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)