tensorflow_probability/python/internal/distribution_util.py
def rotate_transpose(x, shift, name="rotate_transpose"):
  """Circularly moves dims left or right.

  Effectively identical to:

  ```python
  numpy.transpose(x, numpy.roll(numpy.arange(len(x.shape)), shift))
  ```

  When `validate_args=False` additional graph-runtime checks are
  performed. These checks entail moving data from to GPU to CPU.

  Example:

  ```python
  x = tf.random_normal([1, 2, 3, 4])  # Tensor of shape [1, 2, 3, 4].
  rotate_transpose(x, -1).shape == [2, 3, 4, 1]
  rotate_transpose(x, -2).shape == [3, 4, 1, 2]
  rotate_transpose(x,  1).shape == [4, 1, 2, 3]
  rotate_transpose(x,  2).shape == [3, 4, 1, 2]
  rotate_transpose(x,  7).shape == rotate_transpose(x, 3).shape  # [2, 3, 4, 1]
  rotate_transpose(x, -7).shape == rotate_transpose(x, -3).shape  # [4, 1, 2, 3]
  ```

  Args:
    x: `Tensor`.
    shift: `Tensor`. Number of dimensions to transpose left (shift<0) or
      transpose right (shift>0).
    name: Python `str`. The name to give this op.

  Returns:
    rotated_x: Input `Tensor` with dimensions circularly rotated by shift.

  Raises:
    TypeError: if shift is not integer type.
  """
  with tf.name_scope(name):
    x = tf.convert_to_tensor(value=x, name="x")
    shift = tf.convert_to_tensor(value=shift, name="shift")
    # We do not assign back to preserve constant-ness.
    assert_util.assert_integer(shift)
    shift_value_static = tf.get_static_value(shift)
    ndims = tensorshape_util.rank(x.shape)
    if ndims is not None and shift_value_static is not None:
      if ndims < 2:
        return x
      shift_value_static = np.sign(shift_value_static) * (
          abs(shift_value_static) % ndims)
      if shift_value_static == 0:
        return x
      perm = np.roll(np.arange(ndims), shift_value_static)
      return tf.transpose(a=x, perm=perm)
    else:
      # Consider if we always had a positive shift, and some specified
      # direction.
      # When shifting left we want the new array:
      #   last(x, n-shift) + first(x, shift)
      # and if shifting right then we want:
      #   last(x, shift) + first(x, n-shift)
      # Observe that last(a) == slice(a, n) and first(a) == slice(0, a).
      # Also, we can encode direction and shift as one: direction * shift.
      # Combining these facts, we have:
      #   a = cond(shift<0, -shift, n-shift)
      #   last(x, n-a) + first(x, a) == x[a:n] + x[0:a]
      # Finally, we transform shift by modulo length so it can be specified
      # independently from the array upon which it operates (like python).
      ndims = tf.rank(x)
      shift = tf.where(
          tf.less(shift, 0), -shift % ndims,
          ndims - shift % ndims)
      first = tf.range(0, shift)
      last = tf.range(shift, ndims)
      perm = tf.concat([last, first], 0)
      return tf.transpose(a=x, perm=perm)
def rotate_transpose(x, shift, name="rotate_transpose"):
  with tf.name_scope(name):
    x = tf.convert_to_tensor(value=x, name="x")
    shift = tf.convert_to_tensor(value=shift, name="shift")
    # We do not assign back to preserve constant-ness.
    assert_util.assert_integer(shift)
    shift_value_static = tf.get_static_value(shift)
    ndims = tensorshape_util.rank(x.shape)
    if ndims is not None and shift_value_static is not None:
      if ndims < 2:
        return x
      shift_value_static = np.sign(shift_value_static) * (
          abs(shift_value_static) % ndims)
      if shift_value_static == 0:
        return x
      perm = np.roll(np.arange(ndims), shift_value_static)
      return tf.transpose(a=x, perm=perm)
    else:
      # Consider if we always had a positive shift, and some specified
      # direction.
      # When shifting left we want the new array:
      #   last(x, n-shift) + first(x, shift)
      # and if shifting right then we want:
      #   last(x, shift) + first(x, n-shift)
      # Observe that last(a) == slice(a, n) and first(a) == slice(0, a).
      # Also, we can encode direction and shift as one: direction * shift.
      # Combining these facts, we have:
      #   a = cond(shift<0, -shift, n-shift)
      #   last(x, n-a) + first(x, a) == x[a:n] + x[0:a]
      # Finally, we transform shift by modulo length so it can be specified
      # independently from the array upon which it operates (like python).
      ndims = tf.rank(x)
      shift = tf.where(
          tf.less(shift, 0), -shift % ndims,
          ndims - shift % ndims)
      first = tf.range(0, shift)
      last = tf.range(shift, ndims)
      perm = tf.concat([last, first], 0)
      return tf.transpose(a=x, perm=perm)
def rotate_transpose ( x , shift , name = "rotate_transpose" ) : with tf . name_scope ( name ) : x = tf . convert_to_tensor ( value = x , name = "x" ) shift = tf . convert_to_tensor ( value = shift , name = "shift" ) # We do not assign back to preserve constant-ness. assert_util . assert_integer ( shift ) shift_value_static = tf . get_static_value ( shift ) ndims = tensorshape_util . rank ( x . shape ) if ndims is not None and shift_value_static is not None : if ndims < 2 : return x shift_value_static = np . sign ( shift_value_static ) * ( abs ( shift_value_static ) % ndims ) if shift_value_static == 0 : return x perm = np . roll ( np . arange ( ndims ) , shift_value_static ) return tf . transpose ( a = x , perm = perm ) else : # Consider if we always had a positive shift, and some specified # direction. # When shifting left we want the new array: #   last(x, n-shift) + first(x, shift) # and if shifting right then we want: #   last(x, shift) + first(x, n-shift) # Observe that last(a) == slice(a, n) and first(a) == slice(0, a). # Also, we can encode direction and shift as one: direction * shift. # Combining these facts, we have: #   a = cond(shift<0, -shift, n-shift) #   last(x, n-a) + first(x, a) == x[a:n] + x[0:a] # Finally, we transform shift by modulo length so it can be specified # independently from the array upon which it operates (like python). ndims = tf . rank ( x ) shift = tf . where ( tf . less ( shift , 0 ) , - shift % ndims , ndims - shift % ndims ) first = tf . range ( 0 , shift ) last = tf . range ( shift , ndims ) perm = tf . concat ( [ last , first ] , 0 ) return tf . transpose ( a = x , perm = perm )
Circularly moves dims left or right.

  Effectively identical to:

  ```python
  numpy.transpose(x, numpy.roll(numpy.arange(len(x.shape)), shift))
  ```

  When `validate_args=False` additional graph-runtime checks are
  performed. These checks entail moving data from to GPU to CPU.

  Example:

  ```python
  x = tf.random_normal([1, 2, 3, 4])  # Tensor of shape [1, 2, 3, 4].
  rotate_transpose(x, -1).shape == [2, 3, 4, 1]
  rotate_transpose(x, -2).shape == [3, 4, 1, 2]
  rotate_transpose(x,  1).shape == [4, 1, 2, 3]
  rotate_transpose(x,  2).shape == [3, 4, 1, 2]
  rotate_transpose(x,  7).shape == rotate_transpose(x, 3).shape  # [2, 3, 4, 1]
  rotate_transpose(x, -7).shape == rotate_transpose(x, -3).shape  # [4, 1, 2, 3]
  ```

  Args:
    x: `Tensor`.
    shift: `Tensor`. Number of dimensions to transpose left (shift<0) or
      transpose right (shift>0).
    name: Python `str`. The name to give this op.

  Returns:
    rotated_x: Input `Tensor` with dimensions circularly rotated by shift.

  Raises:
    TypeError: if shift is not integer type.
Circularly moves dims left or right .