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fix: examples in kalman_filter.py #310

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fix: examples in kalman_filter.py #310

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35 changes: 20 additions & 15 deletions filterpy/kalman/kalman_filter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,19 +58,22 @@
from filterpy.kalman import KalmanFilter
from filterpy.common import Q_discrete_white_noise, Saver

rng = np.random.RandomState(0)

r_std, q_std = 2., 0.003
dt = 1
cv = KalmanFilter(dim_x=2, dim_z=1)
cv.x = np.array([[0., 1.]]) # position, velocity
cv.F = np.array([[1, dt],[ [0, 1]])
cv.R = np.array([[r_std^^2]])
f.H = np.array([[1., 0.]])
f.P = np.diag([.1^^2, .03^^2)
f.Q = Q_discrete_white_noise(2, dt, q_std**2)
cv.x = np.array([[0., 1.]]).T # position, velocity
cv.F = np.array([[1, dt], [0, 1]])
cv.R = np.array([[r_std ** 2]])
cv.H = np.array([[1., 0.]])
cv.P = np.diag([.1 ** 2, .03 ** 2])
cv.Q = Q_discrete_white_noise(2, dt, q_std ** 2)

saver = Saver(cv)
for z in range(100):
cv.predict()
cv.update([z + randn() * r_std])
cv.update([z + rng.randn() * r_std])
saver.save() # save the filter's state

saver.to_array()
Expand All @@ -85,16 +88,18 @@

This code implements the same filter using the procedural form

x = np.array([[0., 1.]]) # position, velocity
F = np.array([[1, dt],[ [0, 1]])
R = np.array([[r_std^^2]])
from filterpy.kalman import predict, update
x = np.array([[0., 1.]]).T # position, velocity
F = np.array([[1, dt], [0, 1]])
R = np.array([[r_std ** 2]])
H = np.array([[1., 0.]])
P = np.diag([.1^^2, .03^^2)
Q = Q_discrete_white_noise(2, dt, q_std**2)
P = np.diag([.1 ** 2, .03 ** 2])
Q = Q_discrete_white_noise(2, dt, q_std ** 2)

xs = []
for z in range(100):
x, P = predict(x, P, F=F, Q=Q)
x, P = update(x, P, z=[z + randn() * r_std], R=R, H=H)
x, P = update(x, P, z=[z + rng.randn() * r_std], R=R, H=H)
xs.append(x[0, 0])
plt.plot(xs)

Expand Down Expand Up @@ -200,7 +205,7 @@ class KalmanFilter(object):

f.H = np.array([[1., 0.]])

Define the state's covariance matrix P.
Define the state's covariance matrix P.

.. code::

Expand Down Expand Up @@ -240,7 +245,7 @@ class KalmanFilter(object):
f.predict()
f.update(z)

do_something_with_estimate (f.x)
do_something_with_estimate(f.x)


**Procedural Form**
Expand Down