Kf = kern.rbf(1) + kern.white(1, 1e-6)
S = Kf.K(X)
means = np.vstack([np.tile(np.random.multivariate_normal(np.zeros(Nx),S,1),(N,1)) for N in Nobs]) # GP draws for mean of each cluster
#add GP draw for noise
Ky = kern.rbf(1,0.3,1) + kern.white(1,0.001)
Y = means + np.random.multivariate_normal(np.zeros(Nx),Ky.K(X),means.shape[0])
#construct model
m = MOHGP(X, Kf.copy(), Ky.copy(), Y, K=Nclust)
m.constrain_positive('')
m.optimize()
m.preferred_optimizer='bfgs'
m.systematic_splits()
m.remove_empty_clusters(1e-3)
m.plot(1,1,1,0,0,1)
raw_input('press enter to continue ...')
#and again without structure
Y -= Y.mean(1)[:,None]
Y /= Y.std(1)[:,None]
m2 = MOHGP(X, Kf, kern.white(1), Y, K=Nclust)
m2.constrain_positive('')
m2.preferred_optimizer='bfgs'
m2.optimize()
m2.systematic_splits()
m2.remove_empty_clusters(1e-3)
m2.plot(1,1,1,0,0,1)
np.tile(np.random.multivariate_normal(np.zeros(Nx), S, 1), (N, 1))
for N in Nobs
]) # GP draws for mean of each cluster
#add GP draw for noise
Ky = kern.rbf(1, 0.3, 1) + kern.white(1, 0.001)
Y = means + np.random.multivariate_normal(np.zeros(Nx), Ky.K(X),
means.shape[0])
#construct model
m = MOHGP(X, Kf.copy(), Ky.copy(), Y, K=Nclust)
m.constrain_positive('')
m.optimize()
m.preferred_optimizer = 'bfgs'
m.systematic_splits()
m.remove_empty_clusters(1e-3)
m.plot(1, 1, 1, 0, 0, 1)
raw_input('press enter to continue ...')
#and again without structure
Y -= Y.mean(1)[:, None]
Y /= Y.std(1)[:, None]
m2 = MOHGP(X, Kf, kern.white(1), Y, K=Nclust)
m2.constrain_positive('')
m2.preferred_optimizer = 'bfgs'
m2.optimize()
m2.systematic_splits()
m2.remove_empty_clusters(1e-3)
m2.plot(1, 1, 1, 0, 0, 1)
