def test_kernelsActiveDims(self):
''' Test sum and product compositional kernels '''
Q = 2 # latent dimensions
X_mean = GPflow.gplvm.PCA_reduce(self.Y, Q)
kernsQuadratu = [kernels.RBF(1, active_dims=[0])+kernels.Linear(1, active_dims=[1]),
kernels.RBF(1, active_dims=[0])+kernels.PeriodicKernel(1, active_dims=[1]),
kernels.RBF(1, active_dims=[0])*kernels.Linear(1, active_dims=[1]),
kernels.RBF(Q)+kernels.Linear(Q)] # non-overlapping
kernsAnalytic = [ekernels.Add([ekernels.RBF(1, active_dims=[0]), ekernels.Linear(1, active_dims=[1])]),
ekernels.Add([ekernels.RBF(1, active_dims=[0]), kernels.PeriodicKernel(1, active_dims=[1])]),
ekernels.Prod([ekernels.RBF(1, active_dims=[0]), ekernels.Linear(1, active_dims=[1])]),
ekernels.Add([ekernels.RBF(Q), ekernels.Linear(Q)])]
fOnSeparateDims = [True, True, True, False]
Z = np.random.permutation(X_mean.copy())[:self.M]
# Also test default N(0,1) is used
X_prior_mean = np.zeros((self.N, Q))
X_prior_var = np.ones((self.N, Q))
Xtest = self.rng.randn(10, Q)
for kq, ka, sepDims in zip(kernsQuadratu, kernsAnalytic, fOnSeparateDims):
kq.num_gauss_hermite_points = 20 # speed up quadratic for tests
ka.kern_list[0].num_gauss_hermite_points = 0 # RBF should throw error if quadrature is used
if(sepDims):
self.assertTrue(ka.on_separate_dimensions, 'analytic kernel must not use quadrature')
mq = GPflow.gplvm.BayesianGPLVM(X_mean=X_mean, X_var=np.ones((self.N, Q)), Y=self.Y,
kern=kq, M=self.M, Z=Z, X_prior_mean=X_prior_mean, X_prior_var=X_prior_var)
ma = GPflow.gplvm.BayesianGPLVM(X_mean=X_mean, X_var=np.ones((self.N, Q)), Y=self.Y,
kern=ka, M=self.M, Z=Z)
mq._compile()
ma._compile()
ql = mq.compute_log_likelihood()
al = ma.compute_log_likelihood()
self.assertTrue(np.allclose(ql, al, atol=1e-2), 'Likelihood not equal %f<>%f' % (ql, al))
mu_f_a, var_f_a = ma.predict_f(Xtest)
mu_f_q, var_f_q = mq.predict_f(Xtest)
self.assertTrue(np.allclose(mu_f_a, mu_f_q, atol=1e-4), ('Posterior means different', mu_f_a-mu_f_q))
self.assertTrue(np.allclose(mu_f_a, mu_f_q, atol=1e-4), ('Posterior vars different', var_f_a-var_f_q))
def setUp(self):
self._threshold = 0.5
self.rng = np.random.RandomState(0)
self.N = 4
self.D = 2
# Test summed kernels, non-overlapping
rbfvariance = 0.3 + self.rng.rand()
rbfard = [self.rng.rand() + 0.5]
linvariance = 0.3 + self.rng.rand()
self.kernel = kernels.Prod([
kernels.RBF(1, rbfvariance, rbfard, [1], False),
kernels.Linear(1, linvariance, [0])
])
self.ekernel = ekernels.Prod([
ekernels.RBF(1, rbfvariance, rbfard, [1], False),
ekernels.Linear(1, linvariance, [0])
])
self.Xmu = self.rng.rand(self.N, self.D)
self.Xcov = self.rng.rand(self.N, self.D)
self.Z = self.rng.rand(2, self.D)