Natrik - 1 year ago 78

Python Question

**Goal:**

Given integers

`n`

`k`

`k`

`[1, ..,n]`

Input: n=13, k=2

Output: 10

The lexicographical order is

`[1, 10, 11, 12, 13, 2, 3, 4, 5, 6, 7, 8, 9]`

My code is working for numbers up to 10^5, but fails at around 10^6. I'm not familiar at all with improving runtime in Python.

Would not using Python's sort method be a way of doing this?

`class Solution(object):`

def findKthNumber(self, n, k):

"""

:type n: int

:type k: int

:rtype: int

"""

A = []

for i in range(1,n+1):

A.append(i)

x = (sorted(map(str, A)))

return int(x[k-1])

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Answer Source

Use kth select algorithm instead of sorting.

```
def select_median_of_medians_pivot(array, k):
'''
Implementation of the Blum, Floyd, Pratt, Rivest, Tarjan SELECT
algorithm as described by David Eppstein.
CITATION: http://www.ics.uci.edu/~eppstein/161/960130.html
This algorithm has worst case run time of O(N) where N is the
number of entries in the array.
Although this algorithm has better worst case performance than
select_random_pivot(), that algorithm is preferred because it
is much faster in practice.
Here is how you might use it:
# Create a list of pseudo random numbers.
# Duplicates can occur.
num = 10000
array = [random.randint(1,1000) for i in range(num)]
random.shuffle(array)
random.shuffle(array)
# Get the value of the kth item.
k = 7
kval = select_median_of_medians_pivot(array, k)
# Test it.
sorted_array = sorted(array)
assert sorted_array[k] == kval
@param array the list of values
@param k k-th item to select.
'''
# If the array is short, terminate the recursion and return the
# value without partitioning.
if len(array) <= 10:
array.sort()
return array[k]
# Partition the array into subsets with a maximum of 5 elements
# each.
subset_size = 5 # max items in a subset
subsets = [] # list of subsets
num_medians = len(array) / subset_size
if (len(array) % subset_size) > 0:
num_medians += 1 # not divisible by 5
for i in range(num_medians):
beg = i * subset_size
end = min(len(array), beg + subset_size)
subset = array[beg:end]
subsets.append(subset)
# Find the medians in each subset.
# Note that it calls select_median_of_medians_pivot() recursively taking
# advantage of the fact that for len(array) <= 10, the select
# operation simply sorts the array and returns the k-th item. This
# could be done here but since the termination condition is
# required to get an infinite loop we may as well use it.
medians = [] # list of medians
for subset in subsets:
median = select_median_of_medians_pivot(subset, len(subset)/2)
medians.append(median)
# Now get the median of the medians recursively.
# Assign it to the local pivot variable because
# the pivot handling code is the same regardless
# of how it was generated. See select_random_pivot() for
# a different approach for generating the pivot.
median_of_medians = select_median_of_medians_pivot(medians, len(medians)/2)
pivot = median_of_medians # pivot point value (not index)
# Now select recursively using the pivot.
# At this point we have the pivot. Use it to partition the input
# array into 3 categories: items that are less than the pivot
# value (array_lt), items that are greater than the pivot value
# (array_gt) and items that exactly equal to the pivot value
# (equals_array).
array_lt = []
array_gt = []
array_eq = []
for item in array:
if item < pivot:
array_lt.append(item)
elif item > pivot:
array_gt.append(item)
else:
array_eq.append(item)
# The array values have been partitioned according to their
# relation to the pivot value. The partitions look like this:
#
# +---+---+---+...+---+---+---+...+---+---+---+...
# | 0 | 1 | 2 | | e |e+1|e+2| | g |g+1|g+2|
# +---+---+---+...+---+---+---+...+---+---+---+...
# array_lt array_eq array_gt
#
# If the value of k is in the range [0..e) then we know that
# the desired value is in array_lt so we need to recurse.
#
# If the value of k in the range [e..g) then we know that the
# desired value is in array_eq and we are done.
#
# If the value of k is >= g then we the desired value is in
# array_gt and we need to recurse but we also have to make sure
# that k is normalized with respect to array_gt so that it has the
# proper offset in the recursion. We normalize it by subtracting
# len(array_lt) and len(array_eq).
#
if k < len(array_lt):
return select_fct(array_lt, k)
elif k < len(array_lt) + len(array_eq):
return array_eq[0]
else:
normalized_k = k - (len(array_lt) + len(array_eq))
return select_fct(array_gt, normalized_k)
```

Modified code.

```
class Solution(object):
def findKthNumber(self, n, k):
"""
:type n: int
:type k: int
:rtype: int
"""
A = range(1,n+1)
x = (map(str, A))
return select_median_of_medians_pivot(x,k-1)
```

It will reduce your worst case running time from O(n*log n) to O(n).

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