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mindspore.ops.EditDistance

class mindspore.ops.EditDistance(normalize=True)

Computes the Levenshtein Edit Distance. It is used to measure the similarity of two sequences. The inputs are variable-length sequences provided by SparseTensors (hypothesis_indices, hypothesis_values, hypothesis_shape) and (truth_indices, truth_values, truth_shape).

$$\begin{array}{r}{\mathrm{lev}}_{a,b}(i,j)=\{\begin{array}{l}max(i,j)\text{ if }min(i,j)=0\\ min\{\begin{array}{ll}{\mathrm{lev}}_{a,b}(i-1,j)+1& \\ {\mathrm{lev}}_{a,b}(i,j-1)+1& \text{ otherwise. }\\ {\mathrm{lev}}_{a,b}(i-1,j-1)+1_{(a_i\ne b_j)}\end{array}& \end{array}\end{array}$$

Where the $a$ indicates the hypothesis and the $a$ indicates the truth. For ease of understanding, i and j here in may be considered as lengths of a and b.

Parameters

normalize (bool) – If true, edit distances are normalized by length of truth. Default: True.

Inputs:

• hypothesis_indices (Tensor) - The indices of the hypothesis list SparseTensor. With int64 data type. The shape of tensor is $(N,R)$.

• hypothesis_values (Tensor) - The values of the hypothesis list SparseTensor. With float32 data type. Must be 1-D vector with length of N.

• hypothesis_shape (Tensor) - The shape of the hypothesis list SparseTensor. Must be R-length vector with int64 data type. Only constant value is allowed.

• truth_indices (Tensor) - The indices of the truth list SparseTensor. With int64 data type. The shape of tensor is $(M,R)$.

• truth_values (Tensor) - The values of the truth list SparseTensor. Must be 1-D vector with length of M. With float32 data type.

• truth_shape (Tensor) - The shape of the truth list SparseTensor. Must be R-length vector with int64 data type. Only constant value is allowed.

Outputs:

Tensor, a dense tensor with rank R-1 and float32 data type.

Raises

TypeError – If normalize is not a bool.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import context
>>> from mindspore import Tensor
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> class EditDistance(nn.Cell):
...     def __init__(self, hypothesis_shape, truth_shape, normalize=True):
...         super(EditDistance, self).__init__()
...         self.edit_distance = ops.EditDistance(normalize)
...         self.hypothesis_shape = hypothesis_shape
...         self.truth_shape = truth_shape
...
...     def construct(self, hypothesis_indices, hypothesis_values, truth_indices, truth_values):
...         return self.edit_distance(hypothesis_indices, hypothesis_values, self.hypothesis_shape,
...                                   truth_indices, truth_values, self.truth_shape)
...
>>> hypothesis_indices = Tensor(np.array([[0, 0, 0], [1, 0, 1], [1, 1, 1]]).astype(np.int64))
>>> hypothesis_values = Tensor(np.array([1, 2, 3]).astype(np.float32))
>>> hypothesis_shape = Tensor(np.array([1, 1, 2]).astype(np.int64))
>>> truth_indices = Tensor(np.array([[0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1]]).astype(np.int64))
>>> truth_values = Tensor(np.array([1, 3, 2, 1]).astype(np.float32))
>>> truth_shape = Tensor(np.array([2, 2, 2]).astype(np.int64))
>>> edit_distance = EditDistance(hypothesis_shape, truth_shape)
>>> output = edit_distance(hypothesis_indices, hypothesis_values, truth_indices, truth_values)
>>> print(output)
[[1. 1.]
 [1. 1.]]