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209 changes: 178 additions & 31 deletions linked_list/linked_list.py
Original file line number Diff line number Diff line change
@@ -1,75 +1,181 @@

# Defines a node in the singly linked list
# from ast import Pass


class Node:

def __init__(self, value, next_node = None):
self.value = value
self.next = next_node
# self.previous = previous_node

# Defines the singly linked list
class LinkedList:
def __init__(self):
self.head = None # keep the head private. Not accessible outside this class
self.head = None
# keep the head private. Not accessible outside this class
# self.length = 0 # keep the length private. Not accessible outside this class
# if self.head == None:
# self.head = Node(None)
# self.length = 0
# else:
# self.length = 1

# returns the value in the first node
# returns None if the list is empty
# Time Complexity: ?
# Space Complexity: ?
# Time Complexity: O(1)
# Space Complexity: O(1)
def get_first(self):

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pass

if self.head == None:
return None
else:
return self.head.value

# method to add a new node with the specific data value in the linked list
# insert the new node at the beginning of the linked list
# Time Complexity: ?
# Space Complexity: ?
# Time Complexity: O(1)
# Space Complexity: O(1)
def add_first(self, value):

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pass

if self.head == None:
self.head = Node(value)
else:
new_node = Node(value)
new_node.next = self.head
self.head = new_node
return self.head

# if self.head == None:
# return None

# self.head = Node(value, self.head)
# self.length += 1
# return self.head

# method to find if the linked list contains a node with specified value
# returns true if found, false otherwise
# Time Complexity: ?
# Space Complexity: ?
# Time Complexity: O(1)

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⏱ Time complexity is O(n) since in the worst case the node with the value you are searching for will be the last node in the list, and you will have to iterate through every node in the list.

# Space Complexity: O(1)
def search(self, value):

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pass
if self.head == None:
return False

if self.head.value == value:
return True

current = self.head
while current:
if current.value == value:
return True
current = current.next

return False

# method that returns the length of the singly linked list
# Time Complexity: ?
# Space Complexity: ?
# Time Complexity: O(n)
# Space Complexity: O(1)
def length(self):

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pass

length_counter = 0
current = self.head
while (current != None):
length_counter += 1
current = current.next

return length_counter


# method that returns the value at a given index in the linked list
# index count starts at 0
# returns None if there are fewer nodes in the linked list than the index value
# Time Complexity: ?
# Space Complexity: ?
# Time Complexity: O(n)
# Space Complexity: O(1)
def get_at_index(self, index):

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pass

# if self.head == None:
# return None

if self.length() <= index:
return None

current_value = 0
current_node = self.head
while current_node:
if current_value == index:
return current_node.value
current_node = current_node.next
current_value += 1

return current_node.value


# method that returns the value of the last node in the linked list
# returns None if the linked list is empty
# Time Complexity: ?
# Space Complexity: ?
# Time Complexity: O(n)
# Space Complexity: O(1)
def get_last(self):

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pass
if self.head == None:
return None

current = self.head
while current.next:
current = current.next
return current.value

# method that inserts a given value as a new last node in the linked list
# Time Complexity: ?
# Space Complexity: ?
# Time Complexity: O(1)

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⏱ Time complexity is going to be O(n) since you start at the head node and have to traverse the entire list.

# Space Complexity: O(1)
def add_last(self, value):

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pass
if self.head == None:
self.head = Node(value)
else:
current = self.head
while current.next:
current = current.next
current.next = Node(value)

return self.head

# method to return the max value in the linked list
# returns the data value and not the node
def find_max(self):

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pass
if self.head == None:
return None

current = self.head
max_value = current.value
while current != None:
if current.value > max_value:
max_value = current.value
current = current.next

return max_value

# method to delete the first node found with specified value
# Time Complexity: ?
# Space Complexity: ?
# Time Complexity: O(n)
# Space Complexity: O(1)
def delete(self, value):

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pass

if self.head == None:
return None

current = self.head
previous = None
while current:
if current.value == value:
if previous == None:
self.head = current.next
return
else:
previous.next = current.next
return
else:
previous = current
current = current.next

return None

# method to print all the values in the linked list
# Time Complexity: ?
Expand All @@ -89,40 +195,81 @@ def visit(self):
# Time Complexity: ?
# Space Complexity: ?
Comment on lines 195 to 196

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⏱🪐 Time and space complexity?

def reverse(self):
pass
if self.head == None:
return None

previous = None
current = self.head

while current != None:
next_node = current.next
current.next = previous
previous = current
current = next_node
self.head = previous
return previous

## Advanced/ Exercises
# returns the value at the middle element in the singly linked list
# Time Complexity: ?
# Space Complexity: ?
Comment on lines 214 to 215

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⏱🪐 Time and space complexity?

def find_middle_value(self):

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pass
if self.head == None:
return None

current = self.head
middle_value = None
for i in range(self.length // 2):
current = current.next
middle_value = current.value

return middle_value

# find the nth node from the end and return its value
# assume indexing starts at 0 while counting to n
# Time Complexity: ?
# Space Complexity: ?
Comment on lines 230 to 231

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⏱🪐 Time and space complexity?

def find_nth_from_end(self, n):

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You could also take advantage of the get_at_index method you wrote

pass
if self.head == None:
return None

current = self.head
for i in range(n):
current = current.next

return current.value

# checks if the linked list has a cycle. A cycle exists if any node in the
# linked list links to a node already visited.
# returns true if a cycle is found, false otherwise.
# Time Complexity: ?
# Space Complexity: ?
Comment on lines 245 to 246

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⏱🪐 Time and space complexity?

def has_cycle(self):

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pass
if self.head == None:
return None

current = self.head
visited = []
while current:
if current in visited:
return True
else:
visited.append(current)
current = current.next
return False

# Helper method for tests
# Creates a cycle in the linked list for testing purposes
# Assumes the linked list has at least one node
def create_cycle(self):
if self.head == None:
return
return None

# navigate to last node
current = self.head
while current.next != None:
current = current.next

current.next = self.head # make the last node link to first node

return self.head