/
test_modelops.py
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/
test_modelops.py
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import unittest
import numpy as np
from kalman import GaussianDensity
from linear_models import BasicLinearModel
class LinearModelOperationsTestCase(unittest.TestCase):
def setUp(self):
# Basic parameters
self.K = 10
self.ds = 2
self.do = 1
# System matrices
params = dict()
params['F'] = np.array([[0.9,0.81],[0,0.9]])
params['Q'] = np.array([[1,0],[0,1]])
params['H'] = np.array([[1,0]])
params['R'] = np.array([[1]])
# Create model
prior = GaussianDensity(np.array([0,0]), np.array([[100,0],[0,100]]))
self.model = BasicLinearModel(self.ds, self.do, prior, params)
class SimulateDataTestCase(LinearModelOperationsTestCase):
def runTest(self):
true_state = np.array([ [ 17.64052346, 4.00157208],
[ 20.09648249, 5.84230807],
[ 24.68666177, 4.28079939],
[ 26.63553151, 3.70136224],
[ 26.86686292, 3.74182452],
[ 27.35509806, 4.82191557],
[ 29.2863776 , 4.46139903],
[ 30.41533628, 4.34893346],
[ 32.39051783, 3.70888185],
[ 32.46872804, 2.48389792] ])
true_observ = np.array([[ 15.08753364],
[ 20.75010108],
[ 25.55109797],
[ 25.89336649],
[ 29.13661755],
[ 25.90073239],
[ 29.33213611],
[ 30.22815243],
[ 33.92329704],
[ 33.93808681]])
np.random.seed(0)
state, observ = self.model.simulate_data(self.K)
np.testing.assert_almost_equal(state,true_state)
np.testing.assert_almost_equal(observ,true_observ)
class FilterTestCase(LinearModelOperationsTestCase):
def runTest(self):
true_prd_mn = np.array([[ 0. , 0. ],
[ 13.44433691, 0. ],
[ 24.88486417, 7.00653871],
[ 28.88907476, 6.64060538],
[ 28.20304549, 4.84335329],
[ 30.25419611, 4.68487149],
[ 26.64137582, 2.72030464],
[ 28.88527605, 3.37102832],
[ 30.0768482 , 3.49437001],
[ 33.7651837 , 4.46380605]])
true_prd_vr = np.array([[[ 100. , 0. ],
[ 0. , 100. ]],
[[ 67.4119802 , 72.9 ],
[ 72.9 , 82. ]],
[[ 6.18455567, 4.01063471],
[ 4.01063471, 4.49721824]],
[[ 3.99286853, 2.09851187],
[ 2.09851187, 2.82927283]],
[[ 3.53816888, 1.76000025],
[ 1.76000025, 2.57728417]],
[[ 3.44008235, 1.69538493],
[ 1.69538493, 2.53472152]],
[[ 3.42258652, 1.68517428],
[ 1.68517428, 2.52876314]],
[[ 3.42023228, 1.68400688],
[ 1.68400688, 2.5281844 ]],
[[ 3.42002527, 1.6839348 ],
[ 1.6839348 , 2.52815936]],
[[ 3.42001882, 1.68393592],
[ 1.68393592, 2.52815922]]])
true_flt_mn =np.array([[ 14.93815212, 0. ],
[ 20.64331037, 7.78504301],
[ 25.45836658, 7.37845042],
[ 26.49336392, 5.38150366],
[ 28.93090196, 5.20541277],
[ 26.88122405, 3.02256071],
[ 28.72372285, 3.74558702],
[ 29.92435021, 3.88263335],
[ 33.05306473, 4.9597845 ],
[ 33.89896863, 4.52967857]])
true_flt_vr = np.array([[[ 0.99009901, 0. ],
[ 0. , 100. ]],
[[ 0.98538268, 1.06560283],
[ 1.06560283, 4.31755338]],
[[ 0.86081255, 0.55823003],
[ 0.55823003, 2.25836151]],
[[ 0.79971433, 0.42030185],
[ 0.42030185, 1.94726441]],
[[ 0.77964681, 0.38782167],
[ 0.38782167, 1.89471793]],
[[ 0.77477895, 0.38183637],
[ 0.38183637, 1.8873619 ]],
[[ 0.77388797, 0.38103817],
[ 0.38103817, 1.88664741]],
[[ 0.77376754, 0.38097701],
[ 0.38097701, 1.88661649]],
[[ 0.77375695, 0.38097855],
[ 0.38097855, 1.88661633]],
[[ 0.77375662, 0.38097936],
[ 0.38097936, 1.8866144 ]]])
np.random.seed(0)
state, observ = self.model.simulate_data(self.K)
flt, prd, lhood = self.model.kalman_filter(observ)
np.testing.assert_almost_equal(flt.mn,true_flt_mn)
np.testing.assert_almost_equal(flt.vr,true_flt_vr)
np.testing.assert_almost_equal(prd.mn,true_prd_mn)
np.testing.assert_almost_equal(prd.vr,true_prd_vr)
class SmootherTestCase(LinearModelOperationsTestCase):
def runTest(self):
true_smt_mn = np.array([[ 15.25478089, 8.16168127],
[ 20.69559248, 7.11640343],
[ 24.72456361, 5.8493775 ],
[ 26.44311329, 5.13963541],
[ 27.96497005, 4.48424331],
[ 27.50228336, 5.04726022],
[ 29.17714049, 5.36323359],
[ 30.80565571, 5.5569894 ],
[ 33.09237748, 5.03297619],
[ 33.89896863, 4.52967857]])
true_smt_vr = np.array([[[ 0.84212747, -0.47046419],
[-0.47046419, 1.36093023]],
[[ 0.5485336 , -0.07533884],
[-0.07533884, 0.83231186]],
[[ 0.54774982, -0.06397322],
[-0.06397322, 0.70775746]],
[[ 0.54235149, -0.07488472],
[-0.07488472, 0.68605901]],
[[ 0.5383068 , -0.0784352 ],
[-0.0784352 , 0.68349283]],
[[ 0.53735838, -0.07998772],
[-0.07998772, 0.68531949]],
[[ 0.53810584, -0.084037 ],
[-0.084037 , 0.70020252]],
[[ 0.538553 , -0.09038111],
[-0.09038111, 0.7801361 ]],
[[ 0.54525736, -0.04443734],
[-0.04443734, 1.09458568]],
[[ 0.77375662, 0.38097936],
[ 0.38097936, 1.8866144 ]]])
np.random.seed(0)
state, observ = self.model.simulate_data(self.K)
flt, prd, lhood = self.model.kalman_filter(observ)
smt = self.model.rts_smoother(flt, prd)
np.testing.assert_almost_equal(smt.mn,true_smt_mn)
np.testing.assert_almost_equal(smt.vr,true_smt_vr)
class SamplerTestCase(LinearModelOperationsTestCase):
def runTest(self):
true_smp = np.array([[ 16.76187561, 6.85037407],
[ 21.91276618, 6.65131843],
[ 25.44617624, 5.59531396],
[ 28.24896593, 4.31521661],
[ 27.7783341 , 3.35303131],
[ 27.50559835, 5.15217254],
[ 29.30521224, 6.26581596],
[ 31.00831517, 4.75796016],
[ 32.67351594, 3.90126029],
[ 34.25639136, 4.6486885 ]])
np.random.seed(0)
state, observ = self.model.simulate_data(self.K)
smp = self.model.sample_posterior(observ)
np.testing.assert_almost_equal(smp,true_smp)