from mvn import Mvn
from mvn.matrix import Matrix
from mvn.mixture import Mixture
import pylab
pylab.ion()
source = Mixture([
Mvn.rand(2),
Mvn.rand(2),
])
data = source.sample(200)
W1, R1 = [1e7], Mvn(mean=[10.0, 10.0], var=numpy.array([10.0, 10.0])**2)
W2, R2 = [1e7], Mvn(mean=[-10.0, -10.0], var=numpy.array([10.0, 10.0])**2)
old_p = numpy.inf
for N in range(10):
pylab.gcf().clear()
pi1 = sum(W1)
pi2 = sum(W2)
(pi1, pi2) = [pi1 / (pi1 + pi2), pi2 / (pi1 + pi2)]
d1 = R1.density(data) * pi1
d2 = R2.density(data) * pi2
#directory for resulting figures
path = 'kalman'
#seed the rng so results are reproducible.
seed(path)
#create publisher
P = Publisher(path)
#create figure
fig = pylab.figure(figsize=(6, 6))
## kalman filter parameters
#the actual, hidden state
actual = numpy.array([[0, 5]])
#the sensor
sensor = Mvn(vectors=[[1, 0], [0, 1]], var=[1, numpy.inf])
#the system noise
noise = Mvn(vectors=[[1, 0], [0, 1]], var=numpy.array([0.5, 1])**2)
#the shear transform to move the system forward
transform = Matrix([[1, 0], [0.5, 1]])
filtered = sensor.measure(actual)
## initial plot
ax = newAx(fig)
#plot the initial actual position
ax.plot(actual[:, 0], actual[:, 1], **actualParams)
