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FIT2004 2024 Assignment

创建日期：2024-06-18 17:26:51

所属分类：计算机computer science

标签： FIT2004

FIT2004 2024 Assignment

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FIT2004 Assignment

LATE SUBMISSION PENALTY: 10% penalty per day. Submissions more than 7
calendar days late will receive 0. The number of days late is rounded up, e.g. 5 seconds
late means 1 day late, 27 hours late is 2 days late.
The deadlines in this unit are strict, last minute submissions are at your own risk.
PROGRAMMING CRITERIA: It is required that you implement this exercise strictly
using the Python programming language (version should not be earlier than 3.5). This
practical work will be marked on the time complexity, space complexity and functionality
of your program, and your documentation.
Your program will be tested using automated test scripts. It is therefore critically impor-
tant that you name your files and functions as specified in this document. If you do not, it
will make your submission difficult to mark, and you will be penalised.
SUBMISSION REQUIREMENT: You will submit a single python file containing all
of the questions you have answered, assignment2.py. Moodle will not accept submissions
of other file types.
PLAGIARISM: The assignments will be checked for plagiarism using an advanced pla-
giarism detector. In previous semesters, many students were detected by the plagiarism
detector and almost all got zero mark for the assignment (or even zero marks for the unit as
penalty) and, as a result, the large majority of those students failed the unit. Helping others
to solve the assignment is NOT ACCEPTED. Please do not share your solutions partially
or completely to others. Even after the deadline, your solutions/approaches should not be
shared before the grades and feedback are released by the teaching team. Using contents
from the Internet, books etc without citing is plagiarism (if you use such content as part
of your solution and properly cite it, it is not plagiarism; but you wouldn’t be getting any
marks that are possibly assigned for that part of the task as it is not your own work).
The use of generative AI and similar tools for the completion of your assignment
is not allowed in this unit!
1
Learning Outcomes
This assignment achieves the Learning Outcomes of:
• Analyse general problem solving strategies and algorithmic paradigms, and apply them
to solving new problems;
• Prove correctness of programs, analyse their space and time complexities;
• Compare and contrast various abstract data types and use them appropriately;
• Develop and implement algorithms to solve computational problems.
In addition, you will develop the following employability skills:
• Text comprehension.
• Designing test cases.
• Ability to follow specifications precisely.
Assignment timeline
In order to be successful in this assessment, the following steps are provided as a suggestion.
This is an approach which will be useful to you both in future units, and in industry.
Planning
1. Read the assignment specification as soon as possible and write out a list of questions
you have about it.
2. Try to resolve these questions by viewing the FAQ on Ed, or by thinking through the
problems over time.
3. As soon as possible, start thinking about the problems in the assignment.
• It is strongly recommended that you do not write code until you have a solid feeling
for how the problem works and how you will solve it.
4. Writing down small examples and solving them by hand is an excellent tool for coming
to a better understanding of the problem.
• As you are doing this, you will also get a feel for the kinds of edge cases your code
will have to deal with.
5. Write down a high-level description of the algorithm you will use.
6. Determine the complexity of your algorithm idea, ensuring it meets the requirements.
2
Implementing
1. Think of test cases that you can use to check if your algorithm works.
• Use the edge cases you found during the previous phase to inspire your test cases.
• It is also a good idea to generate large random test cases.
• Sharing test cases is allowed, as it is not helping solve the assignment.
2. Code up your algorithm (remember decomposition and comments), and test it on the
tests you have thought of.
3. Try to break your code. Think of what kinds of inputs you could be presented with which
your code might not be able to handle.
• Large inputs
• Small inputs
• Inputs with strange properties
• What if everything is the same?
• What if everything is different?
• etc...
Before submission
• Make sure that the input/output format of your code matches the specification.
• Make sure your filenames match the specification.
• Make sure your functions are named correctly and take the correct inputs.
• Remove print statements and test code from the file you are going to submit.
3
Documentation
For this assignment (and all assignments in this unit) you are required to document and com-
ment your code appropriately. Whilst part of the marks of each question are for documentation,
there is a baseline level of documentation you must have in order for your code to receive marks.
In other words:
Insufficient documentation might result in you getting 0 for the entire question for which it is
insufficient.
This documentation/commenting must consist of (but is not limited to):
• For each function, high-level description of that function. This should be a two or three
sentence explanation of what this function does.
• Your main function in the assignment should contain a generalised description of the
approach your solution uses to solve the assignment task.
• For each function, specify what the input to the function is, and what output the function
produces or returns (if appropriate).
• For each function, the appropriate Big-O or Big-Θ time and space complexity of that
function, in terms of the input size. Make sure you specify what the variables involved
in your complexity refer to. Remember that the complexity of a function includes the
complexity of any function calls it makes.
• Within functions, comments where appropriate. Generally speaking, you would comment
complicated lines of code (which you should try to minimise) or a large block of code
which performs a clear and distinct task (often blocks like this are good candidates to be
their own functions!).
A suggested function documentation layout would be as follows:
def my_function(argv1, argv2):
"""
Function description:
Approach description (if main function):
:Input:
argv1:
argv2:
:Output, return or postcondition:
:Time complexity:
:Time complexity analysis:
:Space complexity:
:Space complexity analysis:
"""
# Write your codes here.
There is a documentation guide available on Moodle in the Assignment section, which contains
a demonstration of how to document code to the level required in the unit.
4
1 Open reading frames
(10 marks, including 2 marks for documentation)
In Molecular Genetics, there is a notion of an Open Reading Frame (ORF). An ORF is a
portion of DNA that is used as the blueprint for a protein. All ORFs start with a particular
sequence, and end with a particular sequence.
In this task, we wish to find all sections of a genome which start with a given sequence of
characters, and end with a (possibly) different given sequence of characters.
To solve this problem, you will need to create a class OrfFinder. The constructor for this class
takes a string genome as a parameter. Additionally, this class will need a method find(start,
end).
1.1 Input
genome is a single non-empty string consisting only of uppercase [A-D]. genome is passed as an
arguement to the __init__ method of OrfFinder (i.e. it gets used when creating an instance
of the class).
start and end are each a single non-empty string consisting of only uppercase [A-D].
1.2 Output
find returns a list of strings. This list contains all the substrings of genome which have start
as a prefix and end as a suffix. There is no particular requirement for the order of these strings.
start and end must not overlap in the substring (see the last two cases of the example below).
1.3 Example
genome1 = OrfFinder("AAABBBCCC")
genome1.find("AAA","BB")
>>> ["AAABB","AAABBB"]
genome1.find("BB","A")
>>>[]
genome1.find("AA","BC")
>>>["AABBBC","AAABBBC"]
genome1.find("A","B")
>>> ["AAAB","AAABB","AAABBB","AAB","AABB","AABBB","AB","ABB","ABBB"]
genome1.find("AA","A")
>>> ["AAA"]
#note that "AA" is not valid, since start and end would need to overlap
genome1.find("AAAB","BBB")
>>> []
# note that "AAABBB" is not valid, since start and end would need to overlap
5
1.4 Complexity Requirements
• The __init__ method of OrfFinder must run in time complexity O(N2), where N is the
length of genome.
• Let T be the length of the string start, U be the length of the string end, and V be the
number of characters in the output list (for a correctly generated output list according to
the instructions in 1.2), then find must run in time complexity (T + U + V ).
As an example of what the complexity for find means, consider a string consisting of N
2
"B"s
followed by N
2
"A"s. If we call find("A","B"), the output is empty, so V is O(1). On the other
hand, if we call find("B", "A") then V is O(N2).
6
2 Securing the companies
(10 marks, including 2 marks for documentation)
Assume that you are the manager of a security company tasked with assigning security officers
(n) to a group of companies (m) in a certain month with 30 days numbered D0, D1, . . . , D29. As-
sume that security officers are denoted by SO0, SO1, . . . SOn−1 and the companies are represented
by C0, C1, . . . Cm−1. There are three 8-hour shifts per day: S0 (midnight-8am), S1 (8am-4pm),
and S2 (4pm-midnight). For each pair company/shift (Cj, Sk), the company Cj has a different
number of security officers required for the shift Sk, however, this is constant for each day of
the month.
The security officers specify the shifts they are interested in working on, and they have the
opportunity to indicate more than one (1) preferred shift. However, they cannot be allocated
to more than one (1) shift per day. Further, the shift preference of each security officer should
be the same for all the days of the month (and therefore specified only once per security officer).
Your task is to provide a plan for assigning officers to the companies for the coming month
according to the requirement of the number of security officers per shift by each company. For
each day of the month, and for each shift, each company should be allocated exactly the same
number of security officers it requests. There are 2 integer parameters, 0 ≤min_shifts≤ 30
(minimum number of shifts) and 0 ≤max_shifts ≤ 30 (maximum number of shifts), and the
total number of shifts allocated to each security officer in a month should be within these two
integer parameters (closed interval).
As long as you comply with the constraints above, you are free to allocate the security officers
any way you want.
The input to your function allocate consists of a list of preferred shifts provided by each
security officer and the number of security officers requested by the companies for each shift:
preferences is a list of lists, where preferences[i][k], is a binary value (0 or 1), indicating
whether security officer SOi is interested in working on shift Sk in that month (1 indicates that
the officer is interested). officers_per_org is a list of lists, where officers_per_org[j][k]
is a non-negative number that specifies how many security officers company Cj needs for shift
Sk on each day of that month.
Your task is to implement a function allocate(preferences, officers_per_org, min_shifts,
max_shifts) which returns:
• None (i.e., Python NoneType), if no allocation satisfying all constraints exists.
• Otherwise, it returns a list of lists allocation, where allocation[i][j][d][k] is equal
to 1 if security officer SOi is allocated to work for company Cj during shift Sk on day Dd.
Complexity requirement: The worst-case time complexity of your solution should be
O(m ∗ n ∗ n).
7
Warning
For all assignments in this unit, you may not use python dictionaries or sets. This is because
the complexity requirements for the assignment are all deterministic worst-case requirements,
and dictionaries/sets are based on hash tables, for which it is difficult to determine the deter-
ministic worst-case behaviour.
Please ensure that you carefully check the complexity of each in-built python function and
data structure that you use, as many of them make the complexities of your algorithms worse.
Common examples which cause students to lose marks are list slicing, inserting or deleting
elements in the middle or front of a list (linear time), using the in keyword to check for
membership of an iterable (linear time), or building a string using repeated concatenation
of characters. Note that use of these functions/techniques is not forbidden, however you
should exercise care when using them.
Please be reasonable with your submissions and follow the coding practices you’ve been taught
in prior units (for example, modularising functions, type hinting, appropriate spacing). While
not an otherwise stated requirement, extremely inefficient or convoluted code will result in
mark deductions.
These are just a few examples, so be careful. Remember that you are responsible for the
complexity of every line of code you write!

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FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 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EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 ACCOUNTING BUSS1040 CHEM 181 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