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 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 = 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 = [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 = 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 = [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 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 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.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()
