CCS592: Sorting Algorithms in Python - Bubble-Sort - IT/Computer Science Assignment help

Assignment Task:

Instruction: 
1. You are required to implement these following 5 sorting algorithms in Python: Bubble-Sort, Merge-Sort, Selection-Sort, Bucket-Sort and Radix-Sort. 
Note: You might want to include other sorting algorithms to enrich your analysis. Among suggested algorithms are: Timsort, Library sort, Shell sort, and Bitonic sort. 
Instruction: 
2. Perform an experimental 

•  time analysis
•  number of comparison analysis
•  array access analysis on these algorithms as n grows (suggestion: from n=100 to n=5000, with increment of 100), with multiple runs (average reading from at least 5 runs) for each problem size. Determine the relative efficiencies of these algorithms in the worst and average cases as a function of n. Perform the experiments for each of the following distributions:
•  Uniformly distributed permutations, where all permutations are equally likely. 
•  Almost-sorted permutations. These are generated by starting with a sorted array of n numbers. Then, independently choose ⌊log2 n⌋ pairs, (i,j), where i and j are uniformly-chosen random integers in the range from 0 to n-1, and swap the numbers at positions i and j in the array. 

Instruction: 
3. Plot the results on a log-log scale (6 plots: { (time analysis, uniformly distributed), (time analysis, almost sorted), ..., (array access analysis, almost sorted)} ) to empirically determine the algorithm's asymptotic running time for each type of the analysis- distribution cases. If the data is well-fit with a line, then the slope of the line can be used to determine the exponent of the algorithm’s efficiency (under 6 different cases). Rank all your sorting implementations from asymptotically slowest to fastest based on your experimental results. 
Instruction: 
4. Deliverables: 

•  Python source code of the 5 sorting algorithms with their corresponding runtime results.
•  A report (in PDF) that includes: Comparative experimental analysis and conclusions drawn from the runtime performance of the algorithms tested. Please include summary of your algorithm, design choices implemented in the source code (such as data structures and representations), and visual plots showing comparative empirical performance of the tested algorithms. The plots should be on a log-log scale. 

Note: There will be a submission link in e-learn, Week-8’s block. 
Instruction: 
5. Suggested reference: Data Structures and Algorithms in Python.

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