def visual_test_sample_gp_no_noise():
n_in = 3
x_in = np.array([3.5, 10.5, 12.5], dtype=np.float64)
f_in = np.array([3.0, 10.0, 12.0], dtype=np.float64)
n_out = 20
ell = 10.0
x_out = np.array(range(n_out), dtype=np.float64)
mean_out, S_out = gp.squared_exponential_conditional(x_in, f_in, x_out, ell, 0.0)
mean_out = np.array(mean_out)
S_out = np.array(S_out)
nsamples = 5
eval, evec = npl.eigh(S_out)
eval = np.sqrt(eval)
L = evec.dot(diag(eval))
C = npl.cholesky(S_out)
figure()
for i in range(nsamples):
r = np.random.normal(size=n_out)
s = L.dot(r)
plot(s + mean_out)
grid()
figure()
for i in range(nsamples):
r = np.random.normal(size=n_out)
s = C.dot(r)
plot(s + mean_out)
grid()
def visual_test_sample_gp_noise():
x_in = np.array([3.5, 10.5, 12.5, 15.0, 20.0], dtype=np.float64)
f_in = np.array([1.0, 10.0, 7.0, 0.0, 5.0], dtype=np.float64)
n_in = x_in.shape[0]
n_out = 20
ell = .1
sigma_noise = 0.1
#x_out = np.array(range(n_out), dtype=np.float64)
x_out = x_in.copy()
n_out = x_out.shape[0]
mean_out, S_out = gp.squared_exponential_conditional(x_in, f_in, x_out, ell, sigma_noise)
mean_out = np.array(mean_out)
S_out = np.array(S_out)
nsamples = 1
eval, evec = npl.eigh(S_out)
eval = np.sqrt(eval)
L = evec.dot(diag(eval))
C = npl.cholesky(S_out)
figure()
plot(f_in)
for i in range(nsamples):
r = np.random.normal(size=n_out)
r[:] = 0
s = L.dot(r)
plot(s + mean_out)
grid()
figure()
plot(f_in)
for i in range(nsamples):
r = np.random.normal(size=n_out)
r[:] = 0
s = C.dot(r)
plot(s + mean_out)
grid()