def test_filt_KPM(self):
kpm=scipy.io.loadmat(pkg_resources.resource_filename(__name__,'kpm_results'))
# process model
A = numpy.array([[1, 0, 1, 0],
[0, 1, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 1]],
dtype=numpy.float64)
# observation model
C = numpy.array([[1, 0, 0, 0],
[0, 1, 0, 0]],
dtype=numpy.float64)
ss=4; os=2
# process covariance
Q = 0.1*numpy.eye(ss)
# measurement covariance
R = 1.0*numpy.eye(os)
initx = numpy.array([10, 10, 1, 0],dtype=numpy.float64)
initV = 10.0*numpy.eye(ss)
x = kpm['x'].T
y = kpm['y'].T
xfilt, Vfilt = adskalman.kalman_filter(y, A, C, Q, R, initx, initV)
assert numpy.allclose(xfilt.T,kpm['xfilt'])
assert_3d_vs_kpm_close(Vfilt,kpm['Vfilt'])
xsmooth, Vsmooth = adskalman.kalman_smoother(y,A,C,Q,R,initx,initV)
assert numpy.allclose(xsmooth.T,kpm['xsmooth'])
assert_3d_vs_kpm_close(Vsmooth,kpm['Vsmooth'])
def test_DRO_smooth(self):
kpm=scipy.io.loadmat(pkg_resources.resource_filename(__name__,'kpm_results'))
# process model
A = numpy.array([[1, 0, 1, 0],
[0, 1, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 1]],
dtype=numpy.float64)
# observation model
C = numpy.array([[1, 0, 0, 0],
[0, 1, 0, 0]],
dtype=numpy.float64)
ss=4; os=2
# process covariance
Q = 0.1*numpy.eye(ss)
# measurement covariance
R = 1.0*numpy.eye(os)
initx = numpy.array([10, 10, 1, 0],dtype=numpy.float64)
initV = 10.0*numpy.eye(ss)
x = kpm['x'].T
y = kpm['y'].T
xfilt, Vfilt = adskalman.DROsmooth(y,A,C,Q,R,initx,initV,mode='forward_only')
if 0:
xfilt_kpm, Vfilt_kpm = adskalman.kalman_filter(y, A, C, Q, R, initx, initV)
print 'xfilt.T',xfilt.T
print 'xfilt_kpm.T',xfilt_kpm.T
print "kpm['xfilt']",kpm['xfilt']
assert numpy.allclose(xfilt.T,kpm['xfilt'])
assert_3d_vs_kpm_close(Vfilt,kpm['Vfilt'])
xsmooth, Vsmooth = adskalman.DROsmooth(y,A,C,Q,R,initx,initV)
if 0:
xsmooth_kpm, Vsmooth_kpm = adskalman.kalman_smoother(y,A,C,Q,R,initx,initV)
print 'xsmooth.T',xsmooth.T
print 'xsmooth_kpm.T',xsmooth_kpm.T
print "kpm['xsmooth']",kpm['xsmooth']
print 'Vsmooth',Vsmooth
assert numpy.allclose(xsmooth.T[:,:-1],kpm['xsmooth'][:,:-1]) # KPM doesn't update last timestep
assert_3d_vs_kpm_close(Vsmooth[:-1],kpm['Vsmooth'][:,:,:-1])
def test_loglik_KPM(self):
# this test broke the loglik calculation
kpm=scipy.io.loadmat(pkg_resources.resource_filename(__name__,'kpm_learn_results'))
y = kpm['y'].T # data vector is transposed from KPM
F1 = kpm['F1']
H1 = kpm['H1']
Q1 = kpm['Q1']
R1 = kpm['R1']
initx1 = kpm['initx']
initV1 = kpm['initV']
xfilt, Vfilt, VVfilt, loglik_filt = adskalman.kalman_filter(
y, F1, H1, Q1, R1, initx1, initV1,full_output=True)
assert numpy.allclose(xfilt, kpm['xfilt'].T)
assert_3d_vs_kpm_close(Vfilt, kpm['Vfilt'])
assert_3d_vs_kpm_close(VVfilt, kpm['VVfilt'])
assert numpy.allclose(loglik_filt, kpm['loglik_filt'])
xsmooth, Vsmooth, VVsmooth, loglik_smooth = adskalman.kalman_smoother(
y, F1, H1, Q1, R1, initx1, initV1,full_output=True)
assert numpy.allclose(xsmooth, kpm['xsmooth'].T)
assert_3d_vs_kpm_close(Vsmooth, kpm['Vsmooth'])
assert_3d_vs_kpm_close(VVsmooth, kpm['VVsmooth'])
assert numpy.allclose(loglik_smooth, kpm['loglik_smooth'])
