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()