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sorts/stopwatch_sort.py
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sorts/stopwatch_sort.py
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"""
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One of reasons for learning about algorithms is
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that they can affect the efficiency of a program.
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More info on: https://en.wikipedia.org/wiki/Sorting_algorithm
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Therefore, it will be helpful for learners
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to see each algorithm's sorting process speed
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and compare the results of different algorithms.
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This function, stopwatch_sort, will return a list of sorting results,
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so it can be used to see how long each sorting algorithm
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takes to complete the sorting process.
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"""
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import os
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import random
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import sys
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import time
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# Modify sys.path to include the 'sorts' directory by adding the parent directory
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from sorts.binary_insertion_sort import binary_insertion_sort
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from sorts.bubble_sort import bubble_sort_iterative
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from sorts.bucket_sort import bucket_sort
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from sorts.counting_sort import counting_sort
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from sorts.heap_sort import heap_sort
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from sorts.merge_sort import merge_sort
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from sorts.quick_sort import quick_sort
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from sorts.radix_sort import radix_sort
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from sorts.selection_sort import selection_sort
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def stopwatch_sort(func_list: list, number_of_integers: int = 10000) -> list:
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"""
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implementation of comparing sorting algorithms
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:param func_list: list of sorting functions
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:param number_of_integers: number of randomly chosen integers to sort
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:return: list of results, where each result is a list including:
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the function name, the number of sorted integers,
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whether the integers were sorted properly,
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and milliseconds taken to complete the sorting process.
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For example, when the following code is executed:
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results = stopwatch_sort([binary_insertion_sort, bubble_sort_iterative], 8000)
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The results will be similar to:
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[['binary_insertion_sort', 8000, True, 2186.258316040039],
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['bubble_sort_iterative', 8000, True, 7760.7762813568115]]
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Examples:
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>>> first_results = stopwatch_sort([binary_insertion_sort], 5000)
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>>> len(first_results)
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1
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>>> len(first_results[0])
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4
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>>> first_results[0][0]
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'binary_insertion_sort'
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>>> first_results[0][1]
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5000
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>>> first_results[0][2]
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True
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>>> float(first_results[0][3]) >= 0
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True
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>>> second_results = stopwatch_sort([binary_insertion_sort, merge_sort])
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>>> len(second_results)
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2
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>>> len(second_results[1])
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4
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>>> second_results[1][0]
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'merge_sort'
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>>> second_results[1][1]
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10000
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>>> second_results[1][2]
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True
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>>> float(second_results[1][3]) >= 0
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True
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"""
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range_multiplier = 2
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int_range = (
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number_of_integers * range_multiplier
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) # Extendable range of random choice
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input_integers = [random.randint(0, int_range) for _ in range(number_of_integers)]
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sorted_integers = sorted(input_integers)
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result_list = []
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for func in func_list:
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# To prevent input_integers from being changed by function
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instance_integers = input_integers.copy()
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# Record the start and end time of sorting
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start_time = time.time()
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sorted_numbers = func(instance_integers)
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end_time = time.time()
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# Each result consists of four elements
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func_name = func.__name__
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length_of_sorted_numbers = len(sorted_numbers)
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properly_sorted = sorted_numbers == sorted_integers
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process_time_milliseconds = (end_time - start_time) * 1000
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process_result = [
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func_name,
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length_of_sorted_numbers,
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properly_sorted,
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process_time_milliseconds,
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]
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result_list.append(process_result)
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return result_list
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if __name__ == "__main__":
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user_input = input("Enter how many random numbers to be sorted: ")
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user_input_int = int(user_input)
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algorithm_list = [
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binary_insertion_sort,
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bubble_sort_iterative,
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bucket_sort,
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counting_sort,
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heap_sort,
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merge_sort,
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quick_sort,
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radix_sort,
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selection_sort,
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]
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results = stopwatch_sort(algorithm_list, user_input_int)
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for result in results:
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print(result)
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