def check(D, N, mu=None, Lambda=None, rho=None, A=None): if mu is None: mu = np.zeros(D) if Lambda is None: Lambda = np.identity(D) if rho is None: rho = np.ones(D) if A is None: A = GaussianARD(3, 5, shape=(D, ), plates=(D, )) V = np.identity(D) + np.ones((D, D)) # Construct model X = GaussianMarkovChain(mu, Lambda, A, rho, n=N + 1, initialize=False) Y = Gaussian(X, V, initialize=False) # Posterior estimation Y.observe(np.random.randn(*(Y.get_shape(0)))) X.update() try: A.update() except: pass try: mu.update() except: pass try: Lambda.update() except: pass try: rho.update() except: pass # Construct rotator rotA = RotateGaussianARD(A, axis=-1) rotX = RotateGaussianMarkovChain(X, rotA) rotX.setup() # Check gradient with respect to R R = np.random.randn(D, D) def cost(r): (b, dr) = rotX.bound(np.reshape(r, np.shape(R))) return (b, np.ravel(dr)) err = optimize.check_gradient(cost, np.ravel(R), verbose=False) self.assertAllClose(err, 0, atol=1e-5, msg="Gradient incorrect") return
def check(D, N, mu=None, Lambda=None, rho=None, A=None): if mu is None: mu = np.zeros(D) if Lambda is None: Lambda = np.identity(D) if rho is None: rho = np.ones(D) if A is None: A = GaussianARD(3, 5, shape=(D,), plates=(D,)) V = np.identity(D) + np.ones((D,D)) # Construct model X = GaussianMarkovChain(mu, Lambda, A, rho, n=N+1, initialize=False) Y = Gaussian(X, V, initialize=False) # Posterior estimation Y.observe(np.random.randn(*(Y.get_shape(0)))) X.update() try: A.update() except: pass try: mu.update() except: pass try: Lambda.update() except: pass try: rho.update() except: pass # Construct rotator rotA = RotateGaussianARD(A, axis=-1) rotX = RotateGaussianMarkovChain(X, rotA) rotX.setup() # Check gradient with respect to R R = np.random.randn(D, D) def cost(r): (b, dr) = rotX.bound(np.reshape(r, np.shape(R))) return (b, np.ravel(dr)) err = optimize.check_gradient(cost, np.ravel(R), verbose=False)[1] self.assertAllClose(err, 0, atol=1e-5, msg="Gradient incorrect") return
def check(D, N, mu=None, Lambda=None, rho=None, A=None): if mu is None: mu = np.zeros(D) if Lambda is None: Lambda = np.identity(D) if rho is None: rho = np.ones(D) if A is None: A = GaussianARD(3, 5, shape=(D, ), plates=(D, )) V = np.identity(D) + np.ones((D, D)) # Construct model X = GaussianMarkovChain(mu, Lambda, A, rho, n=N + 1, initialize=False) Y = Gaussian(X, V, initialize=False) # Posterior estimation Y.observe(np.random.randn(*(Y.get_shape(0)))) X.update() try: A.update() except: pass try: mu.update() except: pass try: Lambda.update() except: pass try: rho.update() except: pass # Construct rotator rotA = RotateGaussianARD(A, axis=-1) rotX = RotateGaussianMarkovChain(X, rotA) # Rotation true_cost0 = X.lower_bound_contribution() rotX.setup() I = np.identity(D) R = np.random.randn(D, D) rot_cost0 = rotX.get_bound_terms(I) rot_cost1 = rotX.get_bound_terms(R) self.assertAllClose(sum(rot_cost0.values()), rotX.bound(I)[0], msg="Bound terms and total bound differ") self.assertAllClose(sum(rot_cost1.values()), rotX.bound(R)[0], msg="Bound terms and total bound differ") rotX.rotate(R) true_cost1 = X.lower_bound_contribution() self.assertAllClose(true_cost1 - true_cost0, rot_cost1[X] - rot_cost0[X], msg="Incorrect rotation cost for X") return
def check(D, N, mu=None, Lambda=None, rho=None, A=None): if mu is None: mu = np.zeros(D) if Lambda is None: Lambda = np.identity(D) if rho is None: rho = np.ones(D) if A is None: A = GaussianARD(3, 5, shape=(D,), plates=(D,)) V = np.identity(D) + np.ones((D,D)) # Construct model X = GaussianMarkovChain(mu, Lambda, A, rho, n=N+1, initialize=False) Y = Gaussian(X, V, initialize=False) # Posterior estimation Y.observe(np.random.randn(*(Y.get_shape(0)))) X.update() try: A.update() except: pass try: mu.update() except: pass try: Lambda.update() except: pass try: rho.update() except: pass # Construct rotator rotA = RotateGaussianARD(A, axis=-1) rotX = RotateGaussianMarkovChain(X, rotA) # Rotation true_cost0 = X.lower_bound_contribution() rotX.setup() I = np.identity(D) R = np.random.randn(D, D) rot_cost0 = rotX.get_bound_terms(I) rot_cost1 = rotX.get_bound_terms(R) self.assertAllClose(sum(rot_cost0.values()), rotX.bound(I)[0], msg="Bound terms and total bound differ") self.assertAllClose(sum(rot_cost1.values()), rotX.bound(R)[0], msg="Bound terms and total bound differ") rotX.rotate(R) true_cost1 = X.lower_bound_contribution() self.assertAllClose(true_cost1 - true_cost0, rot_cost1[X] - rot_cost0[X], msg="Incorrect rotation cost for X") return