jax.numpy.searchsorted¶
-
jax.numpy.
searchsorted
(a, v, side='left', sorter=None)[source]¶ Find indices where elements should be inserted to maintain order.
LAX-backend implementation of
searchsorted()
. Original docstring below.Find the indices into a sorted array a such that, if the corresponding elements in v were inserted before the indices, the order of a would be preserved.
Assuming that a is sorted:
side
returned index i satisfies
left
a[i-1] < v <= a[i]
right
a[i-1] <= v < a[i]
- Parameters
a (1-D array_like) – Input array. If sorter is None, then it must be sorted in ascending order, otherwise sorter must be an array of indices that sort it.
v (array_like) – Values to insert into a.
side ({'left', 'right'}, optional) – If ‘left’, the index of the first suitable location found is given. If ‘right’, return the last such index. If there is no suitable index, return either 0 or N (where N is the length of a).
sorter (1-D array_like, optional) – Optional array of integer indices that sort array a into ascending order. They are typically the result of argsort.
- Returns
indices – Array of insertion points with the same shape as v.
- Return type
array of ints
See also
sort()
Return a sorted copy of an array.
histogram()
Produce histogram from 1-D data.
Notes
Binary search is used to find the required insertion points.
As of NumPy 1.4.0 searchsorted works with real/complex arrays containing nan values. The enhanced sort order is documented in sort.
This function uses the same algorithm as the builtin python bisect.bisect_left (
side='left'
) and bisect.bisect_right (side='right'
) functions, which is also vectorized in the v argument.Examples
>>> np.searchsorted([1,2,3,4,5], 3) 2 >>> np.searchsorted([1,2,3,4,5], 3, side='right') 3 >>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3]) array([0, 5, 1, 2])