CS Algorithms & Programming Lab 10 Solution

The objective of this lab is to learn how to create, manipulate and use arrays. Remember that analyzing your problems and designing them on a piece of paper before starting implementation/coding is always a best practice. Specifically for this lab, you are to both organize your data and methods working on them.

0. Setup Workspace

Start VSC and open the previously created workspace named labs_ws. Now, under the labs folder, create a new folder named lab10.

In this lab, you are to create Java classes/files (under labs/lab10 folder) as described below. We expect you to submit a total of 4 files including:

    • the Project class,

    • the Hackathon class with the main method,

    • the modified Project class,

    • the modified Hackathon class with the main method Outputs of sample runs are shown as brown.

Angel Investor

“An angel investor is an individual who provides capital for a business or businesses start-up, usually in exchange for convertible debt or ownership equity. Angel investors usually give support to start-ups at the initial moments, where risks of the start-ups failing are relatively high, and when most investors are not prepared to back them.” 1

“A hackathon is a design sprint-like event; often, in which computer programmers and others involved in software development, including graphic designers, interface designers, project managers, domain experts, and others collaborate intensively on software projects. The goal of a hackathon is to create functioning software or hardware by the end of the event. Hackathons tend to have a specific focus, which can include the programming language used, the operating system, an application, an API, or the subject and the demographic group of the programmers. In other cases, there is no restriction on the type of software being created.” 2

For this lab, we assume that you are an angel investor and joined a start-up idea hackathon to find projects. This hackathon takes 3 days and each day there will be 3 project presentations. In total, you will see 9 projects and you will choose 6 of them to add to your evaluation list.

Each project has been evaluated in terms of 4 indicators, and as shown in the parenthesis, each indicator has a different degree of influence on the final score. Thus, the final score is a weighted average of scores gained by these indicators. Projects are separated into segments according to their final scores and


    • “Angel investor”. https://en.wikipedia.org/wiki/Angel_investor [Accessed 8 Dec 2021]
2 “Hackathon”. https://en.wikipedia.org/wiki/Hackathon [Accessed 8 Dec 2021]
according to their segments, they take a credibility decision statement. You will use these credibility statements to consider whether you should invest or not.

    • Indicator 1. Score gained at presentation (%10)

    • Indicator 2. Number of people who tested the algorithm (%25)

    • Indicator 3. Expected time duration to complete all alpha and beta tests (%30)

    • Indicator 4. Potential payback rate (%35)

Because you enjoy programming as much as finding promising projects and investing in them, you decided to write an object-oriented algorithm for this decision-making process. For this purpose, you will create two classes, Project and Hackathon (with the main method).

1. Project Class

Create a new/empty file to define a class named Project. Your implementation will provide a simplified definition for a project, which has the following data members and methods:

Static Data Members:

    • INITIAL, MODIFIED, FINAL: Score types, with values 0, 1, and 2, respectively. It denotes the type of score(s) that are currently available and may be accessed about the project.

Instance Data Members:

    • ID: An identifier that is unique for each project, such as A1 and A2.

    • finalScore: Stores the weighted average of modified scores.

    • segment: Stores the segment of projects, it must be initialized with ‘?’.

    • credibility: Stores the credibility of projects, it must be initialized with ‘?’.

    • rawScores: It is an array that holds the related raw scores for each project.

    • modifiedScores: It is an array that holds the scaled scores for each project.

    • hackathon: It is a reference to a Hackathon instance, as part of which this project is being evaluated.

Methods:

    • Constructor: Takes values for and initializes the data members ID and rawScores.

    • Accessor methods:

        ◦ getHackathon: Method to access the hackathon reference.

    • Mutator methods:

        ◦ setHackathon: Method to set the associated hackathon object.

    • Other methods:

        ◦ calcModifiedScores: Method to update the elements of modifiedScores, which is initially a copy of rawScores, which is then scaled as follows.
Indicator 1. x = x (use as is)

Indicator 2.
(0 < x ≤
500)
→ x = 20

(500 < x ≤ 1000)
→ x = 80

(1000 < x)
→ x = 100
Indicator 3.
(0 < x ≤
6)
→ x = 100

(6 < x ≤
12)
→ x = 80

(12 < x)

→ x =20
Indicator 4. (0 ≤ x <
10)
→ x = 20
(10
≤ x < 15)
→ x = 80
(15
≤ x)

→ x = 100

    • calcWeightAvg: Method to update the finalScore by calculating weighted average of

∑  ·  




scores. Weighted average is calculated by the formula

, where

is score  and
is













∑










related weight to score .






        ◦ calcSegment: Method to update the segment according to the final score of the project. The scale for segmentation is as follows.
Project Final Score = 100 → A+

                    ◦ 90→A

                        ▪ 75→B

                        ▪ 60→C

                            • 40→D

        ◦ calcCredibility: Method to update the credibility according to the segment of the project. The scale for credibility is as follows.
Project Segment = A+ → Perfect

            ▪ A  → Very Good

                • B  → Good

                • C → Considerable

                • D → Not appropriate

        ◦ toString: Returns a string representation that contains all initial, modified, or final information stored about the project according to the accepted score type.

    2. Hackathon Class

Create a new/empty file to define a class named Hackathon. Your implementation will provide a simplified definition for a hackathon, which has the following data members and methods:

Static Data Members:

    • MAX_NO_OF_PROJECTS: Stores the maximum number of projects you can add to your evaluation list. You want it to remain unchanged throughout the program because this year you can invest in at most 6 projects.

    • INDICATOR_COUNT: Stores the number of indicators. It remains unchanged throughout the program.
    • INDICATOR_WEIGHTS: It is an array that holds the related weights of indicators. It remains unchanged throughout the program.

Instance Data Members:

    • projectCount: Counts the number of projects that you chose to evaluate.

    • projects: It is a reference to an array of Project instances.

Methods:

    • Constructor:
        ◦ Initializes the data members projectCount and projects as an array of Project objects.

    • Accessor methods:

        ◦ getMaxNoOfProjects: Method to access the MAX_NO_OF_PROJECTS data member.

        ◦ getIndicatorCount: Method to access the INDICATOR_COUNT data member.

        ◦ getIndicatorWeights: Method to access the INDICATOR_WEIGHT data member.

    • Other methods:

        ◦ addProject: Method that accepts a Project object project and adds it into the first available index of projects array. Then, it increments projectCount to pass the next available index. Lastly, it calls the setHackathon method to update the associated hackathon reference of the provided object project.

        ◦ toString: Returns a string table representation that contains all initial, modified, or final information stored about all projects in the hackathon according to the provided score type.

    • Main method:

        ◦ Create a Hackathon object.

        ◦ Add the following projects into this hackathon object.

Project ID
Indicator 1
Indicator 2
Indicator 3
Indicator4





A1
100
240
15
26





A2
20
407
13
11





A3
100
281
13
39





A4
80
1264
4
38





A5
20
1020
12
11





A6
100
1162
17
34






    • Print out the initial table.

    • Calculate modified scores for all projects in the hackathon.

    • Print out the modified table.

    • Calculate final score, segment, and credibility for all projects in the hackathon.

    • Print out the final table.
Sample run:


Initial Table

***********************************************************************

Project ID
Indicator 1
Indicator 2
Indicator 3
Indicator 4
A1
100
240
15
26
A2
20
407
13
11
A3
100
281
13
39
A4
80
1264
4
38
A5
20
1020
12
11
A6
100
1162
17
34

***********************************************************************

Modified Table

***********************************************************************

Project ID
Indicator 1
Indicator 2
Indicator 3
Indicator 4
A1
100
20
20
100
A2
20
20
20
80
A3
100
20
20
100
A4
80
100
100
100
A5
20
100
80
80
A6
100
100
20
100

***********************************************************************

Final Table

************************************************************************************************************************

Project ID
Indicator 1
Indicator 2
Indicator 3
Indicator 4
Weighted Mean
Segment
Credibility
A1
100
20
20
100
56.0
D
Not appropriate
A2
20
20
20
80
41.0
D
Not appropriate
A3
100
20
20
100
56.0
D
Not appropriate
A4
80
100
100
100
98.0
A
Very Good
A5
20
100
80
80
79.0
B
Good
A6
100
100
20
100
76.0
B
Good

************************************************************************************************************************