mindspore.ops.EditDistance
- class mindspore.ops.EditDistance(normalize=True)[源代码]
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).
\[\begin{split}\operatorname{lev}_{a, b}(i, j)=\left\{\begin{array}{ll} \max (i, j) \qquad \qquad \qquad \qquad \qquad \quad \ \text { if } \min (i, j)=0 \\ \min \left\{\begin{array}{ll} \operatorname{lev}_{a, b}(i-1, j)+1 & \\ \operatorname{lev}_{a, b}(i, j-1)+1 & \text { otherwise. } \\ \operatorname{lev}_{a, b}(i-1, j-1)+1_{\left(a_{i} \neq b_{j}\right)} \end{array}\right. & \end{array}\right.\end{split}\]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. 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.
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 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.]]