from pyhsmm.util.stats import whiten, cov
import autoregressive.models as m
import autoregressive.distributions as d
###################
# generate data #
###################
As = [0.99*np.hstack((-np.eye(2),2*np.eye(2))),
0.99*np.array([[np.cos(np.pi/6),-np.sin(np.pi/6)],[np.sin(np.pi/6),np.cos(np.pi/6)]]).dot(np.hstack((-np.eye(2),np.eye(2)))) + np.hstack((np.zeros((2,2)),np.eye(2))),
0.99*np.array([[np.cos(-np.pi/6),-np.sin(-np.pi/6)],[np.sin(-np.pi/6),np.cos(-np.pi/6)]]).dot(np.hstack((-np.eye(2),np.eye(2)))) + np.hstack((np.zeros((2,2)),np.eye(2)))]
truemodel = m.ARHSMM(
alpha=2.,init_state_distn='uniform',
obs_distns=[d.AutoRegression(A=A,sigma=np.eye(2)) for A in As],
dur_distns=[pyhsmm.basic.distributions.PoissonDuration(alpha_0=3*50,beta_0=3)
for state in range(len(As))],
)
data, labels = truemodel.generate(1000)
data += np.random.normal(size=data.shape) # some extra noise
fig, spa = plt.subplots(2,1)
spa[0].plot(data[:,0],data[:,1],'bx-')
spa[1].plot(data,'bx-')
spa[1].set_xlim(0,data.shape[0])
fig.suptitle('data')
truemodel.plot()
plt.gcf().suptitle('truth')
from pyhsmm.basic.distributions import NegativeBinomialIntegerR2Duration
import autoregressive.models as m
import autoregressive.distributions as d
###################
# generate data #
###################
As = [np.hstack((-np.eye(2),2*np.eye(2))),
np.array([[np.cos(np.pi/6),-np.sin(np.pi/6)],[np.sin(np.pi/6),np.cos(np.pi/6)]]).dot(np.hstack((-np.eye(2),np.eye(2)))) + np.hstack((np.zeros((2,2)),np.eye(2))),
np.array([[np.cos(-np.pi/6),-np.sin(-np.pi/6)],[np.sin(-np.pi/6),np.cos(-np.pi/6)]]).dot(np.hstack((-np.eye(2),np.eye(2)))) + np.hstack((np.zeros((2,2)),np.eye(2)))]
truemodel = m.ARHSMM(
alpha=4.,init_state_concentration=4.,
obs_distns=[d.AutoRegression(A=A,sigma=0.1*np.eye(2)) for A in As],
dur_distns=[pyhsmm.basic.distributions.PoissonDuration(alpha_0=4*25,beta_0=4)
for state in range(len(As))],
)
data = truemodel.rvs(1000)
plt.figure()
plt.plot(data[:,0],data[:,1],'bx-')
##################
# create model #
##################
Nmax = 20
affine = True