Implementation of merge sort that takes sublinear extra space
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I implemented an algorithm that takes only sublinear extra space as required in the exercise description:
Develop a merge implementation that reduces the extra space
requirement to max(M, N/M). (dividing the array into N/M blocks of
size M).
def insert_sort(a, lo, hi):
for i in range(lo+1, hi):
j = i
while(j>lo and a[j]<a[j-1]):
a[j], a[j-1] = a[j-1], a[j]
j -= 1
def in_place_merge(a, lo, mi, hi):
aux_hi = a[mi:hi]
for i in reversed(range(lo, hi)):
if aux_hi:
last_a = i - len(aux_hi)
if (last_a < lo or aux_hi[-1] > a[last_a]):
a[i] = aux_hi.pop()
else:
a[i] = a[last_a]
else:
break
def block_insertion_sort(a):
M = 5
for i in range(0, len(a), M):
insert_sort(a, i, min(i+M, len(a)))
if i > 0:
in_place_merge(a, 0, i, min(i+M, len(a)))
In addition, I don't understand the max(M, N/M) part, then I don't know if I have made it. Any suggestions are highly appreciated.
python algorithm mergesort genetic-algorithm
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I implemented an algorithm that takes only sublinear extra space as required in the exercise description:
Develop a merge implementation that reduces the extra space
requirement to max(M, N/M). (dividing the array into N/M blocks of
size M).
def insert_sort(a, lo, hi):
for i in range(lo+1, hi):
j = i
while(j>lo and a[j]<a[j-1]):
a[j], a[j-1] = a[j-1], a[j]
j -= 1
def in_place_merge(a, lo, mi, hi):
aux_hi = a[mi:hi]
for i in reversed(range(lo, hi)):
if aux_hi:
last_a = i - len(aux_hi)
if (last_a < lo or aux_hi[-1] > a[last_a]):
a[i] = aux_hi.pop()
else:
a[i] = a[last_a]
else:
break
def block_insertion_sort(a):
M = 5
for i in range(0, len(a), M):
insert_sort(a, i, min(i+M, len(a)))
if i > 0:
in_place_merge(a, 0, i, min(i+M, len(a)))
In addition, I don't understand the max(M, N/M) part, then I don't know if I have made it. Any suggestions are highly appreciated.
python algorithm mergesort genetic-algorithm
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I implemented an algorithm that takes only sublinear extra space as required in the exercise description:
Develop a merge implementation that reduces the extra space
requirement to max(M, N/M). (dividing the array into N/M blocks of
size M).
def insert_sort(a, lo, hi):
for i in range(lo+1, hi):
j = i
while(j>lo and a[j]<a[j-1]):
a[j], a[j-1] = a[j-1], a[j]
j -= 1
def in_place_merge(a, lo, mi, hi):
aux_hi = a[mi:hi]
for i in reversed(range(lo, hi)):
if aux_hi:
last_a = i - len(aux_hi)
if (last_a < lo or aux_hi[-1] > a[last_a]):
a[i] = aux_hi.pop()
else:
a[i] = a[last_a]
else:
break
def block_insertion_sort(a):
M = 5
for i in range(0, len(a), M):
insert_sort(a, i, min(i+M, len(a)))
if i > 0:
in_place_merge(a, 0, i, min(i+M, len(a)))
In addition, I don't understand the max(M, N/M) part, then I don't know if I have made it. Any suggestions are highly appreciated.
python algorithm mergesort genetic-algorithm
I implemented an algorithm that takes only sublinear extra space as required in the exercise description:
Develop a merge implementation that reduces the extra space
requirement to max(M, N/M). (dividing the array into N/M blocks of
size M).
def insert_sort(a, lo, hi):
for i in range(lo+1, hi):
j = i
while(j>lo and a[j]<a[j-1]):
a[j], a[j-1] = a[j-1], a[j]
j -= 1
def in_place_merge(a, lo, mi, hi):
aux_hi = a[mi:hi]
for i in reversed(range(lo, hi)):
if aux_hi:
last_a = i - len(aux_hi)
if (last_a < lo or aux_hi[-1] > a[last_a]):
a[i] = aux_hi.pop()
else:
a[i] = a[last_a]
else:
break
def block_insertion_sort(a):
M = 5
for i in range(0, len(a), M):
insert_sort(a, i, min(i+M, len(a)))
if i > 0:
in_place_merge(a, 0, i, min(i+M, len(a)))
In addition, I don't understand the max(M, N/M) part, then I don't know if I have made it. Any suggestions are highly appreciated.
python algorithm mergesort genetic-algorithm
python algorithm mergesort genetic-algorithm
edited 19 mins ago
Jamal♦
30.2k11115226
30.2k11115226
asked 1 hour ago
lerner
1365
1365
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