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[hyogshin] WEEK 01 solutions #1723

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17 changes: 17 additions & 0 deletions contains-duplicate/hyogshin.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,17 @@
class Solution:
def containsDuplicate(self, nums: List[int]) -> bool:
if len(nums) == len(set(nums)):
return False
else:
return True



'''
시간 복잡도: O(n)
- set(nums)는 내부적으로 nums의 모든 원소에 대해 __hash__() 및 __eq__() 호출 -> O(n)
- len() 함수는 O(1) -> 무시

공간 복잡도: O(n)
- set(nums)는 nums의 원소를 모두 저장할 수 있게 공간 사용 -> 최악의 경우 O(n)
'''
25 changes: 25 additions & 0 deletions house-robber/hyogshin.py
Original file line number Diff line number Diff line change
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class Solution:
def rob(self, nums: List[int]) -> int:
dp = [0] * (len(nums))
dp[0] = nums[0]
if len(nums) > 1:
dp[1] = max(dp[0], nums[1])

for i in range(2, len(nums)):
dp[i] = max(dp[i-2] + nums[i], dp[i-1])

return max(dp)

'''
시간 복잡도: O(n log n)
- set() -> O(n)
- sorted() -> O(n log n)
- for loop -> O(n)
- max() -> O(n)

공간 복잡도: O(n)
- set() -> O(n)
- sorted list -> O(n)
- rs -> O(n)
- O(3n) => O(n)
'''
32 changes: 32 additions & 0 deletions longest-consecutive-sequence/hyogshin.py
Original file line number Diff line number Diff line change
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class Solution:
def longestConsecutive(self, nums: List[int]) -> int:
if not nums:
return 0
s = set(nums)
nums = sorted(list(s))
rs = []
cnt = 1

for i in range(len(nums)-1):
if (nums[i] + 1) == nums[i+1]:
cnt += 1
else:
rs.append(cnt)
cnt = 1

rs.append(cnt)
return max(rs)

'''
시간 복잡도: O(n log n)
- set(nums) -> O(n)
- sorted(list(s)) -> O(n log n)
- for loop -> O(n)
- O(2n) + O(n log n) => O(2n) 이 아니라 왜 O(n log n) 이지?
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2N은 즉, N의 그래프의 배수이기 때문에 NlogN의 그래프와 비교해서 항상 작은 값을 가지게 됩니다
그래서 항상 더 큰 값을 가지는 NlogN으로 시간복잡도가 정해집니다
표기를 빅오 표기법이라, 최악의 상황으로 시간복잡도를 정해서 NlogN이 됩니다


공간 복잡도: O(n)
- set -> O(n)
- sorted() -> O(n)
- rs -> O(n)
- O(3n) => O(n)
'''
33 changes: 33 additions & 0 deletions top-k-frequent-elements/hyogshin.py
Original file line number Diff line number Diff line change
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class Solution:
def topKFrequent(self, nums: List[int], k: int) -> List[int]:
plus = [0] * (10**4 + 1)
minus = [0] * (10**4 + 1)

for i in range(len(nums)):
if nums[i] < 0:
minus[-(nums[i])] += 1
else:
plus[nums[i]] += 1

ans = []
for i in range(k):
if max(max(minus), max(plus)) == max(plus):
idx = plus.index(max(plus))
ans.append(idx)
plus[idx] = 0
else:
idx = minus.index(max(minus))
ans.append(-(idx))
minus[idx] = 0

return ans

'''
시간 복잡도: O(1)
- for loop -> 보통 O(n) 이지만, 길이 10001 짜리 고정 배열 -> O(1)로 취급 가능

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고정 배열이라도 결국 nums를 처음 for문에서 반복하고 있으므로 O(N)이라고 생각이 드는데 어떻게 생각하시나요?

공간 복잡도: 입력 제외 -> O(1), 입력 포함 -> O(n)
- plus 배열: 10001 -> O(1)
- minus 배열: 10001 -> O(1)
- ans 배열: 길이 k -> O(k)
'''
23 changes: 23 additions & 0 deletions two-sum/hyogshin.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,23 @@
class Solution:
def twoSum(self, nums: List[int], target: int) -> List[int]:
rs = []
for i in range(len(nums) - 1, -1, -1):
pair = target - nums[i]
if pair not in nums or i == nums.index(pair):
continue
idx = nums.index(pair)
if pair in nums:
rs.append(i)
rs.append(idx)
break
return rs

'''
시간 복잡도: O(n^2)
- nums.index(pair) -> O(n)
- for loop 안에서 nums.index(pair) 최대 2번 호출 -> O(2n^2) -> O(n^2)

공간 복잡도: O(1)
- rs 배열에서 number 2개 저장 -> O(1) 공간
- nums 복사나 set/dict 없음
'''