Factor an integer to something as close to a square as possible
Question:
I have a function that reads a file byte by byte and converts it to a floating point array. It also returns the number of elements in said array.
Now I want to reshape the array into a 2D array with the shape being as close to a square as possible.
As an example let’s look at the number 800:
sqrt(800) = 28.427...
Now by I can figure out by trial and error that 25*32 would be the solution I am looking for.
I do this by decrementing the sqrt (rounded to nearest integer) if the result of multiplying the integers is to high, or incrementing them if the result is too low.
I know about algorithms that do this for primes, but this is not a requirement for me. My problem is that even the brute force method I implemented will sometimes get stuck and never finish (which is the reason for my arbitrary limit of iterations):
import math
def factor_int(n):
nsqrt = math.ceil(math.sqrt(n))
factors = [nsqrt, nsqrt]
cd = 0
result = factors[0] * factors[1]
ii = 0
while (result != n or ii > 10000):
if(result > n):
factors[cd] -= 1
else:
factors[cd] += 1
result = factors[0] * factors[1]
print factors, result
cd = 1 - cd
ii += 1
return "resulting factors: {0}".format(factors)
input = 80000
factors = factor_int(input)
using this script above the output will get stuck in a loop printing
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
[274.0, 292.0] 80008.0
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
[274.0, 292.0] 80008.0
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
[274.0, 292.0] 80008.0
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
[274.0, 292.0] 80008.0
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
But I wonder if there are more efficient solutions for this? Certainly I can’t be the first to want to do something like this.
Answers:
def factor_int(n):
val = math.ceil(math.sqrt(n))
val2 = int(n/val)
while val2 * val != float(n):
val -= 1
val2 = int(n/val)
return val, val2, n
try it with:
for x in xrange(10, 20):
print factor_int(x)
Interesting question, here’s a possible solution to your problem:
import math
def min_dist(a, b):
dist = []
for Pa in a:
for Pb in b:
d = math.sqrt(
math.pow(Pa[0] - Pb[0], 2) + math.pow(Pa[1] - Pb[1], 2))
dist.append([d, Pa])
return sorted(dist, key=lambda x: x[0])
def get_factors(N):
if N < 1:
return N
N2 = N / 2
NN = math.sqrt(N)
result = []
for a in range(1, N2 + 1):
for b in range(1, N2 + 1):
if N == (a * b):
result.append([a, b])
result = min_dist(result, [[NN, NN]])
if result:
return result[0][1]
else:
return [N, 1]
for i in range(801):
print i, get_factors(i)
The key of this method is finding the minimum distance to the cartesian point of [math.sqrt(N), math.sqrt(N)] which meets the requirements N=a*b, a&b integers.
I think the modulus operator is a good fit for this problem:
import math
def factint(n):
pos_n = abs(n)
max_candidate = int(math.sqrt(pos_n))
for candidate in xrange(max_candidate, 0, -1):
if pos_n % candidate == 0:
break
return candidate, n / candidate
Here’s a direct method that finds the smallest, closest integers a, b, such that a * b >= n, and a <= b, where n is any number:
def factor_int(n):
a = math.floor(math.sqrt(n))
b = math.ceil(n/float(a))
return a, b
try it with:
for x in xrange(10, 20):
print factor_int(x)
Here’s a shorter code based on the currently accepted answer that is shorter and takes about 25%-75% less time to run than their code (from basic timeit tests):
from math import sqrt, ceil
def factor_int_2(n):
val = ceil(sqrt(n))
while True:
if not n%val:
val2 = n//val
break
val -= 1
return val, val2
And here’s a small and messy test I made to test the efficiency of the method:
print("Method 2 is shorter and about {}% quicker".format(100*(1 - timeit(lambda: factor_int_2(12345))/timeit(lambda: factor_int(12345)))))
#returns 'Method 2 is shorter and about 75.03810670186826% quicker'
Oneline code for one number:
import numpy as np
n = 800
(np.ceil(np.sqrt(n)), np.ceil(n/np.ceil(np.sqrt(n))))
>>> (29.0, 28.0)
Not sure if it is what you asked for, but this is much closer to square than (25,32) as you mentioned in the description. Hope it helps!
This is equivalent to finding factors (b,c) for a=b*c such that the smaller factor, b, is the largest number not larger than sqrt(a). This can be solved by:
import math
def closest_factors(a):
for b in range(int(math.sqrt(a)), 0, -1):
if a%b == 0:
c = a // b
return (b,c)
print(closest_factors(800))
returns (25,32).
I have a function that reads a file byte by byte and converts it to a floating point array. It also returns the number of elements in said array.
Now I want to reshape the array into a 2D array with the shape being as close to a square as possible.
As an example let’s look at the number 800:
sqrt(800) = 28.427...
Now by I can figure out by trial and error that 25*32 would be the solution I am looking for.
I do this by decrementing the sqrt (rounded to nearest integer) if the result of multiplying the integers is to high, or incrementing them if the result is too low.
I know about algorithms that do this for primes, but this is not a requirement for me. My problem is that even the brute force method I implemented will sometimes get stuck and never finish (which is the reason for my arbitrary limit of iterations):
import math
def factor_int(n):
nsqrt = math.ceil(math.sqrt(n))
factors = [nsqrt, nsqrt]
cd = 0
result = factors[0] * factors[1]
ii = 0
while (result != n or ii > 10000):
if(result > n):
factors[cd] -= 1
else:
factors[cd] += 1
result = factors[0] * factors[1]
print factors, result
cd = 1 - cd
ii += 1
return "resulting factors: {0}".format(factors)
input = 80000
factors = factor_int(input)
using this script above the output will get stuck in a loop printing
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
[274.0, 292.0] 80008.0
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
[274.0, 292.0] 80008.0
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
[274.0, 292.0] 80008.0
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
[274.0, 292.0] 80008.0
[273.0, 292.0] 79716.0
[273.0, 293.0] 79989.0
[274.0, 293.0] 80282.0
But I wonder if there are more efficient solutions for this? Certainly I can’t be the first to want to do something like this.
def factor_int(n):
val = math.ceil(math.sqrt(n))
val2 = int(n/val)
while val2 * val != float(n):
val -= 1
val2 = int(n/val)
return val, val2, n
try it with:
for x in xrange(10, 20):
print factor_int(x)
Interesting question, here’s a possible solution to your problem:
import math
def min_dist(a, b):
dist = []
for Pa in a:
for Pb in b:
d = math.sqrt(
math.pow(Pa[0] - Pb[0], 2) + math.pow(Pa[1] - Pb[1], 2))
dist.append([d, Pa])
return sorted(dist, key=lambda x: x[0])
def get_factors(N):
if N < 1:
return N
N2 = N / 2
NN = math.sqrt(N)
result = []
for a in range(1, N2 + 1):
for b in range(1, N2 + 1):
if N == (a * b):
result.append([a, b])
result = min_dist(result, [[NN, NN]])
if result:
return result[0][1]
else:
return [N, 1]
for i in range(801):
print i, get_factors(i)
The key of this method is finding the minimum distance to the cartesian point of [math.sqrt(N), math.sqrt(N)] which meets the requirements N=a*b, a&b integers.
I think the modulus operator is a good fit for this problem:
import math
def factint(n):
pos_n = abs(n)
max_candidate = int(math.sqrt(pos_n))
for candidate in xrange(max_candidate, 0, -1):
if pos_n % candidate == 0:
break
return candidate, n / candidate
Here’s a direct method that finds the smallest, closest integers a, b, such that a * b >= n, and a <= b, where n is any number:
def factor_int(n):
a = math.floor(math.sqrt(n))
b = math.ceil(n/float(a))
return a, b
try it with:
for x in xrange(10, 20):
print factor_int(x)
Here’s a shorter code based on the currently accepted answer that is shorter and takes about 25%-75% less time to run than their code (from basic timeit tests):
from math import sqrt, ceil
def factor_int_2(n):
val = ceil(sqrt(n))
while True:
if not n%val:
val2 = n//val
break
val -= 1
return val, val2
And here’s a small and messy test I made to test the efficiency of the method:
print("Method 2 is shorter and about {}% quicker".format(100*(1 - timeit(lambda: factor_int_2(12345))/timeit(lambda: factor_int(12345)))))
#returns 'Method 2 is shorter and about 75.03810670186826% quicker'
Oneline code for one number:
import numpy as np
n = 800
(np.ceil(np.sqrt(n)), np.ceil(n/np.ceil(np.sqrt(n))))
>>> (29.0, 28.0)
Not sure if it is what you asked for, but this is much closer to square than (25,32) as you mentioned in the description. Hope it helps!
This is equivalent to finding factors (b,c) for a=b*c such that the smaller factor, b, is the largest number not larger than sqrt(a). This can be solved by:
import math
def closest_factors(a):
for b in range(int(math.sqrt(a)), 0, -1):
if a%b == 0:
c = a // b
return (b,c)
print(closest_factors(800))
returns (25,32).