Performance of the recursive implementation Clone this git…

Question Performance of the recursive implementation Clone this git… Performance of the recursive implementation Clone this git repository: https://github.com/kate-holdener/performance_exampleI already calculated 10 latency values of fib.py using n = 1, 10, 20, 30, 40. The raw latencies can be found in the performance.csv file. There are two columns in this file: the first column is the value of nthe second column is the latency of fib(n) in seconds (using fib.py implementation)Calculate the 99th, 90th, 70th, and 50th percentiles for each n. Enter your results in the table below: Percentile99907050n = 1    n = 10    n = 20    n = 30    n = 40     Plot the 90th percentile as a function of n. Insert your plot below. Using the cProfile tool, examine the number of times fib() function is called when n = 1, 10, 20, 30, 40. Enter the results in the table below n110203040# fib() calls      What do the answers from questions 3 and 4 tell you about the asymptotic complexity of fib.py implementation? (does the latency grow logarithmically, linearly, polynomially, or exponentially) B.  Performance of the cached implementation Calculate latency percentiles of fib2.py for n = 1, 10, 20, 30, 40. Note that you can copy and modify run_fib.py to output the raw results to a file for you.  Enter your results in the table below: Percentile99907050n = 1    n = 10    n = 20    n = 30    n = 40     Plot the 90th percentile as a function of n. Insert your plot below. Using the cProfile tool, examine the number of times fib() function is called when n = 1, 10, 20, 30, 40. Enter the results in the table below n110203040# fib() calls      What do the answers from questions 7 and 8 tell you about the asymptotic complexity of fib2.py implementation? (does the latency grow logarithmically, linearly, polynomially, or exponentially)  Computer Science Engineering & Technology Python Programming CSCI 3300 Share QuestionEmailCopy link Comments (0)