コード例 #1
0
ファイル: test_transformations.py プロジェクト: vlall/bayespy
        def test(shape, plates, 
                 axis=-1, 
                 alpha_plates=None, 
                 plate_axis=None,
                 mu=3):

            if plate_axis is not None:
                precomputes = [False, True]
            else:
                precomputes = [False]
                
            for precompute in precomputes:
                # Construct the model
                D = shape[axis]
                if alpha_plates is not None:
                    alpha = Gamma(2, 2,
                                  plates=alpha_plates)
                    alpha.initialize_from_random()
                else:
                    alpha = 2
                X = GaussianArrayARD(mu, alpha,
                                     shape=shape,
                                     plates=plates)

                # Some initial learning and rotator constructing
                X.initialize_from_random()
                Y = GaussianArrayARD(X, 1)
                Y.observe(np.random.randn(*(Y.get_shape(0))))
                X.update()
                if alpha_plates is not None:
                    alpha.update()
                    true_cost0_alpha = alpha.lower_bound_contribution()
                    rotX = RotateGaussianArrayARD(X, alpha, 
                                                  axis=axis,
                                                  precompute=precompute)
                else:
                    rotX = RotateGaussianArrayARD(X, 
                                                  axis=axis,
                                                  precompute=precompute)
                true_cost0_X = X.lower_bound_contribution()

                # Rotation matrices
                I = np.identity(D)
                R = np.random.randn(D, D)
                if plate_axis is not None:
                    C = plates[plate_axis]
                    Q = np.random.randn(C, C)
                    Ic = np.identity(C)
                else:
                    Q = None
                    Ic = None

                # Compute bound terms
                rotX.setup(plate_axis=plate_axis)
                rot_cost0 = rotX.get_bound_terms(I, Q=Ic)
                rot_cost1 = rotX.get_bound_terms(R, Q=Q)
                self.assertAllClose(sum(rot_cost0.values()),
                                    rotX.bound(I, Q=Ic)[0],
                                    msg="Bound terms and total bound differ")
                self.assertAllClose(sum(rot_cost1.values()),
                                    rotX.bound(R, Q=Q)[0],
                                    msg="Bound terms and total bound differ")
                # Perform rotation
                rotX.rotate(R, Q=Q)
                # Check bound terms
                true_cost1_X = X.lower_bound_contribution()
                self.assertAllClose(true_cost1_X - true_cost0_X,
                                    rot_cost1[X] - rot_cost0[X],
                                    msg="Incorrect rotation cost for X")
                if alpha_plates is not None:
                    true_cost1_alpha = alpha.lower_bound_contribution()
                    self.assertAllClose(true_cost1_alpha - true_cost0_alpha,
                                        rot_cost1[alpha] - rot_cost0[alpha],
                                        msg="Incorrect rotation cost for alpha")
            return
コード例 #2
0
ファイル: test_transformations.py プロジェクト: vlall/bayespy
        def test(shape, plates, 
                 axis=-1, 
                 alpha_plates=None, 
                 plate_axis=None,
                 mu=3):
            
            if plate_axis is not None:
                precomputes = [False, True]
            else:
                precomputes = [False]
                
            for precompute in precomputes:
                # Construct the model
                D = shape[axis]
                if alpha_plates is not None:
                    alpha = Gamma(3, 5,
                                  plates=alpha_plates)
                    alpha.initialize_from_random()
                else:
                    alpha = 2
                X = GaussianArrayARD(mu, alpha,
                                     shape=shape,
                                     plates=plates)

                # Some initial learning and rotator constructing
                X.initialize_from_random()
                Y = GaussianArrayARD(X, 1)
                Y.observe(np.random.randn(*(Y.get_shape(0))))
                X.update()
                if alpha_plates is not None:
                    alpha.update()
                    rotX = RotateGaussianArrayARD(X, alpha, 
                                                  axis=axis,
                                                  precompute=precompute)
                else:
                    rotX = RotateGaussianArrayARD(X, 
                                                  axis=axis,
                                                  precompute=precompute)
                try:
                    mu.update()
                except:
                    pass

                # Rotation matrices
                R = np.random.randn(D, D)
                if plate_axis is not None:
                    C = plates[plate_axis]
                    Q = np.random.randn(C, C)
                else:
                    Q = None

                # Compute bound terms
                rotX.setup(plate_axis=plate_axis)

                if plate_axis is None:
                    def f_r(r):
                        (b, dr) = rotX.bound(np.reshape(r, np.shape(R)))
                        return (b, np.ravel(dr))
                else:
                    def f_r(r):
                        (b, dr, dq) = rotX.bound(np.reshape(r, np.shape(R)),
                                             Q=Q)
                        return (b, np.ravel(dr))

                    def f_q(q):
                        (b, dr, dq) = rotX.bound(R,
                                             Q=np.reshape(q, np.shape(Q)))
                        return (b, np.ravel(dq))

                # Check gradient with respect to R
                err = optimize.check_gradient(f_r, 
                                              np.ravel(R), 
                                              verbose=False)
                self.assertAllClose(err, 0, 
                                    atol=1e-4,
                                    msg="Gradient incorrect for R")

                # Check gradient with respect to Q
                if plate_axis is not None:
                    err = optimize.check_gradient(f_q, 
                                                  np.ravel(Q), 
                                                  verbose=False)
                    self.assertAllClose(err, 0,
                                        atol=1e-4,
                                        msg="Gradient incorrect for Q")

            return