/TensorFlow 2.4

# tf.edit_distance

Computes the Levenshtein distance between sequences.

This operation takes variable-length sequences (`hypothesis` and `truth`), each provided as a `SparseTensor`, and computes the Levenshtein distance. You can normalize the edit distance by length of `truth` by setting `normalize` to true.

#### For example:

Given the following input,

```hypothesis = tf.SparseTensor(
[[0, 0, 0],
[1, 0, 0]],
["a", "b"],
(2, 1, 1))
truth = tf.SparseTensor(
[[0, 1, 0],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0]],
["a", "b", "c", "a"],
(2, 2, 2))
tf.edit_distance(hypothesis, truth, normalize=True)
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[inf, 1. ],
[0.5, 1. ]], dtype=float32)>
```

The operaton returns a dense Tensor of shape `[2, 2]` with edit distances normalized by `truth` lengths.

Note: It is possible to calculate edit distance between two sparse tensors with variable-length values. However, attempting to create them while eager execution is enabled will result in a `ValueError`.

For the following inputs,

```# 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
#   (0,0) = ["a"]
#   (1,0) = ["b"]
hypothesis = tf.sparse.SparseTensor(
[[0, 0, 0],
[1, 0, 0]],
["a", "b"],
(2, 1, 1))

# 'truth' is a tensor of shape `[2, 2]` with variable-length values:
#   (0,0) = []
#   (0,1) = ["a"]
#   (1,0) = ["b", "c"]
#   (1,1) = ["a"]
truth = tf.sparse.SparseTensor(
[[0, 1, 0],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0]],
["a", "b", "c", "a"],
(2, 2, 2))

normalize = True

# The output would be a dense Tensor of shape `(2,)`, with edit distances
noramlized by 'truth' lengths.
# output => array([0., 0.5], dtype=float32)
```
Args
`hypothesis` A `SparseTensor` containing hypothesis sequences.
`truth` A `SparseTensor` containing truth sequences.
`normalize` A `bool`. If `True`, normalizes the Levenshtein distance by length of `truth.`
`name` A name for the operation (optional).
Returns
A dense `Tensor` with rank `R - 1`, where R is the rank of the `SparseTensor` inputs `hypothesis` and `truth`.
Raises
`TypeError` If either `hypothesis` or `truth` are not a `SparseTensor`.