mars.tensor.searchsorted¶

mars.tensor.
searchsorted
(a, v, side='left', sorter=None, combine_size=None)[source]¶ Find indices where elements should be inserted to maintain order.
Find the indices into a sorted tensor 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[i1] < v <= a[i]
right
a[i1] <= v < a[i]
 Parameters
a (1D array_like) – Input tensor. 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 (1D array_like, optional) – Optional tensor of integer indices that sort array a into ascending order. They are typically the result of argsort.
combine_size (int, optional) – The number of chunks to combine.
 Returns
indices – Array of insertion points with the same shape as v.
 Return type
tensor of ints
Notes
Binary search is used to find the required insertion points.
This function is a faster version of the builtin python bisect.bisect_left (
side='left'
) and bisect.bisect_right (side='right'
) functions, which is also vectorized in the v argument.Examples
>>> import mars.tensor as mt
>>> mt.searchsorted([1,2,3,4,5], 3).execute() 2 >>> mt.searchsorted([1,2,3,4,5], 3, side='right').execute() 3 >>> mt.searchsorted([1,2,3,4,5], [10, 10, 2, 3]).execute() array([0, 5, 1, 2])