partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | lcs | The length of longest common subsequence among the two given strings s1 and s2 | algorithms/strings/min_distance.py | 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)) | 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)) | [
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train | lca | :type root: TreeNode
:type p: TreeNode
:type q: TreeNode
:rtype: TreeNode | algorithms/tree/lowest_common_ancestor.py | 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:
return root
return left if left else right | 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:
return root
return left if left else right | [
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train | lowest_common_ancestor | :type root: Node
:type p: Node
:type q: Node
:rtype: Node | algorithms/tree/bst/lowest_common_ancestor.py | 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 | 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 | [
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train | climb_stairs | :type n: int
:rtype: int | algorithms/dp/climbing_stairs.py | 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] | 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] | [
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train | find_nth_digit | 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 | algorithms/maths/nth_digit.py | 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 -= length * count
length += 1
count *= 10
start *= 10
start += (n-1) / length
s = str(start)
return int(s[(n-1) % length]) | 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 -= length * count
length += 1
count *= 10
start *= 10
start += (n-1) / length
s = str(start)
return int(s[(n-1) % length]) | [
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train | hailstone | Return the 'hailstone sequence' from n to 1
n: The starting point of the hailstone sequence | algorithms/maths/hailstone.py | 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 | 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 | [
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train | word_break | :type s: str
:type word_dict: Set[str]
:rtype: bool | algorithms/dp/word_break.py | 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 dp[-1] | 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 dp[-1] | [
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train | prime_check | Return True if n is a prime number
Else return False. | algorithms/maths/prime_check.py | 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:
return False
j += 6
return True | 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:
return False
j += 6
return True | [
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train | longest_non_repeat_v1 | Find the length of the longest substring
without repeating characters. | algorithms/arrays/longest_non_repeat.py | 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)
dict[string[i]] = i + 1
max_length = max(max_length, i - j + 1)
return max_length | 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)
dict[string[i]] = i + 1
max_length = max(max_length, i - j + 1)
return max_length | [
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train | longest_non_repeat_v2 | Find the length of the longest substring
without repeating characters.
Uses alternative algorithm. | algorithms/arrays/longest_non_repeat.py | 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 and start <= used_char[char]:
start = used_char[char] + 1
else:
max_len = max(max_len, index - start + 1)
used_char[char] = index
return max_len | 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 and start <= used_char[char]:
start = used_char[char] + 1
else:
max_len = max(max_len, index - start + 1)
used_char[char] = index
return max_len | [
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train | get_longest_non_repeat_v1 | Find the length of the longest substring
without repeating characters.
Return max_len and the substring as a tuple | algorithms/arrays/longest_non_repeat.py | 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)):
if string[i] in dict:
j = max(dict[string[i]], j)
dict[string[i]] = i + 1
if i - j + 1 > max_length:
max_length = i - j + 1
sub_string = string[j: i + 1]
return max_length, sub_string | 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)):
if string[i] in dict:
j = max(dict[string[i]], j)
dict[string[i]] = i + 1
if i - j + 1 > max_length:
max_length = i - j + 1
sub_string = string[j: i + 1]
return max_length, sub_string | [
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train | get_longest_non_repeat_v2 | Find the length of the longest substring
without repeating characters.
Uses alternative algorithm.
Return max_len and the substring as a tuple | algorithms/arrays/longest_non_repeat.py | 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 = {}
for index, char in enumerate(string):
if char in used_char and start <= used_char[char]:
start = used_char[char] + 1
else:
if index - start + 1 > max_len:
max_len = index - start + 1
sub_string = string[start: index + 1]
used_char[char] = index
return max_len, sub_string | 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 = {}
for index, char in enumerate(string):
if char in used_char and start <= used_char[char]:
start = used_char[char] + 1
else:
if index - start + 1 > max_len:
max_len = index - start + 1
sub_string = string[start: index + 1]
used_char[char] = index
return max_len, sub_string | [
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train | PriorityQueue.push | Push the item in the priority queue.
if priority is not given, priority is set to the value of item. | algorithms/queues/priority_queue.py | 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.priority_queue_list):
if current.priority < node.priority:
self.priority_queue_list.insert(index, node)
return
# when traversed complete queue
self.priority_queue_list.append(node) | 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.priority_queue_list):
if current.priority < node.priority:
self.priority_queue_list.insert(index, node)
return
# when traversed complete queue
self.priority_queue_list.append(node) | [
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train | factorial | Calculates factorial iteratively.
If mod is not None, then return (n! % mod)
Time Complexity - O(n) | algorithms/maths/factorial.py | 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):
raise ValueError("'mod' must be a positive integer")
result = 1
if n == 0:
return 1
for i in range(2, n+1):
result *= i
if mod:
result %= mod
return result | 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):
raise ValueError("'mod' must be a positive integer")
result = 1
if n == 0:
return 1
for i in range(2, n+1):
result *= i
if mod:
result %= mod
return result | [
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train | factorial_recur | Calculates factorial recursively.
If mod is not None, then return (n! % mod)
Time Complexity - O(n) | algorithms/maths/factorial.py | 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 > 0):
raise ValueError("'mod' must be a positive integer")
if n == 0:
return 1
result = n * factorial(n - 1, mod)
if mod:
result %= mod
return result | 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 > 0):
raise ValueError("'mod' must be a positive integer")
if n == 0:
return 1
result = n * factorial(n - 1, mod)
if mod:
result %= mod
return result | [
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train | selection_sort | Selection Sort
Complexity: O(n^2) | algorithms/sort/selection_sort.py | 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 correct value
if arr[j] < arr[minimum]:
minimum = j
arr[minimum], arr[i] = arr[i], arr[minimum]
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
return arr | 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 correct value
if arr[j] < arr[minimum]:
minimum = j
arr[minimum], arr[i] = arr[i], arr[minimum]
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
return arr | [
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train | remove_dups | Time Complexity: O(N)
Space Complexity: O(N) | algorithms/linkedlist/remove_duplicates.py | 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 | 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 | [
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train | remove_dups_wothout_set | Time Complexity: O(N^2)
Space Complexity: O(1) | algorithms/linkedlist/remove_duplicates.py | 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:
runner = runner.next
current = current.next | 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:
runner = runner.next
current = current.next | [
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train | RBTree.transplant | replace u with v
:param node_u: replaced node
:param node_v:
:return: None | algorithms/tree/red_black_tree/red_black_tree.py | 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
elif node_u is node_u.parent.right:
node_u.parent.right = node_v
# check is node_v is None
if node_v:
node_v.parent = node_u.parent | 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
elif node_u is node_u.parent.right:
node_u.parent.right = node_v
# check is node_v is None
if node_v:
node_v.parent = node_u.parent | [
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train | RBTree.maximum | find the max node when node regard as a root node
:param node:
:return: max node | algorithms/tree/red_black_tree/red_black_tree.py | 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 | 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 | [
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train | RBTree.minimum | find the minimum node when node regard as a root node
:param node:
:return: minimum node | algorithms/tree/red_black_tree/red_black_tree.py | 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 | 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 | [
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train | modular_exponential | Computes (base ^ exponent) % mod.
Time complexity - O(log n)
Use similar to Python in-built function pow. | algorithms/maths/modular_exponential.py | 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 last bit is 1, add 2^k.
if exponent & 1:
result = (result * base) % mod
exponent = exponent >> 1
# Utilize modular multiplication properties to combine the computed mod C values.
base = (base * base) % mod
return result | 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 last bit is 1, add 2^k.
if exponent & 1:
result = (result * base) % mod
exponent = exponent >> 1
# Utilize modular multiplication properties to combine the computed mod C values.
base = (base * base) % mod
return result | [
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train | can_attend_meetings | :type intervals: List[Interval]
:rtype: bool | algorithms/sort/meeting_rooms.py | 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)):
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return False
return True | 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 | [
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train | Solution.delete_node | :type root: TreeNode
:type key: int
:rtype: TreeNode | algorithms/tree/bst/delete_node.py | 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 = root.left
while left_right_most.right:
left_right_most = left_right_most.right
# Attach right child to the right of that leaf
left_right_most.right = root.right
# Return left child instead of root, a.k.a delete root
return root.left
else:
return root.right
# If left or right child got deleted, the returned root is the child of the deleted node.
elif root.val > key:
root.left = self.deleteNode(root.left, key)
else:
root.right = self.deleteNode(root.right, key)
return root | 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 = root.left
while left_right_most.right:
left_right_most = left_right_most.right
# Attach right child to the right of that leaf
left_right_most.right = root.right
# Return left child instead of root, a.k.a delete root
return root.left
else:
return root.right
# If left or right child got deleted, the returned root is the child of the deleted node.
elif root.val > key:
root.left = self.deleteNode(root.left, key)
else:
root.right = self.deleteNode(root.right, key)
return root | [
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train | simplify_path | :type path: str
:rtype: str | algorithms/stack/simplify_path.py | 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 '/' + '/'.join(stack) | 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 '/' + '/'.join(stack) | [
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train | subsets | O(2**n) | algorithms/backtrack/subsets.py | 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()
# dont take nums[pos]
backtrack(res, nums, stack, pos+1)
res = []
backtrack(res, nums, [], 0)
return res | 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()
# dont take nums[pos]
backtrack(res, nums, stack, pos+1)
res = []
backtrack(res, nums, [], 0)
return res | [
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train | jump_search | 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 | algorithms/search/jump_search.py | 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.wikipedia.org/wiki/Jump_search
"""
n = len(arr)
block_size = int(math.sqrt(n))
block_prev = 0
block= block_size
# return -1 means that array doesn't contain taget value
# find block that contains target value
if arr[n - 1] < target:
return -1
while block <= n and arr[block - 1] < target:
block_prev = block
block += block_size
# find target value in block
while arr[block_prev] < target :
block_prev += 1
if block_prev == min(block, n) :
return -1
# if there is target value in array, return it
if arr[block_prev] == target :
return block_prev
else :
return -1 | 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.wikipedia.org/wiki/Jump_search
"""
n = len(arr)
block_size = int(math.sqrt(n))
block_prev = 0
block= block_size
# return -1 means that array doesn't contain taget value
# find block that contains target value
if arr[n - 1] < target:
return -1
while block <= n and arr[block - 1] < target:
block_prev = block
block += block_size
# find target value in block
while arr[block_prev] < target :
block_prev += 1
if block_prev == min(block, n) :
return -1
# if there is target value in array, return it
if arr[block_prev] == target :
return block_prev
else :
return -1 | [
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train | flatten_iter | Takes as input multi dimensional iterable and
returns generator which produces one dimensional output. | algorithms/arrays/flatten.py | 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 | 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 | [
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train | ladder_length | Bidirectional BFS!!!
:type begin_word: str
:type end_word: str
:type word_list: Set[str]
:rtype: int | algorithms/bfs/word_ladder.py | 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
# when only differ by 1 character
if sum(c1 != c2 for c1, c2 in zip(begin_word, end_word)) == 1:
return 1
begin_set = set()
end_set = set()
begin_set.add(begin_word)
end_set.add(end_word)
result = 2
while begin_set and end_set:
if len(begin_set) > len(end_set):
begin_set, end_set = end_set, begin_set
next_begin_set = set()
for word in begin_set:
for ladder_word in word_range(word):
if ladder_word in end_set:
return result
if ladder_word in word_list:
next_begin_set.add(ladder_word)
word_list.remove(ladder_word)
begin_set = next_begin_set
result += 1
# print(begin_set)
# print(result)
return -1 | 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
# when only differ by 1 character
if sum(c1 != c2 for c1, c2 in zip(begin_word, end_word)) == 1:
return 1
begin_set = set()
end_set = set()
begin_set.add(begin_word)
end_set.add(end_word)
result = 2
while begin_set and end_set:
if len(begin_set) > len(end_set):
begin_set, end_set = end_set, begin_set
next_begin_set = set()
for word in begin_set:
for ladder_word in word_range(word):
if ladder_word in end_set:
return result
if ladder_word in word_list:
next_begin_set.add(ladder_word)
word_list.remove(ladder_word)
begin_set = next_begin_set
result += 1
# print(begin_set)
# print(result)
return -1 | [
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train | convolved | 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 produce the following result:
`[[42, 42], [42, 1], [1, 2], [2, 3], [3, 42], [42, 42]]`
Arguments:
iterable: An object to iterate on. It should support slice indexing if `padding == 0`.
kernel_size: The number of items yielded at every iteration.
stride: The step size between each iteration.
padding: Padding must be an integer or a string with value `SAME` or `VALID`. If it is an integer, it represents
how many values we add with `default_value` on the borders. If it is a string, `SAME` means that the
convolution will add some padding according to the kernel_size, and `VALID` is the same as
specifying `padding=0`.
default_value: Default fill value for padding and values outside iteration range.
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 | algorithms/iterables/convolved.py | 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]]`.
`convolved([1, 2, 3], kernel_size=2, stride=1, padding=2, default_value=42)`
will produce the following result:
`[[42, 42], [42, 1], [1, 2], [2, 3], [3, 42], [42, 42]]`
Arguments:
iterable: An object to iterate on. It should support slice indexing if `padding == 0`.
kernel_size: The number of items yielded at every iteration.
stride: The step size between each iteration.
padding: Padding must be an integer or a string with value `SAME` or `VALID`. If it is an integer, it represents
how many values we add with `default_value` on the borders. If it is a string, `SAME` means that the
convolution will add some padding according to the kernel_size, and `VALID` is the same as
specifying `padding=0`.
default_value: Default fill value for padding and values outside iteration range.
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
"""
# Input validation and error messages
if not hasattr(iterable, '__iter__'):
raise ValueError(
"Can't iterate on object.".format(
iterable))
if stride < 1:
raise ValueError(
"Stride must be of at least one. Got `stride={}`.".format(
stride))
if not (padding in ['SAME', 'VALID'] or type(padding) in [int]):
raise ValueError(
"Padding must be an integer or a string with value `SAME` or `VALID`.")
if not isinstance(padding, str):
if padding < 0:
raise ValueError(
"Padding must be of at least zero. Got `padding={}`.".format(
padding))
else:
if padding == 'SAME':
padding = kernel_size // 2
elif padding == 'VALID':
padding = 0
if not type(iterable) == list:
iterable = list(iterable)
# Add padding to iterable
if padding > 0:
pad = [default_value] * padding
iterable = pad + list(iterable) + pad
# Fill missing value to the right
remainder = (kernel_size - len(iterable)) % stride
extra_pad = [default_value] * remainder
iterable = iterable + extra_pad
i = 0
while True:
if i > len(iterable) - kernel_size:
break
yield iterable[i:i + kernel_size]
i += stride | 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]]`.
`convolved([1, 2, 3], kernel_size=2, stride=1, padding=2, default_value=42)`
will produce the following result:
`[[42, 42], [42, 1], [1, 2], [2, 3], [3, 42], [42, 42]]`
Arguments:
iterable: An object to iterate on. It should support slice indexing if `padding == 0`.
kernel_size: The number of items yielded at every iteration.
stride: The step size between each iteration.
padding: Padding must be an integer or a string with value `SAME` or `VALID`. If it is an integer, it represents
how many values we add with `default_value` on the borders. If it is a string, `SAME` means that the
convolution will add some padding according to the kernel_size, and `VALID` is the same as
specifying `padding=0`.
default_value: Default fill value for padding and values outside iteration range.
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
"""
# Input validation and error messages
if not hasattr(iterable, '__iter__'):
raise ValueError(
"Can't iterate on object.".format(
iterable))
if stride < 1:
raise ValueError(
"Stride must be of at least one. Got `stride={}`.".format(
stride))
if not (padding in ['SAME', 'VALID'] or type(padding) in [int]):
raise ValueError(
"Padding must be an integer or a string with value `SAME` or `VALID`.")
if not isinstance(padding, str):
if padding < 0:
raise ValueError(
"Padding must be of at least zero. Got `padding={}`.".format(
padding))
else:
if padding == 'SAME':
padding = kernel_size // 2
elif padding == 'VALID':
padding = 0
if not type(iterable) == list:
iterable = list(iterable)
# Add padding to iterable
if padding > 0:
pad = [default_value] * padding
iterable = pad + list(iterable) + pad
# Fill missing value to the right
remainder = (kernel_size - len(iterable)) % stride
extra_pad = [default_value] * remainder
iterable = iterable + extra_pad
i = 0
while True:
if i > len(iterable) - kernel_size:
break
yield iterable[i:i + kernel_size]
i += stride | [
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train | convolved_1d | 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 | algorithms/iterables/convolved.py | 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-chevalier/python-conv-lib
- MIT License, Copyright (c) 2018 Guillaume Chevalier
"""
return convolved(iterable, kernel_size, stride, padding, default_value) | 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-chevalier/python-conv-lib
- MIT License, Copyright (c) 2018 Guillaume Chevalier
"""
return convolved(iterable, kernel_size, stride, padding, default_value) | [
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train | convolved_2d | 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 | algorithms/iterables/convolved.py | 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-chevalier/python-conv-lib
- MIT License, Copyright (c) 2018 Guillaume Chevalier
"""
kernel_size = dimensionize(kernel_size, nd=2)
stride = dimensionize(stride, nd=2)
padding = dimensionize(padding, nd=2)
for row_packet in convolved(iterable, kernel_size[0], stride[0], padding[0], default_value):
transposed_inner = []
for col in tuple(row_packet):
transposed_inner.append(list(
convolved(col, kernel_size[1], stride[1], padding[1], default_value)
))
if len(transposed_inner) > 0:
for col_i in range(len(transposed_inner[0])):
yield tuple(row_j[col_i] for row_j in transposed_inner) | 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-chevalier/python-conv-lib
- MIT License, Copyright (c) 2018 Guillaume Chevalier
"""
kernel_size = dimensionize(kernel_size, nd=2)
stride = dimensionize(stride, nd=2)
padding = dimensionize(padding, nd=2)
for row_packet in convolved(iterable, kernel_size[0], stride[0], padding[0], default_value):
transposed_inner = []
for col in tuple(row_packet):
transposed_inner.append(list(
convolved(col, kernel_size[1], stride[1], padding[1], default_value)
))
if len(transposed_inner) > 0:
for col_i in range(len(transposed_inner[0])):
yield tuple(row_j[col_i] for row_j in transposed_inner) | [
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train | dimensionize | 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]`.
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 | algorithms/iterables/convolved.py | 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 the following result:
`[3, 1]`.
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
"""
if not hasattr(maybe_a_list, '__iter__'):
# Argument is probably an integer so we map it to a list of size `nd`.
now_a_list = [maybe_a_list] * nd
return now_a_list
else:
# Argument is probably an `nd`-sized list.
return maybe_a_list | 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 the following result:
`[3, 1]`.
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
"""
if not hasattr(maybe_a_list, '__iter__'):
# Argument is probably an integer so we map it to a list of size `nd`.
now_a_list = [maybe_a_list] * nd
return now_a_list
else:
# Argument is probably an `nd`-sized list.
return maybe_a_list | [
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train | max_sliding_window | :type nums: List[int]
:type k: int
:rtype: List[int] | algorithms/heap/sliding_window_max.py | def max_sliding_window(nums, k):
"""
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:type k: int
:rtype: List[int]
"""
if not nums:
return nums
queue = collections.deque()
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queue.append(num)
else:
res.append(max(queue))
queue.popleft()
queue.append(num)
res.append(max(queue))
return res | 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))
queue.popleft()
queue.append(num)
res.append(max(queue))
return res | [
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train | merge_intervals | Merge intervals in the form of a list. | algorithms/arrays/merge_intervals.py | 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:
out.append(i)
return out | 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:
out.append(i)
return out | [
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train | Interval.merge | Merge two intervals into one. | algorithms/arrays/merge_intervals.py | 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 | 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 | [
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train | Interval.print_intervals | Print out the intervals. | algorithms/arrays/merge_intervals.py | def print_intervals(intervals):
""" Print out the intervals. """
res = []
for i in intervals:
res.append(repr(i))
print("".join(res)) | def print_intervals(intervals):
""" Print out the intervals. """
res = []
for i in intervals:
res.append(repr(i))
print("".join(res)) | [
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train | rotate_v1 | 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. | algorithms/arrays/rotate.py | 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
temp = array[n - 1]
for j in range(n-1, 0, -1):
array[j] = array[j - 1]
array[0] = temp
return array | 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
temp = array[n - 1]
for j in range(n-1, 0, -1):
array[j] = array[j - 1]
array[0] = temp
return array | [
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train | rotate_v2 | 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. | algorithms/arrays/rotate.py | 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:
arr[a], arr[b] = arr[b], arr[a]
a += 1
b -= 1
n = len(array)
k = k % n
reverse(array, 0, n - k - 1)
reverse(array, n - k, n - 1)
reverse(array, 0, n - 1)
return array | 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:
arr[a], arr[b] = arr[b], arr[a]
a += 1
b -= 1
n = len(array)
k = k % n
reverse(array, 0, n - k - 1)
reverse(array, n - k, n - 1)
reverse(array, 0, n - 1)
return array | [
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train | pacific_atlantic | :type matrix: List[List[int]]
:rtype: List[List[int]] | algorithms/dfs/pacific_atlantic.py | 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 _ in range(m)]
for i in range(n):
dfs(pacific, matrix, float("-inf"), i, 0)
dfs(atlantic, matrix, float("-inf"), i, m-1)
for i in range(m):
dfs(pacific, matrix, float("-inf"), 0, i)
dfs(atlantic, matrix, float("-inf"), n-1, i)
for i in range(n):
for j in range(m):
if pacific[i][j] and atlantic[i][j]:
res.append([i, j])
return res | 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 _ in range(m)]
for i in range(n):
dfs(pacific, matrix, float("-inf"), i, 0)
dfs(atlantic, matrix, float("-inf"), i, m-1)
for i in range(m):
dfs(pacific, matrix, float("-inf"), 0, i)
dfs(atlantic, matrix, float("-inf"), n-1, i)
for i in range(n):
for j in range(m):
if pacific[i][j] and atlantic[i][j]:
res.append([i, j])
return res | [
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train | quick_sort | Quick sort
Complexity: best O(n log(n)) avg O(n log(n)), worst O(N^2) | algorithms/sort/quick_sort.py | 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 | 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 | [
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train | is_palindrome | :type s: str
:rtype: bool | algorithms/strings/is_palindrome.py | 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 = i+1, j-1
return True | 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 = i+1, j-1
return True | [
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train | plus_one_v1 | :type digits: List[int]
:rtype: List[int] | algorithms/arrays/plus_one.py | 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.append(summ % 10)
ten = summ // 10
i -= 1
return res[::-1] | 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.append(summ % 10)
ten = summ // 10
i -= 1
return res[::-1] | [
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train | rotate_right | :type head: ListNode
:type k: int
:rtype: ListNode | algorithms/linkedlist/rotate_list.py | 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 circular
current.next = head
k = k % length
# rotate until length-k
for i in range(length-k):
current = current.next
head = current.next
current.next = None
return head | 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 circular
current.next = head
k = k % length
# rotate until length-k
for i in range(length-k):
current = current.next
head = current.next
current.next = None
return head | [
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train | num_decodings | :type s: str
:rtype: int | algorithms/dp/num_decodings.py | 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_last
wo_last = x+y
return wo_last | 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_last
wo_last = x+y
return wo_last | [
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train | search_range | :type nums: List[int]
:type target: int
:rtype: List[int] | algorithms/search/search_range.py | 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]:
low = mid + 1
else:
break
for j in range(len(nums) - 1, -1, -1):
if nums[j] == target:
return [mid, j]
return [-1, -1] | 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]:
low = mid + 1
else:
break
for j in range(len(nums) - 1, -1, -1):
if nums[j] == target:
return [mid, j]
return [-1, -1] | [
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train | first_cyclic_node | :type head: Node
:rtype: Node | algorithms/linkedlist/first_cyclic_node.py | 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
walker = head
while runner is not walker:
runner, walker = runner.next, walker.next
return runner | 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
walker = head
while runner is not walker:
runner, walker = runner.next, walker.next
return runner | [
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train | max_heap_sort | Heap Sort that uses a max heap to sort an array in ascending order
Complexity: O(n log(n)) | algorithms/sort/heap_sort.py | 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_heapify(arr, i, simulation, iteration)
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
return arr | 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_heapify(arr, i, simulation, iteration)
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
return arr | [
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train | max_heapify | Max heapify helper for max_heap_sort | algorithms/sort/heap_sort.py | 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
while current_parent <= last_parent:
# Find greatest child of current_parent
child = 2 * current_parent + 1
if child + 1 <= end and arr[child] < arr[child + 1]:
child = child + 1
# Swap if child is greater than parent
if arr[child] > arr[current_parent]:
arr[current_parent], arr[child] = arr[child], arr[current_parent]
current_parent = child
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
# If no swap occured, no need to keep iterating
else:
break
arr[0], arr[end] = arr[end], arr[0]
return iteration | 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
while current_parent <= last_parent:
# Find greatest child of current_parent
child = 2 * current_parent + 1
if child + 1 <= end and arr[child] < arr[child + 1]:
child = child + 1
# Swap if child is greater than parent
if arr[child] > arr[current_parent]:
arr[current_parent], arr[child] = arr[child], arr[current_parent]
current_parent = child
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
# If no swap occured, no need to keep iterating
else:
break
arr[0], arr[end] = arr[end], arr[0]
return iteration | [
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train | min_heap_sort | Heap Sort that uses a min heap to sort an array in ascending order
Complexity: O(n log(n)) | algorithms/sort/heap_sort.py | 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(arr, i, simulation, iteration)
return arr | 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(arr, i, simulation, iteration)
return arr | [
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train | min_heapify | Min heapify helper for min_heap_sort | algorithms/sort/heap_sort.py | 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
# Iterate from last parent to first
for parent in range(last_parent, -1, -1):
current_parent = parent
# Iterate from current_parent to last_parent
while current_parent <= last_parent:
# Find lesser child of current_parent
child = 2 * current_parent + 1
if child + 1 <= end - start and arr[child + start] > arr[
child + 1 + start]:
child = child + 1
# Swap if child is less than parent
if arr[child + start] < arr[current_parent + start]:
arr[current_parent + start], arr[child + start] = \
arr[child + start], arr[current_parent + start]
current_parent = child
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
# If no swap occured, no need to keep iterating
else:
break
return iteration | 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
# Iterate from last parent to first
for parent in range(last_parent, -1, -1):
current_parent = parent
# Iterate from current_parent to last_parent
while current_parent <= last_parent:
# Find lesser child of current_parent
child = 2 * current_parent + 1
if child + 1 <= end - start and arr[child + start] > arr[
child + 1 + start]:
child = child + 1
# Swap if child is less than parent
if arr[child + start] < arr[current_parent + start]:
arr[current_parent + start], arr[child + start] = \
arr[child + start], arr[current_parent + start]
current_parent = child
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
# If no swap occured, no need to keep iterating
else:
break
return iteration | [
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train | generate_key | the RSA key generating algorithm
k is the number of bits in n | algorithms/maths/rsa.py | 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
return b
def gen_prime(k, seed=None):
"""generate a prime with k bits"""
def is_prime(num):
if num == 2:
return True
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
return False
return True
random.seed(seed)
while True:
key = random.randrange(int(2 ** (k - 1)), int(2 ** k))
if is_prime(key):
return key
# size in bits of p and q need to add up to the size of n
p_size = k / 2
q_size = k - p_size
e = gen_prime(k, seed) # in many cases, e is also chosen to be a small constant
while True:
p = gen_prime(p_size, seed)
if p % e != 1:
break
while True:
q = gen_prime(q_size, seed)
if q % e != 1:
break
n = p * q
l = (p - 1) * (q - 1) # calculate totient function
d = modinv(e, l)
return int(n), int(e), int(d) | 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
return b
def gen_prime(k, seed=None):
"""generate a prime with k bits"""
def is_prime(num):
if num == 2:
return True
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
return False
return True
random.seed(seed)
while True:
key = random.randrange(int(2 ** (k - 1)), int(2 ** k))
if is_prime(key):
return key
# size in bits of p and q need to add up to the size of n
p_size = k / 2
q_size = k - p_size
e = gen_prime(k, seed) # in many cases, e is also chosen to be a small constant
while True:
p = gen_prime(p_size, seed)
if p % e != 1:
break
while True:
q = gen_prime(q_size, seed)
if q % e != 1:
break
n = p * q
l = (p - 1) * (q - 1) # calculate totient function
d = modinv(e, l)
return int(n), int(e), int(d) | [
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train | square_root | Return square root of n, with maximum absolute error epsilon | algorithms/maths/sqrt_precision_factor.py | 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 | 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 | [
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train | counting_sort | 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) | algorithms/sort/counting_sort.py | 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
Complexity: 0(n)
"""
m = min(arr)
# in case there are negative elements, change the array to all positive element
different = 0
if m < 0:
# save the change, so that we can convert the array back to all positive number
different = -m
for i in range(len(arr)):
arr[i] += -m
k = max(arr)
temp_arr = [0] * (k + 1)
for i in range(0, len(arr)):
temp_arr[arr[i]] = temp_arr[arr[i]] + 1
# temp_array[i] contain the times the number i appear in arr
for i in range(1, k + 1):
temp_arr[i] = temp_arr[i] + temp_arr[i - 1]
# temp_array[i] contain the number of element less than or equal i in arr
result_arr = arr.copy()
# creating a result_arr an put the element in a correct positon
for i in range(len(arr) - 1, -1, -1):
result_arr[temp_arr[arr[i]] - 1] = arr[i] - different
temp_arr[arr[i]] = temp_arr[arr[i]] - 1
return result_arr | 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
Complexity: 0(n)
"""
m = min(arr)
# in case there are negative elements, change the array to all positive element
different = 0
if m < 0:
# save the change, so that we can convert the array back to all positive number
different = -m
for i in range(len(arr)):
arr[i] += -m
k = max(arr)
temp_arr = [0] * (k + 1)
for i in range(0, len(arr)):
temp_arr[arr[i]] = temp_arr[arr[i]] + 1
# temp_array[i] contain the times the number i appear in arr
for i in range(1, k + 1):
temp_arr[i] = temp_arr[i] + temp_arr[i - 1]
# temp_array[i] contain the number of element less than or equal i in arr
result_arr = arr.copy()
# creating a result_arr an put the element in a correct positon
for i in range(len(arr) - 1, -1, -1):
result_arr[temp_arr[arr[i]] - 1] = arr[i] - different
temp_arr[arr[i]] = temp_arr[arr[i]] - 1
return result_arr | [
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train | powerset | 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 | algorithms/set/set_covering.py | 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])) --> [(), (1,), (2,), (3,), (1,2), (1,3), (2,3), (1,2,3)]"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1)) | 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])) --> [(), (1,), (2,), (3,), (1,2), (1,3), (2,3), (1,2,3)]"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1)) | [
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train | optimal_set_cover | 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, S2:cost...} | algorithms/set/set_covering.py | 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}
costs (dict): Costs of each subset in S - {S1:cost, S2:cost...}
"""
pset = powerset(subsets.keys())
best_set = None
best_cost = float("inf")
for subset in pset:
covered = set()
cost = 0
for s in subset:
covered.update(subsets[s])
cost += costs[s]
if len(covered) == len(universe) and cost < best_cost:
best_set = subset
best_cost = cost
return best_set | 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}
costs (dict): Costs of each subset in S - {S1:cost, S2:cost...}
"""
pset = powerset(subsets.keys())
best_set = None
best_cost = float("inf")
for subset in pset:
covered = set()
cost = 0
for s in subset:
covered.update(subsets[s])
cost += costs[s]
if len(covered) == len(universe) and cost < best_cost:
best_set = subset
best_cost = cost
return best_set | [
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train | greedy_set_cover | 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...} | algorithms/set/set_covering.py | 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 each subset in S - {S1:cost, S2:cost...}
"""
elements = set(e for s in subsets.keys() for e in subsets[s])
# elements don't cover universe -> invalid input for set cover
if elements != universe:
return None
# track elements of universe covered
covered = set()
cover_sets = []
while covered != universe:
min_cost_elem_ratio = float("inf")
min_set = None
# find set with minimum cost:elements_added ratio
for s, elements in subsets.items():
new_elements = len(elements - covered)
# set may have same elements as already covered -> new_elements = 0
# check to avoid division by 0 error
if new_elements != 0:
cost_elem_ratio = costs[s] / new_elements
if cost_elem_ratio < min_cost_elem_ratio:
min_cost_elem_ratio = cost_elem_ratio
min_set = s
cover_sets.append(min_set)
# union
covered |= subsets[min_set]
return cover_sets | 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 each subset in S - {S1:cost, S2:cost...}
"""
elements = set(e for s in subsets.keys() for e in subsets[s])
# elements don't cover universe -> invalid input for set cover
if elements != universe:
return None
# track elements of universe covered
covered = set()
cover_sets = []
while covered != universe:
min_cost_elem_ratio = float("inf")
min_set = None
# find set with minimum cost:elements_added ratio
for s, elements in subsets.items():
new_elements = len(elements - covered)
# set may have same elements as already covered -> new_elements = 0
# check to avoid division by 0 error
if new_elements != 0:
cost_elem_ratio = costs[s] / new_elements
if cost_elem_ratio < min_cost_elem_ratio:
min_cost_elem_ratio = cost_elem_ratio
min_set = s
cover_sets.append(min_set)
# union
covered |= subsets[min_set]
return cover_sets | [
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train | num_trees | :type n: int
:rtype: int | algorithms/tree/bst/unique_bst.py | 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] | 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] | [
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train | MovingAverage.next | :type val: int
:rtype: float | algorithms/queues/moving_average.py | def next(self, val):
"""
:type val: int
:rtype: float
"""
self.queue.append(val)
return sum(self.queue) / len(self.queue) | def next(self, val):
"""
:type val: int
:rtype: float
"""
self.queue.append(val)
return sum(self.queue) / len(self.queue) | [
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train | n_sum | 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, return bool.
return: list[list[object]]
Note:
1. type of sum_closure's return should be same
as type of compare_closure's first param | algorithms/arrays/n_sum.py | 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, optional
Given two object of nums, return bool.
return: list[list[object]]
Note:
1. type of sum_closure's return should be same
as type of compare_closure's first param
"""
def sum_closure_default(a, b):
return a + b
def compare_closure_default(num, target):
""" above, below, or right on? """
if num < target:
return -1
elif num > target:
return 1
else:
return 0
def same_closure_default(a, b):
return a == b
def n_sum(n, nums, target):
if n == 2: # want answers with only 2 terms? easy!
results = two_sum(nums, target)
else:
results = []
prev_num = None
for index, num in enumerate(nums):
if prev_num is not None and \
same_closure(prev_num, num):
continue
prev_num = num
n_minus1_results = (
n_sum( # recursive call
n - 1, # a
nums[index + 1:], # b
target - num # c
) # x = n_sum( a, b, c )
) # n_minus1_results = x
n_minus1_results = (
append_elem_to_each_list(num, n_minus1_results)
)
results += n_minus1_results
return union(results)
def two_sum(nums, target):
nums.sort()
lt = 0
rt = len(nums) - 1
results = []
while lt < rt:
sum_ = sum_closure(nums[lt], nums[rt])
flag = compare_closure(sum_, target)
if flag == -1:
lt += 1
elif flag == 1:
rt -= 1
else:
results.append(sorted([nums[lt], nums[rt]]))
lt += 1
rt -= 1
while (lt < len(nums) and
same_closure(nums[lt - 1], nums[lt])):
lt += 1
while (0 <= rt and
same_closure(nums[rt], nums[rt + 1])):
rt -= 1
return results
def append_elem_to_each_list(elem, container):
results = []
for elems in container:
elems.append(elem)
results.append(sorted(elems))
return results
def union(duplicate_results):
results = []
if len(duplicate_results) != 0:
duplicate_results.sort()
results.append(duplicate_results[0])
for result in duplicate_results[1:]:
if results[-1] != result:
results.append(result)
return results
sum_closure = kv.get('sum_closure', sum_closure_default)
same_closure = kv.get('same_closure', same_closure_default)
compare_closure = kv.get('compare_closure', compare_closure_default)
nums.sort()
return n_sum(n, nums, target) | 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, optional
Given two object of nums, return bool.
return: list[list[object]]
Note:
1. type of sum_closure's return should be same
as type of compare_closure's first param
"""
def sum_closure_default(a, b):
return a + b
def compare_closure_default(num, target):
""" above, below, or right on? """
if num < target:
return -1
elif num > target:
return 1
else:
return 0
def same_closure_default(a, b):
return a == b
def n_sum(n, nums, target):
if n == 2: # want answers with only 2 terms? easy!
results = two_sum(nums, target)
else:
results = []
prev_num = None
for index, num in enumerate(nums):
if prev_num is not None and \
same_closure(prev_num, num):
continue
prev_num = num
n_minus1_results = (
n_sum( # recursive call
n - 1, # a
nums[index + 1:], # b
target - num # c
) # x = n_sum( a, b, c )
) # n_minus1_results = x
n_minus1_results = (
append_elem_to_each_list(num, n_minus1_results)
)
results += n_minus1_results
return union(results)
def two_sum(nums, target):
nums.sort()
lt = 0
rt = len(nums) - 1
results = []
while lt < rt:
sum_ = sum_closure(nums[lt], nums[rt])
flag = compare_closure(sum_, target)
if flag == -1:
lt += 1
elif flag == 1:
rt -= 1
else:
results.append(sorted([nums[lt], nums[rt]]))
lt += 1
rt -= 1
while (lt < len(nums) and
same_closure(nums[lt - 1], nums[lt])):
lt += 1
while (0 <= rt and
same_closure(nums[rt], nums[rt + 1])):
rt -= 1
return results
def append_elem_to_each_list(elem, container):
results = []
for elems in container:
elems.append(elem)
results.append(sorted(elems))
return results
def union(duplicate_results):
results = []
if len(duplicate_results) != 0:
duplicate_results.sort()
results.append(duplicate_results[0])
for result in duplicate_results[1:]:
if results[-1] != result:
results.append(result)
return results
sum_closure = kv.get('sum_closure', sum_closure_default)
same_closure = kv.get('same_closure', same_closure_default)
compare_closure = kv.get('compare_closure', compare_closure_default)
nums.sort()
return n_sum(n, nums, target) | [
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train | pattern_match | :type pattern: str
:type string: str
:rtype: bool | algorithms/backtrack/pattern_match.py | 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 range(1, len(string)-len(pattern)+2):
if pattern[0] not in dic and string[:end] not in dic.values():
dic[pattern[0]] = string[:end]
if backtrack(pattern[1:], string[end:], dic):
return True
del dic[pattern[0]]
elif pattern[0] in dic and dic[pattern[0]] == string[:end]:
if backtrack(pattern[1:], string[end:], dic):
return True
return False
return backtrack(pattern, string, {}) | 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 range(1, len(string)-len(pattern)+2):
if pattern[0] not in dic and string[:end] not in dic.values():
dic[pattern[0]] = string[:end]
if backtrack(pattern[1:], string[end:], dic):
return True
del dic[pattern[0]]
elif pattern[0] in dic and dic[pattern[0]] == string[:end]:
if backtrack(pattern[1:], string[end:], dic):
return True
return False
return backtrack(pattern, string, {}) | [
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train | bogo_sort | Bogo Sort
Best Case Complexity: O(n)
Worst Case Complexity: O(∞)
Average Case Complexity: O(n(n-1)!) | algorithms/sort/bogo_sort.py | 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 is inorder
i = 0
arr_len = len(arr)
while i+1 < arr_len:
if arr[i] > arr[i+1]:
return False
i += 1
return True
while not is_sorted(arr):
random.shuffle(arr)
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
return arr | 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 is inorder
i = 0
arr_len = len(arr)
while i+1 < arr_len:
if arr[i] > arr[i+1]:
return False
i += 1
return True
while not is_sorted(arr):
random.shuffle(arr)
if simulation:
iteration = iteration + 1
print("iteration",iteration,":",*arr)
return arr | [
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train | AvlTree.insert | Insert new key into node | algorithms/tree/avl/avl.py | 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.left.insert(key)
elif key > self.node.val:
self.node.right.insert(key)
self.re_balance() | 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.left.insert(key)
elif key > self.node.val:
self.node.right.insert(key)
self.re_balance() | [
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train | AvlTree.re_balance | Re balance tree. After inserting or deleting a node, | algorithms/tree/avl/avl.py | 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:
self.node.left.rotate_left()
self.update_heights()
self.update_balances()
self.rotate_right()
self.update_heights()
self.update_balances()
if self.balance < -1:
if self.node.right.balance > 0:
self.node.right.rotate_right()
self.update_heights()
self.update_balances()
self.rotate_left()
self.update_heights()
self.update_balances() | 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:
self.node.left.rotate_left()
self.update_heights()
self.update_balances()
self.rotate_right()
self.update_heights()
self.update_balances()
if self.balance < -1:
if self.node.right.balance > 0:
self.node.right.rotate_right()
self.update_heights()
self.update_balances()
self.rotate_left()
self.update_heights()
self.update_balances() | [
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train | AvlTree.update_heights | Update tree height | algorithms/tree/avl/avl.py | 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()
self.height = 1 + max(self.node.left.height, self.node.right.height)
else:
self.height = -1 | 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()
self.height = 1 + max(self.node.left.height, self.node.right.height)
else:
self.height = -1 | [
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] | keon/algorithms | python | https://github.com/keon/algorithms/blob/4d6569464a62a75c1357acc97e2dd32ee2f9f4a3/algorithms/tree/avl/avl.py#L57-L70 | [
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train | AvlTree.update_balances | Calculate tree balance factor | algorithms/tree/avl/avl.py | 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_balances()
self.balance = self.node.left.height - self.node.right.height
else:
self.balance = 0 | 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_balances()
self.balance = self.node.left.height - self.node.right.height
else:
self.balance = 0 | [
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train | AvlTree.rotate_right | Right rotation | algorithms/tree/avl/avl.py | 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 | 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 | [
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] | keon/algorithms | python | https://github.com/keon/algorithms/blob/4d6569464a62a75c1357acc97e2dd32ee2f9f4a3/algorithms/tree/avl/avl.py#L88-L98 | [
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train | AvlTree.rotate_left | Left rotation | algorithms/tree/avl/avl.py | 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 | 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 | [
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] | keon/algorithms | python | https://github.com/keon/algorithms/blob/4d6569464a62a75c1357acc97e2dd32ee2f9f4a3/algorithms/tree/avl/avl.py#L100-L110 | [
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train | AvlTree.in_order_traverse | In-order traversal of the tree | algorithms/tree/avl/avl.py | 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())
return result | 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())
return result | [
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train | strobogrammatic_in_range | :type low: str
:type high: str
:rtype: int | algorithms/maths/generate_strobogrammtic.py | 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 int(perm) < int(low):
continue
elif len(perm) == high_len and int(perm) > int(high):
continue
else:
count += 1
return count | 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 int(perm) < int(low):
continue
elif len(perm) == high_len and int(perm) > int(high):
continue
else:
count += 1
return count | [
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train | find_keyboard_row | :type words: List[str]
:rtype: List[str] | algorithms/set/find_keyboard_row.py | 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):
result.append(word)
return result | 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):
result.append(word)
return result | [
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train | kth_to_last_eval | 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 | algorithms/linkedlist/kth_to_last.py | 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 = str('.'.join(['head', nexts]))
while head:
if eval(seeker) is None:
return head
else:
head = head.next
return False | 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 = str('.'.join(['head', nexts]))
while head:
if eval(seeker) is None:
return head
else:
head = head.next
return False | [
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train | kth_to_last_dict | 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 | algorithms/linkedlist/kth_to_last.py | 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()
count = 0
while head:
d[count] = head
head = head.next
count += 1
return len(d)-k in d and d[len(d)-k] | 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()
count = 0
while head:
d[count] = head
head = head.next
count += 1
return len(d)-k in d and d[len(d)-k] | [
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train | kth_to_last | 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. | algorithms/linkedlist/kth_to_last.py | 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:
# Went too far, k is not valid
raise IndexError
p1 = p1.next
while p1:
p1 = p1.next
p2 = p2.next
return p2 | 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:
# Went too far, k is not valid
raise IndexError
p1 = p1.next
while p1:
p1 = p1.next
p2 = p2.next
return p2 | [
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train | get_skyline | Wortst Time Complexity: O(NlogN)
:type buildings: List[List[int]]
:rtype: List[List[int]] | algorithms/heap/skyline.py | 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 < n and lrh[i][0] == x:
heapq.heappush(live, (-lrh[i][2], -lrh[i][1]))
i += 1
else:
x = -live[0][1]
while live and -live[0][1] <= x:
heapq.heappop(live)
height = len(live) and -live[0][0]
if not skyline or height != skyline[-1][1]:
skyline += [x, height],
return skyline | 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 < n and lrh[i][0] == x:
heapq.heappush(live, (-lrh[i][2], -lrh[i][1]))
i += 1
else:
x = -live[0][1]
while live and -live[0][1] <= x:
heapq.heappop(live)
height = len(live) and -live[0][0]
if not skyline or height != skyline[-1][1]:
skyline += [x, height],
return skyline | [
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train | summarize_ranges | :type array: List[int]
:rtype: List[] | algorithms/arrays/summarize_ranges.py | 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[i] != num:
res.append((num, array[i]))
else:
res.append((num, num))
i += 1
return res | 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[i] != num:
res.append((num, array[i]))
else:
res.append((num, num))
i += 1
return res | [
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train | encode | Encodes a list of strings to a single string.
:type strs: List[str]
:rtype: str | algorithms/strings/encode_decode.py | 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 | 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 | [
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train | decode | Decodes a single string to a list of strings.
:type s: str
:rtype: List[str] | algorithms/strings/encode_decode.py | 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)
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i = index+1+size
return strs | 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 | [
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train | multiply | :type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]] | algorithms/matrix/multiply.py | 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[0])
if(multiplicand_col != multiplier_row):
raise Exception(
"Multiplicand matrix not compatible with Multiplier matrix.")
# create a result matrix
result = [[0] * multiplier_col for i in range(multiplicand_row)]
for i in range(multiplicand_row):
for j in range(multiplier_col):
for k in range(len(multiplier)):
result[i][j] += multiplicand[i][k] * multiplier[k][j]
return result | 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[0])
if(multiplicand_col != multiplier_row):
raise Exception(
"Multiplicand matrix not compatible with Multiplier matrix.")
# create a result matrix
result = [[0] * multiplier_col for i in range(multiplicand_row)]
for i in range(multiplicand_row):
for j in range(multiplier_col):
for k in range(len(multiplier)):
result[i][j] += multiplicand[i][k] * multiplier[k][j]
return result | [
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train | combination | This function calculates nCr. | algorithms/maths/combination.py | 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) | 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) | [
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train | combination_memo | This function calculates nCr using memoization method. | algorithms/maths/combination.py | 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, r) | 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, r) | [
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train | is_anagram | :type s: str
:type t: str
:rtype: bool | algorithms/map/is_anagram.py | 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 | 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 | [
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train | pancake_sort | Pancake_sort
Sorting a given array
mutation of selection sort
reference: https://www.geeksforgeeks.org/pancake-sorting/
Overall time complexity : O(N^2) | algorithms/sort/pancake_sort.py | 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):
#Finding index of maximum number in arr
index_max = arr.index(max(arr[0:cur]))
if index_max+1 != cur:
#Needs moving
if index_max != 0:
#reverse from 0 to index_max
arr[:index_max+1] = reversed(arr[:index_max+1])
# Reverse list
arr[:cur] = reversed(arr[:cur])
return arr | 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):
#Finding index of maximum number in arr
index_max = arr.index(max(arr[0:cur]))
if index_max+1 != cur:
#Needs moving
if index_max != 0:
#reverse from 0 to index_max
arr[:index_max+1] = reversed(arr[:index_max+1])
# Reverse list
arr[:cur] = reversed(arr[:cur])
return arr | [
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train | ZigZagIterator.next | :rtype: int | algorithms/queues/zigzagiterator.py | def next(self):
"""
:rtype: int
"""
v=self.queue.pop(0)
ret=v.pop(0)
if v: self.queue.append(v)
return ret | def next(self):
"""
:rtype: int
"""
v=self.queue.pop(0)
ret=v.pop(0)
if v: self.queue.append(v)
return ret | [
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train | max_profit_naive | :type prices: List[int]
:rtype: int | algorithms/dp/buy_sell_stock.py | 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 | 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 | [
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train | max_profit_optimized | input: [7, 1, 5, 3, 6, 4]
diff : [X, -6, 4, -2, 3, -2]
:type prices: List[int]
:rtype: int | algorithms/dp/buy_sell_stock.py | 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, cur_max)
return max_so_far | 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, cur_max)
return max_so_far | [
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train | first_unique_char | :type s: str
:rtype: int | algorithms/strings/first_unique_char.py | 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 | 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 | [
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train | Solution.kth_smallest | :type root: TreeNode
:type k: int
:rtype: int | algorithms/tree/bst/kth_smallest.py | def kth_smallest(self, root, k):
"""
:type root: TreeNode
:type k: int
:rtype: int
"""
count = []
self.helper(root, count)
return count[k-1] | def kth_smallest(self, root, k):
"""
:type root: TreeNode
:type k: int
:rtype: int
"""
count = []
self.helper(root, count)
return count[k-1] | [
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train | int_to_roman | :type num: int
:rtype: str | algorithms/strings/int_to_roman.py | 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"];
return m[num//1000] + c[(num%1000)//100] + x[(num%100)//10] + i[num%10]; | 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"];
return m[num//1000] + c[(num%1000)//100] + x[(num%100)//10] + i[num%10]; | [
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train | length_longest_path | :type input: str
:rtype: int | algorithms/stack/longest_abs_path.py | 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') # the depth of current dir or file
print("depth: ", depth)
print("stack: ", stack)
print("curlen: ", curr_len)
while len(stack) > depth: # go back to the correct depth
curr_len -= stack.pop()
stack.append(len(s.strip('\t'))+1) # 1 is the length of '/'
curr_len += stack[-1] # increase current length
print("stack: ", stack)
print("curlen: ", curr_len)
if '.' in s: # update maxlen only when it is a file
max_len = max(max_len, curr_len-1) # -1 is to minus one '/'
return max_len | 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') # the depth of current dir or file
print("depth: ", depth)
print("stack: ", stack)
print("curlen: ", curr_len)
while len(stack) > depth: # go back to the correct depth
curr_len -= stack.pop()
stack.append(len(s.strip('\t'))+1) # 1 is the length of '/'
curr_len += stack[-1] # increase current length
print("stack: ", stack)
print("curlen: ", curr_len)
if '.' in s: # update maxlen only when it is a file
max_len = max(max_len, curr_len-1) # -1 is to minus one '/'
return max_len | [
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train | multiply | :type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]] | algorithms/matrix/sparse_mul.py | 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 = [[0 for _ in range(l)] for _ in range(m)]
for i, row in enumerate(a):
for k, eleA in enumerate(row):
if eleA:
for j, eleB in enumerate(b[k]):
if eleB: c[i][j] += eleA * eleB
return c | 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 = [[0 for _ in range(l)] for _ in range(m)]
for i, row in enumerate(a):
for k, eleA in enumerate(row):
if eleA:
for j, eleB in enumerate(b[k]):
if eleB: c[i][j] += eleA * eleB
return c | [
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train | multiply | :type A: List[List[int]]
:type B: List[List[int]]
:rtype: List[List[int]] | algorithms/matrix/sparse_mul.py | 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])
table_a, table_b = {}, {}
for i, row in enumerate(a):
for j, ele in enumerate(row):
if ele:
if i not in table_a: table_a[i] = {}
table_a[i][j] = ele
for i, row in enumerate(b):
for j, ele in enumerate(row):
if ele:
if i not in table_b: table_b[i] = {}
table_b[i][j] = ele
c = [[0 for j in range(l)] for i in range(m)]
for i in table_a:
for k in table_a[i]:
if k not in table_b: continue
for j in table_b[k]:
c[i][j] += table_a[i][k] * table_b[k][j]
return c | 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])
table_a, table_b = {}, {}
for i, row in enumerate(a):
for j, ele in enumerate(row):
if ele:
if i not in table_a: table_a[i] = {}
table_a[i][j] = ele
for i, row in enumerate(b):
for j, ele in enumerate(row):
if ele:
if i not in table_b: table_b[i] = {}
table_b[i][j] = ele
c = [[0 for j in range(l)] for i in range(m)]
for i in table_a:
for k in table_a[i]:
if k not in table_b: continue
for j in table_b[k]:
c[i][j] += table_a[i][k] * table_b[k][j]
return c | [
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train | bitonic_sort | 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)^2)
Worst-case in non-parallel: O(nlog(n)^2)
reference: https://en.wikipedia.org/wiki/Bitonic_sorter | algorithms/sort/bitonic_sort.py | 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(decreasing)
Worst-case in parallel: O(log(n)^2)
Worst-case in non-parallel: O(nlog(n)^2)
reference: https://en.wikipedia.org/wiki/Bitonic_sorter
"""
def compare(arr, reverse):
n = len(arr)//2
for i in range(n):
if reverse != (arr[i] > arr[i+n]):
arr[i], arr[i+n] = arr[i+n], arr[i]
return arr
def bitonic_merge(arr, reverse):
n = len(arr)
if n <= 1:
return arr
arr = compare(arr, reverse)
left = bitonic_merge(arr[:n // 2], reverse)
right = bitonic_merge(arr[n // 2:], reverse)
return left + right
#end of function(compare and bitionic_merge) definition
n = len(arr)
if n <= 1:
return arr
# checks if n is power of two
if not (n and (not(n & (n - 1))) ):
raise ValueError("the size of input should be power of two")
left = bitonic_sort(arr[:n // 2], True)
right = bitonic_sort(arr[n // 2:], False)
arr = bitonic_merge(left + right, reverse)
return arr | 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(decreasing)
Worst-case in parallel: O(log(n)^2)
Worst-case in non-parallel: O(nlog(n)^2)
reference: https://en.wikipedia.org/wiki/Bitonic_sorter
"""
def compare(arr, reverse):
n = len(arr)//2
for i in range(n):
if reverse != (arr[i] > arr[i+n]):
arr[i], arr[i+n] = arr[i+n], arr[i]
return arr
def bitonic_merge(arr, reverse):
n = len(arr)
if n <= 1:
return arr
arr = compare(arr, reverse)
left = bitonic_merge(arr[:n // 2], reverse)
right = bitonic_merge(arr[n // 2:], reverse)
return left + right
#end of function(compare and bitionic_merge) definition
n = len(arr)
if n <= 1:
return arr
# checks if n is power of two
if not (n and (not(n & (n - 1))) ):
raise ValueError("the size of input should be power of two")
left = bitonic_sort(arr[:n // 2], True)
right = bitonic_sort(arr[n // 2:], False)
arr = bitonic_merge(left + right, reverse)
return arr | [
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train | scc | Computes the strongly connected components of a graph | algorithms/graph/satisfiability.py | 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)
for v in graph:
if not vis[v]:
dfs_transposed(v, graph_transposed, order, vis)
vis = {vertex: False for vertex in graph}
vertex_scc = {}
current_comp = 0
for v in reversed(order):
if not vis[v]:
# Each dfs will visit exactly one component
dfs(v, current_comp, vertex_scc, graph, vis)
current_comp += 1
return vertex_scc | 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)
for v in graph:
if not vis[v]:
dfs_transposed(v, graph_transposed, order, vis)
vis = {vertex: False for vertex in graph}
vertex_scc = {}
current_comp = 0
for v in reversed(order):
if not vis[v]:
# Each dfs will visit exactly one component
dfs(v, current_comp, vertex_scc, graph, vis)
current_comp += 1
return vertex_scc | [
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train | build_graph | Builds the implication graph from the formula | algorithms/graph/satisfiability.py | 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, a_neg), (b_lit, not b_neg))
add_edge(graph, (b_lit, b_neg), (a_lit, not a_neg))
return graph | 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, a_neg), (b_lit, not b_neg))
add_edge(graph, (b_lit, b_neg), (a_lit, not a_neg))
return graph | [
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train | unique_array_sum_combinations | 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.
Complexity: O(n(m+p)) | algorithms/backtrack/array_sum_combinations.py | 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 doing the above procedure while going through complete c array.
Complexity: O(n(m+p))
"""
def check_sum(n, *nums):
if sum(x for x in nums) == n:
return (True, nums)
else:
return (False, nums)
pro = itertools.product(A, B, C)
func = partial(check_sum, target)
sums = list(itertools.starmap(func, pro))
res = set()
for s in sums:
if s[0] is True and s[1] not in res:
res.add(s[1])
return list(res) | 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 doing the above procedure while going through complete c array.
Complexity: O(n(m+p))
"""
def check_sum(n, *nums):
if sum(x for x in nums) == n:
return (True, nums)
else:
return (False, nums)
pro = itertools.product(A, B, C)
func = partial(check_sum, target)
sums = list(itertools.starmap(func, pro))
res = set()
for s in sums:
if s[0] is True and s[1] not in res:
res.add(s[1])
return list(res) | [
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train | is_bst | :type root: TreeNode
:rtype: bool | algorithms/tree/bst/is_bst.py | 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 = root
root = root.right
return True | 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 = root
root = root.right
return True | [
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train | __get_depth | return 0 if unbalanced else depth + 1 | algorithms/tree/is_balanced.py | 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) | 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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train | copy_random_pointer_v1 | :type head: RandomListNode
:rtype: RandomListNode | algorithms/linkedlist/copy_random_pointer.py | def copy_random_pointer_v1(head):
"""
:type head: RandomListNode
:rtype: RandomListNode
"""
dic = dict()
m = n = head
while m:
dic[m] = RandomListNode(m.label)
m = m.next
while n:
dic[n].next = dic.get(n.next)
dic[n].random = dic.get(n.random)
n = n.next
return dic.get(head) | def copy_random_pointer_v1(head):
"""
:type head: RandomListNode
:rtype: RandomListNode
"""
dic = dict()
m = n = head
while m:
dic[m] = RandomListNode(m.label)
m = m.next
while n:
dic[n].next = dic.get(n.next)
dic[n].random = dic.get(n.random)
n = n.next
return dic.get(head) | [
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train | copy_random_pointer_v2 | :type head: RandomListNode
:rtype: RandomListNode | algorithms/linkedlist/copy_random_pointer.py | def copy_random_pointer_v2(head):
"""
:type head: RandomListNode
:rtype: RandomListNode
"""
copy = defaultdict(lambda: RandomListNode(0))
copy[None] = None
node = head
while node:
copy[node].label = node.label
copy[node].next = copy[node.next]
copy[node].random = copy[node.random]
node = node.next
return copy[head] | def copy_random_pointer_v2(head):
"""
:type head: RandomListNode
:rtype: RandomListNode
"""
copy = defaultdict(lambda: RandomListNode(0))
copy[None] = None
node = head
while node:
copy[node].label = node.label
copy[node].next = copy[node.next]
copy[node].random = copy[node.random]
node = node.next
return copy[head] | [
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] | 4d6569464a62a75c1357acc97e2dd32ee2f9f4a3 |
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