def setUp(self): self.rng = np.random.RandomState(0) self.N = 4 self.D = 2 self.Xmu = self.rng.rand(self.N, self.D) self.Z = self.rng.rand(2, self.D) self.Xcov_diag = 0.05 + self.rng.rand(self.N, self.D) self.Xcov = np.zeros((self.Xcov_diag.shape[0], self.Xcov_diag.shape[1], self.Xcov_diag.shape[1])) self.Xcov[(np.s_[:], ) + np.diag_indices(self.Xcov_diag.shape[1])] = self.Xcov_diag # Set up "normal" kernels ekernel_classes = [ekernels.RBF, ekernels.Linear] kernel_classes = [kernels.RBF, kernels.Linear] params = [(self.D, 0.3 + self.rng.rand(), self.rng.rand(2) + [0.5, 1.5], None, True), (self.D, 0.3 + self.rng.rand(), None)] self.ekernels = [c(*p) for c, p in zip(ekernel_classes, params)] self.kernels = [c(*p) for c, p in zip(kernel_classes, params)] # Test summed kernels, non-overlapping rbfvariance = 0.3 + self.rng.rand() rbfard = [self.rng.rand() + 0.5] linvariance = 0.3 + self.rng.rand() self.kernels.append( kernels.Add([ kernels.RBF(1, rbfvariance, rbfard, [1], False), kernels.Linear(1, linvariance, [0]) ])) self.kernels[-1].input_size = self.kernels[-1].input_dim for k in self.kernels[-1].kern_list: k.input_size = self.kernels[-1].input_size self.ekernels.append( ekernels.Add([ ekernels.RBF(1, rbfvariance, rbfard, [1], False), ekernels.Linear(1, linvariance, [0]) ])) self.ekernels[-1].input_size = self.ekernels[-1].input_dim for k in self.ekernels[-1].kern_list: k.input_size = self.ekernels[-1].input_size # Test summed kernels, overlapping rbfvariance = 0.3 + self.rng.rand() rbfard = [self.rng.rand() + 0.5] linvariance = 0.3 + self.rng.rand() self.kernels.append( kernels.Add([ kernels.RBF(self.D, rbfvariance, rbfard), kernels.Linear(self.D, linvariance) ])) self.ekernels.append( ekernels.Add([ ekernels.RBF(self.D, rbfvariance, rbfard), ekernels.Linear(self.D, linvariance) ])) self.assertTrue(self.ekernels[-2].on_separate_dimensions) self.assertTrue(not self.ekernels[-1].on_separate_dimensions)
def setUp(self): self.rng = np.random.RandomState( 1) # this seed works with 60 GH points self.N = 4 self.D = 2 self.Xmu = self.rng.rand(self.N, self.D) self.Z = self.rng.rand(2, self.D) unconstrained = self.rng.randn(self.N, 2 * self.D, self.D) t = TriDiagonalBlockRep() self.Xcov = t.forward(unconstrained) # Set up "normal" kernels ekernel_classes = [ekernels.RBF, ekernels.Linear] kernel_classes = [kernels.RBF, kernels.Linear] params = [(self.D, 0.3 + self.rng.rand(), self.rng.rand(2) + [0.5, 1.5], None, True), (self.D, 0.3 + self.rng.rand(), None)] self.ekernels = [c(*p) for c, p in zip(ekernel_classes, params)] self.kernels = [c(*p) for c, p in zip(kernel_classes, params)] # Test summed kernels, non-overlapping rbfvariance = 0.3 + self.rng.rand() rbfard = [self.rng.rand() + 0.5] linvariance = 0.3 + self.rng.rand() self.kernels.append( kernels.Add([ kernels.RBF(1, rbfvariance, rbfard, [1], False), kernels.Linear(1, linvariance, [0]) ])) self.kernels[-1].input_size = self.kernels[-1].input_dim for k in self.kernels[-1].kern_list: k.input_size = self.kernels[-1].input_size self.ekernels.append( ekernels.Add([ ekernels.RBF(1, rbfvariance, rbfard, [1], False), ekernels.Linear(1, linvariance, [0]) ])) self.ekernels[-1].input_size = self.ekernels[-1].input_dim for k in self.ekernels[-1].kern_list: k.input_size = self.ekernels[-1].input_size # Test summed kernels, overlapping rbfvariance = 0.3 + self.rng.rand() rbfard = [self.rng.rand() + 0.5] linvariance = 0.3 + self.rng.rand() self.kernels.append( kernels.Add([ kernels.RBF(self.D, rbfvariance, rbfard, active_dims=[0, 1]), kernels.Linear(self.D, linvariance, active_dims=[0, 1]) ])) self.ekernels.append( ekernels.Add([ ekernels.RBF(self.D, rbfvariance, rbfard, active_dims=[0, 1]), ekernels.Linear(self.D, linvariance, active_dims=[0, 1]) ])) self.assertTrue(self.ekernels[-2].on_separate_dimensions) self.assertTrue(not self.ekernels[-1].on_separate_dimensions)
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.rng = np.random.RandomState(0) self.N = 4 self.D = 2 self.rbf = ekernels.RBF(self.D, ARD=True) self.rbf.lengthscales = self.rng.rand(2) + [0.5, 1.5] self.rbf.variance = 0.3 + self.rng.rand() self.lin = ekernels.Linear(self.D) self.lin.variance = 0.3 + self.rng.rand() self.add = ekernels.Add([self.rbf, self.lin]) self.Xmu = self.rng.rand(self.N, self.D) self.Z = self.rng.rand(2, self.D) unconstrained = self.rng.randn(self.N, 2 * self.D, self.D) t = TriDiagonalBlockRep() self.Xcov = t.forward(unconstrained)[0, :, :, :]