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 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.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 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 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 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 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 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 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.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 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.data = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
self.ar4 = autoReg(degree=4, name='ar4', padding=0, w=1.0)
def setUp(self):
data = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
self.ar4 = autoReg(degree=4, data=data, name='ar4', padding=0, w=1.0)
