def testForwardFilter(self):
dlm = builder()
dlm.add(trend(degree=1, discount=1, w=1.0))
dlm.initialize()
self.kf1.predict(dlm.model)
self.assertAlmostEqual(dlm.model.prediction.obs, 0)
# the prior on the mean is zero, but observe 1, with
# discount = 1, one should expect the filterd mean to be 0.5
self.kf1.forwardFilter(dlm.model, 1)
self.assertAlmostEqual(dlm.model.obs, 0.5)
self.assertAlmostEqual(dlm.model.prediction.obs, 0)
self.assertAlmostEqual(dlm.model.sysVar, 0.375)
self.kf1.predict(dlm.model)
self.assertAlmostEqual(dlm.model.obs, 0.5)
self.assertAlmostEqual(dlm.model.prediction.obs, 0.5)
dlm.initialize()
self.kf0.predict(dlm.model)
self.assertAlmostEqual(dlm.model.prediction.obs, 0)
# the prior on the mean is zero, but observe 1, with discount = 0
# one should expect the filtered mean close to 1
self.kf0.forwardFilter(dlm.model, 1)
self.assertAlmostEqual(dlm.model.obs[0, 0], 1)
self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0)
self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5)
self.kf0.predict(dlm.model)
self.assertAlmostEqual(dlm.model.obs[0, 0], 1)
self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1)
def setUp(self):
self.data = np.random.rand(10).tolist()
self.features = np.random.rand(10, 2).tolist()
self.trend = trend(degree=2, w=1.0)
self.seasonality = seasonality(period=7, w=1.0)
self.dynamic = dynamic(self.features, w=1.0)
self.autoReg = autoReg(degree=3, w=1.0)
self.builder1 = builder()
def testMissingEvaluation(self):
dlm = builder()
dlm.add(trend(degree=1, discount=1, w=1.0))
dlm.initialize()
dlm.model.evaluation = np.matrix([[None]])
self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True)
self.assertAlmostEqual(dlm.model.obs, 0.0)
self.assertAlmostEqual(dlm.model.transition, 1.0)
def setUp(self):
self.data = np.random.rand(10).tolist()
self.features = np.random.rand(10, 2).tolist()
self.trend = trend(degree=2, w=1.0)
self.seasonality = seasonality(period=7, w=1.0)
self.dynamic = dynamic(self.features, w=1.0)
self.autoReg = autoReg(degree=3,
w=1.0)
self.builder1 = builder()
def __init__(self, data):
self.data = list(data)
self.n = len(data)
self.result = None
self.builder = builder()
self.Filter = None
self.initialized = False
self.options = self._defaultOptions()
self.time = None
def __init__(self, data, **options):
self.data = list(data)
self.n = len(data)
self.result = None
self.builder = builder()
self.Filter = None
self.initialized = False
self.options = self._defaultOptions(**options)
self.time = None
self._printInfo = options.get('printInfo', True)
def __init__(self, data, **options):
self.data = { idx: value for idx, value in enumerate(data) }
self.n = len(data)
self.result = None
self.builder = builder()
self.Filter = None
self.initialized = False
self.options = self._defaultOptions(**options)
self.time = None
self._printInfo = options.get('printInfo', True)
def testForwardFilterMultiDim(self):
dlm = builder()
dlm.add(seasonality(period=2, discount=1, w=1.0))
dlm.initialize()
self.kf11.forwardFilter(dlm.model, 1)
self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333)
self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333)
self.kf11.forwardFilter(dlm.model, -1)
self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5)
self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5)
def testEvolveMode(self):
dlm = builder()
dlm.add(trend(degree=1, discount=0.9, w=1.0))
dlm.add(trend(degree=1, discount=0.98, name='a', w=1.0))
dlm.initialize()
kf2 = kalmanFilter(discount=[0.9, 0.98],
updateInnovation='component',
index=dlm.componentIndex)
kf2.forwardFilter(dlm.model, 1.0)
self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0)
self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0)
def testBackwardSmoother(self):
dlm = builder()
dlm.add(trend(degree=1, discount=1, w=1.0))
dlm.initialize()
# with mean being 0 and observe 1 and 0 consectively, one shall
# expect the smoothed mean at 1 will be 1/3, for discount = 1
self.kf1.forwardFilter(dlm.model, 1)
self.kf1.forwardFilter(dlm.model, 0)
self.kf1.backwardSmoother(dlm.model, \
np.matrix([[0.5]]), \
np.matrix([[0.375]]))
self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3)
self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519)
def __init__(self, data, **options):
self.data = list(data)
# padded_data is used by auto regressor. It is the raw data with missing value
# replaced by forward filter results. (Missing value include the out of scope
# predictions.
self.padded_data = self.data
self.n = len(data)
self.result = None
self.builder = builder()
self.Filter = None
self.initialized = False
self.options = self._defaultOptions(**options)
self.time = None
self._printInfo = options.get('printInfo', True)
def testBackwardSmootherMultiDim(self):
dlm = builder()
dlm.add(trend(degree=2, discount=1, w=1.0))
dlm.initialize()
self.kf11.forwardFilter(dlm.model, 1)
state1 = dlm.model.state
cov1 = dlm.model.sysVar
self.kf11.forwardFilter(dlm.model, -1)
self.kf11.backwardSmoother(dlm.model, \
rawState = state1, \
rawSysVar = cov1)
self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0)
def __init__(self, data, **options):
self.data = list(data)
# padded_data is used by auto regressor. It is the raw data with missing value
# replaced by forward filter results. (Missing value include the out of scope
# predictions.
self.padded_data = self.data
self.n = len(data)
self.result = None
self.builder = builder()
self.Filter = None
self.initialized = False
self.options = self._defaultOptions(**options)
self.time = None
self._printInfo = options.get('printInfo', True)
self.builder._printInfo = self._printInfo
def testMissingData(self):
dlm = builder()
dlm.add(trend(degree=1, discount=1, w=1.0))
dlm.initialize()
self.kf0.forwardFilter(dlm.model, 1)
self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)
self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0)
self.kf0.forwardFilter(dlm.model, None)
self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)
self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5)
self.kf0.forwardFilter(dlm.model, None)
self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)
self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5)
self.kf0.forwardFilter(dlm.model, 0)
self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0)
def setUp(self):
self.features = np.random.rand(10, 2).tolist()
self.trend = trend(degree = 3)
self.seasonality = seasonality(period = 7)
self.dynamic = dynamic(self.features)
self.builder1 = builder()
