def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.features = np.random.random((20, 2)).tolist()
self.trend0 = trend(degree=0, discount=1.0, w=1.0)
self.trend1 = trend(degree=0, discount=1.0)
self.dlm1 = dlm(self.data)
self.dlm2 = dlm(self.data)
self.dlm3 = dlm([-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
self.dlm4 = dlm([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
self.dlm5 = dlm(range(100))
self.dlm1 + trend(degree=0, discount=1, w=1.0)
self.dlm2 + trend(degree=0, discount=1e-12, w=1.0)
self.dlm3 + seasonality(period=2, discount=1, w=1.0)
self.dlm4 + dynamic(features=[[0]
for i in range(5)] + [[1]
for i in range(5)],
discount=1,
w=1.0)
self.dlm5 + trend(degree=0, discount=1, w=1.0) + \
autoReg(degree=1, data=range(100), discount=1, w=1.0)
self.dlm1.evolveMode('dependent')
self.dlm2.evolveMode('dependent')
self.dlm3.evolveMode('dependent')
self.dlm4.evolveMode('dependent')
self.dlm5.evolveMode('dependent')
def testAlter(self):
dlm4 = dlm(self.data)
dlm4 + trend(degree = 1, discount = 1) + dynamic(features = \
self.features, \
discount = 1)
dlm4.fitForwardFilter()
# the filtered step range should be (0, 19)
self.assertEqual(dlm4.result.filteredSteps, [0, 19])
dlm4.alter(date = 15, data = 1, component = 'main')
self.assertEqual(dlm4.result.filteredSteps, [0, 14])
dlm4.fitForwardFilter()
newData = [0] * 9 + [1] + [0] * 10
newData[15] = 1
dlm5 = dlm(newData)
dlm5 + trend(degree = 1, discount = 1) + dynamic(features = \
self.features, \
discount = 1)
dlm5.fitForwardFilter()
# The two chain should have the same filtered obs
self.assertAlmostEqual(np.sum(np.array(dlm4.result.filteredObs) - \
np.array(dlm5.result.filteredObs)), 0.0)
# test alter the feature
dlm4.alter(date=0, data=[1,1], component='dynamic')
self.assertAlmostEqual(dlm4.builder.dynamicComponents['dynamic'].features[0],
[1, 1])
def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.dlm1 = _dlm(self.data)
self.dlm2 = _dlm(self.data)
self.dlm3 = _dlm([-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
self.dlm4 = _dlm([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
self.dlm5 = _dlm(range(100))
self.dlm6 = _dlm(range(100))
self.dlm1.builder + trend(degree=1, discount=1, w=1.0)
self.dlm2.builder + trend(degree=1, discount=1e-12, w=1.0)
self.dlm3.builder + seasonality(period=2, discount=1, w=1.0)
self.dlm4.builder + dynamic(
features=[[0] for i in range(5)] + [[1] for i in range(5)],
discount=1,
w=1.0)
self.dlm5.builder + trend(degree=1, discount=1, w=1.0) + \
autoReg(degree=1, data=range(100), discount=1, w=1.0)
self.dlm6.builder + trend(degree=1, discount=0.9, w=1.0) + \
seasonality(period=2, discount=0.8, w=1.0) + \
autoReg(degree=3, data=range(100), discount=1.0)
self.dlm1._initialize()
self.dlm2._initialize()
self.dlm3._initialize()
self.dlm4._initialize()
self.dlm5._initialize()
self.dlm6._initialize()
self.dlm1.options.innovationType = 'whole'
self.dlm2.options.innovationType = 'whole'
self.dlm3.options.innovationType = 'whole'
self.dlm4.options.innovationType = 'whole'
self.dlm5.options.innovationType = 'whole'
self.dlm6.options.innovationType = 'whole'
def setUp(self):
self.kf1 = kalmanFilter(discount=[1])
self.kf0 = kalmanFilter(discount=[1e-10])
self.kf11 = kalmanFilter(discount=[1, 1])
self.trend0 = trend(degree=0, discount=1, w=1.0)
self.trend0_90 = trend(degree=0, discount=0.9, w=1.0)
self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a')
self.trend1 = trend(degree=1, discount=1, w=1.0)
def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.features = np.random.random((20, 2)).tolist()
self.dlm1 = dlm(self.data)
self.dlm2 = dlm(self.data)
self.dlm3 = dlm([-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
self.dlm1 + trend(degree=1, discount=1)
self.dlm2 + trend(degree=1, discount=1e-12)
self.dlm3 + seasonality(period=2, discount=1)
def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.dlm1 = _dlm(self.data)
self.dlm2 = _dlm(self.data)
self.dlm1.builder + trend(degree=0, discount=1, w=1.0)
self.dlm2.builder + trend(degree=0, discount=1e-12, w=1.0)
self.dlm1._initialize()
self.dlm2._initialize()
self.dlm1.options.innovationType='whole'
self.dlm2.options.innovationType='whole'
def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.dlm1 = _dlm(self.data)
self.dlm2 = _dlm(self.data)
self.dlm1.builder + trend(degree=0, discount=1, w=1.0)
self.dlm2.builder + trend(degree=0, discount=1e-12, w=1.0)
self.dlm1._initialize()
self.dlm2._initialize()
self.dlm1.options.innovationType = 'whole'
self.dlm2.options.innovationType = 'whole'
def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.dlm1 = _dlm(self.data)
self.dlm2 = _dlm(self.data)
self.dlm3 = _dlm([-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
self.dlm1.builder + trend(degree=1, discount=1)
self.dlm2.builder + trend(degree=1, discount=1e-12)
self.dlm3.builder + seasonality(period=2, discount=1)
self.dlm1._initialize()
self.dlm2._initialize()
self.dlm3._initialize()
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 testPredictionNotChangeModel(self):
timeSeries = [1, 2, 1, 5, 3, 5, 4, 8, 1, 2]
dlm1 = dlm(timeSeries) + trend(degree=2, discount=0.95)
dlm1.fitForwardFilter()
(obs1, var1) = dlm1.predictN(N=1, date=dlm1.n-1)
dlm2 = dlm([]) + trend(degree=2, discount=0.95)
for d in timeSeries:
dlm2.append([d], component='main')
dlm2.fitForwardFilter()
(obs2, var2) = dlm2.predictN(N=1, date=dlm2.n-1)
self.assertAlmostEqual(obs1, obs2)
self.assertAlmostEqual(var1, var2)
def testPredictionNotChangeModel(self):
timeSeries = [1, 2, 1, 5, 3, 5, 4, 8, 1, 2]
dlm1 = dlm(timeSeries) + trend(degree=2, discount=0.95)
dlm1.fitForwardFilter()
(obs1, var1) = dlm1.predictN(N=1, date=dlm1.n - 1)
dlm2 = dlm([]) + trend(degree=2, discount=0.95)
for d in timeSeries:
dlm2.append([d], component='main')
dlm2.fitForwardFilter()
(obs2, var2) = dlm2.predictN(N=1, date=dlm2.n - 1)
self.assertAlmostEqual(obs1, obs2)
self.assertAlmostEqual(var1, var2)
def setUp(self):
self.data5 = range(100)
self.dlm5 = _dlmGet(self.data5)
self.dlm5.builder + trend(degree=0, discount=1, w=1.0) + \
autoReg(degree=1, discount=1, w=1.0)
self.dlm5._initialize()
self.dlm5.options.innovationType = 'whole'
def testAppendAutomatic(self):
# we feed the data to dlm4 via two segments
dlm4 = dlm(self.data[0:11])
dlm4 + trend(degree=1, discount=1) + autoReg(
degree=3, data=self.data[0:11], discount=1)
dlm4.fitForwardFilter()
dlm4.append(self.data[11:20])
dlm4.fitForwardFilter()
# we feed the data to dlm5 all at once
dlm5 = dlm(self.data)
dlm5 + trend(degree=1, discount=1) + autoReg(
degree=3, data=self.data, discount=1)
dlm5.fitForwardFilter()
self.assertAlmostEqual(np.sum(np.array(dlm4.result.filteredObs) - \
np.array(dlm5.result.filteredObs)), 0.0)
def setUp(self):
self.data5 = range(100)
self.dlm5 = _dlmGet(self.data5)
self.dlm5.builder + trend(degree=0, discount=1, w=1.0) + \
autoReg(degree=1, discount=1, w=1.0)
self.dlm5._initialize()
self.dlm5.options.innovationType='whole'
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 testAppendDynamic(self):
# we feed the data to dlm4 via two segments
dlm4 = dlm(self.data[0:11])
dlm4 + trend(degree = 1, discount = 1) + dynamic(features = self.features[0:11], \
discount = 1)
dlm4.fitForwardFilter()
dlm4.append(self.data[11 : 20])
dlm4.append(self.features[11 : 20], component = 'dynamic')
dlm4.fitForwardFilter()
# we feed the data to dlm5 all at once
dlm5 = dlm(self.data)
dlm5 + trend(degree = 1, discount = 1) + dynamic(features = self.features, \
discount = 1)
dlm5.fitForwardFilter()
self.assertAlmostEqual(np.sum(np.array(dlm4.result.filteredObs) - \
np.array(dlm5.result.filteredObs)), 0.0)
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 testAdd(self):
trend2 = trend(2, name='trend2')
self.dlm1 = self.dlm1 + trend2
self.assertEqual(self.dlm1.builder.staticComponents['trend2'], trend2)
dynamic2 = dynamic(features=self.features, name='d2')
self.dlm1 = self.dlm1 + dynamic2
self.assertEqual(self.dlm1.builder.dynamicComponents['d2'], dynamic2)
ar3 = autoReg(degree=3, data=self.data, name='ar3')
self.dlm1 = self.dlm1 + ar3
self.assertEqual(self.dlm1.builder.automaticComponents['ar3'], ar3)
def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.data5 = range(100)
self.dlm1 = _dlmTune(self.data)
self.dlm2 = _dlmTune(self.data)
self.dlm6 = _dlmTune(self.data5)
self.dlm7 = _dlmTune([0, 1, None, 1, 0, 1, -1])
self.dlm1.builder + trend(degree=0, discount=1, w=1.0)
self.dlm2.builder + trend(degree=0, discount=1e-12, w=1.0)
self.dlm6.builder + trend(degree=0, discount=0.9, w=1.0) + \
seasonality(period=2, discount=0.8, w=1.0) + \
autoReg(degree=3, discount=1.0)
self.dlm7.builder + trend(degree=0, discount=1, w=1.0)
self.dlm1._initialize()
self.dlm2._initialize()
self.dlm6._initialize()
self.dlm7._initialize()
self.dlm1.options.innovationType='whole'
self.dlm2.options.innovationType='whole'
self.dlm6.options.innovationType='whole'
self.dlm7.options.innovationType='whole'
def testAdd(self):
trend2 = trend(2, name='trend2')
self.dlm1 = self.dlm1 + trend2
self.assertEqual(self.dlm1.builder.staticComponents['trend2'], trend2)
dynamic2 = dynamic(features=self.features, name='d2')
self.dlm1 = self.dlm1 + dynamic2
self.assertEqual(self.dlm1.builder.dynamicComponents['d2'], dynamic2)
ar3 = autoReg(degree=3, name='ar3')
self.dlm1 = self.dlm1 + ar3
self.assertEqual(self.dlm1.builder.automaticComponents['ar3'], ar3)
def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.data5 = range(100)
self.features = np.random.random((20, 2)).tolist()
self.trend0 = trend(degree=0, discount=1.0, w=1.0)
self.trend1 = trend(degree=0, discount=1.0)
self.dlm1 = dlm(self.data)
self.dlm2 = dlm(self.data)
self.dlm3 = dlm([-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
self.dlm4 = dlm([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
self.dlm5 = dlm(self.data5)
self.dlm6 = dlm(self.data5)
self.dlm1 + trend(degree=0, discount=1, w=1.0)
self.dlm2 + trend(degree=0, discount=1e-12, w=1.0)
self.dlm3 + seasonality(period=2, discount=1, w=1.0)
self.dlm4 + dynamic(features=[[0] for i in range(5)] +
[[1] for i in range(5)], discount=1, w=1.0)
self.dlm5 + trend(degree=0, discount=1, w=1.0) + \
autoReg(degree=1, discount=1, w=1.0)
self.dlm6 + trend(degree=0, discount=1, w=1.0) + \
autoReg(degree=2, discount=1, w=1.0)
self.dlm1.evolveMode('dependent')
self.dlm2.evolveMode('dependent')
self.dlm3.evolveMode('dependent')
self.dlm4.evolveMode('dependent')
self.dlm5.evolveMode('dependent')
self.dlm6.evolveMode('dependent')
def testAppend(self):
dlm4 = dlm(self.data[0:11])
dlm4 + trend(degree=1, discount=1)
dlm4.fitForwardFilter()
self.assertEqual(dlm4.n, 11)
dlm4.append(self.data[11:20])
self.assertEqual(dlm4.n, 20)
dlm4.fitForwardFilter()
self.dlm1.fitForwardFilter()
self.assertAlmostEqual(np.sum(np.array(dlm4.result.filteredObs) - \
np.array(self.dlm1.result.filteredObs)), 0.0)
def testPopout(self):
dlm4 = dlm(self.data)
dlm4 + trend(degree = 1, discount = 1) + dynamic(features = \
self.features, \
discount = 1)
dlm4.fitForwardFilter()
# the filtered step range should be (0, 19)
self.assertEqual(dlm4.result.filteredSteps, [0, 19])
# pop out the first date, the filtered range should be (0, -1)
dlm4.popout(0)
self.assertEqual(dlm4.result.filteredSteps, [0, -1])
dlm4.fitForwardFilter()
dlm5 = dlm(self.data[1 : 20])
dlm5 + trend(degree = 1, discount = 1) + dynamic(features = \
self.features[1 : 20], \
discount = 1)
dlm5.fitForwardFilter()
# The two chain should have the same filtered obs
self.assertAlmostEqual(np.sum(np.array(dlm4.result.filteredObs) - \
np.array(dlm5.result.filteredObs)), 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 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 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.data = [0] * 9 + [1] + [0] * 10
self.data5 = range(100)
self.dlm3 = _dlmPredict([-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
self.dlm4 = _dlmPredict([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
self.dlm5 = _dlmPredict(self.data5)
self.dlm3.builder + seasonality(period=2, discount=1, w=1.0)
self.dlm4.builder + dynamic(features=[[0] for i in range(5)] +
[[1] for i in range(5)], discount=1,
w=1.0)
self.dlm5.builder + trend(degree=0, discount=1, w=1.0) + \
autoReg(degree=1, discount=1, w=1.0)
self.dlm3._initialize()
self.dlm4._initialize()
self.dlm5._initialize()
self.dlm3.options.innovationType='whole'
self.dlm4.options.innovationType='whole'
self.dlm5.options.innovationType='whole'
def setUp(self):
self.data = [0] * 9 + [1] + [0] * 10
self.data5 = range(100)
self.dlm3 = _dlmPredict([-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
self.dlm4 = _dlmPredict([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
self.dlm5 = _dlmPredict(self.data5)
self.dlm3.builder + seasonality(period=2, discount=1, w=1.0)
self.dlm4.builder + dynamic(
features=[[0] for i in range(5)] + [[1] for i in range(5)],
discount=1,
w=1.0)
self.dlm5.builder + trend(degree=0, discount=1, w=1.0) + \
autoReg(degree=1, discount=1, w=1.0)
self.dlm3._initialize()
self.dlm4._initialize()
self.dlm5._initialize()
self.dlm3.options.innovationType = 'whole'
self.dlm4.options.innovationType = 'whole'
self.dlm5.options.innovationType = 'whole'
def testDelete(self):
trend2 = trend(2, name='trend2')
self.dlm1 = self.dlm1 + trend2
self.dlm1.delete('trend2')
self.assertEqual(len(self.dlm1.builder.staticComponents), 1)
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()
def testDelete(self):
trend2 = trend(2, name='trend2')
self.dlm1 = self.dlm1 + trend2
self.dlm1.delete('trend2')
self.assertEqual(len(self.dlm1.builder.staticComponents), 1)
def setUp(self):
self.mydlm = dlm(np.random.random(100)) + trend(2, discount=0.95)
self.mytuner = modelTuner()
def testPlot(self):
dlm1 = dlm(range(100)) + trend(1)
dlm1.fit()
dlm1.plot()
def setUp(self):
self.mydlm = dlm(np.random.random(100)) + trend(2, discount=0.95)
self.mytuner = modelTuner()
