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 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 _copyToFeatures(self, name, filterType):
current = self.dlms[name]
# if the feature has not been created,
# we need to first add it as a feature
if 'mvdlmFeatures' not in current.dynamicComponents:
# initialize the new feature
newFeature = [[0] * (len(self.order) - 1) for i in range(self.n)]
current.add(
dynamic(features=newFeature,
name='mvdlmFeatures',
discount=1.0))
# fetch the features
theFeature = current.dynamicComponents['mvdlmFeatures'].features
# update the features
for i in range(self.n):
count = 0
for otherdlm in self.order:
if otherdlm != name:
if filterType == 'forwardFilter':
theFeature[i][count] = self.dlms[otherdlm] \
.result.filteredObs[i]
elif filterType == 'backwardSmoother':
theFeature[i][count] = self.dlms[otherdlm] \
.result.smoothedObs[i]
elif filterType == 'original':
if self.dlms[otherdlm].data[i] is not None:
theFeature[i][count] = self.dlms[otherdlm].data[i]
else:
theFeature[i][count] = 0.0
count += 1
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 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.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.dlm6 = 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.dlm6 + trend(degree=0, discount=1, w=1.0) + \
autoReg(degree=2, 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')
self.dlm6.evolveMode('dependent')
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 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 + 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 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 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 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 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.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 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 setUp(self):
self.features = np.matrix(np.random.rand(10, 2)).tolist()
self.newDynamic = dynamic(features=self.features, w=1.0)
def testInputNumpyMatrix(self):
dynamic(features=np.random.rand(10, 2), w=1.0)
pass
def setUp(self):
self.features = np.matrix(np.random.rand(10, 2)).tolist()
self.newDynamic = dynamic(features=self.features, w=1.0)
def testInputNumpyMatrix(self):
dynamic(features=np.random.rand(10, 2), w=1.0)
pass
