f.close()
duplicates = [] # Return the list of duplicates in this data structure
#Runtime complexity is O(n2)
# 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)
tree = BSTNode(names_1[0])
names_1_checker = [name for name in names_1[1:]]
#Create a new array, without first value to distinguish
for name in names_1_checker:
tree.insert(name)
for name in names_2:
if 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? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
# 0(n^2)
# for name_1 in names_1:0(n)
# for name_2 in names_2: 0(n)
# if name_1 == name_2: 0(1)
# duplicates.append(name_1) 0(1)
bst = None #0(1)
for name_1 in names_1: #0(n) # followed the bst tester
if bst is None: #0(1)
bst = BSTNode(name_1) #0(1)
else:
bst.insert(name_1) #0(log n)
#first loop 0(n log n)
for name_2 in names_2: #0(n)
if bst.contains(name_2): #0(log n)
duplicates.append(name_2) #0(1)
#0(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
bst = BSTNode("Root Node")
for name_1 in names_1:
bst.insert(name_1)
for name_2 in names_2:
if bst.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.
# # 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)
#Using BST to cut runtime
head = names_1[0]
names_1 = names_1[1:]
bst = BSTNode(head)
for name in names_1:
bst.insert(name)
for name in names_2:
if bst.contains(name):
duplicates.append(name)
# RUNTIME : 0.16530179977416992 seconds , 64 Duplicates
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
list_one = BSTNode(names_1[0])
for name in names_1:
list_one.insert(name)
for name in names_2:
if list_one.contains(name):
duplicates.append(name)
# 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 -----------
f = open('names_1.txt', 'r')
bst_names_1 = f.read().split("\n") # List containing 10000 names
f.close()
f = open('names_2.txt', 'r')
bst_names_2 = f.read().split("\n") # List containing 10000 names
f.close()
# initialize an empty list for the duplicates
bst_duplicates = []
# initialize the BSTNode
names_bst = BSTNode('findnames')
# for every name in the first list
for name in bst_names_1:
# add every name to the binary search tree
names_bst.insert(name)
# for every name in teh second list
for name in bst_names_2:
# check to see if the binary search tree already contains that name
if names_bst.contains(name):
# if it does, add it to the duplicates list
bst_duplicates.append(name)
fin_end_time = time.time()
print(f"{len(bst_duplicates)} duplicates: \n\n{',' .join(bst_duplicates)}\n\n")
# print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
# print (f"initial runtime: {end_time - start_time} seconds") # with the nested loops implementation it took 15.864028692245483 seconds to run.
print(f"final tuntime: {fin_end_time - fin_start_time} secones")
print(
f"\ninitial runtime complexity: {initial_runtime_complexity}, final runtime complexity: {final_runtime_complexity}\n"