def test_fit(self):
#create optimization object
self.gpopt = dlimix.CGPopt(self.gp)
#run
RV = self.gpopt.opt()
RV = self.gpopt.opt()
m = (SP.absolute(self.gp.LMLgrad()['X']).max() +
SP.absolute(self.gp.LMLgrad()['covar']).max() +
SP.absolute(self.gp.LMLgrad()['lik']).max())
np.testing.assert_almost_equal(m, 0., decimal=1)
def test_fit(self):
#create optimization object
self.gpopt = dlimix.CGPopt(self.gp)
self.gpopt.setOptBoundLower(self.constrainL)
self.gpopt.setOptBoundUpper(self.constrainU)
#run
self.gpopt.opt()
params = SP.concatenate(
(self.gp.getParams()['covar'], self.gp.getParams()['lik']))[:, 0]
params_true = SP.array(
[0.28822188, 0.35271548, 0.13709146, 0.49447424])
RV = ((params - params_true)**2).max() < 1e-6
RV = RV & (SP.absolute(self.gp.LMLgrad()['lik']).max() < 1E-4)
RV = RV & (SP.absolute(self.gp.LMLgrad()['covar']).max() < 1E-4)
self.assertTrue(RV)