EditDistance

tensorflow C++ API

tensorflow::ops::EditDistance

Computes the (possibly normalized) Levenshtein Edit Distance.

Summary

The inputs are variable-length sequences provided by SparseTensors (hypothesis_indices, hypothesis_values, hypothesis_shape) and (truth_indices, truth_values, truth_shape).

The inputs are:

Arguments:

• scope: A Scope object
• hypothesis_indices: The indices of the hypothesis list SparseTensor. This is an N x R int64 matrix.
• hypothesis_values: The values of the hypothesis list SparseTensor. This is an N-length vector.
• hypothesis_shape: The shape of the hypothesis list SparseTensor. This is an R-length vector.
• truth_indices: The indices of the truth list SparseTensor. This is an M x R int64 matrix.
• truth_values: The values of the truth list SparseTensor. This is an M-length vector.
• truth_shape: truth indices, vector.

Optional attributes (seeAttrs):

• normalize: boolean (if true, edit distances are normalized by length of truth).

The output is:

Returns:

• Output: A dense float tensor with rank R - 1.

For the example input:

// hypothesis represents a 2x1 matrix with variable-length values:
//   (0,0) = ["a"]
//   (1,0) = ["b"]
hypothesis_indices=[[0,0,0],
                    [1,0,0]]
hypothesis_values=["a","b"]
hypothesis_shape=[2,1,1]

// truth represents a 2x2 matrix with variable-length values:
//   (0,0) = []
//   (0,1) = ["a"]
//   (1,0) = ["b", "c"]
//   (1,1) = ["a"]
truth_indices=[[0,1,0],
               [1,0,0],
               [1,0,1],
               [1,1,0]]
truth_values=["a","b","c","a"]
truth_shape=[2,2,2]
normalize=true

The output will be:

// output is a 2x2 matrix 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

EditDistance block

Source link :https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_array_ops.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input hypothesis_indices: The indices of the hypothesis list SparseTensor. This is an N x R int64 matrix.
• hypothesis_values: The values of the hypothesis list SparseTensor. This is an N-length vector.
• hypothesis_shape: The shape of the hypothesis list SparseTensor. This is an int64 & R-length vector.
• truth_indices: The indices of the truth list SparseTensor. This is an M x R int64 matrix.
• truth_values: The values of the truth list SparseTensor. This is an M-length vector.
• truth_shape: truth indices, int64 vector.

Return:

• Output output : Output object of Diag class object.

Result:

• std::vector(Tensor) result_output : Assume inputhas dimensions [D1,..., Dk, D1,..., Dk] , then the output is a tensor of rank k with dimensions [D1,..., Dk]

Using Method

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