ws = np.array(
[np.exp(-(rg - 3)**2 / 2 / 0.8**2),
np.exp(-(rg - 7)**2 / 2 / 1.0**2)]).T #filters
W = np.outer(a, ws[:, 0]) + np.outer((1 - a), ws[:, 1])
eta = (X * W).sum(1) - 1
#emission
p = 1 / (1 + np.exp(-eta))
#nonlinearity
y = (p > np.random.rand(len(p)))
plt.plot(y)
plt.figure()
plt.plot(ws)
# %% full GLM-HMM inference
glmopts = DotMap()
glmopts.family = 'binomlogit'
glmopts.familyextra = 1
glmopts.regularize = 10
glmopts.thresh = 10**-5
glmopts.niter = 100 #maximum iteration
glmopts.baseline = 1
glmopts.algo = 1
glmopts.Display = 1
K0 = np.random.rand(K.shape[0], K.shape[1], K.shape[2])
w0 = np.random.randn(ws.shape[0], ws.shape[1]) * 1 + ws
glmopts.w0 = w0.copy()
results = GLMHMM(y, X, w0, K0, glmopts)
plt.figure()
plt.plot(results.W)
