def bst_ordered(n, target):
bst = BSTNode(0)
# Overall for the next two lines O(n^2)
for i in range(1, n): # O(n)
bst.insert(i) # O(n)
bst.contains(target) # O(n)
def bst_random(random_nums, target):
bst = BSTNode(random_nums[0]) # O(1)
# overall: n log n
for num in random_nums[1:]: # iteration ->O(n)
bst.insert(num) # insert is O(log n)
bst.contains(target) # O(log n) -> with each iteration you can throw out each item you are looking at
class BinarySearchTreeTests(unittest.TestCase):
def setUp(self):
self.bst = BSTNode(5)
def test_insert(self):
self.bst.insert(2)
self.bst.insert(3)
self.bst.insert(7)
self.bst.insert(6)
self.assertEqual(self.bst.left.right.value, 3)
self.assertEqual(self.bst.right.left.value, 6)
def test_handle_dupe_insert(self):
self.bst2 = BSTNode(1)
self.bst2.insert(1)
self.assertEqual(self.bst2.right.value, 1)
def test_contains(self):
self.bst.insert(2)
self.bst.insert(3)
self.bst.insert(7)
self.assertTrue(self.bst.contains(7))
self.assertFalse(self.bst.contains(8))
def test_get_max(self):
self.assertEqual(self.bst.get_max(), 5)
self.bst.insert(30)
self.assertEqual(self.bst.get_max(), 30)
self.bst.insert(300)
self.bst.insert(3)
self.assertEqual(self.bst.get_max(), 300)
"""
class BinarySearchTreeTests(unittest.TestCase):
def setUp(self):
self.bst = BSTNode(5)
def test_insert(self):
self.bst.insert(2)
self.bst.insert(3)
self.bst.insert(7)
self.bst.insert(6)
self.assertEqual(self.bst.left.right.value, 3)
self.assertEqual(self.bst.right.left.value, 6)
def test_handle_dupe_insert(self):
self.bst2 = BSTNode(1)
self.bst2.insert(1)
self.assertEqual(self.bst2.right.value, 1)
def test_contains(self):
self.bst.insert(2)
self.bst.insert(3)
self.bst.insert(7)
self.assertTrue(self.bst.contains(7))
self.assertFalse(self.bst.contains(8))
def test_get_max(self):
self.assertEqual(self.bst.get_max(), 5)
self.bst.insert(30)
self.assertEqual(self.bst.get_max(), 30)
self.bst.insert(300)
self.bst.insert(3)
self.assertEqual(self.bst.get_max(), 300)
def test_for_each(self):
arr = []
def cb(x):
return arr.append(x)
v1 = random.randint(1, 101)
v2 = random.randint(1, 101)
v3 = random.randint(1, 101)
v4 = random.randint(1, 101)
v5 = random.randint(1, 101)
self.bst.insert(v1)
self.bst.insert(v2)
self.bst.insert(v3)
self.bst.insert(v4)
self.bst.insert(v5)
self.bst.for_each(cb)
self.assertTrue(5 in arr)
self.assertTrue(v1 in arr)
self.assertTrue(v2 in arr)
self.assertTrue(v3 in arr)
self.assertTrue(v4 in arr)
self.assertTrue(v5 in arr)
def binary_search(lst, lst2):
bst = BST(names_1[0])
for i in range(len(names_1)):
if i != 0:
bst.insert(names_1[i])
for name_2 in names_2:
if bst.contains(name_2):
duplicates.append(name_2)
def binary_search_tree_approach():
duplicates = []
# insert one of the lists into a binary search tree
# uses the binary search tree class we built earlier this week
bst = BSTNode(names_2[0])
for name in names_2:
bst.insert(name)
# search bst for matching names
for name in names_1:
if bst.contains(name):
duplicates.append(name)
return duplicates
class Tree:
def __init__(self):
self.root = None
def insert(self, value):
if self.root:
return self.root.insert(value)
else:
self.root = BSTNode(value)
return True
def find(self, value):
if self.root:
return self.root.contains(value)
else:
return False
# duplicates.append(name_1)
# our Binary Search Tree from this week has a module contains...
# this allows us to search whether a value or input is in the bst
# if we move all the names from one list to their own search trees
# we should be able to traverse thru using contains method.
BST_names_1 = BSTNode('')
for i in names_1:
BST_names_1.insert(i)
# now that we have the names from names_1 in a bst we will itterate
# over the names in names_2, and if the bst of names_1 contains that
# name then we will move it to the duplicates list.
for i in names_2:
if BST_names_1.contains(i) is True:
duplicates.append(i)
duplicates = sorted(duplicates)
# # stretch using only arrays...not using our imported BSTNode Class.
# # can this reach a similar efficiency? using BST we were around a runtime of 0.105 seconds
# # this runs in about .63 seconds if not sorting the list, and .95 seconds if sorting. Not faster, but no need
# # to create a whole new class to implement.
# unique_names = set(names_2+names_1)
# duplicates = sorted([i for i in names_1 if i in names_1 and i in names_2])
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
bst_names = BSTNode(names_1.pop())
for name_1 in names_1:
bst_names.insert(name_1)
for name_2 in names_2:
if bst_names.contains(name_2):
duplicates.append(name_2)
end_time = time.time()
print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print("Binary search tree")
print (f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
t_start = time.time()
hash_duplicates = []
print(f"runtime: {end_time - start_time} seconds\n\n")
""" MY IMPROVEMENT SOULTION """
name_tree = BSTNode("")
start_time = time.time()
duplicates = []
# insert all the names in one list into a BST
for name in names_1:
name_tree.insert(name)
# look at every name in the second list
# and see if the BST cointains it. If it
# does then add it to the duplicates list
for name in names_2:
if name_tree.contains(name):
duplicates.append(name)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very
# efficient approach to this problem
# What's the best time you can accomplish?
# There are no restrictions on techniques or data
# structures, but you may not import any additional
# libraries that you did not write yourself.
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
# runtime: 0.0599980354309082 seconds
first_list = BSTNode(names_1[0])
next(iter(names_1))
for name in names_1:
first_list.insert(name)
for name in names_2:
if first_list.contains(name):
duplicates.append(name)
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# duplicates.append(name_1)
# Insert the names_1 data into a BST
# Use the first name to create the root node of the BST
tree = BSTNode(names_1[0])
# Insert the rest of the names into the existing BST
for name in names_1: # O(n) to insert each each name from name_1 into the tree
tree.insert(name)
# Check to see if the tree contains the names from list 2
for name in names_2: # O(n) to go through each name in names_2 list
# If the tree contains the name, append to duplicates list
if tree.contains(name): # O(log n) to check if the tree contains the name
duplicates.append(name)
'''
Runtime of 0.17413 seconds. Runtime complexity is O(n log n)
'''
end_time = time.time()
print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print (f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
start_time = time.time()
f = open('names_1.txt', 'r')
names_1 = f.read().split("\n") # List containing 10000 names
f.close()
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
bstNames1 = BSTNode(names_1[0])
for name_1 in names_1[1:]:
bstNames1.insert(name_1)
for name_2 in names_2:
if bstNames1.contains(name_2):
duplicates.append(name_1)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
# duplicates = [name for name in names_1 if name in names_2]
duplicates = []
bst = BSTNode('testing')
# # Not quite sure how else to accomplish this at the moment
# # OPTIMIZED TO RUN IN 0.172 SECONDS ON MY MACHINE
# # for each name in names_1
for name in names_1: # O(n)
# add that name to the binary search tree
bst.insert(name)
# for each name in names 2
for name in names_2:
# see if that name is already in the bst
if bst.contains(name):
# if it is, append it to the duplicates array
duplicates.append(name)
# Replace the nested for loops below with your improvements
# RUNTIME IS 7.6484 SECONDS ON MY MACHINE
# Current runtime = O(n) + O(n) + O(1) + O(1) == O(n^2)
# for name_1 in names_1: # O(n)
# for name_2 in names_2: # O(n)
# if name_1 == name_2: # O(1)
# duplicates.append(name_1) # O(1)?
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
bst = BSTNode("NOT TARGET")
for i in names_2:
bst.insert(i)
for i in names_1:
if bst.contains(i):
duplicates.append(i)
print(f"---\n---")
# while not queue.size == len(names_1) - 1:
# queue.enqueue(names_1[queue.size])
# current_node = queue.storage.head
# while queue.size:
# current_node = queue.dequeue()
# if current_node in names_2:
# duplicates.append(current_node)
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
bst1 = BSTNode(names_1[0])
for name_1 in names_1[1:]:
bst1.insert(name_1)
for name_2 in names_2:
if bst1.contains(name_2):
duplicates.append(name_2)
# if name_1 == name_2:
# duplicates.append(name_1)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
duplicates.append(name_1)
The runtime was:
10.852504014968872 seconds
"""
# Create a BST for names_1
bst_1 = BSTNode(names_1[0])
# For range from the second item in names_1 till the rest of it's length
for i in range(1, len(names_1)):
# Add every other item to bst_1
bst_1.insert(names_1[i])
# For range from 0 till the end og names_2
for i in range(0, len(names_2)):
# Compare items in bst_1 to names_2
# If bst contains the name in index i
if bst_1.contains(names_2[i]):
# Store that name in duplicates
duplicates.append(names_2[i])
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Add 2nd list into BST
bst = BSTNode(names_2[0])
for name_2 in names_2:
if name_2 == 'Nathen Bishop':
continue
else:
bst.insert(name_2)
# Replace the nested for loops below with your improvements
# original runtime: ~4 sec
for name_1 in names_1:
if bst.contains(name_1):
duplicates.append(name_1)
end_time = time.time()
print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print (f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
f.close()
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
name1bst = BSTNode('names1')
for name1 in names_1:
name1bst.insert(name1)
for name2 in names_2:
if name1bst.contains(name2):
duplicates.append(name2)
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
f = open('names_1.txt', 'r')
names_1 = f.read().split("\n") # List containing 10000 names
f.close()
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
faster_names = BSTNode(names_1[0])
for name_1 in names_1:
faster_names.insert(name_1)
for name_2 in names_2:
if faster_names.contains(name_2):
duplicates.append(name_2)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
# first runtime was 14seconds optomized runtime is 0.329120397567749 seconds
f = open('names_1.txt', 'r')
names_1 = f.read().split("\n") # List containing 10000 names
f.close()
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
tree = BSTNode("Andrew Sitzes")
for names in names_1:
tree.insert(names)
for checkNames in names_2:
if tree.contains(checkNames):
duplicates.append(checkNames)
# Replace the nested for loops below with your improvements
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
from binary_search_tree import BSTNode
bst_1 = BSTNode(' ')
# bst_2 = BSTNode('Trevor Keith')
for i in names_1:
#print(i)
bst_1.insert(i)
print(bst_1.contains('Regina Molina'))
# for j in names_2:
# bst_2.insert(j)
# duplicates = [j for j in names_2 if bst_2.contains(j)]
for i in names_2:
if bst_1.contains(i):
duplicates.append(i)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# runtime was 0.57 with bst, compared with approx. 20 seconds for the original
f.close()
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
nodes = BSTNode('names')
for names in names_1:
nodes.insert(names)
for name in names_2:
if nodes.contains(name):
duplicates.append(name)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
# Replace the nested for loops below with your improvements
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
i = 0
for name in names_1:
if i == 0:
bt = BSTNode(name)
i = 1
else:
bt.insert(name)
for name in names_2:
if bt.contains(name):
duplicates.append(name)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
start_time = time.time()
duplicates = []
i = 0
names_2 = set(names_2)
# runtime for this seems to be O(n log n)
# because it isn't constant but doesn't exponentially increase
# based on the size of the lists
duplicates = [] # Return the list of duplicates in this data structure
# creating the root node of the tree with the first value of names_1
tree = BSTNode(names_1[0])
# using the insert method to put the rest of the names_1 list to the tree
for name in names_1[1:]:
tree.insert(name)
# checking the 2nd list with the contains method from the tree to
for name in names_2:
if tree.contains(name):
# if the contains method
duplicates.append(name)
# Replace the nested for loops below with your improvements
# for name_1 in names_1:
# if name_1 in names_2:
# duplicates.append(name_1)
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# print (f"\n{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
# print (f"runtime of nested loop (O(n^2)): {end_time - start_time} seconds")
################################################################
# Using BST data structures to reduce time complexity
duplicates1 = [] # Return the list of duplicates in this data structure
binary_tree = BSTNode('names')
start_time1 = time.time()
for name_1 in names_1:
binary_tree.insert(name_1)
for name_2 in names_2:
if binary_tree.contains(name_2):
duplicates1.append(name_2)
end_time1 = time.time()
print(f"\n{len(duplicates1)} duplicates:\n\n{', '.join(duplicates1)}\n\n")
print(
f"runtime of for loop with Linked list: {end_time1 - start_time1} seconds")
# # ---------- Stretch Goal -----------
# # Python has built-in tools that allow for a very efficient approach to this problem
# # What's the best time you can accomplish? Thare are no restrictions on techniques or data
# # structures, but you may not import any additional libraries that you did not write yourself.
####################################################################################
# # O(n)
class BinarySearchTreeTests(unittest.TestCase):
def setUp(self):
self.bst = BSTNode(5)
def test_insert(self):
self.bst.insert(2)
self.bst.insert(3)
self.bst.insert(7)
self.bst.insert(6)
self.assertEqual(self.bst.left.right.value, 3)
self.assertEqual(self.bst.right.left.value, 6)
def test_handle_dupe_insert(self):
self.bst2 = BSTNode(1)
self.bst2.insert(1)
self.assertEqual(self.bst2.right.value, 1)
def test_contains(self):
self.bst.insert(2)
self.bst.insert(3)
self.bst.insert(7)
self.assertTrue(self.bst.contains(7))
self.assertFalse(self.bst.contains(8))
def test_get_max(self):
self.assertEqual(self.bst.get_max(), 5)
self.bst.insert(30)
self.assertEqual(self.bst.get_max(), 30)
self.bst.insert(300)
self.bst.insert(3)
self.assertEqual(self.bst.get_max(), 300)
def test_for_each(self):
arr = []
cb = lambda x: arr.append(x)
v1 = random.randint(1, 101)
v2 = random.randint(1, 101)
v3 = random.randint(1, 101)
v4 = random.randint(1, 101)
v5 = random.randint(1, 101)
self.bst.insert(v1)
self.bst.insert(v2)
self.bst.insert(v3)
self.bst.insert(v4)
self.bst.insert(v5)
self.bst.for_each(cb)
self.assertTrue(5 in arr)
self.assertTrue(v1 in arr)
self.assertTrue(v2 in arr)
self.assertTrue(v3 in arr)
self.assertTrue(v4 in arr)
self.assertTrue(v5 in arr)
def test_print_traversals(self):
# WARNING: Tests are for Print()
# Debug calls to Print() in functions will cause failure
stdout_ = sys.stdout # Keep previous value
sys.stdout = io.StringIO()
self.bst = BSTNode(1)
self.bst.insert(8)
self.bst.insert(5)
self.bst.insert(7)
self.bst.insert(6)
self.bst.insert(3)
self.bst.insert(4)
self.bst.insert(2)
self.bst.in_order_print(self.bst)
output = sys.stdout.getvalue()
self.assertEqual(output, "1\n2\n3\n4\n5\n6\n7\n8\n")
sys.stdout = io.StringIO()
self.bst.bft_print(self.bst)
output = sys.stdout.getvalue()
self.assertTrue(output == "1\n8\n5\n3\n7\n2\n4\n6\n" or
output == "1\n8\n5\n7\n3\n6\n4\n2\n")
sys.stdout = io.StringIO()
self.bst.dft_print(self.bst)
output = sys.stdout.getvalue()
self.assertTrue(output == "1\n8\n5\n7\n6\n3\n4\n2\n" or
output == "1\n8\n5\n3\n2\n4\n7\n6\n")
sys.stdout = io.StringIO()
self.bst.pre_order_dft(self.bst)
output = sys.stdout.getvalue()
self.assertEqual(output, "1\n8\n5\n3\n2\n4\n7\n6\n")
sys.stdout = io.StringIO()
self.bst.post_order_dft(self.bst)
output = sys.stdout.getvalue()
self.assertEqual(output, "2\n4\n3\n6\n7\n5\n8\n1\n")
sys.stdout = stdout_ # Restore stdout
start_time = time.time()
f = open('names_1.txt', 'r')
names_1 = f.read().split("\n") # List containing 10000 names
f.close()
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
tree = BSTNode('')
for name1 in names_1:
tree.insert(name1)
for name2 in names_2:
if tree.contains(name2):
duplicates.append(name2)
end_time = time.time()
print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print (f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
if a > b:
print("true")
else:
print("false")
We should be able to use the first letter of the name to determine
where each name should be in a binary search tree.
'''
duplicates = [] # Return the list of duplicates in this data structure
binary_search_tree = BSTNode("root")
for name_1 in names_1:
binary_search_tree.insert(name_1)
for name_2 in names_2:
if binary_search_tree.contains(name_2):
duplicates.append(name_2)
# Improved runtime = 0.25955724716186523 seconds
# Improved runtime complexity = O(2n) || 2n because 2 loops execute n times
end_time = time.time()
print(f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print(f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
f.close()
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
node = BSTNode(names_1[0])
i = 1
while i < len(names_1):
node.insert(names_1[i])
i += 1
for each in names_2:
if node.contains(each):
duplicates.append(each)
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
end_time = time.time()
print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print (f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
