We have an input integer let's say 13. We can find consistent subarray of fibonacci numbers that sums to 10 - [2,3,5]. I need to find next number that is not a sum of consistent subarray. In this case this number will be 14. I have this code, but the catch is, it can be optimized to not iterate through all of the N's from starting Left Pointer = 1 and Right Pointer = 1 but somehow "import" from previous N and i have no clue how to do it so maybe someone smarter might help.
def fib(n):
if n == 1: return 1
if n == 2: return 1
return fib(n-1) + fib(n-2)
def fibosubarr(n):
L_pointer = 1
R_pointer = 2
sumfibs = 1
while sumfibs != n:
if sumfibs > n and L_pointer < R_pointer:
sumfibs -= fib(L_pointer)
L_pointer += 1
elif sumfibs < n and L_poiner < R_pointer:
sumfibs += fib(R_pointer)
R_pointer += 1
else: return False
return True
n = int(input())
while fibosubarr(n):
n += 1
print(n)
Here's a technique called "memoizing". The fib function here keeps track of the current list and only extends it as necessary. Once it has generated a number, it doesn't need to do it again.
_fib = [1,1]
def fib(n):
while len(_fib) <= n:
_fib.append( _fib[-2]+_fib[-1] )
return _fib[n]
With your scheme, 200000 caused a noticeable delay. With this scheme, even 2 billion runs instantaneously.
To get the next subarray sum, you only need one call of the function -- provided you keep track of the least sum value that was exceeding n.
I would also use a generator for the Fibonacci numbers:
def genfib():
a = 1
b = 1
while True:
yield b
a, b = b, a + b
def fibosubarr(n):
left = genfib()
right = genfib()
sumfibs = next(right)
closest = float("inf")
while sumfibs:
if sumfibs > n:
closest = min(sumfibs, closest)
sumfibs -= next(left)
elif sumfibs < n:
sumfibs += next(right)
else:
return n
return closest
Now you can do as you did -- produce the next valid sum that is at least the input value:
n = int(input())
print(fibosubarr(n))
You could also loop to go from one such sum to the next:
n = 0
for _ in range(10): # first 10 such sums
n = fibosubarr(n+1)
print(n)
Given an array of integers arr of even length n and an integer k.
We want to divide the array into exactly n/2 pairs such that the sum of each pair is divisible by k.
Return True If you can find a way to do that or False otherwise.
e.g.
arr = [1,2,3,4,5,10,6,7,8,9], k = 5, output: True
arr = [-1,-1,-1,-1,2,2,-2,-2], k = 3, output: False
The code i've written:
def canArrange(self, arr: List[int], k: int) -> bool:
dictt = {}
for i in range(len(arr)):
arr[i] = arr[i] % k
if arr[i] not in dictt:
dictt[arr[i]] = 1
else:
dictt[arr[i]] += 1
count = 0
for num in arr:
if (k-num) == num:
if dictt[num] > 1:
dictt[num] -= 2
count += 1
else:
dictt[num] -= 1
if (k-num) in dictt and dictt[(k-num)] != 0 and dictt[num] != 0:
dictt[(k-num)] -= 1
dictt[num] -= 1
count += 1
elif num == 0 and dictt[num] > 1:
dictt[num] -= 2
count += 1
if count == len(arr)//2:
return True
else:
return False
My logic is I am first taking the count of "every element % k" and the finding its another pair and reducing the count of both to avoid duplicates, one special case is when the values in a pair are same so for that i am already decrementing the value in my dictionary and at last i am checking whether the number of pairs are equal to half of elements or not
I am able to pass 93/97 test cases on leetcode, and unable to debug for the rest as the length of these 4 test cases are huge hence not possible manually, i must be missing some points while setting my logic, kindly help
Edit: DONE (I have edited the code above)
For example:
input: A = [ 6 4 3 -5 0 2 -7 1 ]
output: 5
Since 5 is the smallest positive integer that does not occur in the array.
I have written two solutions to that problem. The first one is good but I don't want to use any external libraries + its O(n)*log(n) complexity. The second solution "In which I need your help to optimize it" gives an error when the input is chaotic sequences length=10005 (with minus).
Solution 1:
from itertools import count, filterfalse
def minpositive(a):
return(next(filterfalse(set(a).__contains__, count(1))))
Solution 2:
def minpositive(a):
count = 0
b = list(set([i for i in a if i>0]))
if min(b, default = 0) > 1 or min(b, default = 0) == 0 :
min_val = 1
else:
min_val = min([b[i-1]+1 for i, x in enumerate(b) if x - b[i - 1] >1], default=b[-1]+1)
return min_val
Note: This was a demo test in codility, solution 1 got 100% and
solution 2 got 77 %.
Error in "solution2" was due to:
Performance tests ->
medium chaotic sequences length=10005 (with minus) got 3 expected
10000
Performance tests -> large chaotic + many -1, 1, 2, 3 (with
minus) got 5 expected 10000
Testing for the presence of a number in a set is fast in Python so you could try something like this:
def minpositive(a):
A = set(a)
ans = 1
while ans in A:
ans += 1
return ans
Fast for large arrays.
def minpositive(arr):
if 1 not in arr: # protection from error if ( max(arr) < 0 )
return 1
else:
maxArr = max(arr) # find max element in 'arr'
c1 = set(range(2, maxArr+2)) # create array from 2 to max
c2 = c1 - set(arr) # find all positive elements outside the array
return min(c2)
I have an easy solution. No need to sort.
def solution(A):
s = set(A)
m = max(A) + 2
for N in range(1, m):
if N not in s:
return N
return 1
Note: It is 100% total score (Correctness & Performance)
def minpositive(A):
"""Given an list A of N integers,
returns the smallest positive integer (greater than 0)
that does not occur in A in O(n) time complexity
Args:
A: list of integers
Returns:
integer: smallest positive integer
e.g:
A = [1,2,3]
smallest_positive_int = 4
"""
len_nrs_list = len(A)
N = set(range(1, len_nrs_list+2))
return min(N-set(A)) #gets the min value using the N integers
This solution passes the performance test with a score of 100%
def solution(A):
n = sorted(i for i in set(A) if i > 0) # Remove duplicates and negative numbers
if not n:
return 1
ln = len(n)
for i in range(1, ln + 1):
if i != n[i - 1]:
return i
return ln + 1
def solution(A):
B = set(sorted(A))
m = 1
for x in B:
if x == m:
m+=1
return m
Continuing on from Niroj Shrestha and najeeb-jebreel, added an initial portion to avoid iteration in case of a complete set. Especially important if the array is very large.
def smallest_positive_int(A):
sorted_A = sorted(A)
last_in_sorted_A = sorted_A[-1]
#check if straight continuous list
if len(sorted_A) == last_in_sorted_A:
return last_in_sorted_A + 1
else:
#incomplete list, iterate to find the smallest missing number
sol=1
for x in sorted_A:
if x == sol:
sol += 1
else:
break
return sol
A = [1,2,7,4,5,6]
print(smallest_positive_int(A))
This question doesn't really need another answer, but there is a solution that has not been proposed yet, that I believe to be faster than what's been presented so far.
As others have pointed out, we know the answer lies in the range [1, len(A)+1], inclusively. We can turn that into a set and take the minimum element in the set difference with A. That's a good O(N) solution since set operations are O(1).
However, we don't need to use a Python set to store [1, len(A)+1], because we're starting with a dense set. We can use an array instead, which will replace set hashing by list indexing and give us another O(N) solution with a lower constant.
def minpositive(a):
# the "set" of possible answer - values_found[i-1] will tell us whether i is in a
values_found = [False] * (len(a)+1)
# note any values in a in the range [1, len(a)+1] as found
for i in a:
if i > 0 and i <= len(a)+1:
values_found[i-1] = True
# extract the smallest value not found
for i, found in enumerate(values_found):
if not found:
return i+1
We know the final for loop always finds a value that was not marked, because it has one more element than a, so at least one of its cells was not set to True.
def check_min(a):
x= max(a)
if x-1 in a:
return x+1
elif x <= 0:
return 1
else:
return x-1
Correct me if i'm wrong but this works for me.
def solution(A):
clone = 1
A.sort()
for itr in range(max(A) + 2):
if itr not in A and itr >= 1:
clone = itr
break
return clone
print(solution([2,1,4,7]))
#returns 3
def solution(A):
n = 1
for i in A:
if n in A:
n = n+1
else:
return n
return n
def not_in_A(a):
a=sorted(a)
if max(a)<1:
return(1)
for i in range(0,len(a)-1):
if a[i+1]-a[i]>1:
out=a[i]+1
if out==0 or out<1:
continue
return(out)
return(max(a)+1)
mark and then find the first one that didn't find
nums = [ 6, 4, 3, -5, 0, 2, -7, 1 ]
def check_min(nums):
marks = [-1] * len(nums)
for idx, num in enumerate(nums):
if num >= 0:
marks[num] = idx
for idx, mark in enumerate(marks):
if mark == -1:
return idx
return idx + 1
I just modified the answer by #najeeb-jebreel and now the function gives an optimal solution.
def solution(A):
sorted_set = set(sorted(A))
sol = 1
for x in sorted_set:
if x == sol:
sol += 1
else:
break
return sol
I reduced the length of set before comparing
a=[1,222,3,4,24,5,6,7,8,9,10,15,2,3,3,11,-1]
#a=[1,2,3,6,3]
def sol(a_array):
a_set=set()
b_set=set()
cnt=1
for i in a_array:
#In order to get the greater performance
#Checking if element is greater than length+1
#then it can't be output( our result in solution)
if i<=len(a) and i >=1:
a_set.add(i) # Adding array element in set
b_set.add(cnt) # Adding iterator in set
cnt=cnt+1
b_set=b_set.difference(a_set)
if((len(b_set)) > 1):
return(min(b_set))
else:
return max(a_set)+1
sol(a)
def solution(A):
nw_A = sorted(set(A))
if all(i < 0 for i in nw_A):
return 1
else:
ans = 1
while ans in nw_A:
ans += 1
if ans not in nw_A:
return ans
For better performance if there is a possibility to import numpy package.
def solution(A):
import numpy as np
nw_A = np.unique(np.array(A))
if np.all((nw_A < 0)):
return 1
else:
ans = 1
while ans in nw_A:
ans += 1
if ans not in nw_A:
return ans
def solution(A):
# write your code in Python 3.6
min_num = float("inf")
set_A = set(A)
# finding the smallest number
for num in set_A:
if num < min_num:
min_num = num
# print(min_num)
#if negative make positive
if min_num < 0 or min_num == 0:
min_num = 1
# print(min_num)
# if in set add 1 until not
while min_num in set_A:
min_num += 1
return min_num
Not sure why this is not 100% in correctness. It is 100% performance
def solution(A):
arr = set(A)
N = set(range(1, 100001))
while N in arr:
N += 1
return min(N - arr)
solution([1, 2, 6, 4])
#returns 3
I have a coding challenge next week as the first round interview. The HR said they will use Codility as the coding challenge platform. I have been practicing using the Codility Lessons.
My issue is that I often get a very high score on Correctness, but my Performance score, which measure time complexity, is horrible (I often get 0%).
Here's the question:
https://app.codility.com/programmers/lessons/5-prefix_sums/passing_cars/
My code is:
def solution(A):
N = len(A)
my_list = []
count = 0
for i in range(N):
if A[i] == 1:
continue
else:
my_list = A[i + 1:]
count = count + sum(my_list)
print(count)
return count
It is supposed to be O(N) but mine is O(N**2).
How can someone approach this question to solve it under the O(N) time complexity?
In general, when you look at an algorithm question, how do you come up with an approach?
You should not sum the entire array each time you find a zero. That makes it O(n^2). Instead note that every zero found will give a +1 for each following one:
def solution(A):
zeros = 0
passing = 0
for i in A:
if i == 0:
zeros += 1
else:
passing += zeros
return passing
You may check all codility solutions as well as passingcars example.
Don’t forget the 1000000000 limit.
def solution(a):
pc=0
fz=0
for e in a:
if pc>1000000000:
return -1
if e==0:
fz+=1
else:
pc+=fz
return pc
Regarding the above answer, it is correct but missing checking the cases where passing exceeds 1000000000.
Also, I found a smarter and simple way to count the pairs of cars that could be passed where you just count all existing ones inside the array from the beginning and inside the loop when you find any zero, you say Ok we could possibly pair all the ones with this zero (so we increase the count) and as we are already looping through the whole array, if we find one then we can simply remove that one from ones since we will never need it again.
It takes O(N) as a time complexity since you just need to loop once in the array.
def solution(A):
ones = A.count(1)
c = 0
for i in range(0, len(A)):
if A[i] == 0:
c += ones
else: ones -= 1
if c > 1000000000:
return -1
return c
In a loop, find indices of zeros in list and nb of ones in front of first zero. In another loop, find the next zero, reduce nOnesInFront by the index difference between current and previous zero
def solution(A):
count = 0; zeroIndices = []
nOnesInFront = 0; foundZero = False
for i in range(len(A)):
if A[i] == 0:
foundZero = True
zeroIndices.append(i)
elif foundZero: nOnesInFront += 1;
if nOnesInFront == 0: return 0 #no ones in front of a zero
if not zeroIndices: return 0 #no zeros
iPrev = zeroIndices[0]
count = nOnesInFront
for i in zeroIndices[1:]:
nOnesInFront -= (i-iPrev) - 1 #decrease nb of ones by the differnce between current and previous zero index
iPrev = i
count += nOnesInFront
if count > 1000000000: return -1
else: return count
Here are some tests cases to verify the solution:
print(solution([0, 1, 0, 1, 1])) # 5
print(solution([0, 1, 1, 0, 1])) # 4
print(solution([0])) # 0
print(solution([1])) # 0
print(solution([1, 0])) # 0
print(solution([0, 1])) # 1
print(solution([1, 0, 0])) # 0
print(solution([1, 0, 1])) # 1
print(solution([0, 0, 1])) # 2
I am writing this program to find the 13 adjacent digits in this number that, when added together, have the largest sum. When I run it, however, the b value does not start at 12; it starts at some obscenely high number and I cannot figure out why. Any idea why my a and b values are not incrementing correctly?
num = "731671765313306249192251196744265747423553491949349698352031277450632623957831801698480186947885184385861560789112949495459501737958331952853208805511125069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450"
a = 0
b = 12
greatest = 0
while b != len(str(num)):
num = str(num)
newNum = num[a:b]
total = 0
for num in newNum:
num = int(num)
total += num
if total > greatest:
greatest = total
a+=1
b+=1
print(b)
print(greatest)
The main issue is that you are reusing num in the inner loop, which renders the "original" num wrong after the first run.
Additionally, if you want a 13 digits run-in, you'd better start with b = 13
And furthermore, there is no need for str(num) since it is already a string, and no need to change b along the program. You can also replace the inner loop with a sum upon map.
Here is what it should look like after these changes:
num = "731671765313306249192251196744265747423553491949349698352031277450632623957831801698480186947885184385861560789112949495459501737958331952853208805511125069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450"
index = 0
run_in = 13
greatest = 0
while index + run_in < len(num):
num_slice = num[index: index + run_in]
slice_sum = sum(map(int, num_slice))
if slice_sum > greatest:
greatest = slice_sum
index += 1
print(greatest)
If you are into super functions, you can create the same effect with a list comprehension and a max closure, iterating all possible indexes (until the length of the number minus the run in):
greatest = max(sum(map(int, num[index: index + run_in])) for index in range(len(num) - run_in))
def largest(num, k):
num = str(num)
if len(num) < k: raise ValueError("Need a number with at least {} digits".format(k))
currsum = sum(int(i) for i in num[:k])
answer = currsum, 0
i = k+1
while i < len(num):
currsum -= int(num[i-k])
currsum += int(num[i])
if currsum > answer[0]: answer = currsum, i
i += 1
return answer
total, ind = largest(myNum, 13)
print("The maximum sum of digits is {}, starting at index {}".format(total, ind))