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EditDistance

public final class EditDistance

Computes the (possibly normalized) Levenshtein Edit Distance.

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:

Nested Classes

class EditDistance.Options Optional attributes for EditDistance  

Public Methods

Output<Float>
asOutput()
Returns the symbolic handle of a tensor.
static <T> EditDistance
create(Scope scope, Operand<Long> hypothesisIndices, Operand<T> hypothesisValues, Operand<Long> hypothesisShape, Operand<Long> truthIndices, Operand<T> truthValues, Operand<Long> truthShape, Options... options)
Factory method to create a class wrapping a new EditDistance operation.
static EditDistance.Options
normalize(Boolean normalize)
Output<Float>
output()
A dense float tensor with rank R - 1.

Inherited Methods

Public Methods

public Output<Float> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static EditDistance create (Scope scope, Operand<Long> hypothesisIndices, Operand<T> hypothesisValues, Operand<Long> hypothesisShape, Operand<Long> truthIndices, Operand<T> truthValues, Operand<Long> truthShape, Options... options)

Factory method to create a class wrapping a new EditDistance operation.

Parameters
scope current scope
hypothesisIndices The indices of the hypothesis list SparseTensor. This is an N x R int64 matrix.
hypothesisValues The values of the hypothesis list SparseTensor. This is an N-length vector.
hypothesisShape The shape of the hypothesis list SparseTensor. This is an R-length vector.
truthIndices The indices of the truth list SparseTensor. This is an M x R int64 matrix.
truthValues The values of the truth list SparseTensor. This is an M-length vector.
truthShape truth indices, vector.
options carries optional attributes values
Returns
  • a new instance of EditDistance

public static EditDistance.Options normalize (Boolean normalize)

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

The output is:

public Output<Float> 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