sorting-algorithms-analysis

Requirement:

You need minimum python 3 latest or later version to run this code. To download python 3, please refer

https://www.python.org/downloads/

Few other dependencies are required by the program like numpy and matplotlib. If they are not installed then run the below commands

	pip3 install matplotlib
	Pip3 install numpy

Execution :

Step 1. Generate the data set.

To generate the data set, please run the below command

python3 dataset_generator.py

User must provide the following inputs to generate the data set.

1. Datasets to generate : Number of datasets for which average to be taken.
2. How many numbers in each dataset : It is the maximum number of elements in each dataset from which inputs are taken. Give a number more or equal to 50,000

Note: This step is optional if user wants to use the data set files provided as part of code submission.

Note : For large dataset, testing might take more time for calculating the average execution time of each sorting algorithm

Step 2. Execute a specific algorithm or all the algorithms and then compare their performances.

To execute a sepecific algorithm or to compare the performace of all the algorithms, please run the below mentioned command

python3 compare.py

It will prompt you for the input like which algorithm do you want to run ?.

Note: If you want to run or compare the performance for all the algorithms then please give input or select '6'.

Give inputs for following :
Select Sorting Algorithm to test :
1. Insertion Sort
2. Merge Sort
3. Heap Sort
4. In-Place Quick Sort
5. Modified Quick Sort
6. All Sorting Algorithms

Once you select the algorithm, it will again prompt you for data set input. For ex: For which data set do you want to run this algorithm ?. Sorted or random etc.

Select Type of Data Set for sorting :
1. Random/Unsorted Input
2. Sorted Input
3. Reversely Sorted Input

Note: Please make sure you have generated data set files before running the executing or comparing the performance of all the algorithms.