Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: THEend8_
Summary
The main objective of this assignment is to get your hands dirty with some simple data structures and algorithms to solve basic computational problems. These data structures will also come in handy for your second assignment, so you should take your time to think about your implementations and try to make them as efficient as possible.
1 Getting Started
Before we get into the nitty gritty, we will discuss the skeleton codebase that will form the basis of your implementations, and provide some rules that must be followed when implementing your solutions.
1.1 Codebase
The codebase contains a number of data structures stubs that you should implement, as well as some scripts that allow your code to be tested. Figure 1 shows a snapshot of the project directory tree with the different files categorized.
test_structures.py
test_warmup.py
test_kmers.py
structures
dynamic_array.py
linked_list.py
bit_vector.py
warmup
warmup.py
malloclabs
kmer_structure.py
analysis.txt
generate_dna.py
Figure 1 The directory tree is organized by task. Blue represents directories, Teal represents files that contain implementations (but are not executable), and Orange represents executable files.
1.2 Implementation Rules
The following list outlines some important information regarding the skeleton code, and your implementation. If you have any doubts, please ask on Ed discussion.
❙ The code is written in Python and, in particular, should be executed with Python 3 or higher. The EAIT student server, moss, has Python 3.11.* installed by default. We recommend using moss for the development and testing of your assignment, but you can use your own system if you wish.
2 COMP3506/7505 – Semester 2, 2024
❙ You are not allowed to use built-in methods or data structures – this is an algorithms and data structures course, after all. If you want to use a dict (aka {}), you will need to imple- ment that yourself. Lists can be used as “dumb arrays” by manually allocating space like myArray = [None] * 10 but you may not use built-ins like append, clear, count, copy, extend, index, insert, pop, remove, reverse, sort, min, max, and so on. List slicing is also prohibited, as are functions like sorted, len, reversed, zip. Be sensible – if you need the functionality provided by these methods, you may implement them yourself. Similarly, don’t use any other collections or structures such as set or tuple (for example; mytup = ("abc", 123)).
❙ You are not allowed to use libraries such as numpy, pandas, scipy, collections, and so on.
❙ Exceptions: The only additional libraries you can use are random and math. You are allowed to use range and enumerate to handle looping. You can also use for item in my_list looping over simple lists.
2 Task 1: Data Structures (5 marks)
We’ll start off by implementing some fundamental data structures. You should write your own tests. We will try to break your code via (hidden) corner cases. You have been warned.
Task 1.1: Doubly Linked List (1.5 Marks)
Your first task is to implement a doubly linked list — your first “pointer-based” data structure. To get started, look at the linked_list .py file. You will notice that this file contains two classes: the Node type, which stores a data payload, as well as a reference to the next node; and the DoublyLinkedList type which tracks the head and tail of the list, as well as the number of nodes in the list.
A basic set of functions that you need to support are provided as function templates, and you will need to implement them. You will also notice that there may be some changes or modifications required to the data structures to support the necessary operations – feel free to add member variables or functions, but please do not change the names of the provided functions as these will be used for marking.
You will need to implement your own tests and run them using: python3 test_structures .py --linkedlist.
Task 1.2: Dynamic Array (2 Marks)
Unlike the linked list discussed above, which can store nodes at any abitrary location in memory, we often prefer to have data items stored contiguously (consecutively in memory), allowing us to access an element x at some index i in constant time. One such way to achieve this is through the use of a dynamic array.
The file dynamic_array.py contains another skeleton for you to implement. You should store your data in self._data, and you can add any other member variables to your Dy-namicArray object. Each function that needs to be supported is provided as a stub. Your implementations should be efficient and correct, and we have provided annotations to describe the expected complexity. In this assignment, we have given you a slightly trickier ADT than the classic append-only array discussed in the lectures. In particular, you must support prepend operations — that is, allowing an element x to be placed at the front of the array — in O(1) amortized time, worst case.
You will need to implement your own tests and run them using: python3 test_structures .py --dynamicarray
Task 1.3: Bitvector (1.5 marks)
In some applications, it is useful to track the state of a collection of objects using simple Boolean (True or False) flags. A naïve way to do this is to simply use a (dynamic) array, storing bool types as the underlying data. However, Boolean types are usually represented by a machine word (32 or 64 bits), meaning that we waste a lot of space with this approach. An alternative approach is to use a bitvector , which stores an array of b-bit integers to represent each item — unset bits (value 0) represent False, and set bits (value 1) represent True. Clearly, this approach uses 64× less space than the naïve approach, as a single b = 64 bit integer can track the state of 64 items.
The file bit_vector.py contains another skeleton for you to implement. Note that it uses a composition based design where a DynamicArray object is used to store the underlying data. Each function that needs to be supported is provided as a stub. Your implementations should be efficient and correct, and we have provided annotations to describe the expected complexity. We describe two of the more exotic operations that should be supported in more detail below.
You will need to implement your own tests and run them using: python3 test_structures .py --bitvector.
Bitvector Operations: Shift
The shift operator handles both left and right shifts, depending on the sign of the dist parameter. If the dist parameter is positive, we do a left shift by dist. A left shift moves all bits in the bitvector left by dist positions, replacing empty positions with 0 bits. For example, the following demonstrates the before (top) and after (bottom) of a left shift by dist = 2:
1011000100011
1100010001100
Notice that the two most significant bits have fallen off (the leftmost 10 on the first bitvector). The right shifts work the same way, but we move the bits dist positions to the right (and the least significant bits will fall off).
Bitvector Operations: Rotate
The rotate operator works exactly the same way as the shift operator, except it ensures that any bits that fall off the end are rotated back onto the start of the bitvector. Using the same example as above with dist = 2:
1011000100011
1100010001110
3 Task 2: Algorithmic Thinking Warm-Up (5 Marks)
Next, we are going to work on some simple warm up problems. These are designed to build your problem solving skills. Some of them may appear tricky at first; you are encouraged to sit down and think about them (a pen and a piece of paper will help). Do not be afraid to get creative, as there may be multiple ways to solve each problem. Each problem will be assessed on three tiers of tests — see the warmup.py file for more details, and test with test_warmup.py (you need to implement your own tests).
3.1 The Main Character
You are given a string S. You need to simply return the first position of a repeated character (indexed from zero), or −1 if there are no repeats.
❙ S = hello → 3.
❙ S = world → −1.
❙ S = algorithmsarefun → 10.
❙ S = ooooohigetitnow → 1.
Here’s the catch: S can be built from an alphabet containing 232 possible characters, represented as integers in the range [0, 232 − 1]. Hint: You should use one of your data structures from part one to help you solve this problem efficiently!
3.2 Sum-Thing Odd
You are given an unsorted list L containing n unique integers. Let min(L) and max (L) represent the smallest and largest integers in L. Your task is to find the sum of the missing odd integers in the range [min(L), max(L)].
❙ Consider L = ⟨6, 4, 9〉: min(L) = 4 and max (L) = 9. Your range of interest is thus [4, 9]. The sum of the missing odd integers will be 5 + 7 = 12.
❙ Consider L = ⟨10, 1, 7, 17〉: min(L) = 1 and max(L) = 17. Your range of interest is [1, 17]. The sum of missing odds in this range will be 3 + 5 + 9 + 11 + 13 + 15 = 56.
3.3 It’s cool, k?
A natural number is k-cool if it can be represented as the sum of unique non-negative powers of k. For example:
❙ 17 is 4-cool because 40 + 42 = 17;
❙ 128 is 2-cool because 27 = 128;
❙ 11 is 10-cool because 100 + 101 = 11.
Given n and k, you must return the n th largest k-cool natural number. Two alternative ways to say this:
❙ Return the n th smallest number that can be created by summing non-negative integer powers of k; or
❙ If you created all combinations of sums of powers of k and sorted them into ascending order, we want you to return the n th one.
Clearly, the first k-cool number is always 1 (k0 = 1) and the second k-cool number is always k 1 = k. Since we will test some very large numbers (up to 101 ,000 ,000 ), you should return the number modulo 1016 + 61.
3.4 Your Number is Up
Alice and Bob are playing a numbers game. The game starts with an unsorted list of integers, L, with n = |L| guaranteed to be even. On each turn, they both remove one number from L. If Alice removes an even number, it is added to her score. If she removes an odd number, her score remains unchanged. The opposite is true for Bob (removing an odd number adds to his score; removing an even number keeps his score unchanged). After n turns, the array is exhausted, and the winner is determined by the player with the largest score — A draw is also possible. Given L, you must return the winner, and their score, assuming both Alice and Bob play optimally. You can assume that Alice always gets the first turn.
3.5 Getting Lit
A straight road of length k is illuminated by n light poles. Each light pole is represented by a natural number i, where i represents the total distance from the start of the road to the given pole. Each light is capable of illuminating a radius r. Given an unsorted list L of poles, you need to return the minimal radius r such that the entire length of the road, k, is illuminated. You may assume that the width of the road is infinitely small.
Figure 2 A sketch of getting lit with n = 4 light poles.
6 COMP3506/7505 – Semester 2, 2024
4 Task 3: Problem Solving with Data Structures (5 marks)
You are an algorithms specialist working at SIGSEGVTM , a world leader in high perform- ance algorithmic solutions. You have been contracted by a bioinformatics company called MallocLabs who require a bespoke system to help them deal with a growing amount of genomic data they need to index. Their lead Bioinformatician, Barry Malloc, has provided you with the following overview of the data and the system requirements. Your job is to design and implement an appropriate data structure — and related algorithms — to match Barry’s requirements. Barry has kindly placed the required time bounds in the function stubs — you should carefully consider these when designing your data structure.
4.1 Data Representation
DNA data is represented as a string S of length |S| over an alphabet Σ = {A, C, G, T}. Each character represents a different base (Adenine , Cytosine , Guanine, and Thymine). For
example, a sequence S with |S| = 32 might look like S = GTCGTGAAGTCGGTTCCTTCAATGGTTAAACC.
Since sequences can be very long, we can break them up into k-mers, all possible substrings of length k. For example, there are 10 individual 23-mers of S:
GTCGTGAAGTCGGTTCCTTCAAT TCGTGAAGTCGGTTCCTTCAATG CGTGAAGTCGGTTCCTTCAATGG GTGAAGTCGGTTCCTTCAATGGT TGAAGTCGGTTCCTTCAATGGTT GAAGTCGGTTCCTTCAATGGTTA AAGTCGGTTCCTTCAATGGTTAA AGTCGGTTCCTTCAATGGTTAAA GTCGGTTCCTTCAATGGTTAAAC TCGGTTCCTTCAATGGTTAAACC
In general, a sequence of length |S| will contain |S|−k +1 k-mers, and there are a total of |Σ|k unique possible k-mers (in our case, |Σ| = 4). You are provided with a tool generate_dna.py that can generate n sequences of length |S| for you to experiment with; it is probably easiest to simply write them out to a file, and use the file as input to your testing program.
4.2 Required Functionality
At run-time, your program will be given two arguments that specify a path to a file containing DNA sequences, as well as the value of k we are interested in working with. For example, you might be given a file containing 50, 000 sequences of length 200, and k = 31. We will always use str types to represent k-mers. The data structure used to solve the following requirements is up to you, and should be designed based on the functionality requested. You may need to use one or more of the structures implemented in part one for example, but the final choice is yours. Implement in kmer_structure .py and test with test_kmers .py (you need to implement your own tests).
Storage and Modification: 2 marks
The first set of functions you need to support allow for reading and modifying data. They are specified as follows:
❙ read: Given a file containing DNA sequences, break them into individual k-mers, and store them in your data structure;
❙ batch_insert(L): Given a list of k-mers L, insert them into your data structure;
❙ batch_delete(L): Given a list of k-mers L, delete all occurences of them from your data structure.
Note that there may be some duplicate k-mers in your data structure. You must keep track of duplicates and their frequency, as these will be required for answering some query types in the next section.
Queries: 3 marks
Your data structure also needs to support the following query types.
❙ freqgeq(n): Return a list of unique k-mers that occur at least n times; ❙ count(q): Return the number of times a k-mer q occurs;
❙ countgeq(q): Return the total number of k-mers that are ≥ q; that is, you need to sum the frequencies of all k-mers lexicographically greater than or equal to q ;
❙ compatible(q): Return the total number of k-mers that are compatible with q.
We provide some further information on the compatibility query as follows. A given k-mer q is called compatible with k-mer b if the last two characters in q are the complement of the first two characters in b. In genomics, the pair A and T is complementary, as is the pair C and G. So, for example, CCTGATG is compatible with ACTTGCG:
q = CCTGATG
| |
ACTTGCG
Note that we always assume we are matching the end of the input query k-mer q with the start of all other k-mers.
Analysis: 2 marks (COMP7505 Students Only)
If you are a COMP7505 student, you must also answer the questions posed in the plain text file called analysis.txt (inside the malloclabs directory).COMP3506 students are encouraged to do this too, but they will not be assessed on this component. Please keep your answers succinct, but make sure to include all details that may be relevant. If in doubt, err on the side of more detail.
5 Assessment
This section briefly describes how your assignment will be assessed.
5.1 Mark Allocation
Marks will be provided based on an extensive (hidden) set of unit tests. These tests will do their best to break your data structure in terms of time and/or correctness, so you need to pay careful attention to the efficiency and the validity of your code. Each test passed will carry some weight, and your autograder score will be computed based on the outcome of the test suite. If you did not rigorously test your programs/code, you should go back and do so! As the famous poet Ice Cube once said: check yourself before you wreck yourself.
The marks (percentages) provided in each task above are indicative of the total score available for each part, but marks may be taken off for poor coding style including lack of commenting, inefficient solutions, and incorrect solutions. Our code quality checks are not as strict as PEP8, but we assume typical best practices are used such as informative variable and function names, commenting, and breaking long lines. While the overall grade/score will be calculated mathematically, an indicative rubric is provided as follows:
❙ Excellent: Passes at least 90% of test cases, failing only sophisticated or tricky tests; well structured and commented code; appropriate design choices; appropriate application of data structures/algorithms for solving Tasks 2/3.
❙ Good: Passes at least 80% of test cases, failing one or two simple tests; well structured and commented code; good design choices with some minor improvements possible; good application of data structures/algorithms for solving Task 2/3 with some minor improvements possible.
❙ Satisfactory: Passes at least 70% of test cases; code is reasonably well structured with some comments; most design choices are reasonable but significant room for improvement; reasonable application of data structures/algorithms for solving Task 2/3, but significant improvements possible.
❙ Poor: Passes less than 70% of test cases; code is difficult to read, not well structured, or lacks comments; design choices do not demonstrate a sound understanding of the desired functionality; little or no suitable application of data structures or algorithms towards solving Task 2/3.
5.2 Plagiarism and Generative AI
If you want to actually learn something in this course, our recommendation is that you avoid using Generative AI tools: You need to think about what you are doing, and why, in order to put the theory (what we talk about in the lectures and tutorials) into practical knowledge that you can use, and this is often what makes things “click” when learning. Mindlessly lifting code from an AI engine won’t teach you how to solve algorithms problems, and if you’re not caught here, you’ll be caught soon enough by prospective employers.
If you are still tempted, note that we will be running your assignments through sophistic- ated software similarity checking systems against a number of samples including including your classmates and our own solutions (including a number that have been developed with AI assistance). If we believe you may have used AI extensively in your solution, you may be called in for an interview to walk through your code. Note also that the final exam may contain questions or scenarios derived from those presented in the assignment work, so cheating could weaken your chances of successfully passing the exam.
As part of your submission, you must create a file called statement .txt. In that file, you must provide attribution to any sources or tools used to help you with your assignment, including any prompts provided to AI tooling. If you did not use any such tooling, you can make a statement outlining that fact. Failing to submit this file will yield you zero marks.
6 Submission
You need to submit your solution to Gradescope under the Assignment 1: Autograder link in your dashboard. Please use the appropriate link as there is a separate submission for 3506 and 7505 students. Once you submit your solution, a series of tests will be conducted and the results of the public tests will be provided to you. However, the assessment will also include a number of additional hidden tests, so you should make sure you test your solutions extensively. You may resubmit as often as you like before the deadline, but we are imposing a limit of ten submissions per 24 hour period. Please write your own tests!
Structure
The easiest way to submit your solution is to submit a .zip file. The autograder expects a specific directory structure for your solution, and the tests will fail if you do not use this structure. In particular, you should use the same structure as the skeleton codebase that was provided. You should also have the statement .txt and analysis .txt (for COMP7505 students). Submissions without the statement .txt will be given zero marks, and the autograder will notify you of this.
- 经济Economics
- 金融Finance
- 会计Accounting
- 工商管理Business Management
- 计算机computer science
- 商业分析business analysis
- 法律Law
- 管理Management
- 市场营销Marketing
- 哲学Philosophy
- 心理学Psychology
- 物理Physical
- 统计学statistics
- 电气工程electric engineering
- 化学chemistry
- 生物biology
- 数学mathematics
- 语言学linguistics
- 土木工程civil engineering
- 工程力学engineering mechanics
- 艺术art
PROFESSIONAL HELP!
在线咨询
立即下单
Wechat：THEend8_ 
Email：