apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-05 01:32:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Added a couple of tests for model predictions

Browse source
This commit is contained in:
Alan Saul 2014-03-21 14:22:42 +00:00
parent ee85229a5d
commit d15c4153f0
4 changed files with 109 additions and 16 deletions
Download patch file Download diff file Expand all files Collapse all files

54
GPy/testing/unit_tests.py → GPy/testing/model_tests.py
View file

@ -6,6 +6,60 @@ import unittest
import numpy as np
import GPy
class MiscTests(unittest.TestCase):
def setUp(self):
self.N = 20
self.N_new = 50
self.D = 1
self.X = np.random.uniform(-3., 3., (self.N, 1))
self.Y = np.sin(self.X) + np.random.randn(self.N, self.D) * 0.05
self.X_new = np.random.uniform(-3., 3., (self.N_new, 1))
def test_raw_predict(self):
k = GPy.kern.RBF(1)
m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
m.randomize()
Kinv = np.linalg.pinv(k.K(self.X) + np.eye(self.N)*m.Gaussian_noise.variance)
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(self.Y)
mu, covar = m._raw_predict(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)
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)
np.testing.assert_almost_equal(mu_hat, mu)
def test_sparse_raw_predict(self):
k = GPy.kern.RBF(1)
m = GPy.models.SparseGPRegression(self.X, self.Y, kernel=k)
m.randomize()
Z = m.Z[:]
X = self.X[:]
#Not easy to check if woodbury_inv is correct in itself as it requires a large derivation and expression
Kinv = m.posterior.woodbury_inv
K_hat = k.K(self.X_new) - k.K(self.X_new, Z).dot(Kinv).dot(k.K(Z, self.X_new))
mu, covar = m._raw_predict(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)
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)
#np.testing.assert_almost_equal(mu_hat, mu)
class GradientTests(unittest.TestCase):
def setUp(self):
######################################