代做DTS103TC Design and Analysis of Algorithms代写留学生Python语言

Module code and Title	DTS103TC Design and Analysis of Algorithms
School Title	School of AI and Advanced Computing
Assignment Title	Coursework
Submission Deadline	Sunday, March 23rd 23:59 (UTC+8 Beijing), 2025

DTS103TC Design and Analysis of Algorithms

Individual Coursework

Due: Sunday March 23rd, 2025 @ 11:59pm

Weight: 40%

Maximum score: 100 points

Learning Outcomes Assessed

• A. Describe the different classes of algorithms and design principles associated with them; Illustrate these classes by examples from classical algorithmic areas, current research and applications.

• B. Identify the design principles used in a given algorithm, and apply design principles to produce efficient algorithmic solutions to a given problem.

•  C. Have fluency in using basic data structures in conjunction with classical algorithmic problems.

General notes

• Please read the coursework instructions and requirements carefully. Not following these instructions and requirements may result in loss of marks.

• The assignment must be submitted via Learning Mall to the correct drop box. Only electronic submission is accepted and no hard copy submission.

• All students must download their file and check that it is viewable after submission.

Documents may become corrupted during the uploading process (e.g.   due to slow internet connections).  However, students themselves are responsible for submitting a functional and correct file for assessments.

• Academic Integrity Policy is strictly followed.

• The use of Generative AI for content generation is not permitted on this assessed coursework. Submissions will be checked through Turnitin.

Coding Policy

• Programming Language: All code must be written in Python, with a version of 3.6 or higher. Students are required to use Jupyter Notebook for their implementation, and the main file must be an ’ .ipynb’ file that includes all outputs (such as printed results, graphs, etc).

• Code Structure: The code must be well-structured, readable, and properly documented. Comments should be included for each function and section, explaining its purpose and functionality. Each notebook cell must contain at one task only.  In other words, you can use several cells for one task but one cell cannot contain more than one task.

• Libraries and Tools: Do not use any libraries or tools. Only the Python builtins and classes provided during the labs are allowed. For example, the class Node is allowed.

• Code Validation: Ensure the code is fully executable without errors for each Jupiter Notebook

cell. All notebooks must be submitted with outputs included (e.g., printed results, graphs, etc), allowing the marker to verify the results without rerunning the notebook. Of course, the code of the cell must match the output of the cell.

Submission Policy

1. Submission Format

• Each student must submit a single ZIP file containing:

(a) The final report in PDF format.  The first page of the PDF report must be the cover sheet provided on Learning Mall.

(b) A single Jupyter Notebook (file name ending with .ipynb) must be provided.

2. File Naming

• The ZIP file must be named in the format: Lastname_Firstname_StudentID_Coursework.zip.

• The PDF report must be named in the format: Lastname_Firstname_StudentID_Coursework.pdf.

• Each individual’s code must be named in the format: Lastname_Firstname_StudentID_Code .ipynb.

Late Policy

5% of the total marks available for the assessment shall be deducted from the assessment mark for each working day after the submission date, up to a maximum of five working days.

Avoid Plagiarism

• Do not submit work from other students.

• Do not share code/work to other students.

• Do not read code/work from other students, discussions should be limited to high level only.

• Do not use open-source code. You must write your own solution and comment your code.

Overview

In this coursework, you are expected to design and implement algorithms to produce solutions to four given problems in Python.  You should have function(s) to receive task input as parameters, implement your algorithm design and return results.

1 Task 1 (20 marks)

Given an m × n grid filled with integers representing the weight of the crop in one cell (or crop area). A farmer (who is gathering the crops) starts at the top left cell and need to find a way to the bottom right cell. The farmer can only move either down or right. What is the maximum weight of crops the farmer can collect through its path from top left to bottom right?  Solve this problem with dynamic programming.

Example 1 of input grid:

10   4    8    3

6    7    5    1

2   4    2    2

Output: 32 because the path giving the maximum reward is 10, 6, 7, 5, 2, 2.

Example 2 of input grid:

10   4    8    3

6    7    5    1

20   4    2    2

Output: 44 because the path giving the maximum reward is 10, 6, 20, 4, 2, 2.

You should have a function named maxPathSum to receive the grid (List[List[int]]) and return the maximum reward (int).

2 Task 2 (20 marks)

Given a directed acyclic graph, use depth first search to find all the paths from node S to node T. Input example:

graph  =  {"S":  ["A","B"],

"A":  ["B","T"],

"B":  ["T"]}

Output:

[["S",  "A",  "T"],

["S",  "A",  "B",  "T"], ["S",  "B",  "T"]]

Answer: There are three paths: S → A → T, S → A → B → T, S → B → T

The adjacency list of the graph can be represented using a dictionary in python, where the keys are the nodes of the graph, and their values are a list storing the neighbors of these nodes. You should have a function named allPaths to receive the adjacency list represented as a dictionary and return a list of paths.

3.1 Introduction

A factory is producing n = 2l objects such that they all have the same weight w. However, the objects sometimes have a different weight and must be identified.  Suppose we are in the case where two objects among the n have different weights such that the first object has weight w1  and the second object has weight w2  with the following properties:  w1  < w2 < w and w1 + w2 = w. Given a list of weights, implement an efficient algorithm to find the index of the two objects with weight different than w. In this problem, we use a precision digital balance to measure accurately the total weight of a list of objects.

Input/Output pair example: weights  =  [w,  w,  w, w_2, w, w, w_1, w],  (3,  6)

In this example, there are n = 23  = 8 weights where w1  is at index 6 and w2  is at index 3.  In other words, (i1, i2) = (3, 6) and, in Python, we have weights[6]  =  w_1 and weights[3]  =  w_2.

The Python function containing your algorithm must be named findWeights2 with function argument named weights to receive a list of float numbers. The output of your function is a tuple of index (i1, i2).

3.2   Algorithm design

This section is meant to guide the student through solving the problem.  Suppose we divide the set of input objects in two disjoint subsets U1  and U2  of equal sizes.

1. At the first iteration (first division of the object list): how many objects are there in U1  and U2?

2. What are the possible values for the weight difference between U1  and U2?  (Hint:  negative weight differences are considered)

3.  In each case, in which set does w1 belong to? What about w2?

4.  In each case, what should the algorithm do? (4 points)

5. A sub-problem: we consider a simpler version of the problem with the following conditions:

• There are n = 2l  objects.

• Among the n objects, only one object has weight w0  < w.

The Python function containing your algorithm must be named findWeight1 with function argument named weights to receive a list of float numbers.  The output of your function is an index i.

6. Based on the previous questions, implement an efficient algorithm (the function is named findWeights2) to find the positions ofw1  and w2 . You may reuse the findWeight1 function.

7. What is the running time of your algorithm?

4   Task 4 (30 marks)

Given a list of intervals, merge all overlapping intervals and return the merged list.

Input example:  [[9,11],  [1,4],  [13,19],  [2,7]]

Output: the merged list should be [[1,7],  [9,11],  [13,19]].

1.  Identify the appropriate data structures and algorithms needed to solve this problem. For each choice, explain your solution in less than 200 words.

2.  Implement your solution in Python language. The function should be named mergeIntervals and receive as input as Python List

3. Provide 5 test cases of your algorithm.

4. Analyze the time and space complexity of your algorithm.