Python tensorflow.matrix_diag函数代码示例

def _expand_independent_outputs(fvar, full_cov, full_output_cov):
    """
    Reshapes fvar to the correct shape, specified by `full_cov` and `full_output_cov`.

    :param fvar: has shape N x P (full_cov = False) or P x N x N (full_cov = True).
    :return:
    1. full_cov: True and full_output_cov: True
       fvar N x P x N x P
    2. full_cov: True and full_output_cov: False
       fvar P x N x N
    3. full_cov: False and full_output_cov: True
       fvar N x P x P
    4. full_cov: False and full_output_cov: False
       fvar N x P
    """
    if full_cov and full_output_cov:
        fvar = tf.matrix_diag(tf.transpose(fvar))   # N x N x P x P
        fvar = tf.transpose(fvar, [0, 2, 1, 3])  # N x P x N x P
    if not full_cov and full_output_cov:
        fvar = tf.matrix_diag(fvar)   # N x P x P
    if full_cov and not full_output_cov:
        pass  # P x N x N
    if not full_cov and not full_output_cov:
        pass  # N x P

    return fvar

def _expectation(p, rbf_kern, feat1, lin_kern, feat2, nghp=None):
    """
    Compute the expectation:
    expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
        - K_lin_{.,.} :: RBF kernel
        - K_rbf_{.,.} :: Linear kernel
    Different Z1 and Z2 are handled if p is diagonal and K_lin and K_rbf have disjoint
    active_dims, in which case the joint expectations simplify into a product of expectations

    :return: NxM1xM2
    """
    if rbf_kern.on_separate_dims(lin_kern) and isinstance(p, DiagonalGaussian):  # no joint expectations required
        eKxz1 = expectation(p, (rbf_kern, feat1))
        eKxz2 = expectation(p, (lin_kern, feat2))
        return eKxz1[:, :, None] * eKxz2[:, None, :]

    if feat1 != feat2:
        raise NotImplementedError("Features have to be the same for both kernels.")

    if rbf_kern.active_dims != lin_kern.active_dims:
        raise NotImplementedError("active_dims have to be the same for both kernels.")

    with params_as_tensors_for(rbf_kern), params_as_tensors_for(lin_kern), \
         params_as_tensors_for(feat1), params_as_tensors_for(feat2):
        # use only active dimensions
        Xcov = rbf_kern._slice_cov(tf.matrix_diag(p.cov) if isinstance(p, DiagonalGaussian) else p.cov)
        Z, Xmu = rbf_kern._slice(feat1.Z, p.mu)

        N = tf.shape(Xmu)[0]
        D = tf.shape(Xmu)[1]

        lin_kern_variances = lin_kern.variance if lin_kern.ARD \
            else tf.zeros((D,), dtype=settings.tf_float) + lin_kern.variance

        rbf_kern_lengthscales = rbf_kern.lengthscales if rbf_kern.ARD \
            else tf.zeros((D,), dtype=settings.tf_float) + rbf_kern.lengthscales  ## Begin RBF eKxz code:

        chol_L_plus_Xcov = tf.cholesky(tf.matrix_diag(rbf_kern_lengthscales ** 2) + Xcov)  # NxDxD

        Z_transpose = tf.transpose(Z)
        all_diffs = Z_transpose - tf.expand_dims(Xmu, 2)  # NxDxM
        exponent_mahalanobis = tf.matrix_triangular_solve(chol_L_plus_Xcov, all_diffs, lower=True)  # NxDxM
        exponent_mahalanobis = tf.reduce_sum(tf.square(exponent_mahalanobis), 1)  # NxM
        exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis)  # NxM

        sqrt_det_L = tf.reduce_prod(rbf_kern_lengthscales)
        sqrt_det_L_plus_Xcov = tf.exp(tf.reduce_sum(tf.log(tf.matrix_diag_part(chol_L_plus_Xcov)), axis=1))
        determinants = sqrt_det_L / sqrt_det_L_plus_Xcov  # N
        eKxz_rbf = rbf_kern.variance * (determinants[:, None] * exponent_mahalanobis)  ## NxM <- End RBF eKxz code

        tiled_Z = tf.tile(tf.expand_dims(Z_transpose, 0), (N, 1, 1))  # NxDxM
        z_L_inv_Xcov = tf.matmul(tiled_Z, Xcov / rbf_kern_lengthscales[:, None] ** 2., transpose_a=True)  # NxMxD

        cross_eKzxKxz = tf.cholesky_solve(
            chol_L_plus_Xcov, (lin_kern_variances * rbf_kern_lengthscales ** 2.)[..., None] * tiled_Z)  # NxDxM

        cross_eKzxKxz = tf.matmul((z_L_inv_Xcov + Xmu[:, None, :]) * eKxz_rbf[..., None], cross_eKzxKxz)  # NxMxM
        return cross_eKzxKxz

  def Test(self):
    np.random.seed(1)
    n = shape_[-1]
    batch_shape = shape_[:-2]
    a = np.random.uniform(
        low=-1.0, high=1.0, size=n * n).reshape([n, n]).astype(dtype_)
    a += a.T
    a = np.tile(a, batch_shape + (1, 1))
    if dtype_ == np.float32:
      atol = 1e-4
    else:
      atol = 1e-12
    for compute_v in False, True:
      np_e, np_v = np.linalg.eig(a)
      with self.test_session():
        if compute_v:
          tf_e, tf_v = tf.self_adjoint_eig(tf.constant(a))

          # Check that V*diag(E)*V^T is close to A.
          a_ev = tf.batch_matmul(
              tf.batch_matmul(tf_v, tf.matrix_diag(tf_e)), tf_v, adj_y=True)
          self.assertAllClose(a_ev.eval(), a, atol=atol)

          # Compare to numpy.linalg.eig.
          CompareEigenDecompositions(self, np_e, np_v, tf_e.eval(), tf_v.eval(),
                                     atol)
        else:
          tf_e = tf.self_adjoint_eigvals(tf.constant(a))
          self.assertAllClose(
              np.sort(np_e, -1), np.sort(tf_e.eval(), -1), atol=atol)

  def testSampleWithBroadcastScale(self):
    # mu corresponds to a 2-batch of 3-variate normals
    mu = np.zeros([2, 3])

    # diag corresponds to no batches of 3-variate normals
    diag = np.ones([3])

    with self.test_session():
      dist = tfd.VectorExponentialDiag(mu, diag, validate_args=True)

      mean = dist.mean()
      self.assertAllEqual([2, 3], mean.get_shape())
      self.assertAllClose(mu + diag, mean.eval())

      n = int(1e4)
      samps = dist.sample(n, seed=0).eval()
      samps_centered = samps - samps.mean(axis=0)
      cov_mat = tf.matrix_diag(diag).eval()**2
      sample_cov = np.matmul(samps_centered.transpose([1, 2, 0]),
                             samps_centered.transpose([1, 0, 2])) / n

      self.assertAllClose(mu + diag, samps.mean(axis=0),
                          atol=0.10, rtol=0.05)
      self.assertAllClose([cov_mat, cov_mat], sample_cov,
                          atol=0.10, rtol=0.05)

  def test_broadcast_apply_and_solve(self):
    # These cannot be done in the automated (base test class) tests since they
    # test shapes that tf.matmul cannot handle.
    # In particular, tf.matmul does not broadcast.
    with self.test_session() as sess:
      x = tf.random_normal(shape=(2, 2, 3, 4))

      # This LinearOperatorDiag will be brodacast to (2, 2, 3, 3) during solve
      # and apply with 'x' as the argument.
      diag = tf.random_uniform(shape=(2, 1, 3))
      operator = linalg.LinearOperatorDiag(diag)
      self.assertAllEqual((2, 1, 3, 3), operator.shape)

      # Create a batch matrix with the broadcast shape of operator.
      diag_broadcast = tf.concat(1, (diag, diag))
      mat = tf.matrix_diag(diag_broadcast)
      self.assertAllEqual((2, 2, 3, 3), mat.get_shape())  # being pedantic.

      operator_apply = operator.apply(x)
      mat_apply = tf.matmul(mat, x)
      self.assertAllEqual(operator_apply.get_shape(), mat_apply.get_shape())
      self.assertAllClose(*sess.run([operator_apply, mat_apply]))

      operator_solve = operator.solve(x)
      mat_solve = tf.matrix_solve(mat, x)
      self.assertAllEqual(operator_solve.get_shape(), mat_solve.get_shape())
      self.assertAllClose(*sess.run([operator_solve, mat_solve]))

 def K(self, X, X2=None, presliced=False):
     if X2 is None:
         d = tf.fill(tf.stack([tf.shape(X)[0]]), tf.squeeze(self.variance))
         return tf.matrix_diag(d)
     else:
         shape = tf.stack([tf.shape(X)[0], tf.shape(X2)[0]])
         return tf.zeros(shape, settings.float_type)

    def _build_predict(self, Xnew, full_cov=False):
        """
        The posterior variance of F is given by
            q(f) = N(f | K alpha + mean, [K^-1 + diag(lambda**2)]^-1)
        Here we project this to F*, the values of the GP at Xnew which is given
        by
           q(F*) = N ( F* | K_{*F} alpha + mean, K_{**} - K_{*f}[K_{ff} +
                                           diag(lambda**-2)]^-1 K_{f*} )
        """

        # compute kernel things
        Kx = self.kern.K(self.X, Xnew)
        K = self.kern.K(self.X)

        # predictive mean
        f_mean = tf.matmul(Kx, self.q_alpha, transpose_a=True) + self.mean_function(Xnew)

        # predictive var
        A = K + tf.matrix_diag(tf.transpose(1. / tf.square(self.q_lambda)))
        L = tf.cholesky(A)
        Kx_tiled = tf.tile(tf.expand_dims(Kx, 0), [self.num_latent, 1, 1])
        LiKx = tf.matrix_triangular_solve(L, Kx_tiled)
        if full_cov:
            f_var = self.kern.K(Xnew) - tf.matmul(LiKx, LiKx, transpose_a=True)
        else:
            f_var = self.kern.Kdiag(Xnew) - tf.reduce_sum(tf.square(LiKx), 1)
        return f_mean, tf.transpose(f_var)

 def testVector(self):
   with self.test_session(use_gpu=self._use_gpu):
     v = np.array([1.0, 2.0, 3.0])
     mat = np.diag(v)
     v_diag = tf.matrix_diag(v)
     self.assertEqual((3, 3), v_diag.get_shape())
     self.assertAllEqual(v_diag.eval(), mat)

def _quadrature_expectation(p, obj1, feature1, obj2, feature2, num_gauss_hermite_points):
    """
    General handling of quadrature expectations for Gaussians and DiagonalGaussians
    Fallback method for missing analytic expectations
    """
    num_gauss_hermite_points = 100 if num_gauss_hermite_points is None else num_gauss_hermite_points

    warnings.warn("Quadrature is used to calculate the expectation. This means that "
                  "an analytical implementations is not available for the given combination.")

    if obj2 is None:
        eval_func = lambda x: get_eval_func(obj1, feature1)(x)
    elif obj1 is None:
        raise NotImplementedError("First object cannot be None.")
    else:
        eval_func = lambda x: (get_eval_func(obj1, feature1, np.s_[:, :, None])(x) *
                               get_eval_func(obj2, feature2, np.s_[:, None, :])(x))

    if isinstance(p, DiagonalGaussian):
        if isinstance(obj1, kernels.Kernel) and isinstance(obj2, kernels.Kernel) \
                and obj1.on_separate_dims(obj2):  # no joint expectations required

            eKxz1 = quadrature_expectation(p, (obj1, feature1),
                                           num_gauss_hermite_points=num_gauss_hermite_points)
            eKxz2 = quadrature_expectation(p, (obj2, feature2),
                                           num_gauss_hermite_points=num_gauss_hermite_points)
            return eKxz1[:, :, None] * eKxz2[:, None, :]

        else:
            cov = tf.matrix_diag(p.cov)
    else:
        cov = p.cov
    return mvnquad(eval_func, p.mu, cov, num_gauss_hermite_points)

 def K(self, X, X2=None, full_output_cov=True):
     K = self.kern.K(X, X2)  # N x N2
     if full_output_cov:
         Ks = tf.tile(K[..., None], [1, 1, self.P])  # N x N2 x P
         return tf.transpose(tf.matrix_diag(Ks), [0, 2, 1, 3])  # N x P x N2 x P
     else:
         return tf.tile(K[None, ...], [self.P, 1, 1])  # P x N x N2

  def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
    shape = list(shape)
    diag_shape = shape[:-1]

    diag = tf.random_normal(diag_shape, dtype=dtype.real_dtype)
    if dtype.is_complex:
      diag = tf.complex(
          diag, tf.random_normal(diag_shape, dtype=dtype.real_dtype))

    diag_ph = tf.placeholder(dtype=dtype)

    if use_placeholder:
      # Evaluate the diag here because (i) you cannot feed a tensor, and (ii)
      # diag is random and we want the same value used for both mat and
      # feed_dict.
      diag = diag.eval()
      operator = linalg.LinearOperatorDiag(diag_ph)
      feed_dict = {diag_ph: diag}
    else:
      operator = linalg.LinearOperatorDiag(diag)
      feed_dict = None

    mat = tf.matrix_diag(diag)

    return operator, mat, feed_dict

  def testSample(self):
    mu = [-1., 1]
    diag = [1., -2]
    dist = tfd.VectorLaplaceDiag(mu, diag, validate_args=True)
    samps = self.evaluate(dist.sample(int(1e4), seed=0))
    cov_mat = 2. * self.evaluate(tf.matrix_diag(diag))**2

    self.assertAllClose(mu, samps.mean(axis=0), atol=0., rtol=0.05)
    self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.05, rtol=0.05)

 def testGrad(self):
   shapes = ((3,), (7, 4))
   with self.test_session(use_gpu=self._use_gpu):
     for shape in shapes:
       x = tf.constant(np.random.rand(*shape), np.float32)
       y = tf.matrix_diag(x)
       error = tf.test.compute_gradient_error(x, x.get_shape().as_list(),
                                              y, y.get_shape().as_list())
       self.assertLess(error, 1e-4)

 def K(self, X, X2=None, presliced=False):
     if X2 is None:
         d = tf.fill(tf.shape(X)[:-1], tf.squeeze(self.variance))
         return tf.matrix_diag(d)
     else:
         shape = tf.concat([tf.shape(X)[:-2],
                            tf.reshape(tf.shape(X)[-2], [1]),
                            tf.reshape(tf.shape(X2)[-2], [1])], 0)
         return tf.zeros(shape, settings.float_type)

  def testMultivariateNormalDiagWithSoftplusStDev(self):
    mu = [-1.0, 1.0]
    diag = [-1.0, -2.0]
    with self.test_session():
      dist = distributions.MultivariateNormalDiagWithSoftplusStDev(mu, diag)
      samps = dist.sample(1000, seed=0).eval()
      cov_mat = tf.matrix_diag(tf.nn.softplus(diag)).eval()**2

      self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
      self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.1)