Python array_ops.batch_matrix_band_part函数代码示例

def _MatrixTriangularSolveGrad(op, grad):
  """Gradient for MatrixTriangularSolve."""
  a = op.inputs[0]
  adjoint_a = op.get_attr("adjoint")
  lower_a = op.get_attr("lower")
  c = op.outputs[0]
  grad_b = linalg_ops.matrix_triangular_solve(
      a, grad, lower=lower_a, adjoint=not adjoint_a)
  if adjoint_a:
    grad_a = -math_ops.batch_matmul(c, grad_b, adj_y=True)
  else:
    grad_a = -math_ops.batch_matmul(grad_b, c, adj_y=True)
  if lower_a:
    grad_a = array_ops.batch_matrix_band_part(grad_a, -1, 0)
  else:
    grad_a = array_ops.batch_matrix_band_part(grad_a, 0, -1)
  return (grad_a, grad_b)

  def _sample_n(self, n, seed):
    batch_shape = self.batch_shape()
    event_shape = self.event_shape()
    batch_ndims = array_ops.shape(batch_shape)[0]

    ndims = batch_ndims + 3  # sample_ndims=1, event_ndims=2
    shape = array_ops.concat(0, ((n,), batch_shape, event_shape))

    # Complexity: O(nbk^2)
    x = random_ops.random_normal(shape=shape,
                                 mean=0.,
                                 stddev=1.,
                                 dtype=self.dtype,
                                 seed=seed)

    # Complexity: O(nbk)
    # This parametrization is equivalent to Chi2, i.e.,
    # ChiSquared(k) == Gamma(alpha=k/2, beta=1/2)
    g = random_ops.random_gamma(shape=(n,),
                                alpha=self._multi_gamma_sequence(
                                    0.5 * self.df, self.dimension),
                                beta=0.5,
                                dtype=self.dtype,
                                seed=seed)

    # Complexity: O(nbk^2)
    x = array_ops.batch_matrix_band_part(x, -1, 0)  # Tri-lower.

    # Complexity: O(nbk)
    x = array_ops.batch_matrix_set_diag(x, math_ops.sqrt(g))

    # Make batch-op ready.
    # Complexity: O(nbk^2)
    perm = array_ops.concat(0, (math_ops.range(1, ndims), (0,)))
    x = array_ops.transpose(x, perm)
    shape = array_ops.concat(0, (batch_shape, (event_shape[0], -1)))
    x = array_ops.reshape(x, shape)

    # Complexity: O(nbM) where M is the complexity of the operator solving a
    # vector system.  E.g., for OperatorPDDiag, each matmul is O(k^2), so
    # this complexity is O(nbk^2). For OperatorPDCholesky, each matmul is
    # O(k^3) so this step has complexity O(nbk^3).
    x = self.scale_operator_pd.sqrt_matmul(x)

    # Undo make batch-op ready.
    # Complexity: O(nbk^2)
    shape = array_ops.concat(0, (batch_shape, event_shape, (n,)))
    x = array_ops.reshape(x, shape)
    perm = array_ops.concat(0, ((ndims-1,), math_ops.range(0, ndims-1)))
    x = array_ops.transpose(x, perm)

    if not self.cholesky_input_output_matrices:
      # Complexity: O(nbk^3)
      x = math_ops.batch_matmul(x, x, adj_y=True)

    return x

  def sqrt_matmul(self, x, name='sqrt_matmul'):
    """Left (batch) matmul `x` by a sqrt of this matrix:  `Sx` where `A = S S^T.

    Args:
      x: `Tensor` with shape broadcastable to `[N1,...,Nb, k]` and same `dtype`
        as self.
      name:  A name scope to use for ops added by this method.

    Returns:
      Shape `[N1,...,Nb, k]` `Tensor` holding the product `S x`.
    """
    with ops.name_scope(self.name):
      with ops.op_scope([x] + self.inputs, name):
        chol_lower = array_ops.batch_matrix_band_part(self._chol, -1, 0)
        return math_ops.batch_matmul(chol_lower, x)

def _BatchMatrixBandPartGrad(op, grad):
  num_lower = op.inputs[1]
  num_upper = op.inputs[2]
  return (array_ops.batch_matrix_band_part(grad, num_lower, num_upper), None,
          None)

  def __init__(self, mu, sigma=None, sigma_chol=None, name=None):
    """Multivariate Normal distributions on `R^k`.

    User must provide means `mu`, which are tensors of rank `N+1` (`N >= 0`)
    with the last dimension having length `k`.

    User must provide exactly one of `sigma` (the covariance matrices) or
    `sigma_chol` (the cholesky decompositions of the covariance matrices).
    `sigma` or `sigma_chol` must be of rank `N+2`.  The last two dimensions
    must both have length `k`.  The first `N` dimensions correspond to batch
    indices.

    If `sigma_chol` is not provided, the batch cholesky factorization of `sigma`
    is calculated for you.

    The shapes of `mu` and `sigma` must match for the first `N` dimensions.

    Regardless of which parameter is provided, the covariance matrices must all
    be **positive definite** (an error is raised if one of them is not).

    Args:
      mu: (N+1)-D.  `float` or `double` tensor, the means of the distributions.
      sigma: (N+2)-D.  (optional) `float` or `double` tensor, the covariances
        of the distribution(s).  The first `N+1` dimensions must match
        those of `mu`.  Must be batch-positive-definite.
      sigma_chol: (N+2)-D.  (optional) `float` or `double` tensor, a
        lower-triangular factorization of `sigma`
        (`sigma = sigma_chol . sigma_chol^*`).  The first `N+1` dimensions
        must match those of `mu`.  The tensor itself need not be batch
        lower triangular: we ignore the upper triangular part.  However,
        the batch diagonals must be positive (i.e., sigma_chol must be
        batch-positive-definite).
      name: The name to give Ops created by the initializer.

    Raises:
      ValueError: if neither sigma nor sigma_chol is provided.
      TypeError: if mu and sigma (resp. sigma_chol) are different dtypes.
    """
    if (sigma is None) == (sigma_chol is None):
      raise ValueError("Exactly one of sigma and sigma_chol must be provided")

    with ops.op_scope([mu, sigma, sigma_chol], name, "MultivariateNormal"):
      sigma_or_half = sigma_chol if sigma is None else sigma

      mu = ops.convert_to_tensor(mu)
      sigma_or_half = ops.convert_to_tensor(sigma_or_half)

      contrib_tensor_util.assert_same_float_dtype((mu, sigma_or_half))

      with ops.control_dependencies([
          _assert_compatible_shapes(mu, sigma_or_half)]):
        mu = array_ops.identity(mu, name="mu")

        # Store the dimensionality of the MVNs
        self._k = array_ops.gather(array_ops.shape(mu), array_ops.rank(mu) - 1)

        if sigma_chol is not None:
          # Ensure we only keep the lower triangular part.
          sigma_chol = array_ops.batch_matrix_band_part(
              sigma_chol, num_lower=-1, num_upper=0)
          sigma_det = _determinant_from_sigma_chol(sigma_chol)
          with ops.control_dependencies([
              _assert_batch_positive_definite(sigma_chol)]):
            self._sigma = math_ops.batch_matmul(
                sigma_chol, sigma_chol, adj_y=True, name="sigma")
            self._sigma_chol = array_ops.identity(sigma_chol, "sigma_chol")
            self._sigma_det = array_ops.identity(sigma_det, "sigma_det")
            self._mu = array_ops.identity(mu, "mu")
        else:  # sigma is not None
          sigma_chol = linalg_ops.batch_cholesky(sigma)
          sigma_det = _determinant_from_sigma_chol(sigma_chol)
          # batch_cholesky checks for PSD; so we can just use it here.
          with ops.control_dependencies([sigma_chol]):
            self._sigma = array_ops.identity(sigma, "sigma")
            self._sigma_chol = array_ops.identity(sigma_chol, "sigma_chol")
            self._sigma_det = array_ops.identity(sigma_det, "sigma_det")
            self._mu = array_ops.identity(mu, "mu")

    def sample_n(self, n, seed=None, name="sample"):
        # pylint: disable=line-too-long
        """Generate `n` samples.

    Complexity: O(nbk^3)

    The sampling procedure is based on the [Bartlett decomposition](
    https://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)
    and [using a Gamma distribution to generate Chi2 random variates](
    https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_and_related_distributions).

    Args:
      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
        observations to sample.
      seed: Python integer; random number generator seed.
      name: The name of this op.

    Returns:
      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
          with values of type `self.dtype`.
    """
        with ops.name_scope(self.name):
            with ops.name_scope(name, values=[n] + list(self.inputs.values())):
                n = ops.convert_to_tensor(n, name="n")
                if n.dtype != dtypes.int32:
                    raise TypeError("n.dtype=%s which is not int32" % n.dtype)
                batch_shape = self.batch_shape()
                event_shape = self.event_shape()
                batch_ndims = array_ops.shape(batch_shape)[0]

                ndims = batch_ndims + 3  # sample_ndims=1, event_ndims=2
                shape = array_ops.concat(0, ((n,), batch_shape, event_shape))

                # Complexity: O(nbk^2)
                x = random_ops.random_normal(shape=shape, mean=0.0, stddev=1.0, dtype=self.dtype, seed=seed)

                # Complexity: O(nbk)
                # This parametrization is equivalent to Chi2, i.e.,
                # ChiSquared(k) == Gamma(alpha=k/2, beta=1/2)
                g = random_ops.random_gamma(
                    shape=(n,),
                    alpha=self._multi_gamma_sequence(0.5 * self.df, self.dimension),
                    beta=0.5,
                    dtype=self.dtype,
                    seed=seed,
                )

                # Complexity: O(nbk^2)
                x = array_ops.batch_matrix_band_part(x, -1, 0)  # Tri-lower.

                # Complexity: O(nbk)
                x = array_ops.batch_matrix_set_diag(x, math_ops.sqrt(g))

                # Make batch-op ready.
                # Complexity: O(nbk^2)
                perm = array_ops.concat(0, (math_ops.range(1, ndims), (0,)))
                x = array_ops.transpose(x, perm)
                shape = array_ops.concat(0, (batch_shape, (event_shape[0], -1)))
                x = array_ops.reshape(x, shape)

                # Complexity: O(nbM) where M is the complexity of the operator solving a
                # vector system.  E.g., for OperatorPDDiag, each matmul is O(k^2), so
                # this complexity is O(nbk^2). For OperatorPDCholesky, each matmul is
                # O(k^3) so this step has complexity O(nbk^3).
                x = self.scale_operator_pd.sqrt_matmul(x)

                # Undo make batch-op ready.
                # Complexity: O(nbk^2)
                shape = array_ops.concat(0, (batch_shape, event_shape, (n,)))
                x = array_ops.reshape(x, shape)
                perm = array_ops.concat(0, ((ndims - 1,), math_ops.range(0, ndims - 1)))
                x = array_ops.transpose(x, perm)

                if not self.cholesky_input_output_matrices:
                    # Complexity: O(nbk^3)
                    x = math_ops.batch_matmul(x, x, adj_y=True)

                # Set shape hints.
                if self.scale_operator_pd.get_shape().ndims is not None:
                    x.set_shape(
                        tensor_shape.TensorShape(
                            [tensor_util.constant_value(n)] + self.scale_operator_pd.get_shape().as_list()
                        )
                    )
                elif x.get_shape().ndims is not None:
                    x.get_shape()[0].merge_with(tensor_shape.TensorDimension(tensor_util.constant_value(n)))

                return x

 def _to_dense(self):
   chol = array_ops.batch_matrix_band_part(self._chol, -1, 0)
   return math_ops.batch_matmul(chol, chol, adj_y=True)

 def _sqrt_to_dense(self):
   chol = array_ops.batch_matrix_band_part(self._chol, -1, 0)
   return array_ops.identity(chol)

 def _batch_sqrt_matmul(self, x, transpose_x=False):
   chol = array_ops.batch_matrix_band_part(self._chol, -1, 0)
   # tf.batch_matmul is defined x * y, so "y" is on the right, not "x".
   return math_ops.batch_matmul(chol, x, adj_y=transpose_x)

 def _sqrt_matmul(self, x, transpose_x=False):
   chol = array_ops.batch_matrix_band_part(self._chol, -1, 0)
   # tf.matmul is defined a * b
   return math_ops.matmul(chol, x, transpose_b=transpose_x)

 def _batch_matmul(self, x, transpose_x=False):
   # tf.batch_matmul is defined x * y, so "y" is on the right, not "x".
   chol = array_ops.batch_matrix_band_part(self._chol, -1, 0)
   chol_times_x = math_ops.batch_matmul(
       chol, x, adj_x=True, adj_y=transpose_x)
   return math_ops.batch_matmul(chol, chol_times_x)

 def _matmul(self, x, transpose_x=False):
   # tf.matmul is defined a * b.
   chol = array_ops.batch_matrix_band_part(self._chol, -1, 0)
   chol_times_x = math_ops.matmul(
       chol, x, transpose_a=True, transpose_b=transpose_x)
   return math_ops.matmul(chol, chol_times_x)