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])
# pop out the first date, the filtered range should be (0, -1)
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)
def testAlter(self):
dlm4 = dlm(self.data)
dlm4 + self.trend1 + 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 + self.trend1 + 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.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 + self.trend1 + 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 + self.trend1 + 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.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 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 testAppendAutomatic(self):
# we feed the data to dlm4 via two segments
dlm4 = dlm(self.data[0:11])
dlm4 + self.trend1 + 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 + self.trend1 + 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 testAppendDynamic(self):
# we feed the data to dlm4 via two segments
dlm4 = dlm(self.data[0:11])
dlm4 + self.trend1 + 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 + self.trend1 + 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 testAppendDynamic(self):
# we feed the data to dlm4 via two segments
dlm4 = dlm(self.data[0:11])
dlm4 + self.trend1 + 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 + self.trend1 + 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 testPopout(self):
dlm4 = dlm(self.data)
dlm4 + self.trend1 + 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 + self.trend1 + 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 testPopout(self):
dlm4 = dlm(self.data)
dlm4 + self.trend1 + 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 + self.trend1 + 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 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 testAppend(self):
dlm4 = dlm(self.data[0:11])
dlm4 + self.trend0
dlm4.evolveMode('dependent')
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 testAppend(self):
dlm4 = dlm(self.data[0:11])
dlm4 + self.trend0
dlm4.evolveMode('dependent')
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 setUp(self):
self.mydlm = dlm(np.random.random(100)) + trend(2, discount=0.95)
self.mytuner = modelTuner()
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()
