# Finding the closest pair of points divide-and-conquer

Here is my code for the famous "finding the closest pair of points" problem from CLRS book section 33.4 using a divide-and-conquer approach (this code returns the minimum distance between pair of points).

This code works correctly but it's slow. As seen in the code, I pre-sort so I avoid sorting at every recursive call, however, my run time is still too long.

import statistics as stats
import math

# helper functions:
def two_point_distance(p0,p1):
# returns distance between two (x,y) pairs
return ( (p0[0]-p1[0])**2 + (p0[1] - p1[1])**2 )**0.5

def combine_xy(x_arr,y_arr):
# combine x_arr and y_arr to combined list of (x,y) tuples
return list(zip(x_arr,y_arr))

def find_closest_distance_brute(xy_arr):
# brute force approach to find closest distance
dmin = math.inf
for i in range (len(xy_arr)-1):
dis_storage = []
for j in range (1,len(xy_arr)-i):
d_i_ipj = two_point_distance(xy_arr[i], xy_arr[i+j])
dis_storage.append(d_i_ipj)
dis_storage_min = min(dis_storage)
if dis_storage_min < dmin:
dmin = dis_storage_min

return dmin

def calc_median_x(xy_arr):
# return median of x values in list of (x,y) points
return stats.median([val[0] for val in xy_arr ])

def filter_set(xy_arr_y_sorted, median, distance):
# filter the entire set such than |x-median|<=min distance in halves
out = []
for val in xy_arr_y_sorted:
val_x = val[0]
if abs(val_x-median) <= distance:
out.append(val)
return out

def x_sort(xy_arr):
# sort array according to x value
return sorted(xy_arr, key=lambda val: val[0])

def y_sort(xy_arr):
# sort array according to y value
return sorted(xy_arr, key=lambda val: val[1])

def split_array(arr_x_sorted, arr_y_sorted,median):
# split array of size n to two arrays of n/2
# input is the same array twice, one sorted wrt x, the other wrt y
leq_arr_x_sorted, grt_arr_x_sorted = [],[]
dmy_x = 0 # switch between left and right when val_x==median
for val in arr_x_sorted:
val_x = val[0]
if val_x < median:
leq_arr_x_sorted.append(val)
if val_x > median:
grt_arr_x_sorted.append(val)
if val_x == median:
if dmy_x == 0:
leq_arr_x_sorted.append(val)
dmy_x = 1
else:
grt_arr_x_sorted.append(val)
dmy_x = 0

leq_arr_y_sorted, grt_arr_y_sorted = [],[]
dmy_y = 0 # switch between left and right when val_x==median
for val in arr_y_sorted:
val_x = val[0]
if val_x < median:
leq_arr_y_sorted.append(val)
if val_x > median:
grt_arr_y_sorted.append(val)
if val_x == median:
if dmy_y == 0:
leq_arr_y_sorted.append(val)
dmy_y = 1
else:
grt_arr_y_sorted.append(val)
dmy_y = 0
return leq_arr_x_sorted, leq_arr_y_sorted, grt_arr_x_sorted, grt_arr_y_sorted

def find_min_distance_in_rec(xy_arr_y_sorted,dmin):
# takes in array sorted in y, and minimum distance of n/2 halves
# for each point it computes distance to 7 subsequent points
# output min distance encountered

dmin_rec = dmin

if len(xy_arr_y_sorted) == 1:
return math.inf

if len(xy_arr_y_sorted) > 7:
for i in range(len(xy_arr_y_sorted)-7):
dis_storage = []
for j in range(1,8):
d_i_ipj = two_point_distance(xy_arr_y_sorted[i],xy_arr_y_sorted[i+j])
dis_storage.append(d_i_ipj)
dis_storage_min = min(dis_storage)
if dis_storage_min < dmin_rec:
dmin_rec = dis_storage_min
for k in range(len(xy_arr_y_sorted)-7, len(xy_arr_y_sorted)-1):
dis_storage = []
for l in range(1,len(xy_arr_y_sorted)-k):
d_k_kpl = two_point_distance(xy_arr_y_sorted[k], xy_arr_y_sorted[k+l])
dis_storage.append(d_k_kpl)
dis_storage_min = min(dis_storage)
if dis_storage_min < dmin_rec:
dmin_rec = dis_storage_min
else:
for m in range(0,len(xy_arr_y_sorted)-1):
dis_storage = []
for n in range (1,len(xy_arr_y_sorted)-m):
d_m_mpn = two_point_distance(xy_arr_y_sorted[m], xy_arr_y_sorted[m+n])
dis_storage.append(d_m_mpn)
dis_storage_min = min(dis_storage)
if dis_storage_min < dmin_rec:
dmin_rec = dis_storage_min
return dmin_rec

def find_closest_distance_recur(xy_arr_x_sorted, xy_arr_y_sorted):
# recursive function to find closest distance between points
if len(xy_arr_x_sorted) <=3 :
return find_closest_distance_brute(xy_arr_x_sorted)

median = calc_median_x(xy_arr_x_sorted)
leq_arr_x_sorted, leq_arr_y_sorted , grt_arr_x_sorted, grt_arr_y_sorted = split_array(xy_arr_x_sorted, xy_arr_y_sorted, median)

distance_left = find_closest_distance_recur(leq_arr_x_sorted, leq_arr_y_sorted)
distance_right = find_closest_distance_recur(grt_arr_x_sorted, grt_arr_y_sorted)
distance_min = min(distance_left, distance_right)

filt_out = filter_set(xy_arr_y_sorted, median, distance_min)
distance_filt = find_min_distance_in_rec(filt_out, distance_min)

return min(distance_min, distance_filt)

def find_closest_point(x_arr, y_arr):
# input is x,y points in two arrays, all x's in x_arr, all y's in y_arr
xy_arr = combine_xy(x_arr,y_arr)
xy_arr_x_sorted = x_sort(xy_arr)
xy_arr_y_sored = y_sort(xy_arr)

min_distance = find_closest_distance_recur(xy_arr_x_sorted, xy_arr_y_sored)

return min_distance


Speeding up the helper.

Exponentiation is an expensive operation, time wise. Instead of (p0[0]-p1[0])**2, use (p0[0]-p1[0])*(p0[0]-p1[0])

Consider returning distance-squared. With the “brute force” solution, the minimum distance corresponds to the minimum squared distance, so you can find the square-root of the minimum, instead of the minimum of the square-roots, and save many expensive calculations.

Check: math.sqrt(...) may be faster than (...)**0.5

Speeding up find_closest_distance_brute():

What is dis_storage for? You accumulate distances in a list, take the minimum of the list, and if the minimum is less than a global minimum, you update the global minimum. The list is then thrown away.

There is no need to build up the list! It is a waste of time and memory. A generator expression can pass a “list” of data to the min function, without actually creating the list.

dis_storage_min = min( two_point_distance(xy_arr[i], pnt)
for pnt in xy_arr[i+1:])


Passing xy_arr[i] to the two_point_distance() function is inefficient; you should lookup the value from the array once and save it in a local variable, instead of looking up the same value each time through the loop. The for loop is actually the ideal place to get the value looked up. Instead of looping over the range() of the len() of xy_arr, loop over the values from the array. Looping over the enumeration gives the index at the same time:

dmin = math.inf
for i, pnt_i in enumerate(xy_arr[:-1]):
dis_storage_min = min( two_point_distance(pnt_i, pnt_j)
for pnt_j in xy_arr[i+1:])
if dis_storage_min < dmin:
dmin = dis_storage_min


Note the use of xy_arr[:-1] to avoid the last point of the array, and xy_arr[i+1:] to loop over all points after the ith value.

The above can be refactored into one min(...) statements, with a generator using two for loops. Exercise (temporary) left to student.

Like the min function above, you can use a generator expression to calculate the median without needlessly creating a list. Just remove the []’s from the call:

def calc_median_x(xy_arr):
# return median of x values in list of (x,y) points
return stats.median( val[0] for val in xy_arr )


The filter_set function can be written using list comprehension, instead of the inefficient list.append() approach:

def filter_set(xy_arr_y_sorted, median, distance):
# filter the entire set such than |x-median|<=min distance in halves
return [ val for val in xy_arr_y_sorted
if abs(val[0] - median) <= distance ]


split_array():

Again, list comprehension is better than appending.

leq_arr_x_sorted = [ val for val in arr_x_sorted if val[0] < median ]
geq_arr_x_sorted = [ val for val in arr_x_sorted if val[0] > median ]
eq_x             = [ val for val in arr_x_sorted if val[0] == median ]


You can then add half of the eq_x array to the leq array, the other half to the geq array:

n = len(eq_x)//2
leq_arr_x_sorted = leq_arr_x_sorted + eq_x[:n]
geq_arr_x_sorted = eq_x[n:] + geq_arr_x_sorted


Similar comments of course apply to the arr_y_sorted.

But this may still be busy work. If the array is already sorted by x coordinate, then:

n = len(arr_x_sorted) // 2
leq_arr_x_sorted = arr_x_sorted[:n]
geq_arr_x_sorted = arr_x_sorted[n:]


will give you approximately the same thing. It diverges when multiple copies of the median exist but are not balanced across the centre. Ie:

[ 1, 2, 3, 4, 4, 4, 4, 8 ]


will split as:

[ 1, 2, 3, 4, 4 ]  [ 4, 4, 8 ]  // yours
[ 1, 2, 3, 4 ]  [ 4, 4, 4, 8 ]  // mine


Whether you can use this will depend on whether you need the y sorted data partitioned in exactly the same way as the x.

find_min_distance_in_rect():

The else: clause (7 or less points) looks like you are implementing a brute force search for the minimum distance. But you’ve already written that; just call find_min_dist_brute(xy_arr_y_sorted). It won’t speed up your code, but it will make it shorter.

Looping over all but the last 7 points, and searching the 7 points immediately after them, and then looping over the last 7 points and searching the remaining 7-k points is very awkward code. The theory says you don’t need to compare more than 7 comparisons per point, and that is important to keep the algorithm O(n). But coding wise, that’s a mess.

Better: for every point, compare it with each point after it, until you come to a point whose y coordinate is further than dmin. Not a 7 anywhere in sight. Just a double for loop, with a break statement in the inner loop. The theory guarantees that you won’t need to loop more than 7 times in the inner loop; we don’t need that codified as well. We might actually gain a tiny bit of speed, because you’ll often loop less than 7 times.

• Thanks @AJNeufeld; I incorporated all your suggestions. In addition, I re-wrote the find_min_distance_in_rec based on your earlier suggestions on find_closest_distance_brute() function where I now use list comprehension and generator expressions. The run time improved by 30% which is significant but the code is still a tad bit slow. I appreciate if you have further suggestions for speed improvement. Jul 16, 2018 at 1:52
• I’ve provided a few additional points. Once you’ve incorporated those, if you still need additional help, it would be wise to post a follow up question containing the improved code, with a link back to this one, and a link in this one to the new question. See what should I do when someone answers my question for additional details. Jul 16, 2018 at 21:28
• I created a follow up question since the code changed significantly. link to follow up question Jul 17, 2018 at 17:51
def split_array(arr_x_sorted, arr_y_sorted,median):
# split array of size n to two arrays of n/2
# input is the same array twice, one sorted wrt x, the other wrt y
leq_arr_x_sorted, grt_arr_x_sorted = [],[]
dmy_x = 0 # switch between left and right when val_x==median
for val in arr_x_sorted:
val_x = val[0]
if val_x < median:
leq_arr_x_sorted.append(val)
if val_x > median:
grt_arr_x_sorted.append(val)
if val_x == median:
if dmy_x == 0:
leq_arr_x_sorted.append(val)
dmy_x = 1
else:
grt_arr_x_sorted.append(val)
dmy_x = 0

leq_arr_y_sorted, grt_arr_y_sorted = [],[]
dmy_y = 0 # switch between left and right when val_x==median
for val in arr_y_sorted:
val_x = val[0]
if val_x < median:
leq_arr_y_sorted.append(val)
if val_x > median:
grt_arr_y_sorted.append(val)
if val_x == median:
if dmy_y == 0:
leq_arr_y_sorted.append(val)
dmy_y = 1
else:
grt_arr_y_sorted.append(val)
dmy_y = 0
return leq_arr_x_sorted, leq_arr_y_sorted, grt_arr_x_sorted, grt_arr_y_sorted


There's a 15-line chunk of code there that's repeated twice, so if this is a correct implementation that could be factored out.

However, I don't believe that it is a correct implementation, on two counts.

1. My understanding is that the end result should be four arrays such that leq_arr_x_sorted and leq_arr_y_sorted contain the same points sorted on different projections. But the dmy_x / dmy_y separation of points whose x coordinate is median doesn't seem to guarantee that those points will be sent to the same half in the x-projection and the y-projection. In order to guarantee that an additional pre-condition is needed: most obviously that in arr_x_sorted ties are broken by y; but that pre-condition is not produced by x_sort.

2. My understanding is that the split should be 50-50, even in degenerate cases where all of the points have the same x-coordinate.

The easiest way that I can see to fix both problems is to do a "Dutch flag" partition into less-than, equal, and greater-than; and then to split the values sorted on y and use that split for both (since all the x-coordinates are the same, it's sorted on both coordinates):

def split_array(x_sorted, y_sorted, median_x):
lt_x_sorted = [point for point in x_sorted if point[0] < median_x]
gt_x_sorted = [point for point in x_sorted if point[0] > median_x]

lt_y_sorted = [point for point in y_sorted if point[0] < median_x]
eq_y_sorted = [point for point in y_sorted if point[0] == median_x]
gt_y_sorted = [point for point in y_sorted if point[0] > median_x]

# Make the two halves equal in size
split = len(x_sorted) // 2 - len(lt_x_sorted)
eq_left = eq_y_sorted[:split]
eq_right = eq_y_sorted[split:]

return lt_x_sorted + eq_left, lt_y_sorted + eq_left,
eq_right + gt_x_sorted, eq_right + gt_y_sorted


def find_closest_distance_recur(xy_arr_x_sorted, xy_arr_y_sorted):
# recursive function to find closest distance between points
if len(xy_arr_x_sorted) <=3 :
return find_closest_distance_brute(xy_arr_x_sorted)

median = calc_median_x(xy_arr_x_sorted)


Hang on! The median of a sorted array doesn't need a call to stats.median! xy_arr_x_sorted[len(xy_arr_x_sorted) // 2][0].

    leq_arr_x_sorted, leq_arr_y_sorted , grt_arr_x_sorted, grt_arr_y_sorted = split_array(xy_arr_x_sorted, xy_arr_y_sorted, median)

...

filt_out = filter_set(xy_arr_y_sorted, median, distance_min)
distance_filt = find_min_distance_in_rec(filt_out, distance_min)


By filtering to a single strip, this will end up comparing points which have already been compared. It might be worth filtering leq_arr_y_sorted and grt_arr_y_sorted and then modifying find_min_distance_in_rec to take the strips separately and only do cross-strip comparisons.

• If the arrays become unsorted after calls to split_array that means there's a bug in split_array, but I can't see it. On the other hand, if you're willing to accept that split_array will unsort the elements, I don't see why it's worth sorting them in the first place. In either case, it sounds like the code needs a lot more comments to document what assumptions are made and what assumptions should not be made because they're unsafe. Jul 19, 2018 at 7:21
• I concur. The arrays becoming unsorted in split_array would be a bug, but I can see it either. If an array is sorted, iterating over it and appending each element to one of two arrays, even randomly, will result in two sorted arrays. And the median of a sorted array does not need a call to stats.median. Most worrisome, if you’ve actually accepted that split_array returns unsorted data, why would you assign the returned values to variables with sorted in their name? Jul 19, 2018 at 14:06
• Peter’s calculation of median is slightly incorrect 50% of the time, so could give a different answer than stats.median at those times. When len() is even, the correct median would be the average of the two middle values. I chalk that minor faux-pas up to laziness; the major point was clear: the median of a sorted array can be obtained in O(1) time, instead of from an O(n log n) library call. Jul 19, 2018 at 14:19
• @DRmo This is Code Review, not Code Debugging. Peter’s review caught a bug in one of your functions, based on the understanding one can glean from sparsely commented code, & demonstrated the function’s bug with sample input in the follow up question’s answer. He offered suggestions on how to fix the bug. Asking him to find input that produces buggy final output is beyond the scope of CR. Your argument suggests your program is “bug free” despite a demonstrable bug in a subroutine. This is a risky & questionable stance to take. Say thank-you, upvote the answer, fix the bug, & move on. Jul 19, 2018 at 19:00
• @DRmo, I've just dug out my copy of CLR and I stand by the term bug: the code is documented to implement the book's approach, and the book explicitly says that the points in $X_L$ and $Y_L$ are the same. That aside, I don't understand the asymmetry in insisting that I should provide you with a test case which demonstrates amplification of a bug that is already demonstrated to exist, while you claim that my proposed replacement introduces bugs but don't provide a test case to support that. Post your test suite and I'll fix any bugs in my suggestion. Jul 19, 2018 at 21:30