def test_noise(self,thr=1e-6):
""" Test noise covariance.
"""
n,m,D,X,Z = self.n,self.m,self.D,self.X,self.Z
k = cov.noise()
self.run_verifications(k,thr)
d = np.linalg.norm( k(X) - np.eye(n) )
self.assertLessEqual(d,thr)
d = np.linalg.norm( k(X,Z) - np.eye(n,m) )
self.assertLessEqual(d,thr)
def test_noise(self, thr=1e-6):
""" Test noise covariance.
"""
n, m, D, X, Z = self.n, self.m, self.D, self.X, self.Z
k = cov.noise()
self.run_verifications(k, thr)
d = np.linalg.norm(k(X) - np.eye(n))
self.assertLessEqual(d, thr)
d = np.linalg.norm(k(X, Z) - np.eye(n, m))
self.assertLessEqual(d, thr)
def test_noise():
'''
Tests the noise covariance
WN :math:`=\sigma^2 \delta_{x,x^\prime}`
'''
f = covs.noise(2.1)
cov_train,cov_test = apply_cov(f)
expect_cov_train = 4.41*np.eye(3)
expect_cov_test = np.zeros((3,2))
expect_cov_test[0][0]= 4.41
np.testing.assert_almost_equal(cov_train, expect_cov_train)
np.testing.assert_almost_equal(cov_test, expect_cov_test)