tensorflow_probability/python/internal/distribution_util.py
def fill_triangular(x, upper=False, name=None):
  r"""Creates a (batch of) triangular matrix from a vector of inputs.

  Created matrix can be lower- or upper-triangular. (It is more efficient to
  create the matrix as upper or lower, rather than transpose.)

  Triangular matrix elements are filled in a clockwise spiral. See example,
  below.

  If `x.shape` is `[b1, b2, ..., bB, d]` then the output shape is
  `[b1, b2, ..., bB, n, n]` where `n` is such that `d = n(n+1)/2`, i.e.,
  `n = int(np.sqrt(0.25 + 2. * m) - 0.5)`.

  Example:

  ```python
  fill_triangular([1, 2, 3, 4, 5, 6])
  # ==> [[4, 0, 0],
  #      [6, 5, 0],
  #      [3, 2, 1]]

  fill_triangular([1, 2, 3, 4, 5, 6], upper=True)
  # ==> [[1, 2, 3],
  #      [0, 5, 6],
  #      [0, 0, 4]]
  ```

  The key trick is to create an upper triangular matrix by concatenating `x`
  and a tail of itself, then reshaping.

  Suppose that we are filling the upper triangle of an `n`-by-`n` matrix `M`
  from a vector `x`. The matrix `M` contains n**2 entries total. The vector `x`
  contains `n * (n+1) / 2` entries. For concreteness, we'll consider `n = 5`
  (so `x` has `15` entries and `M` has `25`). We'll concatenate `x` and `x` with
  the first (`n = 5`) elements removed and reversed:

  ```python
  x = np.arange(15) + 1
  xc = np.concatenate([x, x[5:][::-1]])
  # ==> array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 14, 13,
  #            12, 11, 10, 9, 8, 7, 6])

  # (We add one to the arange result to disambiguate the zeros below the
  # diagonal of our upper-triangular matrix from the first entry in `x`.)

  # Now, when reshapedlay this out as a matrix:
  y = np.reshape(xc, [5, 5])
  # ==> array([[ 1,  2,  3,  4,  5],
  #            [ 6,  7,  8,  9, 10],
  #            [11, 12, 13, 14, 15],
  #            [15, 14, 13, 12, 11],
  #            [10,  9,  8,  7,  6]])

  # Finally, zero the elements below the diagonal:
  y = np.triu(y, k=0)
  # ==> array([[ 1,  2,  3,  4,  5],
  #            [ 0,  7,  8,  9, 10],
  #            [ 0,  0, 13, 14, 15],
  #            [ 0,  0,  0, 12, 11],
  #            [ 0,  0,  0,  0,  6]])
  ```

  From this example we see that the resuting matrix is upper-triangular, and
  contains all the entries of x, as desired. The rest is details:
  - If `n` is even, `x` doesn't exactly fill an even number of rows (it fills
    `n / 2` rows and half of an additional row), but the whole scheme still
    works.
  - If we want a lower triangular matrix instead of an upper triangular,
    we remove the first `n` elements from `x` rather than from the reversed
    `x`.

  For additional comparisons, a pure numpy version of this function can be found
  in `distribution_util_test.py`, function `_fill_triangular`.

  Args:
    x: `Tensor` representing lower (or upper) triangular elements.
    upper: Python `bool` representing whether output matrix should be upper
      triangular (`True`) or lower triangular (`False`, default).
    name: Python `str`. The name to give this op.

  Returns:
    tril: `Tensor` with lower (or upper) triangular elements filled from `x`.

  Raises:
    ValueError: if `x` cannot be mapped to a triangular matrix.
  """

  with tf.name_scope(name or "fill_triangular"):
    x = tf.convert_to_tensor(value=x, name="x")
    m = tf.compat.dimension_value(
        tensorshape_util.with_rank_at_least(x.shape, 1)[-1])
    if m is not None:
      # Formula derived by solving for n: m = n(n+1)/2.
      m = np.int32(m)
      n = np.sqrt(0.25 + 2. * m) - 0.5
      if n != np.floor(n):
        raise ValueError("Input right-most shape ({}) does not "
                         "correspond to a triangular matrix.".format(m))
      n = np.int32(n)
      static_final_shape = x.shape[:-1].concatenate([n, n])
    else:
      m = tf.shape(input=x)[-1]
      # For derivation, see above. Casting automatically lops off the 0.5, so we
      # omit it.  We don't validate n is an integer because this has
      # graph-execution cost; an error will be thrown from the reshape, below.
      n = tf.cast(
          tf.sqrt(0.25 + tf.cast(2 * m, dtype=tf.float32)), dtype=tf.int32)
      static_final_shape = tensorshape_util.with_rank_at_least(
          x.shape, 1)[:-1].concatenate([None, None])

    # Try it out in numpy:
    #  n = 3
    #  x = np.arange(n * (n + 1) / 2)
    #  m = x.shape[0]
    #  n = np.int32(np.sqrt(.25 + 2 * m) - .5)
    #  x_tail = x[(m - (n**2 - m)):]
    #  np.concatenate([x_tail, x[::-1]], 0).reshape(n, n)  # lower
    #  # ==> array([[3, 4, 5],
    #               [5, 4, 3],
    #               [2, 1, 0]])
    #  np.concatenate([x, x_tail[::-1]], 0).reshape(n, n)  # upper
    #  # ==> array([[0, 1, 2],
    #               [3, 4, 5],
    #               [5, 4, 3]])
    #
    # Note that we can't simply do `x[..., -(n**2 - m):]` because this doesn't
    # correctly handle `m == n == 1`. Hence, we do nonnegative indexing.
    # Furthermore observe that:
    #   m - (n**2 - m)
    #   = n**2 / 2 + n / 2 - (n**2 - n**2 / 2 + n / 2)
    #   = 2 (n**2 / 2 + n / 2) - n**2
    #   = n**2 + n - n**2
    #   = n
    ndims = prefer_static_rank(x)
    if upper:
      x_list = [x, tf.reverse(x[..., n:], axis=[ndims - 1])]
    else:
      x_list = [x[..., n:], tf.reverse(x, axis=[ndims - 1])]
    new_shape = (
        tensorshape_util.as_list(static_final_shape)
        if tensorshape_util.is_fully_defined(static_final_shape) else tf.concat(
            [tf.shape(input=x)[:-1], [n, n]], axis=0))
    x = tf.reshape(tf.concat(x_list, axis=-1), new_shape)
    x = tf.linalg.band_part(
        x, num_lower=(0 if upper else -1), num_upper=(-1 if upper else 0))
    tensorshape_util.set_shape(x, static_final_shape)
    return x
def fill_triangular(x, upper=False, name=None):
  r"""Creates a (batch of) triangular matrix from a vector of inputs.

  Created matrix can be lower- or upper-triangular. (It is more efficient to
  create the matrix as upper or lower, rather than transpose.)

  Triangular matrix elements are filled in a clockwise spiral. See example,
  below.

  If `x.shape` is `[b1, b2, ..., bB, d]` then the output shape is
  `[b1, b2, ..., bB, n, n]` where `n` is such that `d = n(n+1)/2`, i.e.,
  `n = int(np.sqrt(0.25 + 2. * m) - 0.5)`.

  Example:

  ```python
  fill_triangular([1, 2, 3, 4, 5, 6])
  # ==> [[4, 0, 0],
  #      [6, 5, 0],
  #      [3, 2, 1]]

  fill_triangular([1, 2, 3, 4, 5, 6], upper=True)
  # ==> [[1, 2, 3],
  #      [0, 5, 6],
  #      [0, 0, 4]]
  ```

  The key trick is to create an upper triangular matrix by concatenating `x`
  and a tail of itself, then reshaping.

  Suppose that we are filling the upper triangle of an `n`-by-`n` matrix `M`
  from a vector `x`. The matrix `M` contains n**2 entries total. The vector `x`
  contains `n * (n+1) / 2` entries. For concreteness, we'll consider `n = 5`
  (so `x` has `15` entries and `M` has `25`). We'll concatenate `x` and `x` with
  the first (`n = 5`) elements removed and reversed:

  ```python
  x = np.arange(15) + 1
  xc = np.concatenate([x, x[5:][::-1]])
  # ==> array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 14, 13,
  #            12, 11, 10, 9, 8, 7, 6])

  # (We add one to the arange result to disambiguate the zeros below the
  # diagonal of our upper-triangular matrix from the first entry in `x`.)

  # Now, when reshapedlay this out as a matrix:
  y = np.reshape(xc, [5, 5])
  # ==> array([[ 1,  2,  3,  4,  5],
  #            [ 6,  7,  8,  9, 10],
  #            [11, 12, 13, 14, 15],
  #            [15, 14, 13, 12, 11],
  #            [10,  9,  8,  7,  6]])

  # Finally, zero the elements below the diagonal:
  y = np.triu(y, k=0)
  # ==> array([[ 1,  2,  3,  4,  5],
  #            [ 0,  7,  8,  9, 10],
  #            [ 0,  0, 13, 14, 15],
  #            [ 0,  0,  0, 12, 11],
  #            [ 0,  0,  0,  0,  6]])
  ```

  From this example we see that the resuting matrix is upper-triangular, and
  contains all the entries of x, as desired. The rest is details:
  - If `n` is even, `x` doesn't exactly fill an even number of rows (it fills
    `n / 2` rows and half of an additional row), but the whole scheme still
    works.
  - If we want a lower triangular matrix instead of an upper triangular,
    we remove the first `n` elements from `x` rather than from the reversed
    `x`.

  For additional comparisons, a pure numpy version of this function can be found
  in `distribution_util_test.py`, function `_fill_triangular`.

  Args:
    x: `Tensor` representing lower (or upper) triangular elements.
    upper: Python `bool` representing whether output matrix should be upper
      triangular (`True`) or lower triangular (`False`, default).
    name: Python `str`. The name to give this op.

  Returns:
    tril: `Tensor` with lower (or upper) triangular elements filled from `x`.

  Raises:
    ValueError: if `x` cannot be mapped to a triangular matrix.
  """

  with tf.name_scope(name or "fill_triangular"):
    x = tf.convert_to_tensor(value=x, name="x")
    m = tf.compat.dimension_value(
        tensorshape_util.with_rank_at_least(x.shape, 1)[-1])
    if m is not None:
      # Formula derived by solving for n: m = n(n+1)/2.
      m = np.int32(m)
      n = np.sqrt(0.25 + 2. * m) - 0.5
      if n != np.floor(n):
        raise ValueError("Input right-most shape ({}) does not "
                         "correspond to a triangular matrix.".format(m))
      n = np.int32(n)
      static_final_shape = x.shape[:-1].concatenate([n, n])
    else:
      m = tf.shape(input=x)[-1]
      # For derivation, see above. Casting automatically lops off the 0.5, so we
      # omit it.  We don't validate n is an integer because this has
      # graph-execution cost; an error will be thrown from the reshape, below.
      n = tf.cast(
          tf.sqrt(0.25 + tf.cast(2 * m, dtype=tf.float32)), dtype=tf.int32)
      static_final_shape = tensorshape_util.with_rank_at_least(
          x.shape, 1)[:-1].concatenate([None, None])

    # Try it out in numpy:
    #  n = 3
    #  x = np.arange(n * (n + 1) / 2)
    #  m = x.shape[0]
    #  n = np.int32(np.sqrt(.25 + 2 * m) - .5)
    #  x_tail = x[(m - (n**2 - m)):]
    #  np.concatenate([x_tail, x[::-1]], 0).reshape(n, n)  # lower
    #  # ==> array([[3, 4, 5],
    #               [5, 4, 3],
    #               [2, 1, 0]])
    #  np.concatenate([x, x_tail[::-1]], 0).reshape(n, n)  # upper
    #  # ==> array([[0, 1, 2],
    #               [3, 4, 5],
    #               [5, 4, 3]])
    #
    # Note that we can't simply do `x[..., -(n**2 - m):]` because this doesn't
    # correctly handle `m == n == 1`. Hence, we do nonnegative indexing.
    # Furthermore observe that:
    #   m - (n**2 - m)
    #   = n**2 / 2 + n / 2 - (n**2 - n**2 / 2 + n / 2)
    #   = 2 (n**2 / 2 + n / 2) - n**2
    #   = n**2 + n - n**2
    #   = n
    ndims = prefer_static_rank(x)
    if upper:
      x_list = [x, tf.reverse(x[..., n:], axis=[ndims - 1])]
    else:
      x_list = [x[..., n:], tf.reverse(x, axis=[ndims - 1])]
    new_shape = (
        tensorshape_util.as_list(static_final_shape)
        if tensorshape_util.is_fully_defined(static_final_shape) else tf.concat(
            [tf.shape(input=x)[:-1], [n, n]], axis=0))
    x = tf.reshape(tf.concat(x_list, axis=-1), new_shape)
    x = tf.linalg.band_part(
        x, num_lower=(0 if upper else -1), num_upper=(-1 if upper else 0))
    tensorshape_util.set_shape(x, static_final_shape)
    return x
def fill_triangular ( x , upper = False , name = None ) : with tf . name_scope ( name or "fill_triangular" ) : x = tf . convert_to_tensor ( value = x , name = "x" ) m = tf . compat . dimension_value ( tensorshape_util . with_rank_at_least ( x . shape , 1 ) [ - 1 ] ) if m is not None : # Formula derived by solving for n: m = n(n+1)/2. m = np . int32 ( m ) n = np . sqrt ( 0.25 + 2. * m ) - 0.5 if n != np . floor ( n ) : raise ValueError ( "Input right-most shape ({}) does not " "correspond to a triangular matrix." . format ( m ) ) n = np . int32 ( n ) static_final_shape = x . shape [ : - 1 ] . concatenate ( [ n , n ] ) else : m = tf . shape ( input = x ) [ - 1 ] # For derivation, see above. Casting automatically lops off the 0.5, so we # omit it.  We don't validate n is an integer because this has # graph-execution cost; an error will be thrown from the reshape, below. n = tf . cast ( tf . sqrt ( 0.25 + tf . cast ( 2 * m , dtype = tf . float32 ) ) , dtype = tf . int32 ) static_final_shape = tensorshape_util . with_rank_at_least ( x . shape , 1 ) [ : - 1 ] . concatenate ( [ None , None ] ) # Try it out in numpy: #  n = 3 #  x = np.arange(n * (n + 1) / 2) #  m = x.shape[0] #  n = np.int32(np.sqrt(.25 + 2 * m) - .5) #  x_tail = x[(m - (n**2 - m)):] #  np.concatenate([x_tail, x[::-1]], 0).reshape(n, n)  # lower #  # ==> array([[3, 4, 5], #               [5, 4, 3], #               [2, 1, 0]]) #  np.concatenate([x, x_tail[::-1]], 0).reshape(n, n)  # upper #  # ==> array([[0, 1, 2], #               [3, 4, 5], #               [5, 4, 3]]) # # Note that we can't simply do `x[..., -(n**2 - m):]` because this doesn't # correctly handle `m == n == 1`. Hence, we do nonnegative indexing. # Furthermore observe that: #   m - (n**2 - m) #   = n**2 / 2 + n / 2 - (n**2 - n**2 / 2 + n / 2) #   = 2 (n**2 / 2 + n / 2) - n**2 #   = n**2 + n - n**2 #   = n ndims = prefer_static_rank ( x ) if upper : x_list = [ x , tf . reverse ( x [ ... , n : ] , axis = [ ndims - 1 ] ) ] else : x_list = [ x [ ... , n : ] , tf . reverse ( x , axis = [ ndims - 1 ] ) ] new_shape = ( tensorshape_util . as_list ( static_final_shape ) if tensorshape_util . is_fully_defined ( static_final_shape ) else tf . concat ( [ tf . shape ( input = x ) [ : - 1 ] , [ n , n ] ] , axis = 0 ) ) x = tf . reshape ( tf . concat ( x_list , axis = - 1 ) , new_shape ) x = tf . linalg . band_part ( x , num_lower = ( 0 if upper else - 1 ) , num_upper = ( - 1 if upper else 0 ) ) tensorshape_util . set_shape ( x , static_final_shape ) return x
r"""Creates a (batch of) triangular matrix from a vector of inputs.

  Created matrix can be lower- or upper-triangular. (It is more efficient to
  create the matrix as upper or lower, rather than transpose.)

  Triangular matrix elements are filled in a clockwise spiral. See example,
  below.

  If `x.shape` is `[b1, b2, ..., bB, d]` then the output shape is
  `[b1, b2, ..., bB, n, n]` where `n` is such that `d = n(n+1)/2`, i.e.,
  `n = int(np.sqrt(0.25 + 2. * m) - 0.5)`.

  Example:

  ```python
  fill_triangular([1, 2, 3, 4, 5, 6])
  # ==> [[4, 0, 0],
  #      [6, 5, 0],
  #      [3, 2, 1]]

  fill_triangular([1, 2, 3, 4, 5, 6], upper=True)
  # ==> [[1, 2, 3],
  #      [0, 5, 6],
  #      [0, 0, 4]]
  ```

  The key trick is to create an upper triangular matrix by concatenating `x`
  and a tail of itself, then reshaping.

  Suppose that we are filling the upper triangle of an `n`-by-`n` matrix `M`
  from a vector `x`. The matrix `M` contains n**2 entries total. The vector `x`
  contains `n * (n+1) / 2` entries. For concreteness, we'll consider `n = 5`
  (so `x` has `15` entries and `M` has `25`). We'll concatenate `x` and `x` with
  the first (`n = 5`) elements removed and reversed:

  ```python
  x = np.arange(15) + 1
  xc = np.concatenate([x, x[5:][::-1]])
  # ==> array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 14, 13,
  #            12, 11, 10, 9, 8, 7, 6])

  # (We add one to the arange result to disambiguate the zeros below the
  # diagonal of our upper-triangular matrix from the first entry in `x`.)

  # Now, when reshapedlay this out as a matrix:
  y = np.reshape(xc, [5, 5])
  # ==> array([[ 1,  2,  3,  4,  5],
  #            [ 6,  7,  8,  9, 10],
  #            [11, 12, 13, 14, 15],
  #            [15, 14, 13, 12, 11],
  #            [10,  9,  8,  7,  6]])

  # Finally, zero the elements below the diagonal:
  y = np.triu(y, k=0)
  # ==> array([[ 1,  2,  3,  4,  5],
  #            [ 0,  7,  8,  9, 10],
  #            [ 0,  0, 13, 14, 15],
  #            [ 0,  0,  0, 12, 11],
  #            [ 0,  0,  0,  0,  6]])
  ```

  From this example we see that the resuting matrix is upper-triangular, and
  contains all the entries of x, as desired. The rest is details:
  - If `n` is even, `x` doesn't exactly fill an even number of rows (it fills
    `n / 2` rows and half of an additional row), but the whole scheme still
    works.
  - If we want a lower triangular matrix instead of an upper triangular,
    we remove the first `n` elements from `x` rather than from the reversed
    `x`.

  For additional comparisons, a pure numpy version of this function can be found
  in `distribution_util_test.py`, function `_fill_triangular`.

  Args:
    x: `Tensor` representing lower (or upper) triangular elements.
    upper: Python `bool` representing whether output matrix should be upper
      triangular (`True`) or lower triangular (`False`, default).
    name: Python `str`. The name to give this op.

  Returns:
    tril: `Tensor` with lower (or upper) triangular elements filled from `x`.

  Raises:
    ValueError: if `x` cannot be mapped to a triangular matrix.
r Creates a ( batch of ) triangular matrix from a vector of inputs .