def general_prepare(self): Y = np.random.randn(self.T, self.D) inputs = np.random.randn(self.T - 1, self.input_dim) if self.input_dim > 0 else None Q_diag = np.random.randn(self.E)**2. kern = [ gp.kernels.RBF(self.E + self.input_dim, ARD=True) for _ in range(self.E) ] for k in kern: k.lengthscales = np.random.rand(self.E + self.input_dim) * 2. for k in kern: k.variance = np.random.rand() Z = np.random.randn(self.E, self.n_ind_pts, self.E + self.input_dim) mean_fn = mean_fns.Linear(np.random.randn(self.E, self.E), np.random.randn(self.E)) Umu = np.random.randn(self.E, self.n_ind_pts) Ucov_chol = np.random.randn(self.E, self.n_ind_pts, self.n_ind_pts) Ucov_chol = np.linalg.cholesky( np.matmul(Ucov_chol, np.transpose(Ucov_chol, [0, 2, 1]))) qx1_mu = np.random.randn(self.E) qx1_cov = np.random.randn(self.E, self.E) qx1_cov = qx1_cov @ qx1_cov.T As = np.random.randn(self.T - 1, self.E) bs = np.random.randn(self.T - 1, self.E) Ss = np.random.randn(self.T - 1, self.E)**2. m = GPSSM(self.E, Y, inputs=inputs, emissions=None, px1_mu=None, px1_cov=None, kern=kern, Z=Z, n_ind_pts=None, mean_fn=mean_fn, Q_diag=Q_diag, Umu=Umu, Ucov_chol=Ucov_chol, qx1_mu=qx1_mu, qx1_cov=qx1_cov, As=As, bs=bs, Ss=Ss, n_samples=self.n_samples, seed=self.seed) _ = m.compute_log_likelihood() return m
def lin_mean(): return mean_functions.Linear(A=rng.randn(Data.D_in, Data.D_out), b=rng.randn(Data.D_out))
Xcov = ctt(Xcov @ np.transpose(Xcov, (0, 2, 1))) Z = rng.randn(num_ind, D_in) def markov_gauss(): cov_params = rng.randn(num_data + 1, D_in, 2 * D_in) / 2.0 # (N+1)xDx2D Xcov = cov_params @ np.transpose(cov_params, (0, 2, 1)) # (N+1)xDxD Xcross = cov_params[:-1] @ np.transpose(cov_params[1:], (0, 2, 1)) # NxDxD Xcross = np.concatenate((Xcross, np.zeros((1, D_in, D_in))), 0) # (N+1)xDxD Xcov = np.stack([Xcov, Xcross]) # 2x(N+1)xDxD return MarkovGaussian(Xmu_markov, ctt(Xcov)) _means = { "lin": mf.Linear(A=rng.randn(D_in, D_out), b=rng.randn(D_out)), "identity": mf.Identity(input_dim=D_in), "const": mf.Constant(c=rng.randn(D_out)), "zero": mf.Zero(output_dim=D_out), } _distrs = { "gauss": Gaussian(Xmu, Xcov), "dirac_gauss": Gaussian(Xmu, np.zeros((num_data, D_in, D_in))), "gauss_diag": DiagonalGaussian(Xmu, rng.rand(num_data, D_in)), "dirac_diag": DiagonalGaussian(Xmu, np.zeros((num_data, D_in))), "dirac_markov_gauss":
def lin(): return mean_functions.Linear(rng.rand(Data.D_in, Data.D_out), rng.rand(Data.D_out))
class Data: rng = np.random.RandomState(1) num_data = 5 num_ind = 4 D_in = 2 D_out = 2 Xmu = rng.randn(num_data, D_in) L = gen_L(rng, num_data, D_in, D_in) Xvar = np.array([l @ l.T for l in L]) Z = rng.randn(num_ind, D_in) # distributions don't need to be compiled (No Parameter objects) # but the members should be Tensors created in the same graph graph = tf.Graph() with test_util.session_context(graph) as sess: gauss = Gaussian(tf.constant(Xmu), tf.constant(Xvar)) dirac = Gaussian(tf.constant(Xmu), tf.constant(np.zeros((num_data, D_in, D_in)))) gauss_diag = DiagonalGaussian(tf.constant(Xmu), tf.constant(rng.rand(num_data, D_in))) dirac_diag = DiagonalGaussian(tf.constant(Xmu), tf.constant(np.zeros((num_data, D_in)))) dirac_markov_gauss = MarkovGaussian( tf.constant(Xmu), tf.constant(np.zeros((2, num_data, D_in, D_in)))) # create the covariance for the pairwise markov-gaussian dummy_gen = lambda rng, n, *shape: np.array( [rng.randn(*shape) for _ in range(n)]) L_mg = dummy_gen(rng, num_data, D_in, 2 * D_in) # N+1 x D x 2D LL = np.concatenate((L_mg[:-1], L_mg[1:]), 1) # N x 2D x 2D Xcov = LL @ np.transpose(LL, (0, 2, 1)) Xc = np.concatenate((Xcov[:, :D_in, :D_in], Xcov[-1:, D_in:, D_in:]), 0) # N+1 x D x D Xcross = np.concatenate( (Xcov[:, :D_in, D_in:], np.zeros( (1, D_in, D_in))), 0) # N+1 x D x D Xcc = np.stack([Xc, Xcross]) # 2 x N+1 x D x D markov_gauss = MarkovGaussian(Xmu, Xcc) with gpflow.decors.defer_build(): # features ip = features.InducingPoints(Z) # kernels rbf_prod_seperate_dims = kernels.Product([ kernels.RBF(1, variance=rng.rand(), lengthscales=rng.rand(), active_dims=[0]), kernels.RBF(1, variance=rng.rand(), lengthscales=rng.rand(), active_dims=[1]) ]) rbf_lin_sum = kernels.Sum([ kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand()), kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand()), kernels.Linear(D_in, variance=rng.rand()) ]) rbf = kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand()) lin_kern = kernels.Linear(D_in, variance=rng.rand()) # mean functions lin = mean_functions.Linear(rng.rand(D_in, D_out), rng.rand(D_out)) iden = mean_functions.Identity( D_in) # Note: Identity can only be used if Din == Dout zero = mean_functions.Zero(output_dim=D_out) const = mean_functions.Constant(rng.rand(D_out))