classical', 'Bayesian', 'Classical (2std)', 'Bayesian (2std)']
rmse_table_f = pd.DataFrame(np.hstack((rmseMean_f.reshape(2, 7).T, rmseStd_f.reshape(2, 7).T)),
index=row_labels, columns=col_labels)
nci_table_f = pd.DataFrame(np.hstack((nciMean_f.reshape(2, 7).T, nciStd_f.reshape(2, 7).T)),
index=row_labels, columns=col_labels)
nll_table_f = pd.DataFrame(np.hstack((nllMean_f.reshape(2, 7).T, nllStd_f.reshape(2, 7).T)),
index=row_labels, columns=col_labels)
rmse_table_s = pd.DataFrame(np.hstack((rmseMean_s.reshape(2, 7).T, rmseStd_s.reshape(2, 7).T)),
index=row_labels, columns=col_labels)
nci_table_s = pd.DataFrame(np.hstack((nciMean_s.reshape(2, 7).T, nciStd_s.reshape(2, 7).T)),
index=row_labels, columns=col_labels)
nll_table_s = pd.DataFrame(np.hstack((nllMean_s.reshape(2, 7).T, nllStd_s.reshape(2, 7).T)),
index=row_labels, columns=col_labels)
# print tables
print('Filter RMSE')
print(rmse_table_f)
print('Filter NCI')
print(nci_table_f)
print('Filter NLL')
print(nll_table_f)
print('Smoother RMSE')
print(rmse_table_s)
print('Smoother NCI')
print(nci_table_s)
print('Smoother NLL')
print(nll_table_s)
# return computed metrics for filters and smoothers
return {'filter_RMSE': rmse_table_f, 'filter_NCI': nci_table_f, 'filter_NLL': nll_table_f,
'smoother_RMSE': rmse_table_s, 'smoother_NCI': nci_table_s, 'smoother_NLL': nll_table_s}