lags = 10
# AR Gaussian
trans1 = LinearTransformation(K=K, D=D, lags=lags)
obs1 = ARGaussianObservation(K=K,
D=D,
transformation=trans1,
train_sigma=False)
model1 = HMM(K=K, D=D, observation=obs1)
model2 = HMM(K=K, D=D, observation_kwargs={"lags": lags})
#print(model1.params == model2.params)
model2.params = model1.params
for p1, p2 in zip(model1.params_unpack, model2.params_unpack):
assert torch.all(torch.eq(p1, p2))
# AR LogitNormal
bounds = np.array([[0, 2], [0, 4]])
model1 = HMM(K=K,
D=D,
observation='logitnormal',
observation_kwargs={
"lags": lags,
"bounds": bounds
})
D=D,
M=M,
lags=1,
bounds=bounds,
transformation=tran)
# model
model = HMM(K=K, D=D, M=M, observation=obs)
#model.observation.mus_init = data[0] * torch.ones(K, D, dtype=torch.float64)
params = joblib.load(
"/Users/leah/Columbia/courses/19summer/SocialBehavior/SocialBehaviorptc/project_notebooks/gridmodel/model_k2"
)
model.params = params
obs.log_sigmas = torch.log(torch.ones((2, 4), dtype=torch.float64) * 0.01)
center_z = torch.tensor([0], dtype=torch.int)
center_x = torch.tensor([[150, 190, 200, 200]], dtype=torch.float64)
sample_z_center, sample_x_center = model.sample(20,
prefix=(center_z, center_x))
diff = np.diff(sample_x_center, axis=0)
print("diff in dim 0")
print(diff[:, 0])
print("\ndiff in dim 1")
