def setUp(self):
self.KF = CholeskyKalmanFilter
self.data = load_robot()
import numpy as np
import matplotlib.pyplot as pk
from pykalman.datasets import load_robot
from pykalman import KalmanFilter
# Initialize the Kalman Filter
data = load_robot()
kf = KalmanFilter(
data.transition_matrix,
data.observation_matrix,
data.initial_transition_covariance,
data.initial_observation_covariance,
data.transition_offsets,
data.observation_offset,
data.initial_state_mean,
data.initial_state_covariance,
random_state=0
)
# Estimate mean and covariance of hidden state distribution iteratively. This
# is equivalent to
#
# >>> (filter_state_means, filtered_state_covariance) = kf.filter(data)
n_timesteps = data.observations.shape[0]
n_dim_state = data.transition_matrix.shape[0]
filtered_state_means = np.zeros((n_timesteps, n_dim_state))
filtered_state_covariances = np.zeros((n_timesteps, n_dim_state, n_dim_state))
for t in range(n_timesteps - 1):
if t == 0:
filtered_state_means[t] = data.initial_state_mean
filtered_state_covariances[t] = data.initial_state_covariance
import inspect
import numpy as np
from numpy import ma
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_true
from pykalman import AdditiveUnscentedKalmanFilter, UnscentedKalmanFilter
from pykalman.datasets import load_robot
data = load_robot()
def build_unscented_filter(cls):
'''Instantiate the Unscented Kalman Filter'''
# build transition functions
A = np.array([[1, 1], [0, 1]])
C = np.array([[0.5, -0.3]])
if cls == UnscentedKalmanFilter:
f = lambda x, y: A.dot(x) + y
g = lambda x, y: C.dot(x) + y
elif cls == AdditiveUnscentedKalmanFilter:
f = lambda x: A.dot(x)
g = lambda x: C.dot(x)
else:
raise ValueError("How do I make transition functions for {0}?".format(
cls, ))
x = np.array([1, 1])
P = np.array([[1, 0.1], [0.1, 1]])
def setUp(self):
self.KF = BiermanKalmanFilter
self.data = load_robot()
def setUp(self):
self.KF = BiermanKalmanFilter
self.data = load_robot()
