tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py
def _uniform_correlation_like_matrix(num_rows, batch_shape, dtype, seed):
  """Returns a uniformly random `Tensor` of "correlation-like" matrices.

  A "correlation-like" matrix is a symmetric square matrix with all entries
  between -1 and 1 (inclusive) and 1s on the main diagonal.  Of these,
  the ones that are positive semi-definite are exactly the correlation
  matrices.

  Args:
    num_rows: Python `int` dimension of the correlation-like matrices.
    batch_shape: `Tensor` or Python `tuple` of `int` shape of the
      batch to return.
    dtype: `dtype` of the `Tensor` to return.
    seed: Random seed.

  Returns:
    matrices: A `Tensor` of shape `batch_shape + [num_rows, num_rows]`
      and dtype `dtype`.  Each entry is in [-1, 1], and each matrix
      along the bottom two dimensions is symmetric and has 1s on the
      main diagonal.
  """
  num_entries = num_rows * (num_rows + 1) / 2
  ones = tf.ones(shape=[num_entries], dtype=dtype)
  # It seems wasteful to generate random values for the diagonal since
  # I am going to throw them away, but `fill_triangular` fills the
  # diagonal, so I probably need them.
  # It's not impossible that it would be more efficient to just fill
  # the whole matrix with random values instead of messing with
  # `fill_triangular`.  Then would need to filter almost half out with
  # `matrix_band_part`.
  unifs = uniform.Uniform(-ones, ones).sample(batch_shape, seed=seed)
  tril = util.fill_triangular(unifs)
  symmetric = tril + tf.linalg.matrix_transpose(tril)
  diagonal_ones = tf.ones(
      shape=util.pad(batch_shape, axis=0, back=True, value=num_rows),
      dtype=dtype)
  return tf.linalg.set_diag(symmetric, diagonal_ones)
def _uniform_correlation_like_matrix(num_rows, batch_shape, dtype, seed):
  num_entries = num_rows * (num_rows + 1) / 2
  ones = tf.ones(shape=[num_entries], dtype=dtype)
  # It seems wasteful to generate random values for the diagonal since
  # I am going to throw them away, but `fill_triangular` fills the
  # diagonal, so I probably need them.
  # It's not impossible that it would be more efficient to just fill
  # the whole matrix with random values instead of messing with
  # `fill_triangular`.  Then would need to filter almost half out with
  # `matrix_band_part`.
  unifs = uniform.Uniform(-ones, ones).sample(batch_shape, seed=seed)
  tril = util.fill_triangular(unifs)
  symmetric = tril + tf.linalg.matrix_transpose(tril)
  diagonal_ones = tf.ones(
      shape=util.pad(batch_shape, axis=0, back=True, value=num_rows),
      dtype=dtype)
  return tf.linalg.set_diag(symmetric, diagonal_ones)
def _uniform_correlation_like_matrix ( num_rows , batch_shape , dtype , seed ) : num_entries = num_rows * ( num_rows + 1 ) / 2 ones = tf . ones ( shape = [ num_entries ] , dtype = dtype ) # It seems wasteful to generate random values for the diagonal since # I am going to throw them away, but `fill_triangular` fills the # diagonal, so I probably need them. # It's not impossible that it would be more efficient to just fill # the whole matrix with random values instead of messing with # `fill_triangular`.  Then would need to filter almost half out with # `matrix_band_part`. unifs = uniform . Uniform ( - ones , ones ) . sample ( batch_shape , seed = seed ) tril = util . fill_triangular ( unifs ) symmetric = tril + tf . linalg . matrix_transpose ( tril ) diagonal_ones = tf . ones ( shape = util . pad ( batch_shape , axis = 0 , back = True , value = num_rows ) , dtype = dtype ) return tf . linalg . set_diag ( symmetric , diagonal_ones )
Returns a uniformly random `Tensor` of "correlation-like" matrices.

  A "correlation-like" matrix is a symmetric square matrix with all entries
  between -1 and 1 (inclusive) and 1s on the main diagonal.  Of these,
  the ones that are positive semi-definite are exactly the correlation
  matrices.

  Args:
    num_rows: Python `int` dimension of the correlation-like matrices.
    batch_shape: `Tensor` or Python `tuple` of `int` shape of the
      batch to return.
    dtype: `dtype` of the `Tensor` to return.
    seed: Random seed.

  Returns:
    matrices: A `Tensor` of shape `batch_shape + [num_rows, num_rows]`
      and dtype `dtype`.  Each entry is in [-1, 1], and each matrix
      along the bottom two dimensions is symmetric and has 1s on the
      main diagonal.
Returns a uniformly random Tensor of correlation - like matrices .