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How to use timsort method in hypothesis

Best Python code snippet using hypothesis

performance_compare.py

Source:performance_compare.py

...31            finish_time = time.time() - start_time32            merge_lst.append(finish_time)3334            start_time = time.time()35            timsort(copy)36            finish_time = time.time() - start_time37            timsort_lst.append(finish_time)3839        avg_merge = sum(merge_lst)/len(merge_lst)40        avg_timsort = sum(timsort_lst)/len(timsort_lst)4142        merge_avg.append(avg_merge)43        timsort_avg.append(avg_timsort)4445    # Plot individual graphs for each curve46    plt.plot(merge_avg, color='red', label = 'Traditional merge')47    plt.plot(timsort_avg, color='blue', label = 'TimSort')48    plt.plot(theory_time, color='green', label = 'Theoretical run time O(nlog(n))')49    plt.xlabel("Length of the list")50    plt.ylabel("Averaged run-time")51    plt.legend()52    plt.show()535455# With worst case traditional merge sort - maximum comparisons:56def worstCases(n):57    """Generates the worst case scenario for merge sort58    with maxiimum comparisons possible for every size N"""5960    # Holds base cases of N = 1, N = 261    lst = [[], [1], [2,1]]6263    # Builds worst cases from the bottom up64    for i in range(3, n + 1):65        left = lst[i//2]66        right = lst[i - i//2]67        left = [x*2 for x in left]68        right = [y*2 - 1 for y in right]69        entry = left + right70        lst.append(entry)71    return lst727374def graph_runtimes_worst(length_lst, repeats):75    merge_avg = []76    timsort_avg = []77    theory_time = [0]78    worst_cases = worstCases(length_lst)7980    for i in range(1, length_lst):81        theory_time.append(i*math.log(i)/1000000)8283    for i in worst_cases:84        merge_lst = []85        timsort_lst = []8687        for x in range(repeats):88            mylist = i89            copy = mylist.copy()9091            start_time = time.time()92            mergesort(mylist, 0, len(mylist) - 1)93            finish_time = time.time() - start_time94            merge_lst.append(finish_time)9596            start_time = time.time()97            timsort(copy)98            finish_time = time.time() - start_time99            timsort_lst.append(finish_time)100101        avg_merge = sum(merge_lst)/len(merge_lst)102        avg_timsort = sum(timsort_lst)/len(timsort_lst)103104        merge_avg.append(avg_merge)105        timsort_avg.append(avg_timsort)106107    plt.plot(merge_avg, color='red', label = 'Traditional merge')108    plt.plot(timsort_avg, color='blue', label = 'TimSort')109    plt.plot(theory_time, color='green', label = 'Theoretical run time O(nlog(n))')110    plt.xlabel("Length of the list")111    plt.ylabel("Averaged run-time")112    plt.legend()113    plt.show()114115116# With sorted data - best case Timsort:117def graph_runtimes_best(length_lst, repeats):118    """Identical to graph_runtimes, but with ordered data"""119    merge_avg = []120    timsort_avg = []121    theory_time = [0]122123    for i in range(1, length_lst):124        theory_time.append(i*math.log(i)/1000000)125126    for i in range(length_lst):127        merge_lst = []128        timsort_lst = []129130        for x in range(repeats):131132            # Here is the only change133            mylist = [x for x in range(i)]134            copy = mylist.copy()135136            start_time = time.time()137            mergesort(mylist, 0, len(mylist) - 1)138            finish_time = time.time() - start_time139            merge_lst.append(finish_time)140141            start_time = time.time()142            timsort(copy)143            finish_time = time.time() - start_time144            timsort_lst.append(finish_time)145146        avg_merge = sum(merge_lst)/len(merge_lst)147        avg_timsort = sum(timsort_lst)/len(timsort_lst)148149        merge_avg.append(avg_merge)150        timsort_avg.append(avg_timsort)151152    plt.plot(merge_avg, color='red', label = 'Traditional merge')153    plt.plot(timsort_avg, color='blue', label = 'TimSort')154    plt.plot(theory_time, color='green', label = 'Theoretical run time O(nlog(n))')155    plt.xlabel("Length of the list")156    plt.ylabel("Averaged run-time")
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hw4_ques2.py

Source:hw4_ques2.py

1import math2import sys3import time4import numpy as np5from matplotlib import pyplot as plt6# https://www.geeksforgeeks.org/insertion-sort/7def insertionSort(arr): 8	for i in range(1, len(arr)): 9		key = arr[i] 10		j = i-111		while j >= 0 and key < arr[j] : 12				arr[j + 1] = arr[j] 13				j -= 114		arr[j + 1] = key15# https://www.geeksforgeeks.org/merge-sort/16def mergeSort(arr):17	if len(arr) >1:18		mid = len(arr)//219		L = arr[:mid]20		R = arr[mid:]21		mergeSort(L)22		mergeSort(R)23		i = j = k = 0		 24		while i < len(L) and j < len(R):25			if L[i] < R[j]:26				arr[k] = L[i]27				i+= 128			else:29				arr[k] = R[j]30				j+= 131			k+= 132		while i < len(L):33			arr[k] = L[i]34			i+= 135			k+= 136		while j < len(R):37			arr[k] = R[j]38			j+= 139			k+= 140def TimSort(array, timsort_constant):41	if(len(array)<=1):42		return43	if(len(array) <= timsort_constant):44		insertionSort(array)45	else:46		mid = len(array)//247		L = array[:mid]48		R = array[mid:]49		TimSort(L, timsort_constant)50		TimSort(R, timsort_constant)51		i = j = k = 0		 52		while i < len(L) and j < len(R):53			if L[i] < R[j]:54				array[k] = L[i]55				i+= 156			else:57				array[k] = R[j]58				j+= 159			k+= 160		while i < len(L):61			array[k] = L[i]62			i+= 163			k+= 164		while j < len(R):65			array[k] = R[j]66			j+= 167			k+= 168time_timsort = np.zeros((11,51))69for n in range(0,51):70	time_timsort_n = []71	for t in range(1000):72		time_timsort_t = []73		array = np.random.randint(1000000, size=n)74		for k in range(0,51,5):75			start_time = time.perf_counter()76			TimSort(np.copy(array),k)77			stop_time = time.perf_counter()78			time_timsort_t.append(stop_time - start_time)79		time_timsort_n.append(time_timsort_t)80	time_timsort_n = np.array(time_timsort_n)81	time_timsort_n = np.mean(time_timsort_n, axis=0)82	time_timsort[:,n] = time_timsort_n83# time_timsort = np.array(time_timsort)84time_timsort = 1000. * time_timsort85for k in range(0,51,5):86	plt.plot(range(0, 51), time_timsort[(k//5)], label='k=' + str(k))87plt.xlabel('Length of Array (N)')88plt.ylabel('Average Run time (ms)')89plt.legend()90plt.show()91optimal_k = 1592time_insertion_sort = []93time_merge_sort = []94time_timsort = []95for n in range(2, 51):96	avg_insertion_sort_time = 0.0097	avg_merge_sort_time = 0.0098	avg_timsort_time = 0.0099	for t in range(1000):100		array = np.random.randint(1000000, size=n)101		start_time = time.perf_counter()102		insertionSort(np.copy(array))103		stop_time = time.perf_counter()104		avg_insertion_sort_time = avg_insertion_sort_time + (stop_time - start_time)105		start_time = time.perf_counter()106		mergeSort(np.copy(array))107		stop_time = time.perf_counter()108		avg_merge_sort_time = avg_merge_sort_time + (stop_time - start_time)109		start_time = time.perf_counter()110		TimSort(np.copy(array), optimal_k)111		stop_time = time.perf_counter()112		avg_timsort_time = avg_timsort_time + (stop_time - start_time)113	time_insertion_sort.append(avg_insertion_sort_time/1000.0)114	time_merge_sort.append(avg_merge_sort_time/1000.0)115	time_timsort.append(avg_timsort_time/1000.0)116time_insertion_sort = np.array(time_insertion_sort)117time_merge_sort = np.array(time_merge_sort)118time_timsort = np.array(time_timsort)119time_insertion_sort = 1000. * time_insertion_sort120time_merge_sort = 1000. * time_merge_sort121time_timsort = 1000. * time_timsort122plt.plot(range(2, 51), time_insertion_sort, label='Insertion Sort', color='#28324b')123plt.plot(range(2, 51), time_merge_sort, label='Merge Sort', color='#f55158')124plt.plot(range(2, 51), time_timsort, label='TimSort', color='#f3c417')125plt.xlabel('Length of Array (N)')126plt.ylabel('Average Run time (ms)')127plt.legend()...

test_timsort.py

Source:test_timsort.py

1import pytest2# import os, sys3# myPath = os.path.dirname(os.path.abspath(__file__))4# sys.path.insert(0, myPath + '/../')5from other_sorts import timsort as ts6def test_timsort_already_sorted():7    nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]8    ts.timSort(nums)9    assert nums == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]10def test_timsort_out_of_order():11    nums = [10, 9, 7, 4, 6, 2, 1, 5, 8, 3]12    ts.timSort(nums)13    assert [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] == nums14def test_timsort_size_more_than_run():15    nums = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,16            21,66,23,24,25,26,27,28,29,30,31,32,33]17    ts.timSort(nums)18    assert [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,19            21,23,24,25,26,27,28,29,30,31,32,33,66] == nums20def test_timsort_empty():21    nothing = []22    ts.timSort(nothing)23    assert [] == nothing24##Helpers##25def test_timsort_insertion_already_sorted():26    nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]27    ts.insertionSort(nums, 0, len(nums) - 1)28    assert nums == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]29def test_timsort_insertion_out_of_order():30    nums = [10, 9, 7, 4, 6, 2, 1, 5, 8, 3]31    ts.insertionSort(nums, 0, len(nums) - 1)32    assert [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] == nums33def test_timsort_insertion_empty():34    nothing = []35    ts.insertionSort(nothing, 0, len(nothing) - 1)36    assert [] == nothing37def test_timsort_merge_nonempty_more_left():38    nums = [2,6,7,1,5]39    ts.merge(nums, 0, 2, 4)40    assert nums == [1,2,5,6,7]41def test_timsort_merge_nonempty_more_right():42    nums = [2,6,7,1,5,8,9]43    ts.merge(nums, 0, 2, 5)44    assert nums == [1,2,5,6,7,8, 9]45def test_timsort_merge_empty():46    nums = []47    ts.merge(nums, 0, 0, 0)...

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