INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
[summary]
Works iterative approximate O(n)
Arguments:
n {[int]} -- [description]
Returns:
[int] -- [description] | def fib_iter(n):
"""[summary]
Works iterative approximate O(n)
Arguments:
n {[int]} -- [description]
Returns:
[int] -- [description]
"""
# precondition
assert n >= 0, 'n must be positive integer'
fib_1 = 0
fib_2 = 1
sum = 0
if n <= 1:
return n
... |
:param nums: List[int]
:return: Set[tuple] | def subsets(nums):
"""
:param nums: List[int]
:return: Set[tuple]
"""
n = len(nums)
total = 1 << n
res = set()
for i in range(total):
subset = tuple(num for j, num in enumerate(nums) if i & 1 << j)
res.add(subset)
return res |
The length of longest common subsequence among the two given strings s1 and s2 | def lcs(s1, s2, i, j):
"""
The length of longest common subsequence among the two given strings s1 and s2
"""
if i == 0 or j == 0:
return 0
elif s1[i - 1] == s2[j - 1]:
return 1 + lcs(s1, s2, i - 1, j - 1)
else:
return max(lcs(s1, s2, i - 1, j), lcs(s1, s2, i, j - 1)) |
:type root: TreeNode
:type p: TreeNode
:type q: TreeNode
:rtype: TreeNode | def lca(root, p, q):
"""
:type root: TreeNode
:type p: TreeNode
:type q: TreeNode
:rtype: TreeNode
"""
if root is None or root is p or root is q:
return root
left = lca(root.left, p, q)
right = lca(root.right, p, q)
if left is not None and right is not None:
retur... |
:type root: Node
:type p: Node
:type q: Node
:rtype: Node | def lowest_common_ancestor(root, p, q):
"""
:type root: Node
:type p: Node
:type q: Node
:rtype: Node
"""
while root:
if p.val > root.val < q.val:
root = root.right
elif p.val < root.val > q.val:
root = root.left
else:
return root |
:type n: int
:rtype: int | def climb_stairs(n):
"""
:type n: int
:rtype: int
"""
arr = [1, 1]
for _ in range(1, n):
arr.append(arr[-1] + arr[-2])
return arr[-1] |
find the nth digit of given number.
1. find the length of the number where the nth digit is from.
2. find the actual number where the nth digit is from
3. find the nth digit and return | def find_nth_digit(n):
"""find the nth digit of given number.
1. find the length of the number where the nth digit is from.
2. find the actual number where the nth digit is from
3. find the nth digit and return
"""
length = 1
count = 9
start = 1
while n > length * count:
n -=... |
Return the 'hailstone sequence' from n to 1
n: The starting point of the hailstone sequence | def hailstone(n):
"""Return the 'hailstone sequence' from n to 1
n: The starting point of the hailstone sequence
"""
sequence = [n]
while n > 1:
if n%2 != 0:
n = 3*n + 1
else:
n = int(n/2)
sequence.append(n)
return sequence |
:type s: str
:type word_dict: Set[str]
:rtype: bool | def word_break(s, word_dict):
"""
:type s: str
:type word_dict: Set[str]
:rtype: bool
"""
dp = [False] * (len(s)+1)
dp[0] = True
for i in range(1, len(s)+1):
for j in range(0, i):
if dp[j] and s[j:i] in word_dict:
dp[i] = True
break
... |
Return True if n is a prime number
Else return False. | def prime_check(n):
"""Return True if n is a prime number
Else return False.
"""
if n <= 1:
return False
if n == 2 or n == 3:
return True
if n % 2 == 0 or n % 3 == 0:
return False
j = 5
while j * j <= n:
if n % j == 0 or n % (j + 2) == 0:
retu... |
Find the length of the longest substring
without repeating characters. | def longest_non_repeat_v1(string):
"""
Find the length of the longest substring
without repeating characters.
"""
if string is None:
return 0
dict = {}
max_length = 0
j = 0
for i in range(len(string)):
if string[i] in dict:
j = max(dict[string[i]], j)
... |
Find the length of the longest substring
without repeating characters.
Uses alternative algorithm. | def longest_non_repeat_v2(string):
"""
Find the length of the longest substring
without repeating characters.
Uses alternative algorithm.
"""
if string is None:
return 0
start, max_len = 0, 0
used_char = {}
for index, char in enumerate(string):
if char in used_char an... |
Find the length of the longest substring
without repeating characters.
Return max_len and the substring as a tuple | def get_longest_non_repeat_v1(string):
"""
Find the length of the longest substring
without repeating characters.
Return max_len and the substring as a tuple
"""
if string is None:
return 0, ''
sub_string = ''
dict = {}
max_length = 0
j = 0
for i in range(len(string))... |
Find the length of the longest substring
without repeating characters.
Uses alternative algorithm.
Return max_len and the substring as a tuple | def get_longest_non_repeat_v2(string):
"""
Find the length of the longest substring
without repeating characters.
Uses alternative algorithm.
Return max_len and the substring as a tuple
"""
if string is None:
return 0, ''
sub_string = ''
start, max_len = 0, 0
used_char = ... |
Push the item in the priority queue.
if priority is not given, priority is set to the value of item. | def push(self, item, priority=None):
"""Push the item in the priority queue.
if priority is not given, priority is set to the value of item.
"""
priority = item if priority is None else priority
node = PriorityQueueNode(item, priority)
for index, current in enumerate(self... |
Calculates factorial iteratively.
If mod is not None, then return (n! % mod)
Time Complexity - O(n) | def factorial(n, mod=None):
"""Calculates factorial iteratively.
If mod is not None, then return (n! % mod)
Time Complexity - O(n)"""
if not (isinstance(n, int) and n >= 0):
raise ValueError("'n' must be a non-negative integer.")
if mod is not None and not (isinstance(mod, int) and mod > 0):... |
Calculates factorial recursively.
If mod is not None, then return (n! % mod)
Time Complexity - O(n) | def factorial_recur(n, mod=None):
"""Calculates factorial recursively.
If mod is not None, then return (n! % mod)
Time Complexity - O(n)"""
if not (isinstance(n, int) and n >= 0):
raise ValueError("'n' must be a non-negative integer.")
if mod is not None and not (isinstance(mod, int) and mod... |
Selection Sort
Complexity: O(n^2) | def selection_sort(arr, simulation=False):
""" Selection Sort
Complexity: O(n^2)
"""
iteration = 0
if simulation:
print("iteration",iteration,":",*arr)
for i in range(len(arr)):
minimum = i
for j in range(i + 1, len(arr)):
# "Select" the ... |
Time Complexity: O(N)
Space Complexity: O(N) | def remove_dups(head):
"""
Time Complexity: O(N)
Space Complexity: O(N)
"""
hashset = set()
prev = Node()
while head:
if head.val in hashset:
prev.next = head.next
else:
hashset.add(head.val)
prev = head
head = head.next |
Time Complexity: O(N^2)
Space Complexity: O(1) | def remove_dups_wothout_set(head):
"""
Time Complexity: O(N^2)
Space Complexity: O(1)
"""
current = head
while current:
runner = current
while runner.next:
if runner.next.val == current.val:
runner.next = runner.next.next
else:
... |
replace u with v
:param node_u: replaced node
:param node_v:
:return: None | def transplant(self, node_u, node_v):
"""
replace u with v
:param node_u: replaced node
:param node_v:
:return: None
"""
if node_u.parent is None:
self.root = node_v
elif node_u is node_u.parent.left:
node_u.parent.left = node_v
... |
find the max node when node regard as a root node
:param node:
:return: max node | def maximum(self, node):
"""
find the max node when node regard as a root node
:param node:
:return: max node
"""
temp_node = node
while temp_node.right is not None:
temp_node = temp_node.right
return temp_node |
find the minimum node when node regard as a root node
:param node:
:return: minimum node | def minimum(self, node):
"""
find the minimum node when node regard as a root node
:param node:
:return: minimum node
"""
temp_node = node
while temp_node.left:
temp_node = temp_node.left
return temp_node |
Computes (base ^ exponent) % mod.
Time complexity - O(log n)
Use similar to Python in-built function pow. | def modular_exponential(base, exponent, mod):
"""Computes (base ^ exponent) % mod.
Time complexity - O(log n)
Use similar to Python in-built function pow."""
if exponent < 0:
raise ValueError("Exponent must be positive.")
base %= mod
result = 1
while exponent > 0:
# If the l... |
:type intervals: List[Interval]
:rtype: bool | def can_attend_meetings(intervals):
"""
:type intervals: List[Interval]
:rtype: bool
"""
intervals = sorted(intervals, key=lambda x: x.start)
for i in range(1, len(intervals)):
if intervals[i].start < intervals[i - 1].end:
return False
return True |
:type root: TreeNode
:type key: int
:rtype: TreeNode | def delete_node(self, root, key):
"""
:type root: TreeNode
:type key: int
:rtype: TreeNode
"""
if not root: return None
if root.val == key:
if root.left:
# Find the right most leaf of the left sub-tree
left_right_most =... |
:type path: str
:rtype: str | def simplify_path(path):
"""
:type path: str
:rtype: str
"""
skip = {'..', '.', ''}
stack = []
paths = path.split('/')
for tok in paths:
if tok == '..':
if stack:
stack.pop()
elif tok not in skip:
stack.append(tok)
return '/' + ... |
O(2**n) | def subsets(nums):
"""
O(2**n)
"""
def backtrack(res, nums, stack, pos):
if pos == len(nums):
res.append(list(stack))
else:
# take nums[pos]
stack.append(nums[pos])
backtrack(res, nums, stack, pos+1)
stack.pop()
# do... |
Jump Search
Worst-case Complexity: O(√n) (root(n))
All items in list must be sorted like binary search
Find block that contains target value and search it linearly in that block
It returns a first target value in array
reference: https://en.wikipedia.org/wiki/Jump_search | def jump_search(arr,target):
"""Jump Search
Worst-case Complexity: O(√n) (root(n))
All items in list must be sorted like binary search
Find block that contains target value and search it linearly in that block
It returns a first target value in array
reference: https://en.w... |
Takes as input multi dimensional iterable and
returns generator which produces one dimensional output. | def flatten_iter(iterable):
"""
Takes as input multi dimensional iterable and
returns generator which produces one dimensional output.
"""
for element in iterable:
if isinstance(element, Iterable):
yield from flatten_iter(element)
else:
yield element |
Bidirectional BFS!!!
:type begin_word: str
:type end_word: str
:type word_list: Set[str]
:rtype: int | def ladder_length(begin_word, end_word, word_list):
"""
Bidirectional BFS!!!
:type begin_word: str
:type end_word: str
:type word_list: Set[str]
:rtype: int
"""
if len(begin_word) != len(end_word):
return -1 # not possible
if begin_word == end_word:
return 0
#... |
Iterable to get every convolution window per loop iteration.
For example:
`convolved([1, 2, 3, 4], kernel_size=2)`
will produce the following result:
`[[1, 2], [2, 3], [3, 4]]`.
`convolved([1, 2, 3], kernel_size=2, stride=1, padding=2, default_value=42)`
will pro... | def convolved(iterable, kernel_size=1, stride=1, padding=0, default_value=None):
"""Iterable to get every convolution window per loop iteration.
For example:
`convolved([1, 2, 3, 4], kernel_size=2)`
will produce the following result:
`[[1, 2], [2, 3], [3, 4]]`.
`convolve... |
1D Iterable to get every convolution window per loop iteration.
For more information, refer to:
- https://github.com/guillaume-chevalier/python-conv-lib/blob/master/conv/conv.py
- https://github.com/guillaume-chevalier/python-conv-lib
- MIT License, Copyright (c) 2018 Guillaume Chevalier | def convolved_1d(iterable, kernel_size=1, stride=1, padding=0, default_value=None):
"""1D Iterable to get every convolution window per loop iteration.
For more information, refer to:
- https://github.com/guillaume-chevalier/python-conv-lib/blob/master/conv/conv.py
- https://github.com/guillaume-chevali... |
2D Iterable to get every convolution window per loop iteration.
For more information, refer to:
- https://github.com/guillaume-chevalier/python-conv-lib/blob/master/conv/conv.py
- https://github.com/guillaume-chevalier/python-conv-lib
- MIT License, Copyright (c) 2018 Guillaume Chevalier | def convolved_2d(iterable, kernel_size=1, stride=1, padding=0, default_value=None):
"""2D Iterable to get every convolution window per loop iteration.
For more information, refer to:
- https://github.com/guillaume-chevalier/python-conv-lib/blob/master/conv/conv.py
- https://github.com/guillaume-chevali... |
Convert integers to a list of integers to fit the number of dimensions if
the argument is not already a list.
For example:
`dimensionize(3, nd=2)`
will produce the following result:
`(3, 3)`.
`dimensionize([3, 1], nd=2)`
will produce the following result:
`[3, 1]`.
... | def dimensionize(maybe_a_list, nd=2):
"""Convert integers to a list of integers to fit the number of dimensions if
the argument is not already a list.
For example:
`dimensionize(3, nd=2)`
will produce the following result:
`(3, 3)`.
`dimensionize([3, 1], nd=2)`
will produce ... |
:type nums: List[int]
:type k: int
:rtype: List[int] | def max_sliding_window(nums, k):
"""
:type nums: List[int]
:type k: int
:rtype: List[int]
"""
if not nums:
return nums
queue = collections.deque()
res = []
for num in nums:
if len(queue) < k:
queue.append(num)
else:
res.append(max(queue... |
Merge intervals in the form of a list. | def merge_intervals(intervals):
""" Merge intervals in the form of a list. """
if intervals is None:
return None
intervals.sort(key=lambda i: i[0])
out = [intervals.pop(0)]
for i in intervals:
if out[-1][-1] >= i[0]:
out[-1][-1] = max(out[-1][-1], i[-1])
else:
... |
Merge two intervals into one. | def merge(intervals):
""" Merge two intervals into one. """
out = []
for i in sorted(intervals, key=lambda i: i.start):
if out and i.start <= out[-1].end:
out[-1].end = max(out[-1].end, i.end)
else:
out += i,
return out |
Print out the intervals. | def print_intervals(intervals):
""" Print out the intervals. """
res = []
for i in intervals:
res.append(repr(i))
print("".join(res)) |
Rotate the entire array 'k' times
T(n)- O(nk)
:type array: List[int]
:type k: int
:rtype: void Do not return anything, modify array in-place instead. | def rotate_v1(array, k):
"""
Rotate the entire array 'k' times
T(n)- O(nk)
:type array: List[int]
:type k: int
:rtype: void Do not return anything, modify array in-place instead.
"""
array = array[:]
n = len(array)
for i in range(k): # unused variable is not a problem
... |
Reverse segments of the array, followed by the entire array
T(n)- O(n)
:type array: List[int]
:type k: int
:rtype: void Do not return anything, modify nums in-place instead. | def rotate_v2(array, k):
"""
Reverse segments of the array, followed by the entire array
T(n)- O(n)
:type array: List[int]
:type k: int
:rtype: void Do not return anything, modify nums in-place instead.
"""
array = array[:]
def reverse(arr, a, b):
while a < b:
ar... |
:type matrix: List[List[int]]
:rtype: List[List[int]] | def pacific_atlantic(matrix):
"""
:type matrix: List[List[int]]
:rtype: List[List[int]]
"""
n = len(matrix)
if not n: return []
m = len(matrix[0])
if not m: return []
res = []
atlantic = [[False for _ in range (n)] for _ in range(m)]
pacific = [[False for _ in range (n)] for... |
Quick sort
Complexity: best O(n log(n)) avg O(n log(n)), worst O(N^2) | def quick_sort(arr, simulation=False):
""" Quick sort
Complexity: best O(n log(n)) avg O(n log(n)), worst O(N^2)
"""
iteration = 0
if simulation:
print("iteration",iteration,":",*arr)
arr, _ = quick_sort_recur(arr, 0, len(arr) - 1, iteration, simulation)
return arr |
:type s: str
:rtype: bool | def is_palindrome(s):
"""
:type s: str
:rtype: bool
"""
i = 0
j = len(s)-1
while i < j:
while i < j and not s[i].isalnum():
i += 1
while i < j and not s[j].isalnum():
j -= 1
if s[i].lower() != s[j].lower():
return False
i, j... |
:type digits: List[int]
:rtype: List[int] | def plus_one_v1(digits):
"""
:type digits: List[int]
:rtype: List[int]
"""
digits[-1] = digits[-1] + 1
res = []
ten = 0
i = len(digits)-1
while i >= 0 or ten == 1:
summ = 0
if i >= 0:
summ += digits[i]
if ten:
summ += 1
res.appe... |
:type head: ListNode
:type k: int
:rtype: ListNode | def rotate_right(head, k):
"""
:type head: ListNode
:type k: int
:rtype: ListNode
"""
if not head or not head.next:
return head
current = head
length = 1
# count length of the list
while current.next:
current = current.next
length += 1
# make it circul... |
:type s: str
:rtype: int | def num_decodings(s):
"""
:type s: str
:rtype: int
"""
if not s or s[0] == "0":
return 0
wo_last, wo_last_two = 1, 1
for i in range(1, len(s)):
x = wo_last if s[i] != "0" else 0
y = wo_last_two if int(s[i-1:i+1]) < 27 and s[i-1] != "0" else 0
wo_last_two = wo_... |
:type nums: List[int]
:type target: int
:rtype: List[int] | def search_range(nums, target):
"""
:type nums: List[int]
:type target: int
:rtype: List[int]
"""
low = 0
high = len(nums) - 1
while low <= high:
mid = low + (high - low) // 2
if target < nums[mid]:
high = mid - 1
elif target > nums[mid]:
l... |
:type head: Node
:rtype: Node | def first_cyclic_node(head):
"""
:type head: Node
:rtype: Node
"""
runner = walker = head
while runner and runner.next:
runner = runner.next.next
walker = walker.next
if runner is walker:
break
if runner is None or runner.next is None:
return None... |
Heap Sort that uses a max heap to sort an array in ascending order
Complexity: O(n log(n)) | def max_heap_sort(arr, simulation=False):
""" Heap Sort that uses a max heap to sort an array in ascending order
Complexity: O(n log(n))
"""
iteration = 0
if simulation:
print("iteration",iteration,":",*arr)
for i in range(len(arr) - 1, 0, -1):
iteration = max_heapif... |
Max heapify helper for max_heap_sort | def max_heapify(arr, end, simulation, iteration):
""" Max heapify helper for max_heap_sort
"""
last_parent = (end - 1) // 2
# Iterate from last parent to first
for parent in range(last_parent, -1, -1):
current_parent = parent
# Iterate from current_parent to last_parent
whi... |
Heap Sort that uses a min heap to sort an array in ascending order
Complexity: O(n log(n)) | def min_heap_sort(arr, simulation=False):
""" Heap Sort that uses a min heap to sort an array in ascending order
Complexity: O(n log(n))
"""
iteration = 0
if simulation:
print("iteration",iteration,":",*arr)
for i in range(0, len(arr) - 1):
iteration = min_heapify(ar... |
Min heapify helper for min_heap_sort | def min_heapify(arr, start, simulation, iteration):
""" Min heapify helper for min_heap_sort
"""
# Offset last_parent by the start (last_parent calculated as if start index was 0)
# All array accesses need to be offset by start
end = len(arr) - 1
last_parent = (end - start - 1) // 2
# Itera... |
the RSA key generating algorithm
k is the number of bits in n | def generate_key(k, seed=None):
"""
the RSA key generating algorithm
k is the number of bits in n
"""
def modinv(a, m):
"""calculate the inverse of a mod m
that is, find b such that (a * b) % m == 1"""
b = 1
while not (a * b) % m == 1:
b += 1
retu... |
Return square root of n, with maximum absolute error epsilon | def square_root(n, epsilon=0.001):
"""Return square root of n, with maximum absolute error epsilon"""
guess = n / 2
while abs(guess * guess - n) > epsilon:
guess = (guess + (n / guess)) / 2
return guess |
Counting_sort
Sorting a array which has no element greater than k
Creating a new temp_arr,where temp_arr[i] contain the number of
element less than or equal to i in the arr
Then placing the number i into a correct position in the result_arr
return the result_arr
Complexity: 0(n) | def counting_sort(arr):
"""
Counting_sort
Sorting a array which has no element greater than k
Creating a new temp_arr,where temp_arr[i] contain the number of
element less than or equal to i in the arr
Then placing the number i into a correct position in the result_arr
return the result_arr
... |
Calculate the powerset of any iterable.
For a range of integers up to the length of the given list,
make all possible combinations and chain them together as one object.
From https://docs.python.org/3/library/itertools.html#itertools-recipes | def powerset(iterable):
"""Calculate the powerset of any iterable.
For a range of integers up to the length of the given list,
make all possible combinations and chain them together as one object.
From https://docs.python.org/3/library/itertools.html#itertools-recipes
"""
"list(powerset([1,2,3]... |
Optimal algorithm - DONT USE ON BIG INPUTS - O(2^n) complexity!
Finds the minimum cost subcollection os S that covers all elements of U
Args:
universe (list): Universe of elements
subsets (dict): Subsets of U {S1:elements,S2:elements}
costs (dict): Costs of each subset in S - {S1:cost, ... | def optimal_set_cover(universe, subsets, costs):
""" Optimal algorithm - DONT USE ON BIG INPUTS - O(2^n) complexity!
Finds the minimum cost subcollection os S that covers all elements of U
Args:
universe (list): Universe of elements
subsets (dict): Subsets of U {S1:elements,S2:elements}
... |
Approximate greedy algorithm for set-covering. Can be used on large
inputs - though not an optimal solution.
Args:
universe (list): Universe of elements
subsets (dict): Subsets of U {S1:elements,S2:elements}
costs (dict): Costs of each subset in S - {S1:cost, S2:cost...} | def greedy_set_cover(universe, subsets, costs):
"""Approximate greedy algorithm for set-covering. Can be used on large
inputs - though not an optimal solution.
Args:
universe (list): Universe of elements
subsets (dict): Subsets of U {S1:elements,S2:elements}
costs (dict): Costs of e... |
:type n: int
:rtype: int | def num_trees(n):
"""
:type n: int
:rtype: int
"""
dp = [0] * (n+1)
dp[0] = 1
dp[1] = 1
for i in range(2, n+1):
for j in range(i+1):
dp[i] += dp[i-j] * dp[j-1]
return dp[-1] |
:type val: int
:rtype: float | def next(self, val):
"""
:type val: int
:rtype: float
"""
self.queue.append(val)
return sum(self.queue) / len(self.queue) |
n: int
nums: list[object]
target: object
sum_closure: function, optional
Given two elements of nums, return sum of both.
compare_closure: function, optional
Given one object of nums and target, return -1, 1, or 0.
same_closure: function, optional
Given two object of nums, ret... | def n_sum(n, nums, target, **kv):
"""
n: int
nums: list[object]
target: object
sum_closure: function, optional
Given two elements of nums, return sum of both.
compare_closure: function, optional
Given one object of nums and target, return -1, 1, or 0.
same_closure: function, ... |
:type pattern: str
:type string: str
:rtype: bool | def pattern_match(pattern, string):
"""
:type pattern: str
:type string: str
:rtype: bool
"""
def backtrack(pattern, string, dic):
if len(pattern) == 0 and len(string) > 0:
return False
if len(pattern) == len(string) == 0:
return True
for end in... |
Bogo Sort
Best Case Complexity: O(n)
Worst Case Complexity: O(∞)
Average Case Complexity: O(n(n-1)!) | def bogo_sort(arr, simulation=False):
"""Bogo Sort
Best Case Complexity: O(n)
Worst Case Complexity: O(∞)
Average Case Complexity: O(n(n-1)!)
"""
iteration = 0
if simulation:
print("iteration",iteration,":",*arr)
def is_sorted(arr):
#check the array ... |
Insert new key into node | def insert(self, key):
"""
Insert new key into node
"""
# Create new node
n = TreeNode(key)
if not self.node:
self.node = n
self.node.left = AvlTree()
self.node.right = AvlTree()
elif key < self.node.val:
self.node.l... |
Re balance tree. After inserting or deleting a node, | def re_balance(self):
"""
Re balance tree. After inserting or deleting a node,
"""
self.update_heights(recursive=False)
self.update_balances(False)
while self.balance < -1 or self.balance > 1:
if self.balance > 1:
if self.node.left.balance < 0... |
Update tree height | def update_heights(self, recursive=True):
"""
Update tree height
"""
if self.node:
if recursive:
if self.node.left:
self.node.left.update_heights()
if self.node.right:
self.node.right.update_heights()
... |
Calculate tree balance factor | def update_balances(self, recursive=True):
"""
Calculate tree balance factor
"""
if self.node:
if recursive:
if self.node.left:
self.node.left.update_balances()
if self.node.right:
self.node.right.update... |
Right rotation | def rotate_right(self):
"""
Right rotation
"""
new_root = self.node.left.node
new_left_sub = new_root.right.node
old_root = self.node
self.node = new_root
old_root.left.node = new_left_sub
new_root.right.node = old_root |
Left rotation | def rotate_left(self):
"""
Left rotation
"""
new_root = self.node.right.node
new_left_sub = new_root.left.node
old_root = self.node
self.node = new_root
old_root.right.node = new_left_sub
new_root.left.node = old_root |
In-order traversal of the tree | def in_order_traverse(self):
"""
In-order traversal of the tree
"""
result = []
if not self.node:
return result
result.extend(self.node.left.in_order_traverse())
result.append(self.node.key)
result.extend(self.node.right.in_order_traverse())
... |
:type low: str
:type high: str
:rtype: int | def strobogrammatic_in_range(low, high):
"""
:type low: str
:type high: str
:rtype: int
"""
res = []
count = 0
low_len = len(low)
high_len = len(high)
for i in range(low_len, high_len + 1):
res.extend(helper2(i, i))
for perm in res:
if len(perm) == low_len and... |
:type words: List[str]
:rtype: List[str] | def find_keyboard_row(words):
"""
:type words: List[str]
:rtype: List[str]
"""
keyboard = [
set('qwertyuiop'),
set('asdfghjkl'),
set('zxcvbnm'),
]
result = []
for word in words:
for key in keyboard:
if set(word.lower()).issubset(key):
... |
This is a suboptimal, hacky method using eval(), which is not
safe for user input. We guard against danger by ensuring k in an int | def kth_to_last_eval(head, k):
"""
This is a suboptimal, hacky method using eval(), which is not
safe for user input. We guard against danger by ensuring k in an int
"""
if not isinstance(k, int) or not head.val:
return False
nexts = '.'.join(['next' for n in range(1, k+1)])
seeker... |
This is a brute force method where we keep a dict the size of the list
Then we check it for the value we need. If the key is not in the dict,
our and statement will short circuit and return False | def kth_to_last_dict(head, k):
"""
This is a brute force method where we keep a dict the size of the list
Then we check it for the value we need. If the key is not in the dict,
our and statement will short circuit and return False
"""
if not (head and k > -1):
return False
d = dict()... |
This is an optimal method using iteration.
We move p1 k steps ahead into the list.
Then we move p1 and p2 together until p1 hits the end. | def kth_to_last(head, k):
"""
This is an optimal method using iteration.
We move p1 k steps ahead into the list.
Then we move p1 and p2 together until p1 hits the end.
"""
if not (head or k > -1):
return False
p1 = head
p2 = head
for i in range(1, k+1):
if p1 is None:... |
Wortst Time Complexity: O(NlogN)
:type buildings: List[List[int]]
:rtype: List[List[int]] | def get_skyline(lrh):
"""
Wortst Time Complexity: O(NlogN)
:type buildings: List[List[int]]
:rtype: List[List[int]]
"""
skyline, live = [], []
i, n = 0, len(lrh)
while i < n or live:
if not live or i < n and lrh[i][0] <= -live[0][1]:
x = lrh[i][0]
while i ... |
:type array: List[int]
:rtype: List[] | def summarize_ranges(array):
"""
:type array: List[int]
:rtype: List[]
"""
res = []
if len(array) == 1:
return [str(array[0])]
i = 0
while i < len(array):
num = array[i]
while i + 1 < len(array) and array[i + 1] - array[i] == 1:
i += 1
if array... |
Encodes a list of strings to a single string.
:type strs: List[str]
:rtype: str | def encode(strs):
"""Encodes a list of strings to a single string.
:type strs: List[str]
:rtype: str
"""
res = ''
for string in strs.split():
res += str(len(string)) + ":" + string
return res |
Decodes a single string to a list of strings.
:type s: str
:rtype: List[str] | def decode(s):
"""Decodes a single string to a list of strings.
:type s: str
:rtype: List[str]
"""
strs = []
i = 0
while i < len(s):
index = s.find(":", i)
size = int(s[i:index])
strs.append(s[index+1: index+1+size])
i = index+1+size
return strs |
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]] | def multiply(multiplicand: list, multiplier: list) -> list:
"""
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]]
"""
multiplicand_row, multiplicand_col = len(
multiplicand), len(multiplicand[0])
multiplier_row, multiplier_col = len(multiplier), len(multiplier... |
This function calculates nCr. | def combination(n, r):
"""This function calculates nCr."""
if n == r or r == 0:
return 1
else:
return combination(n-1, r-1) + combination(n-1, r) |
This function calculates nCr using memoization method. | def combination_memo(n, r):
"""This function calculates nCr using memoization method."""
memo = {}
def recur(n, r):
if n == r or r == 0:
return 1
if (n, r) not in memo:
memo[(n, r)] = recur(n - 1, r - 1) + recur(n - 1, r)
return memo[(n, r)]
return recur(n... |
:type s: str
:type t: str
:rtype: bool | def is_anagram(s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
maps = {}
mapt = {}
for i in s:
maps[i] = maps.get(i, 0) + 1
for i in t:
mapt[i] = mapt.get(i, 0) + 1
return maps == mapt |
Pancake_sort
Sorting a given array
mutation of selection sort
reference: https://www.geeksforgeeks.org/pancake-sorting/
Overall time complexity : O(N^2) | def pancake_sort(arr):
"""
Pancake_sort
Sorting a given array
mutation of selection sort
reference: https://www.geeksforgeeks.org/pancake-sorting/
Overall time complexity : O(N^2)
"""
len_arr = len(arr)
if len_arr <= 1:
return arr
for cur in range(len(arr), 1, -1):... |
:rtype: int | def next(self):
"""
:rtype: int
"""
v=self.queue.pop(0)
ret=v.pop(0)
if v: self.queue.append(v)
return ret |
:type prices: List[int]
:rtype: int | def max_profit_naive(prices):
"""
:type prices: List[int]
:rtype: int
"""
max_so_far = 0
for i in range(0, len(prices) - 1):
for j in range(i + 1, len(prices)):
max_so_far = max(max_so_far, prices[j] - prices[i])
return max_so_far |
input: [7, 1, 5, 3, 6, 4]
diff : [X, -6, 4, -2, 3, -2]
:type prices: List[int]
:rtype: int | def max_profit_optimized(prices):
"""
input: [7, 1, 5, 3, 6, 4]
diff : [X, -6, 4, -2, 3, -2]
:type prices: List[int]
:rtype: int
"""
cur_max, max_so_far = 0, 0
for i in range(1, len(prices)):
cur_max = max(0, cur_max + prices[i] - prices[i-1])
max_so_far = max(max_so_far,... |
:type s: str
:rtype: int | def first_unique_char(s):
"""
:type s: str
:rtype: int
"""
if (len(s) == 1):
return 0
ban = []
for i in range(len(s)):
if all(s[i] != s[k] for k in range(i + 1, len(s))) == True and s[i] not in ban:
return i
else:
ban.append(s[i])
return -1 |
:type root: TreeNode
:type k: int
:rtype: int | def kth_smallest(self, root, k):
"""
:type root: TreeNode
:type k: int
:rtype: int
"""
count = []
self.helper(root, count)
return count[k-1] |
:type num: int
:rtype: str | def int_to_roman(num):
"""
:type num: int
:rtype: str
"""
m = ["", "M", "MM", "MMM"];
c = ["", "C", "CC", "CCC", "CD", "D", "DC", "DCC", "DCCC", "CM"];
x = ["", "X", "XX", "XXX", "XL", "L", "LX", "LXX", "LXXX", "XC"];
i = ["", "I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX"];
... |
:type input: str
:rtype: int | def length_longest_path(input):
"""
:type input: str
:rtype: int
"""
curr_len, max_len = 0, 0 # running length and max length
stack = [] # keep track of the name length
for s in input.split('\n'):
print("---------")
print("<path>:", s)
depth = s.count('\t') #... |
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]] | def multiply(self, a, b):
"""
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]]
"""
if a is None or b is None: return None
m, n, l = len(a), len(b[0]), len(b[0])
if len(b) != n:
raise Exception("A's column number must be equal to B's row number.")
c = ... |
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]] | def multiply(self, a, b):
"""
:type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]]
"""
if a is None or b is None: return None
m, n = len(a), len(b[0])
if len(b) != n:
raise Exception("A's column number must be equal to B's row number.")
l = len(b[0])
... |
bitonic sort is sorting algorithm to use multiple process, but this code not containing parallel process
It can sort only array that sizes power of 2
It can sort array in both increasing order and decreasing order by giving argument true(increasing) and false(decreasing)
Worst-case in parallel: O(log(n... | def bitonic_sort(arr, reverse=False):
"""
bitonic sort is sorting algorithm to use multiple process, but this code not containing parallel process
It can sort only array that sizes power of 2
It can sort array in both increasing order and decreasing order by giving argument true(increasing) and false(de... |
Computes the strongly connected components of a graph | def scc(graph):
''' Computes the strongly connected components of a graph '''
order = []
vis = {vertex: False for vertex in graph}
graph_transposed = {vertex: [] for vertex in graph}
for (v, neighbours) in graph.iteritems():
for u in neighbours:
add_edge(graph_transposed, u, v)... |
Builds the implication graph from the formula | def build_graph(formula):
''' Builds the implication graph from the formula '''
graph = {}
for clause in formula:
for (lit, _) in clause:
for neg in [False, True]:
graph[(lit, neg)] = []
for ((a_lit, a_neg), (b_lit, b_neg)) in formula:
add_edge(graph, (a_lit... |
1. Sort all the arrays - a,b,c. - This improves average time complexity.
2. If c[i] < Sum, then look for Sum - c[i] in array a and b.
When pair found, insert c[i], a[j] & b[k] into the result list.
This can be done in O(n).
3. Keep on doing the above procedure while going through complete c array.... | def unique_array_sum_combinations(A, B, C, target):
"""
1. Sort all the arrays - a,b,c. - This improves average time complexity.
2. If c[i] < Sum, then look for Sum - c[i] in array a and b.
When pair found, insert c[i], a[j] & b[k] into the result list.
This can be done in O(n).
3. Keep on... |
:type root: TreeNode
:rtype: bool | def is_bst(root):
"""
:type root: TreeNode
:rtype: bool
"""
stack = []
pre = None
while root or stack:
while root:
stack.append(root)
root = root.left
root = stack.pop()
if pre and root.val <= pre.val:
return False
pre... |
return 0 if unbalanced else depth + 1 | def __get_depth(root):
"""
return 0 if unbalanced else depth + 1
"""
if root is None:
return 0
left = __get_depth(root.left)
right = __get_depth(root.right)
if abs(left-right) > 1 or -1 in [left, right]:
return -1
return 1 + max(left, right) |
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