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# tf.edit_distance

Computes the Levenshtein distance between sequences.

``````tf.edit_distance(
hypothesis, truth, normalize=True, name='edit_distance'
)
``````

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

normalize = True
``````

This operation would return the following:

``````# 'output' is a tensor of shape `[2, 2]` with edit distances normalized
# by 'truth' lengths.
output ==> [[inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
[0.5, 1.0]]  # (1,0): addition, (1,1): no hypothesis
``````

#### 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`.