TensorFlow 1 version | View source on GitHub |
Searches for where a value would go in a sorted sequence.
tf.searchsorted(
sorted_sequence, values, side='left', out_type=tf.dtypes.int32,
name=None
)
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)
Args | |
---|---|
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. |
Returns | |
---|---|
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.
|
Raises | |
---|---|
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.
|