CMPT 145 Assignment 7 Algorithm Analysis, and Recursion solution

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Question 0 (5 points):
Purpose: To force the use of Version Control in Assignment 7
Degree of Diculty: Easy
You are expected to practice using Version Control for Assignment 7. This is a tool that you need to be
required to use, even when you don’t need it, so that when you do need it, you are already familiar with it.
Do the following steps.
1. Create a new PyCharm project for Assignment 7.
2. Use Enable Version Control Integration… to initialize Git for your project.
3. Download the Python and text les provided for you with the Assignment, and add them to your
project.
4. Before you do any coding or start any other questions, make an initial commit.
5. As you work on each question, use Version Control frequently at various times when you have implemented an initial design, xed a bug, completed a question, or want to try something dierent. Make
your most professional attempt to use the software appropriately.
6. When you are nished your assignment, open the terminal in your Assignment 6 project folder, and
enter the command: git –no-pager log (double dash before the word ’no’). The easiest way to do
this is to use PyCharm, locate PyCharm’s Terminal panel at the bottom of the PyCharm window, and
type your command-line work there.
Note: You might have trouble with this if you are using Windows. Hopefully you are using the department’s network lesystem to store your les. If so, you can log into a non-Windows computer (Linux or
Mac) and do this. Just open a command-line, cd to your A6 folder, and run git –no-pager log there.
If you did all your work in this folder, git will be able to see it even if you did your work on Windows.
Git’s information is out of sight, but in your folder.
Note: If you are working at home onWindows, Google for how to make git available on your commandline window. You basically have to tell the command-line app where the git app is.
You may need to work in the lab for this; Git is installed there.
What to Hand In
After completing and submitting your work for Questions 5-7, open a command-line window in your Assignment 7 project folder. Run the following command in the terminal: git –no-pager log (double dash
before the word ’no’). Git will output the full contents of your interactions with Git in the console. Copy/-
paste this into a text le named a7-git.log.
If you are working on several dierent computers, you may copy/paste output from all of them, and submit
them as a single le. It’s not the way to use git, but it is the way students work on assignments.
Be sure to include your name, NSID, student number, course number and laboratory section at the top of
all documents.
Evaluation
• 5 marks: The log le shows that you used Git as part of your work for Assignment 7. For full marks,
your log le contains
– Meaningful commit messages.
– At least two commits per programming question for a total of at least 6 commits.
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CMPT 145
Principles of Computer Science
Question 1 (5 points):
Purpose: To practice using the big-O notation.
Degree of Diculty: Easy
Suppose you had 5 algorithms, and you analyzed each one to determine the number of steps it required,
and expressed the number of steps as a function of a size parameter, as follows:
1. f1(n) = n
49 + 200n
2. f2(n) = n
8
362 + 239n log(n)
3. f3(n) = 70500n
2 + 105n!
4. f4(n) = 3 log(n) + 2n
2
(n−1)
2 + 40n
3
log(n)
5. f5(n) = 8n
4 + n
4.123 + 16n
For each of the given functions, express it using big-O. For example, if f(n) = 17n
4 + 42 then we would
write f(n) = O(n
4
); you could also write f(n) ∈ O(n
4
) showing that the function is in the category O(n
4
).
Justications for your answers are not necessary. Just apply the rules and state the answers.
What to hand in
Include your answers in a le called a7.txt, though PDF and RTF les are acceptable. Clearly identify the
question number and each part. If you are submitting a text le, you can write exponents such as n
2
like
this: n^2.
Evaluation
1 mark for every correct answer. Answers that do not use the big-O notation will not be considered correct.
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CMPT 145
Principles of Computer Science
Question 2 (4 points):
Purpose: To practice analyzing algorithms to assess their run time complexity.
Degree of Diculty: Easy
Analyze the following pseudo-code, and answer the questions below. Each question asks you to analyze
the code under the assumption of a cost for the function doSomething(). For these questions you don’t
need to provide a justication.
1. Consider the following loop:
✞ ☎
i = 0
while i < n :
doSomething (…) # see below !
i = i + 1
✝ ✆
What is the worst case time complexity (Big-O) of this loop if doSomething() is a function requiring
O(1) steps?
2. Consider the following loop:
✞ ☎
i = 0
while i < n :
doSomething (…) # see below !
i = i + 2
✝ ✆
What is the worst case time complexity (Big-O) of this loop if doSomething() is a function requiring
O(n) steps?
3. Consider the following loop:
✞ ☎
i = 1
while i < n : doSomething (…) # see below ! i = i * 2 ✝ ✆ What is the worst case time complexity (Big-O) of this loop if doSomething() is a function requiring O(m) steps? Note: treat m and n as independent input-size parameters. 4. Consider the following loop: ✞ ☎ i = n while i > 0:
doSomething (…) # see below !
i = i – 1
✝ ✆
What is the worst case time complexity (Big-O) of this loop if doSomething() is a function requiring
O(m!) steps? Note: treat m and n as independent input-size parameters.
What to hand in
Include your answer in the a7.txt document. Clearly mark your work using the question number. If you
are submitting a text le, you can write exponents such as n
2
like this: n^2.
Evaluation
• 1 mark for each correct result using big-O notation. No justication needed.
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Principles of Computer Science
Question 3 (9 points):
Purpose: To practice analyzing algorithms to assess their run time complexity.
Degree of Diculty: Easy
(a) (3 points) Analyze the following Python script, and determine the worst-case time complexity. Justify
your work with a brief explanation.
✞ ☎
for i in range ( n ):
j = 0
while j < n :
print ( j – i )
j = j + 1
✝ ✆
(b) (3 points) Analyze the following Python script, and determine the worst-case time complexity. Justify
your work with a brief explanation.
✞ ☎
for i in range (len ( alist )):
j = 0
while j < i :
alist [ j ] = alist [ j ] – alist [ i ]
j = j + 1
✝ ✆
(c) (3 points) Analyze the following Python script, and determine the worst-case time complexity. Justify
your work with a brief explanation.
✞ ☎
1 for i in range (len ( alist )):
2 j = 1
3 while j < len ( alist ): 4 alist [ j ] = alist [ j ] – alist [ i ] 5 j = j * 2 ✝ ✆ What to hand in Include your answer in the a7.txt document. Clearly identify your work using the question number. If you are submitting a text le, you can write exponents such as n 2 like this: n^2. Evaluation • 1 marks for each correct result using big-O notation; • 2 marks for each correct justication. Page 5 Department of Computer Science 176 Thorvaldson Building 110 Science Place, Saskatoon, SK, S7N 5C9, Canada Telephine: (306) 966-4886, Facimile: (306) 966-4884 CMPT 145 Summer-Spring 2019 Principles of Computer Science Question 4 (6 points): Purpose: To practice analyzing algorithms to assess their run time complexity. Degree of Diculty: Easy Phoenix Wright, ace attorney, has just setup a new computer ling system to keep track of his court cases. He had his friend Larry Butz write the code (below) for the ling system. Larry claims his code is super fast, and runs in O(1) time. Is this statement at all correct? What about in the worst CASE? Best CASE? (no need for average case). JUSTICE-ify your answer. Carefully read the evaluation criteria of the question. ✞ ☎ 1 def file_case ( case , closed_cases ): 2 “”” 3 Purpose : 4 Checks whether the given case has been closed , 5 and if so puts it into the closed_cases . 6 If the case has not yet been closed , a search 7 is done to return any previously closed and related cases . 8 Pre : 9 case : a dictionary containing 3 fields – 10 status = a string . Either ” Open ” or ” Closed ” 11 keywords = list of strings . May match tags of other cases . 12 Can be any arbitrary length . 13 crime = string . What the single alleged crime is. 14 E.g. ” murder ” , ” burglary ” , etc . 15 closed_cases : list of cases ( see case above ) 16 Post : case is appended to closed_cases is if the status 17 of case is ” Closed ” 18 Return : Empty list if case ’s status was ” Closed ” , 19 otherwise a list of related cases 20 “”” 21 if case [” status “] == ” Closed “: 22 # Case is closed . Just add it to our list of closed cases 23 closed_cases . append ( case ) 24 return [] 25 # Case is not closed , do a search to return any related closed cases 26 related_cases = [] 27 for search_term in case [” keywords “]: 28 for closed_case in closed_cases : 29 if search_term == closed_case [” crime “]: 30 related_cases . append ( closed_case ) 31 return related_cases ✝ ✆ What to hand in Include your answer in the a7.txt document. Clearly identify your work using the question number. Evaluation • 2 marks: You correctly identied the best and worst case in Big-O, and whether Larry sucks. • 2 mark: You identied any size input size parameters. • 2 marks: Your JUSTICE-ications and analysis are correct (accusing people and OBJECTIONS! are encouraged). Page 6 Department of Computer Science 176 Thorvaldson Building 110 Science Place, Saskatoon, SK, S7N 5C9, Canada Telephine: (306) 966-4886, Facimile: (306) 966-4884 CMPT 145 Summer-Spring 2019 Principles of Computer Science Question 5 (8 points): Purpose: To practice simple recursion on integers. Degree of Diculty: Easy (a) (2 points) The Fibonacci sequence is a well-known sequence of integers that follows a pattern that can be seen to occur in many dierent areas of nature. The sequence looks like 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, . . . . That is, the sequence starts with 0 followed by 1, and then every number to follow is the sum of the previous two numbers. The Fibonacci numbers can be expressed as a mathematical function as follows: f(n) =    0 if n = 0 1 if n = 1 f(n − 1) + f(n − 2) if n > 1
Translate this function into Python, and test it. The function must be recursive.
Your function should only accept a non-negative n integer as input, and return the nth Fibonacci number. No other parameters are allowed. It should not display anything.
(b) (2 points) The Moosonacci sequence is a less well-known sequence of integers that follows a pattern
that is rarely seen to occur in nature. The sequence looks like this:
0, 1, 2, 3, 6, 11, 20, 37, 68, 125 . . .
That is, the sequence starts with 0 followed by 1, and then 2; then every number to follow is the sum
of the previous three numbers. For example:
• m(3) = 3 = 0 + 1 + 2
• m(4) = 6 = 1 + 2 + 3
• m(5) = 11 = 2 + 3 + 6
Write a recursive Python function to calculate the nth number in the Moosonacci sequence. As with
the Fibonacci sequence, we’ll start the sequence with m(0) = 0.
Your function should only accept a non-negative n integer as input, and return the nth Moosonacci
number. No other parameters are allowed. It should not display anything.
(c) (4 points) Design a recursive Python function named substr that takes as input a string s, a target
character c, and a replacement character r, that returns a new string with every occurrence of the
character t replaced by the character r. For example:
✞ ☎
>>> substr (’l’, ’x’, ’Hello , world !’)
’Hexxo , worxd !’
>>> substr (’o’, ’i’, ’Hello , world !’)
’Helli , wirld !’
>>> substr (’z’, ’q’, ’Hello , world !’)
’Hello , world !’
✝ ✆
If the target does not appear in the string, the returned string is identical to the original string.
Addendum: Your function should accept two single character strings and an arbitrary string as input,
and return a new string with the substitutions made. It should not display anything.
Non-credit activities
You should of course test your functions before you submit. There is no credit for testing in this question,
so the issue is to test enough that you are condent without wasting your time. Use the debugger and step
through these functions for a few small values of n (try a base case and a non-base case). Try to identify
the stack frames in the debugging window, and notice how each function call gets its own set of variables.
Page 7
Department of Computer Science
176 Thorvaldson Building
110 Science Place, Saskatoon, SK, S7N 5C9, Canada
Telephine: (306) 966-4886, Facimile: (306) 966-4884
CMPT 145
Summer-Spring 2019
Principles of Computer Science
What to Hand In
A le called a7q5.py containing:
• Your recursive functions.
Be sure to include your name, NSID, student number, course number and laboratory section at the top of
all documents.
Evaluation
This is just a warm up, and the functions are very simple.
• 2 marks: Fibonacci function. Full marks if it is recursive, zero marks otherwise.
• 2 marks: Moosonacci function. Full marks if it is recursive, zero marks otherwise.
• 4 marks: subst function. Full marks if it is recursive, and if it works, zero marks otherwise.
Page 8
Department of Computer Science
176 Thorvaldson Building
110 Science Place, Saskatoon, SK, S7N 5C9, Canada
Telephine: (306) 966-4886, Facimile: (306) 966-4884
CMPT 145
Summer-Spring 2019
Principles of Computer Science
Question 6 (18 points):
Purpose: Practical recursion using the Node ADT.
Degree of Diculty: Moderate
Below are three recursive functions that work on node-chains (not Linked Lists). You MUST implement
them using the node ADT, and you MUST use recursion. We will impose very strict rules on implementing
these functions which will hopefully show you new ways to think about recursion. Keep in mind that the
node ADT is recursively designed, since the next eld refers to another node-chain (possibly empty).
For all of the questions you are not allowed to use any data collections (lists, stacks, queues). Instead,
recursively pass any needed information using the PARAMETERS. DO NOT ADD ANY EXTRA PARAMETERS.
None are needed.
You will implement the following three functions:
(a) (6 points) to_string(node_chain): For this function, you are going to re-implement the to_string()
operation from Assignment 5 using recursion. Recall, the function does not do any console output. It
should return a string that represents the node-chain (e.g. [ 1 | * -]–>[ 2 | * -]–>[ 3 | / ]).
Additionally, for a completely empty chain, the to_string() should return the string EMPTY.
To test this function, create the following test cases:
• An empty chain.
• A chain with one node.
• A chain with several nodes.
(b) (6 points) copy(node_chain): A new node-chain is created, with the same values, in the same order,
but it’s a separate distinct chain. Adding or removing something from the copy must not aect the
original chain. Your function should copy the node chain, and return the reference to the rst node in
the new chain.
To test this function, create the following test cases:
• An empty chain.
• A chain with one node.
• A chain with one several nodes.
Be sure to check that you have two distinct chains with the same values!
(c) (6 points) replace(node_chain, target, replacement): Replace every occurrence of the data target
in node_chain with replacement. Your function should return the reference to the rst node in the chain.
To test this function, create the following test cases:
• An empty chain.
• A chain with no replacements
• A chain with several replacements.
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What to Hand In
A le called a7q6.py containing:
• Your recursive functions for to_string(), copy(), replace() in a le called a7q6.py
• A test-script called a7q6_testing.py, including the cases above, and any other tests you consider
important.
Be sure to include your name, NSID, student number, course number and laboratory section at the top of
all documents.
Evaluation
• (3 marks each) Your to_string(), copy(), replace() functions are recursive, and correctly implement
the intended behaviour.
• (3 marks each) You implemented the test cases given above.
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Question 7 (18 points):
Purpose: To emphasize that recursion is not about code, or functions or Python. It’s about relationships.
Degree of Diculty: Tricky
Problem:
This question is designed to emphasize and reward the use of relationships in designing recursive functions. It’s straight-forward if you look at it from a certain point of view, but insidiously dicult if you insist
that staring at a computer is a good way to solve problems. The function that answers this problem can be
written in 5-8 lines of Python code (not counting the doc-string interface). The entire problem is guring
out what the relationships are. And the code is absolutely trivial once you have the relationship right. You
will know you have the relationships right because they make sense as relationships, not as Python code.
A small part of your grade for this question is the actual code, but much more weight is given to whether
or not you can explain the relationship that the function makes use of.
The problem starts with a room. The room will always be rectangular (a square is a special kind of rectangle,
so they’re allowed), but we will vary the dimensions. The room will be subdivided into an integer number
of squares of equal size. Think of the squares as “tiles.” In general, the room will be a tiles wide, and b tiles
long. Furthermore, a and b are integers, and always positive (a > 0 and b > 0).
Mario is in the room, trying to get from one corner of the room to the other. He will always start on the tile
in the lower left corner of the room, and he will always have to get to the tile in the top right corner of the
room. Mario’s movement is constrained by the rule that he cannot move diagonally from one tile to another.
Mario can only move to an adjacent tile on the same row, or on the same column, but not diagonally. And
of course, Mario cannot step outside the room.
Mario wants to follow a path that is as short as possible, counting tiles as distance. A path is a sequence
of tiles that Mario can move to. The shortest path has a + b − 1 tiles on it, including the starting tile and
the ending tile. Furthermore, and this is where the fun begins, there are possibly several paths that are all
equally short, with the same number of tiles.
These paths all share the same property: on a shortest path, Mario can take a step to an adjacent tile, and
he can only move up one tile (up, or “north”), or to the right one tile (right, or “east”). If Mario moves (“south”),
or left (“west”), he cannot be on a shortest path.
In the diagram below, we show a 3 × 4 room, and two valid shortest paths that Mario could take.
a
b
The question is, in a room of a × b tiles, how many dierent shortest paths could he take (obeying his
movement constraints, above)? We want to count paths that are dierent, and we want to count all the
paths. Some paths have a number of tiles in common. For example, two paths can start the same way,
but then at some tile, they split. Likewise, two paths that start separately might join up and end the same
way (see the red an blue paths above). These would all be separate paths. The only reason to give these
examples is to clarify what a path is.
(a) (5 points) Design and implement a recursive function called marioCount() that is given a and b (the
size of the room, in tiles), and returns the total number of shortest paths Mario could take to get from
the bottom left to the top right of the room.
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(b) (3 points) Use your recursive function to calculate the number of paths for each of the following cases:
• 3 × 3
• 4 × 4
• 10 × 12
This can be done in your script; submit the output of these examples with the answers to the following
questions.
(c) (5 points) For each base case in your function, briey explain (a sentence or two) what the base case
represents, and why the answer (which should be simple) is correct. It’s okay if your function has
several base cases, or only one.
(d) (5 points) For each recursive case in your function, briey explain (2-5 sentences should do) how the
problem size is made smaller, and how you build up the solution using the answer from a smaller
problem size. It’s okay if your function has several recursive cases, or only one.
Here are some hints:
• When you’re thinking about this problem, it’s natural to try to count the paths by nding all of them.
That’s ne, as you’re trying to look for patterns, and for small values of a and b. But your recursive
function should not try to build any paths; counting them is much simpler than nding them all.
• Use the recursion template.
What to Hand In
1. A le called a7q7.py containing: Your recursive marioCount() function and the code for the three
examples listed part (b).
2. Add your explanations for parts (c) and (d) to the le called a7.txt (used for question 1-4). Clearly identify
the question number and each part. You may also use RTF, DOC, DOCX, or PDF formats.
Be sure to include your name, NSID, student number, course number and laboratory section at the top of
all documents.
Evaluation
• (5 marks) Your marioCount() function is recursive, and correctly counts the number of paths.
• (3 marks) The output for the three test cases above is correct.
• (5 marks) Your explanation of the base case(s) conveys that you understood what the code is doing
there.
• (5 marks) Your explanation of the recursive case(s) conveys that you understood what the code is
doing there.
Non-credit activities
Here are some extra things you could do if you want to push yourself a bit.
1. There is a simple recursive program that solves the problem, which will get you full marks, but it’s
very slow when a and b get bigger. Give an argument for the time complexity of the simple program.
Can you establish the space complexity, i.e., how much space it uses?
2. Find a way to make the simple program fast. In other words, nd an algorithm whose worst case time
complexity is much better than the simple program. Establish the time and space complexity of the
improved algorithm.
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Principles of Computer Science
3. Solve the problem without recursion.
None of this extra work gets you any marks. Doing the work is its own reward.
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