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Advanced-Algorithm-Project-2023-2024

This repository is dedicated to solving the Matrix Maximum Segment Problem in a 2-dimensional scenario, commonly known as the Maximum Subarray Problem. Our primary objective is to implement various algorithms for this task and provide a comprehensive experimental analysis of their runtime performance and the quality of results. Explore this repository to access the code, documentation, and data related to our research and implementations.

Project Repository Usage Guide

This guide explains how to use the repository for running and testing different algorithmic approaches.

Setup

First, ensure you have the following packages installed by running the following command in your terminal :

pip install numpy seaborn matplotlib

Creating a Python File

Create a Python file in the project folder.

Import Modules

Import the required modules as shown below:

from test_class.test_algorithm import TestAlgorithm
from visualizations.visualize_sns import create_test_result_plots

Choose an Approach

Import the approach you want to try. For example:

from brute.brute_force import brute_force_non_constrained, brute_force_constrained
from greedy.greedy_algorithm import greedy_algorithm_non_constrained

Testing the Algorithm

  1. Create an instance of the test class:
test_algorithm_instance = TestAlgorithm()
  1. To test a specific approach without constraints, use the following pattern:
algorithm_name = "Name of your algorithm"
test_results = test_algorithm_instance.run_algorithm_tests(approach_name_call, algorithm_name)
create_test_result_plots(test_results, algorithm_name)

For example, to test the brute force approach without constraints:

algorithm_name = "Brute Force Non-Constrained Algorithm"
test_results = test_algorithm_instance.run_algorithm_tests(brute_force_non_constrained, algorithm_name)
create_test_result_plots(test_results, algorithm_name)

Testing with Constraints

  1. To test an approach with constraints, add a new parameter constrained_type in the run_algorithm_tests function. It accepts two string values: 'variable' or 'fixed'.
  • fixed: Tests the approach against fixed constraints with increasing sizes of arrays.
  • variable: Tests the approach against variable constraints with a fixed sized array.

Example for fixed constraints:

algorithm_name = "Brute Force Constrained"
test_results = test_algorithm_instance.run_algorithm_tests(brute_force_constrained, algorithm_name, constrained_type='fixed')
create_test_result_plots(test_results, algorithm_name)

Example for variable constraints:

test_results = test_algorithm_instance.run_algorithm_tests(brute_force_constrained, algorithm_name, constrained_type='variable')
create_test_result_plots(test_results, algorithm_name)