def generate_n_lists(n, m):
start = ts()
output_lists = []
x = 107
for i in range(n):
temp_list = []
for j in range(m):
x = (x * 37 + 103) % 1013
temp_list.append(1)
output_lists.append(temp_list)
end = ts()
delta(start, end)
return output_lists
# print(gen_lists(10, 5))
def find_pattern_exact(line: List[int], pattern: List[int]) -> int:
s_line = ''
for x in line:
s_line += str(x)
s_pattern = ''
for x in pattern:
s_pattern += str(x)
idx = s_line.find(s_pattern) # tu trzeba sprawdzić czy rzeczywiście szukanie podnapisów w pythonie jest szybkie...
if idx == -1:
return 0
# teraz trzeba sprawdzić czy tylko ten pattern jest tutaj...
rest = s_line[:idx] + s_line[idx + len(pattern):]
if int(rest) == 0:
return 1
else:
return 0
st = ts()
# w = gen_lists(10 ** 5, 20)
print(find_pattern_exact([0, 0, 0, 1, 1, 0, 0], [1, 1]))
en = ts()
delta(st, en)
# print('00010'.find('sim_annealing'))
# print(int('00000100'))
return sublen # [!!!!!!!!!!!!...................] # ---> O(N * log(N)) letters = 26 LEN = 1600 st = ts() a = random_string(nsymbols=letters, length=LEN) b = random_string(nsymbols=letters, length=LEN) # gg = long(random_string(nsymbols=NS, length=LEN), random_string(nsymbols=NS, length=LEN)) # gg = faster(a, b) gg = fast_hash_solution(a, b) ed = ts() delta(st, ed) """ Simple algo, O(N**3) 100 1.571 200 7.853 300 24.333 400 50.992 600 160.7 800 366.106 1600 2631.371 3200 19903 Faster, N**2 log(N) 1600 12.895