def test_1d_0P():
global inf
f = KalmanFilter (dim_x=2, dim_z=1)
inf = InformationFilter (dim_x=2, dim_z=1)
f.x = np.array([[2.],
[0.]]) # initial state (location and velocity)
f.F = (np.array([[1., 1.],
[0., 1.]])) # state transition matrix
f.H = np.array([[1., 0.]]) # Measurement function
f.R = np.array([[5.]]) # state uncertainty
f.Q = np.eye(2)*0.0001 # process uncertainty
f.P = np.diag([20., 20.])
inf.x = f.x.copy()
inf.F = f.F.copy()
inf.H = np.array([[1.,0.]]) # Measurement function
inf.R_inv *= 1./5 # state uncertainty
inf.Q = np.eye(2)*0.0001
inf.P_inv = 0.000000000000000000001
#inf.P_inv = inv(f.P)
m = []
r = []
r2 = []
zs = []
for t in range (50):
# create measurement = t plus white noise
z = t + random.randn()* np.sqrt(5)
zs.append(z)
# perform kalman filtering
f.predict()
f.update(z)
inf.predict()
inf.update(z)
try:
print(t, inf.P)
except:
pass
# save data
r.append (f.x[0,0])
r2.append (inf.x[0,0])
m.append(z)
#assert np.allclose(f.x, inf.x), f'{t}: {f.x.T} {inf.x.T}'
if DO_PLOT:
plt.plot(m)
plt.plot(r)
plt.plot(r2)
def test_against_kf():
inv = np.linalg.inv
dt = 1.0
IM = np.eye(2)
Q = np.array([[.25, 0.5], [0.5, 1]])
F = np.array([[1, dt], [0, 1]])
#QI = inv(Q)
P = inv(IM)
from filterpy.kalman import InformationFilter
from filterpy.common import Q_discrete_white_noise
#f = IF2(2, 1)
r_std = .2
R = np.array([[r_std*r_std]])
RI = inv(R)
'''f.F = F.copy()
f.H = np.array([[1, 0.]])
f.RI = RI.copy()
f.Q = Q.copy()
f.IM = IM.copy()'''
kf = KalmanFilter(2, 1)
kf.F = F.copy()
kf.H = np.array([[1, 0.]])
kf.R = R.copy()
kf.Q = Q.copy()
f0 = InformationFilter(2, 1)
f0.F = F.copy()
f0.H = np.array([[1, 0.]])
f0.R_inv = RI.copy()
f0.Q = Q.copy()
#f.IM = np.zeros((2,2))
for i in range(1, 50):
z = i + (np.random.rand() * r_std)
f0.predict()
#f.predict()
kf.predict()
f0.update(z)
#f.update(z)
kf.update(z)
print(f0.x.T, kf.x.T)
assert np.allclose(f0.x, kf.x)
def test_1d_0P():
f = KalmanFilter (dim_x=2, dim_z=1)
inf = InformationFilter (dim_x=2, dim_z=1)
f.x = np.array([[2.],
[0.]]) # initial state (location and velocity)
f.F = (np.array([[1.,1.],
[0.,1.]])) # state transition matrix
f.H = np.array([[1.,0.]]) # Measurement function
f.R = np.array([[5.]]) # state uncertainty
f.Q = np.eye(2)*0.0001 # process uncertainty
f.P = np.diag([20., 20.])
inf.x = f.x.copy()
inf.F = f.F.copy()
inf.H = np.array([[1.,0.]]) # Measurement function
inf.R_inv *= 1./5 # state uncertainty
inf.Q *= 0.0001
inf.P_inv = 0
#inf.P_inv = inv(f.P)
m = []
r = []
r2 = []
zs = []
for t in range (100):
# create measurement = t plus white noise
z = t + random.randn()*20
zs.append(z)
# perform kalman filtering
f.predict()
f.update(z)
inf.predict()
inf.update(z)
# save data
r.append (f.x[0,0])
r2.append (inf.x[0,0])
m.append(z)
#assert abs(f.x[0,0] - inf.x[0,0]) < 1.e-12
if DO_PLOT:
plt.plot(m)
plt.plot(r)
plt.plot(r2)
def test_1d():
f = KalmanFilter(dim_x=2, dim_z=1)
inf = InformationFilter(dim_x=2, dim_z=1)
# ensure __repr__ doesn't assert
str(inf)
f.x = np.array([[2.],
[0.]]) # initial state (location and velocity)
inf.x = f.x.copy()
f.F = (np.array([[1.,1.],
[0.,1.]])) # state transition matrix
inf.F = f.F.copy()
f.H = np.array([[1.,0.]]) # Measurement function
inf.H = np.array([[1.,0.]]) # Measurement function
f.R *= 5 # state uncertainty
inf.R_inv *= 1./5 # state uncertainty
f.Q *= 0.0001 # process uncertainty
inf.Q *= 0.0001
m = []
r = []
r2 = []
zs = []
s = Saver(inf)
for t in range (100):
# create measurement = t plus white noise
z = t + random.randn()*20
zs.append(z)
# perform kalman filtering
f.update(z)
f.predict()
inf.update(z)
inf.predict()
# save data
r.append (f.x[0,0])
r2.append (inf.x[0,0])
m.append(z)
print(inf.y)
print(inf.SI)
s.save()
assert abs(f.x[0,0] - inf.x[0,0]) < 1.e-12
if DO_PLOT:
plt.plot(m)
plt.plot(r)
plt.plot(r2)
def test_against_kf():
inv = np.linalg.inv
dt = 1.0
IM = np.eye(2)
Q = np.array([[.25, 0.5], [0.5, 1]])
F = np.array([[1, dt], [0, 1]])
#QI = inv(Q)
P = inv(IM)
from filterpy.kalman import InformationFilter
from filterpy.common import Q_discrete_white_noise
#f = IF2(2, 1)
r_std = .2
R = np.array([[r_std * r_std]])
RI = inv(R)
'''f.F = F.copy()
f.H = np.array([[1, 0.]])
f.RI = RI.copy()
f.Q = Q.copy()
f.IM = IM.copy()'''
kf = KalmanFilter(2, 1)
kf.F = F.copy()
kf.H = np.array([[1, 0.]])
kf.R = R.copy()
kf.Q = Q.copy()
f0 = InformationFilter(2, 1)
f0.F = F.copy()
f0.H = np.array([[1, 0.]])
f0.R_inv = RI.copy()
f0.Q = Q.copy()
#f.IM = np.zeros((2,2))
for i in range(1, 50):
z = i + (np.random.rand() * r_std)
f0.predict()
#f.predict()
kf.predict()
f0.update(z)
#f.update(z)
kf.update(z)
print(f0.x.T, kf.x.T)
assert np.allclose(f0.x, kf.x)
def test_1d():
f = KalmanFilter (dim_x=2, dim_z=1)
inf = InformationFilter (dim_x=2, dim_z=1)
f.x = np.array([[2.],
[0.]]) # initial state (location and velocity)
inf.x = f.x.copy()
f.F = (np.array([[1.,1.],
[0.,1.]])) # state transition matrix
inf.F = f.F.copy()
f.H = np.array([[1.,0.]]) # Measurement function
inf.H = np.array([[1.,0.]]) # Measurement function
f.R = 5. # state uncertainty
inf.R_inv = 1./5 # state uncertainty
f.Q = 0.0001 # process uncertainty
inf.Q = 0.0001
m = []
r = []
r2 = []
zs = []
for t in range (100):
# create measurement = t plus white noise
z = t + random.randn()*20
zs.append(z)
# perform kalman filtering
f.update(z)
f.predict()
inf.update(z)
inf.predict()
# save data
r.append (f.x[0,0])
r2.append (inf.x[0,0])
m.append(z)
assert abs(f.x[0,0] - inf.x[0,0]) < 1.e-12
if DO_PLOT:
plt.plot(m)
plt.plot(r)
plt.plot(r2)
def test_1d_0P():
global inf
f = KalmanFilter(dim_x=2, dim_z=1)
inf = InformationFilter(dim_x=2, dim_z=1)
f.x = np.array([[2.], [0.]]) # initial state (location and velocity)
f.F = (np.array([[1., 1.], [0., 1.]])) # state transition matrix
f.H = np.array([[1., 0.]]) # Measurement function
f.R = np.array([[5.]]) # state uncertainty
f.Q = np.eye(2) * 0.0001 # process uncertainty
f.P = np.diag([20., 20.])
inf.x = f.x.copy()
inf.F = f.F.copy()
inf.H = np.array([[1., 0.]]) # Measurement function
inf.R_inv *= 1. / 5 # state uncertainty
inf.Q = np.eye(2) * 0.0001
inf.P_inv = 0.000000000000000000001
#inf.P_inv = inv(f.P)
m = []
r = []
r2 = []
zs = []
for t in range(50):
# create measurement = t plus white noise
z = t + random.randn() * np.sqrt(5)
zs.append(z)
# perform kalman filtering
f.predict()
f.update(z)
inf.predict()
inf.update(z)
try:
print(t, inf.P)
except:
pass
# save data
r.append(f.x[0, 0])
r2.append(inf.x[0, 0])
m.append(z)
#assert np.allclose(f.x, inf.x), f'{t}: {f.x.T} {inf.x.T}'
if DO_PLOT:
plt.plot(m)
plt.plot(r)
plt.plot(r2)