tf_quant_finance.math.segment_ops.segment_diff

Last updated: 2023-03-16.

tf_quant_finance.math.segment_ops.segment_diff#

View source

Computes difference of successive elements in a segment.

tf_quant_finance.math.segment_ops.segment_diff(
    x, segment_ids, order=1, exclusive=False, dtype=None, name=None
)

For a complete description of segment_* ops see documentation of tf.segment_max. This op extends the diff functionality to segmented inputs.

The behaviour of this op is the same as that of the op diff within each segment. The result is effectively a concatenation of the results of diff applied to each segment.

Example#

  x = tf.constant([2, 5, 1, 7, 9] + [32, 10, 12, 3] + [4, 8, 5])
  segments = tf.constant([0, 0, 0, 0, 0] + [1, 1, 1, 1] + [2, 2, 2])
  # First order diff. Expected result: [3, -4, 6, 2, -22, 2, -9, 4, -3]
  dx1 = segment_diff(
      x, segment_ids=segments, order=1, exclusive=True)
  # Non-exclusive, second order diff.
  # Expected result: [2, 5, -1, 2, 8, 32, 10, -20, -7, 4, 8, 1]
  dx2 = segment_diff(
      x, segment_ids=segments, order=2, exclusive=False)

Args:#

  • x: A rank 1 Tensor of any dtype for which arithmetic operations are permitted.

  • segment_ids: A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of x. Values should be sorted and can be repeated.

  • order: Positive Python int. The order of the difference to compute. order = 1 corresponds to the difference between successive elements. Default value: 1

  • exclusive: Python bool. See description above. Default value: False

  • dtype: Optional tf.Dtype. If supplied, the dtype for x to use when converting to Tensor. Default value: None which maps to the default dtype inferred by TF.

  • name: Python str name prefixed to Ops created by this class. Default value: None which is mapped to the default name ‘segment_diff’.

Returns:#

  • diffs: A Tensor of the same dtype as x. Assuming that each segment is of length greater than or equal to order, if exclusive is True, then the size is n-order*k where n is the size of x, k is the number of different segment ids supplied if segment_ids is not None or 1 if segment_ids is None. If any of the segments is of length less than the order, then the size is: n-sum(min(order, length(segment_j)), j) where the sum is over segments. If exclusive is False, then the size is n.

Contents