Download code/train/Python/0001749_complexity_class.py from Variable-role/sajaniemi_variable_dataset_large: direct link, hf CLI and curl.
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4.8 kB
| __author__ = 'ferrard' | |
| # --------------------------------------------------------------- | |
| # Imports | |
| # --------------------------------------------------------------- | |
| import time | |
| import matplotlib.pyplot as plt | |
| import scipy as sp | |
| # --------------------------------------------------------------- | |
| # Interface - Timing function | |
| # --------------------------------------------------------------- | |
| def time_them(k, m, *functions): | |
| """Times the functions (accepting one argument - n) on k values of n up to m | |
| Stops the timing once the function's execution takes: | |
| - more then 2 sec | |
| - more then 1 sec longer then on previous value of n | |
| """ | |
| n_values = list(range(1, m)) | |
| if m > k: | |
| n_values = list(range(1, m, m//k)) | |
| results = [] | |
| for i in range(len(functions)): | |
| print("Testing function " + functions[i].__name__) | |
| results_for_f = [] | |
| for n in n_values: | |
| print("\tInput size " + str(n)) | |
| before = time.time() | |
| functions[i](n) | |
| after = time.time() | |
| results_for_f.append(after - before) | |
| if results_for_f[-1] > 2 or (len(results_for_f) > 1 and results_for_f[-1] - results_for_f[-2] > 1): | |
| break | |
| results.append(results_for_f) | |
| for i in range(len(functions)): | |
| plt.plot(n_values[:len(results[i])], results[i], label=functions[i].__name__) | |
| plt.legend() | |
| plt.show() | |
| # --------------------------------------------------------------- | |
| # Interface - try out | |
| # --------------------------------------------------------------- | |
| def n_sqrt_n(n): | |
| res = 0 | |
| for i in range(n*int(sp.sqrt(n))): | |
| res += 1 | |
| return res | |
| def n_squared(n): | |
| res = 0 | |
| for i in range(n*n): | |
| res += 1 | |
| return res | |
| # --------------------------------------------------------------- | |
| # Interface - Sum to | |
| # --------------------------------------------------------------- | |
| def sum_builtin(n): | |
| """Sums numbers up to n using built-in function - O(n)""" | |
| print(sum(range(n))) | |
| def sum_explicit(n): | |
| """Sums numbers up to n explicitely - O(n)""" | |
| total = 0 | |
| for i in range(n): | |
| total += i | |
| print(total) | |
| def sum_analytic(n): | |
| """Sums numbers up to n, analytically - O(1)""" | |
| print(n*(n + 1)//2) | |
| # --------------------------------------------------------------- | |
| # Fibonnachi numbers | |
| # --------------------------------------------------------------- | |
| def fib_n_naive(n): | |
| """Naive (recursive) way to compute Fibonacci's numbers. O(F(n))""" | |
| if n == 0: | |
| return 0 | |
| if n == 1: | |
| return 1 | |
| return fib_n_naive(n - 1) + fib_n_naive(n - 2) | |
| def fib_n_efficient(n): | |
| """Efficient way to compute Fibonacci's numbers. Complexity = O(n)""" | |
| a = 0 | |
| b = 1 | |
| for i in range(n - 1): | |
| c = a + b | |
| a = b | |
| b = c | |
| print(b) | |
| return b | |
| def fib_n_closed(n): | |
| """Closed-form computation Fibonacci's numbers. Complexity = O(n) | |
| WRONG! Problems with precision! | |
| """ | |
| fi = (1 + sp.sqrt(5))/2 | |
| res = int(round((fi**n - (-fi)**(-n))/sp.sqrt(5))) | |
| print(res) | |
| return res | |
| # --------------------------------------------------------------- | |
| # Sorting | |
| # --------------------------------------------------------------- | |
| BOUND = 1000 | |
| # BOUND = 1000000 # try this bound - the linear sort will much more slow down | |
| def sort_selection(n): | |
| """Sort n random numbers - using inefficient quadratic sort - O(n^2)""" | |
| l = list(sp.random.random_integers(0, BOUND, n)) | |
| for i in range(n): | |
| for j in range(i + 1, n): | |
| if l[i] > l[j]: | |
| tmp = l[i] | |
| l[i] = l[j] | |
| l[j] = tmp | |
| def sort_inbuilt(n): | |
| """Sorts n random numbers - using efficient inbuilt function - O(n log n)""" | |
| l = list(sp.random.random_integers(0, BOUND, n)) | |
| l.sort() | |
| def sort_linear(n): | |
| """Sorts n random numbers bounded in a small range - using efficient linear sort, called Counting sort - O(n)""" | |
| l = list(sp.random.random_integers(0, BOUND, n)) | |
| counts = [0]*(BOUND + 1) | |
| for i in l: | |
| counts[i] += 1 | |
| counter = 0 | |
| for i in range(len(counts)): | |
| for j in range(counts[i]): | |
| l[counter] = i | |
| counter += 1 | |
| # --------------------------------------------------------------- | |
| # Main | |
| # --------------------------------------------------------------- | |
| def main(): | |
| # time_them(20, 1000, n_sqrt_n, n_squared) | |
| # time_them(20, 1000000, sum_builtin, sum_explicit, sum_analytic) | |
| # time_them(20, 100, fib_n_naive, fib_n_closed, fib_n_efficient) | |
| # time_them(20, 1000, fib_n_closed, fib_n_efficient) | |
| # time_them(20, 10000, sort_inbuilt, sort_linear, sort_selection) | |
| time_them(20, 1000000, sort_inbuilt, sort_linear) | |
| if __name__ == '__main__': | |
| main() | |