Searches for where a value would go in a sorted sequence.

This is not a method for checking containment (like python in).

The typical use case for this operation is "binning", "bucketing", or "discretizing". The values are assigned to bucket-indices based on the edges listed in sorted_sequence. This operation returns the bucket-index for each value.

edges = [-1, 3.3, 9.1, 10.0]
values = [0.0, 4.1, 12.0]
tf.searchsorted(edges, values).numpy()
array([1, 2, 4], dtype=int32)

The side argument controls which index is returned if a value lands exactly on an edge:

seq = [0, 3, 9, 10, 10]
values = [0, 4, 10]
tf.searchsorted(seq, values).numpy()
array([0, 2, 3], dtype=int32)
tf.searchsorted(seq, values, side="right").numpy()
array([1, 2, 5], dtype=int32)

The axis is not settable for this operation. It always operates on the innermost dimension (axis=-1). The operation will accept any number of outer dimensions. Here it is applied to the rows of a matrix:

sorted_sequence = [[0., 3., 8., 9., 10.],
                   [1., 2., 3., 4., 5.]]
values = [[9.8, 2.1, 4.3],
          [0.1, 6.6, 4.5, ]]
tf.searchsorted(sorted_sequence, values).numpy()
array([[4, 1, 2],
       [0, 5, 4]], dtype=int32)

sorted_sequence N-D Tensor containing a sorted sequence.
values N-D Tensor containing the search values.
side 'left' or 'right'; 'left' corresponds to lower_bound and 'right' to upper_bound.
out_type The output type (int32 or int64). Default is tf.int32.
name Optional name for the operation.

An N-D Tensor the size of values containing the result of applying either lower_bound or upper_bound (depending on side) to each value. The result is not a global index to the entire Tensor, but the index in the last dimension.

ValueError If the last dimension of sorted_sequence >= 2^31-1 elements. If the total size of values exceeds 2^31 - 1 elements. If the first N-1 dimensions of the two tensors don't match.