apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-06 02:24:17 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[predict] added noiseless convenience function to gp, bc of whining about it...

Browse source
This commit is contained in:
Max Zwiessele 2016-04-01 15:27:03 +01:00
parent 403dceacc3
commit 2001cd6dfd
2 changed files with 54 additions and 19 deletions
Download patch file Download diff file Expand all files Collapse all files

24
GPy/testing/model_tests.py
View file

@ -27,7 +27,7 @@ class MiscTests(unittest.TestCase):
Test whether the predicted variance of normal GP goes negative under numerical unstable situation.
Thanks simbartonels@github for reporting the bug and providing the following example.
"""
# set seed for reproducability
np.random.seed(3)
# Definition of the Branin test function
@ -69,13 +69,13 @@ class MiscTests(unittest.TestCase):
K_hat = k.K(self.X_new) - k.K(self.X_new, self.X).dot(Kinv).dot(k.K(self.X, self.X_new))
mu_hat = k.K(self.X_new, self.X).dot(Kinv).dot(m.Y_normalized)
mu, covar = m._raw_predict(self.X_new, full_cov=True)
mu, covar = m.predict_noiseless(self.X_new, full_cov=True)
self.assertEquals(mu.shape, (self.N_new, self.D))
self.assertEquals(covar.shape, (self.N_new, self.N_new))
np.testing.assert_almost_equal(K_hat, covar)
np.testing.assert_almost_equal(mu_hat, mu)
mu, var = m._raw_predict(self.X_new)
mu, var = m.predict_noiseless(self.X_new)
self.assertEquals(mu.shape, (self.N_new, self.D))
self.assertEquals(var.shape, (self.N_new, 1))
np.testing.assert_almost_equal(np.diag(K_hat)[:, None], var)
@ -166,7 +166,7 @@ class MiscTests(unittest.TestCase):
# S = \int (f(x) - m)^2q(x|mu,S) dx = \int f(x)^2 q(x) dx - mu**2 = 4(mu^2 + S) - (2.mu)^2 = 4S
Y_mu_true = 2*X_pred_mu
Y_var_true = 4*X_pred_var
Y_mu_pred, Y_var_pred = m._raw_predict(X_pred)
Y_mu_pred, Y_var_pred = m.predict_noiseless(X_pred)
np.testing.assert_allclose(Y_mu_true, Y_mu_pred, rtol=1e-4)
np.testing.assert_allclose(Y_var_true, Y_var_pred, rtol=1e-4)
@ -181,13 +181,13 @@ class MiscTests(unittest.TestCase):
K_hat = k.K(self.X_new) - k.K(self.X_new, Z).dot(Kinv).dot(k.K(Z, self.X_new))
K_hat = np.clip(K_hat, 1e-15, np.inf)
mu, covar = m._raw_predict(self.X_new, full_cov=True)
mu, covar = m.predict_noiseless(self.X_new, full_cov=True)
self.assertEquals(mu.shape, (self.N_new, self.D))
self.assertEquals(covar.shape, (self.N_new, self.N_new))
np.testing.assert_almost_equal(K_hat, covar)
# np.testing.assert_almost_equal(mu_hat, mu)
mu, var = m._raw_predict(self.X_new)
mu, var = m.predict_noiseless(self.X_new)
self.assertEquals(mu.shape, (self.N_new, self.D))
self.assertEquals(var.shape, (self.N_new, 1))
np.testing.assert_almost_equal(np.diag(K_hat)[:, None], var)
@ -365,7 +365,7 @@ class MiscTests(unittest.TestCase):
warp_m = GPy.models.WarpedGP(self.X, self.Y, kernel=warp_k, warping_function=warp_f)
warp_m.optimize()
warp_preds = warp_m.predict(self.X)
np.testing.assert_almost_equal(preds, warp_preds)
@unittest.skip('Comment this to plot the modified sine function')
@ -378,7 +378,7 @@ class MiscTests(unittest.TestCase):
Y = np.sin(X) + np.random.normal(0,0.1,151)
Y = np.exp(Y) - 5
#Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y]) + 0
#np.seterr(over='raise')
import matplotlib.pyplot as plt
warp_k = GPy.kern.RBF(1)
@ -390,7 +390,7 @@ class MiscTests(unittest.TestCase):
#warp_m.randomize()
#warp_m['.*warp_tanh.psi*'][:,0:2].constrain_bounded(0,100)
#warp_m['.*warp_tanh.psi*'][:,0:1].constrain_fixed(1)
#print(warp_m.checkgrad())
#warp_m.plot()
#plt.show()
@ -406,7 +406,7 @@ class MiscTests(unittest.TestCase):
warp_f.plot(X.min()-10, X.max()+10)
plt.show()
class GradientTests(np.testing.TestCase):
def setUp(self):
@ -738,12 +738,12 @@ class GradientTests(np.testing.TestCase):
lik = GPy.likelihoods.Gaussian()
m = GPy.models.GPVariationalGaussianApproximation(X, Y, kernel=kern, likelihood=lik)
self.assertTrue(m.checkgrad())
def test_ssgplvm(self):
from GPy import kern
from GPy.models import SSGPLVM
from GPy.examples.dimensionality_reduction import _simulate_matern
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]