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from cython cimport floating
cdef inline void dual_swap(
floating* darr,
ITYPE_t *iarr,
ITYPE_t a,
ITYPE_t b,
) nogil:
"""Swap the values at index a and b of both darr and iarr"""
cdef floating dtmp = darr[a]
darr[a] = darr[b]
darr[b] = dtmp
cdef ITYPE_t itmp = iarr[a]
iarr[a] = iarr[b]
iarr[b] = itmp
cdef int simultaneous_sort(
floating* values,
ITYPE_t* indices,
ITYPE_t size,
) nogil:
"""
Perform a recursive quicksort on the values array as to sort them ascendingly.
This simultaneously performs the swaps on both the values and the indices arrays.
The numpy equivalent is:
def simultaneous_sort(dist, idx):
i = np.argsort(dist)
return dist[i], idx[i]
Notes
-----
Arrays are manipulated via a pointer to there first element and their size
as to ease the processing of dynamically allocated buffers.
"""
# TODO: In order to support discrete distance metrics, we need to have a
# simultaneous sort which breaks ties on indices when distances are identical.
# The best might be using a std::stable_sort and a Comparator which might need
# an Array of Structures (AoS) instead of the Structure of Arrays (SoA)
# currently used.
cdef:
ITYPE_t pivot_idx, i, store_idx
floating pivot_val
# in the small-array case, do things efficiently
if size <= 1:
pass
elif size == 2:
if values[0] > values[1]:
dual_swap(values, indices, 0, 1)
elif size == 3:
if values[0] > values[1]:
dual_swap(values, indices, 0, 1)
if values[1] > values[2]:
dual_swap(values, indices, 1, 2)
if values[0] > values[1]:
dual_swap(values, indices, 0, 1)
else:
# Determine the pivot using the median-of-three rule.
# The smallest of the three is moved to the beginning of the array,
# the middle (the pivot value) is moved to the end, and the largest
# is moved to the pivot index.
pivot_idx = size // 2
if values[0] > values[size - 1]:
dual_swap(values, indices, 0, size - 1)
if values[size - 1] > values[pivot_idx]:
dual_swap(values, indices, size - 1, pivot_idx)
if values[0] > values[size - 1]:
dual_swap(values, indices, 0, size - 1)
pivot_val = values[size - 1]
# Partition indices about pivot. At the end of this operation,
# pivot_idx will contain the pivot value, everything to the left
# will be smaller, and everything to the right will be larger.
store_idx = 0
for i in range(size - 1):
if values[i] < pivot_val:
dual_swap(values, indices, i, store_idx)
store_idx += 1
dual_swap(values, indices, store_idx, size - 1)
pivot_idx = store_idx
# Recursively sort each side of the pivot
if pivot_idx > 1:
simultaneous_sort(values, indices, pivot_idx)
if pivot_idx + 2 < size:
simultaneous_sort(values + pivot_idx + 1,
indices + pivot_idx + 1,
size - pivot_idx - 1)
return 0
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