def get_multi_model():
cp6 = datasets.table_111()
E2 = [[1, 0]]
G = tools.jordan_form(2)
order = ['standard', 'outlier', 'level', 'growth']
prior_model_prob = {
'standard': 0.85,
'outlier': 0.07,
'level': 0.05,
'growth': 0.03
}
# order = ['standard', 'outlier']
# prior_model_prob = {'standard' : 0.90,
# 'outlier' : 0.10}
models = {}
standard = Model(G, [0.9, 0.9], obs_var_mult=1.)
models['standard'] = standard
models['outlier'] = Model(G, [0.9, 0.9], obs_var_mult=100.)
models['level'] = Model(G, [0.01, 0.9], obs_var_mult=1.)
models['growth'] = Model(G, [0.9, 0.01], obs_var_mult=1.)
# priors
m0 = np.array([600., 10.])
C0 = np.diag([10000., 25.])
n0, d0 = 10., 1440.
multi = MultiProcessDLM(cp6,
E2,
models,
order,
prior_model_prob,
m0=m0,
C0=C0,
n0=n0,
s0=d0 / n0,
approx_steps=1)
return multi
def get_multi_model():
cp6 = datasets.table_111()
E2 = [[1, 0]]
G = tools.jordan_form(2)
order = ['standard', 'outlier', 'level', 'growth']
prior_model_prob = {'standard' : 0.85,
'outlier' : 0.07,
'level' : 0.05,
'growth' : 0.03}
# order = ['standard', 'outlier']
# prior_model_prob = {'standard' : 0.90,
# 'outlier' : 0.10}
models = {}
standard = Model(G, [0.9, 0.9], obs_var_mult=1.)
models['standard'] = standard
models['outlier'] = Model(G, [0.9, 0.9], obs_var_mult=100.)
models['level'] = Model(G, [0.01, 0.9], obs_var_mult=1.)
models['growth'] = Model(G, [0.9, 0.01], obs_var_mult=1.)
# priors
m0 = np.array([600., 10.])
C0 = np.diag([10000., 25.])
n0, d0 = 10., 1440.
multi = MultiProcessDLM(cp6, E2, models, order,
prior_model_prob,
m0=m0, C0=C0, n0=n0, s0=d0 / n0,
approx_steps=1)
return multi
def __init__(self, order, lam=1., discount=None):
self.order = order
F = _e_vector(order)
G = tools.jordan_form(order, lam)
ConstantComponent.__init__(self, F, G, discount=discount)
def __init__(self, order, lam=1., discount=None):
self.order = order
F = _e_vector(order)
G = tools.jordan_form(order, lam)
ConstantComponent.__init__(self, F, G, discount=discount)