def main(query): x = int(sys.argv[1]) if(x==1): kmp.kmp(sys.argv[2]) elif(x==2): bm.bm(sys.argv[2]) elif(x==3): regex.regex(sys.argv[2])
def test_kmp(self):
self.assertListEqual(kmp("THIS IS A TEST TEXT", "TEST"),
[10])
self.assertListEqual(kmp("AABAACAADAABAAABAA", "AABA"),
[0, 9, 13])
self.assertListEqual(kmp("AAAAAAAAAAAAAAAAAB", "AAAAB"),
[13])
self.assertListEqual(kmp("ABABABCABABABCABABABC", "ABABAC"),
[])
self.assertListEqual(kmp("ABC ABCDAB ABCDABCDABDE", "ABCDABD"),
[15])
def __init__(self, uploaded_files, text, option):
if (option == "pilihan1"):
kmp_0 = kmp.kmp()
kmp_0.convertText(uploaded_files)
if (kmp_0.kmpMatch(text.lower()) == -1):
self.hasil = [[
'', "Tidak ditemukan " + text + " pada file.", ''
]]
else:
self.hasil = [[
'',
"indeks pada file: " + str(kmp_0.kmpMatch(text.lower())),
''
]]
elif (option == "pilihan2"):
boyer = boyce.boyce()
boyer.convertText(uploaded_files)
if (boyer.bmMatch(text.lower()) == -1):
self.hasil = [[
'', "Tidak ditemukan " + text + " pada file.", ''
]]
else:
self.hasil = [[
'',
"indeks pada file: " + str(boyer.bmMatch(text.lower())), ''
]]
else:
reg = regex.regex(uploaded_files)
self.hasil = reg.regexMatch(text)
def extractor(keyword, sentences, option):
informations = []
for sentence in sentences[1]:
# bisa diganti dengan regex ataupun boyermoore sesuai pilihan nantinya
if option == 'kmp':
positions = kmp(keyword, sentence)
elif option == 'bm':
positions = boyer_moore(keyword, sentence)
elif option == 'regex':
positions = [m.start(0) for m in re.finditer(keyword, sentence)]
if positions:
time = find_time(sentence)
if len(time) == 0:
for other_sentence in sentences[1]:
time = find_time(other_sentence)
if time:
break
if len(time) == 0:
time = ['tidak ada penanda waktu']
amount = find_amount_info(sentence)
closest_amount = ''
if amount:
closest_amount = amount[0][1]
if len(amount) > 1:
# find the closest one with the keyword
minimal = len(sentence)
for amount_candidate in amount:
if abs(amount_candidate[0] - positions[0]) < minimal:
closest_amount = amount_candidate[1]
minimal = abs(amount_candidate[0] - positions[0])
informations.append(
[keyword, sentences[0], time[0], closest_amount, sentence])
return informations
def analyseMsg(recvMsg):
position = kmp.kmp(recvMsg, DELIMITER, KMP_TABLE)
if position == len(recvMsg) - len(DELIMITER):
formatMsg(recvMsg)
else:
preMsg = recvMsg[:position + len(DELIMITER)]
formatMsg(preMsg)
# analyse last msg
analyseMsg(recvMsg[position + len(DELIMITER) + IGNORE_LEN:])
def analyseMsg(recvMsg, roomid):
position = kmp.kmp(recvMsg, DELIMITER, KMP_TABLE)
if position == len(recvMsg) - len(DELIMITER):
formatMsg(recvMsg, roomid)
else:
preMsg = recvMsg[:position + len(DELIMITER)]
formatMsg(preMsg, roomid)
# analyse last msg
analyseMsg(recvMsg[position + len(DELIMITER) + IGNORE_LEN:], roomid)
def naive_kmp(s1, s2):
"""Same as `naive` but using KMP instead of `_strcmp`."""
if len(s1) != len(s2):
return False
# builds the shift table once
T = kmp.get_shift_table(s1)
for i in range(len(s2)):
if kmp.kmp(Shifted(s2, i), s1, T=T) is not None:
return True
return False
i += j - pmt(small[:j])
else:
i += 1
return False
if __name__ == "__main__":
words = read_file('words.txt')
n = -1
build_tree(words[:n])
tree = build_tree(words[:n])
lines = read_file('1984.txt')
start = time.time()
for line in lines:
match_w = match_doc(tree, line)
if match_w != "":
print("{} \t {}\n".format(match_w, line))
end = time.time()
print(end-start) # 0.10075116157531738
# 测试kmp
start = time.time()
for line in lines:
for word in words:
result = kmp(line, word)
if result:
print(word)
end = time.time()
print(end - start) # > 30min 我等了8分钟才找出100个， 上个算法找出了400多个，不能再等了。
if __name__ == "__main__":
searchKey = sys.argv[2].lower()
query = api.search(q=searchKey, count=100)
tweet = []
for tweetapi in query:
tweet.append(tweetapi.text)
data = {}
data['query'] = sys.argv[2]
data['pattern'] = sys.argv[3]
data['tweets'] = []
if (sys.argv[1] == '-kmp'):
for isi in tweet:
spamidx = kmp.kmp(isi.lower(), sys.argv[3])
data['tweets'].append({'tweet': isi, 'spamidx': spamidx})
elif (sys.argv[1] == '-bm'):
for isi in tweet:
spamidx = boyer_moore.bmMatch(isi.lower(), sys.argv[3])
data['tweets'].append({'tweet': isi, 'spamidx': spamidx})
elif (sys.argv[1] == '-re'):
for isi in tweet:
spamidx = regex.regexMatch(isi.lower(), sys.argv[3])
data['tweets'].append({'tweet': isi, 'spamidx': spamidx})
with open('data.txt', "w") as outfile:
json.dump(data, outfile)
def is_rotation(s1, s2):
"""Returns True if `s2` is a rotation of `s1` using one pass of KMP."""
if len(s1) != len(s2):
return False
return kmp.kmp(s2 + s2, s1) is not None
sys.path.append("..")
from lib import spacedPrint
from kmp import kmp
###############################
######## PATTERN MATCHING #####
#finds all the occurrences of a pattern in a text and returns the start indices of those occurrences
def findOccurrences(pattern, text):
patternLength = len(pattern)
positions = []
for i in range(len(text) - patternLength + 1):
if pattern == text[i:i + patternLength]:
positions.append(i)
return positions
if __name__ == "__main__":
pattern_pm = "TGTTCATTG"
text_pm = "TATGTTCATATGTGTTCATGTTCTTACACTAATGCAGTGTTCATTGTTCATGCCTTGTTCATTGTTCATTGTTCATGTTGTTCATCTGTTCATCACTGTTCATTCTTGTTCATATCTTTGGTGTTCATACTGGCCGTGTTCATGCAGCTCCGCTGTGTTCATATGTTCATGCAGTGTTCATTGTTCATATGTTCATATGTTCATTTGTTCATGGGACGATTTGTTCATTAGATTGTGTGTTCATTGTTCATAATGTTCATAATGTTCATCCGATGTTCATATGTTCATCTGTTCATCGTGTTCATACTGAAAAACTTGTTCATACAGTGTTCATTCTGTTCATCAAACAAGATGGTTTGTTCATTGTTCATACGAGGCTGTTCATCTCTGTTCATTTGTTCATTAGGTGTTCATTGCTGTTCATATGTTCATTGTTCATGTGTTCATAAGTGTTCATCGTTTCGTTTACGGGATCTGTTCATTTGTGTTCATATTGTTCATGATTTCTGGTTGAATGTTCATTGTTCATCTCGTGTACCTGTTCATATGTTCATCCGTGTTCATGTGTTCATTGTTCATGCCTGTTCATCGTGTTCATGTGTGTTCATTATAATGTTCATATGAATGTTCATAAATGACTTGTTCATCGTGTTCATCTGTGTTCATTTCGCTGTTCATACTGTTCATAAGCGTAATGTTCATTCTGTTCATTGTTCATTGTTCATGCTACCTTGTTCATATCACGTGTTCATCCGTGTTCATTGTTCATTCTCATTGTTCATTTTATGTGTTCATTGTTCATGCTTGATGTTCATTTGTTCATTCTGTTCATGTGTTCATTGTTCATTTACGTGTTCATTTGTTCATCGCCTGTTCATTGTTCATAGATGTTCATTTGTTCATCCGTGTTCATTACGGTGTTCATAGGACTGTTCATCTATGTTCATATGTTCATTGTTCATGTGTTCATTAAGGTGTTCATGACCTTGTTCATCCTTGTTCATCTTACTGTTCATCAAGTGTTCATATGTTCATTAACTGATGTTCATTGCTGTTCATCGTTGTTCATTGTTCATTCTGTTCATGGTGTTCATACCATGTTCATCGGCGGTTGTTCATGTCTGTTCATGATTCTGTTCATTTGTTCATTGTTCATGCTGTTCATAGTGTTCATTGTTCATTTGTTCATATTGTTCATGATTTGTTGTTCATGATTGTTCATTTACAATGTTCATTGTTCATAGTGTTCATTTGTTCATATGTTCATATGTTCATTCTTGTTCATTGTTCATTAGCCATTGTTCATTGTTCATTGTTCATATGATGTTCATTGTTCATTGTTCATTTTGTTCATTGTTCATTTGTTCATACCTGTTCATGACCTGTCCTTTGTTCATCCTGTTCATGTAGGTGTTCATGGGTGTTCATCTTGTTCATAATGTTCATTCAGTGTTCATAGCTATTGTTCATATTTGTTCATTTTGTTCATACTGTTCATTTGTTCATTGTTCATATGTTCATTGTTCATGGTCGCTTCCGGTTTAAGATGTTCATGTACAATTGTTCATACATGTTCATTTGTTCATTGTTCATTTGTTCATTAAGTGTGTTCATCTGTTCATTGTTCATACTATGTTCATTGTTCATTGTTCATCTACGATGTTCATTTTATGTTCATGATGTTCATTGTTCATGCTCCATCGTTTTGTTCATCTGTTCATGTGTTCATCGGCTGTTCATCTGTTCATAAAGCTTGTTCATTTGTTCATTGTTCATTTTATTTGTTCATGCTGTTCATAGTGTTCATTGTTCATCCCTGTTCATCCAATGTGTTCATGTGTTCATGCCGATGTGGTGTTCATGTGTTCATCAGTGGATGTTCATTCCTGTTCATCTGTTCATCCCGGTGTTCATACTGTTCATGAGTGTTCATGCACAACGTAAGCCTATGTTCATATTGTTCATTGCACGTACTCTGTTCATTTGTTCATATGTGTTCATATTGTTCATTGTTCATGTCGTTGTTCATCTGTTCATAACAGTGTTCATTGTTCATTGTTCATTGTTCATAGCTGTTCATTGTTCATATTGTTCATGATGTTCATTGTTCATATGTTCATCGTGTTCATCTGTTCATCACGTTAGGGTGTGTTCATTGTTCATGCCTGTTCATTGTTCATTATTGTGTTCATCAATGTATGTTCATTGTTCATCCATTCTGTTCATGGTGTTCATTGTTCATCCTTTAGATGTATGTTCATCACTGTTCATCATGTTCATGTGTTCATTTTGTTCATCTGTTGTTCATATGTTCATGCCTGTTCATATGTTCATCTGTTCATTGTTCATGCACCTGGTGTTCATTGTTCATTACTCCTGTTCATGCTGTTCATTCTGTTCATAGTGTGTTCATTGTTCATCTGTTCATCTGCTGTTCATATGTTCATTTGTTCATAACTGTTCATTTGTTCATCAGACTGTTCATAGTGTTCATCTGTTCATTGTTCATACTTGTTCATGTTGTTCATCACAGGTGTTCATTGTTCATTGTTCATCACGATAATGCTTATATTGATGTTCATACCTATTATGTTCATTGATGTTCATCCATGTGTTCATCAAGGGTTCTGTTCATTGTCATGTTCATAGCCAGTGTTTGTTCATTATGTTCATGGTGTTCATAAACGGAGTGTTCATGTAACATGTTCATTGTTCATTATGTGTTCATTGTTCATCCCCATTGTTCATCGTGTTCATGTTCCCCTATGTTCATTAGTGGTAGTCTGTTCATTTGTTCATCTGTTCATTGTTCATATGTTCATTGTTCATCTAGTGTTCATTGTTCATAATGTTCATACTGCCTGTTCATAAATGTTCATAAGACATAGCTGTTCATTGTTCATTGTTCATATCCTGTTCATGTGTTCATTCATTGTTCATATGCAATGTTCATGGATGTTCATCAAGTGTTCATATGTTCATAAATGTTCATGTGTTCATTTCTGTTCATTGTTCATTCAATACGCAGGTATGTGTTCATAATGTTCATATGTTCATACGATTGTTCATACCTGTTCATAGTGTTCATTGTCCTGTTCATACTATGTTCATTTTGTTCATTGTTCATGAAGTGCATTACTGATGTTCATTGTTCATGCTGTGTTCATTGTTCATGTTGTTCATTCTGTTCATGAGCTGAACTGTTCATGTGTTCATCCCTGTTCATGCTATGTTCATTGGGATGTTCATATGTTCATATTGTCAGCGATGTTCATCGTCATGTTCATATGTTCATATGTTCATCACGATGTTCATAGTGTTCATTGTTCATTGTTCATCGTGCTGTTCATACGTGTTCATTGTTCATGCCTGTTCATTGTTCATTGTTCATCTGTTCATGTTGTTCATTTGCCGTGTTCATATGTTCATGTTAGTTGTTCATGGTGTTCATTGTTCATTGTTCATTGTTCATTGTTCATTCCGTACACTGTTCATATGTTCATTGTTCATAGTCTTTGTTCATGCACGATGTTCATTGTTCATTGTTCATATGTTCATGAATGTTCATCCTGCGTAGATATGTTCATATGTTCATGTGTTCATTGTTCATTACTTGTTCATAATGTTCATTGGTTGTTCATCCAGTGTTCATCTGTTCATCTAATGTTCATTGTTCATTGTTCATAGTGTTCATGGCGTGTTCATTGTTCATGGATCGTGTTCATCGTGTTCATTTAGTTTTGTACATAGTGCTGTTCATTGTTCATGGGATTGTGTTCATATATGTTCATTGTTCATTGTTCATTGTTCATAATGTTCATTATCCTCTTGTTCATTGGAACCATGTTCATAGCACAGTGTTCATATGTTCATTTGTTCATCCCTGTTCATACGTCACTGTTCATTGAATGTTCATGGCTGTTCATGATATGTTCATGCCGATGTTCATGGGAACTGTTCATTATGTTCATCTGTAGCGTGCGAGTTGTTCATGTGCAAATGTTCATCACGATGGCCGGAGGTGTTCATCTGTGTTCATTGTTCATTGTTCATTGTTCATGATGTTCATATGTTCATCTATGTTCATCGGTCTGTTCATGGGTGTTCATGAAATTGTTCATGATGTTCATTGTTCATTGCTCTGTTCATTTGTTCATCTGTTCATGCTGTTGTTCATTCAGGCATGGTTGTTCATGATCCCCGTGTTCATGTGTTCATGTGTTCATCTTGTGTTCATTGTTCATGTGTTCATTGTTCATATGGTATACGCAAATGTTCATTGTTCATTCATGTTCATGTGTTCATCAAGCTCTGTTCATAGCTGTTCATGGTGTTCATAAGTGTTCATAGGTGTTCATCATGTTCATTGTTCATGTTAGTGTTCATGTCTGTTCATACCTGTTCATGTTGTTCATTGTTCATCCTGTTCATGTTGTTCATCTTGTTCATCGTTGTTCATCTTGTTCATACTGTTCATTGTTCATATTGTTCATATAGTCGTTTGTTCATCATGTTCATGGTTGTTCATTGTTCATGCATGTTGTTCATACAAGACCTAAACTGTTCATTTTGTTCATGTGTTCATCTTTGTTCATGTGTTCATCGGTGTTCATGTTGTTCATTGAGGTGTTCATACACGGTGTTCATATCTGTTCATTGTTCATATGTTCATTGTTCATTCGTGTTCATGTGTTCATAAAATGTTCATTTATGTTCATATGTTCATTTGTGTTCATGGTATTGTTCATATGTTCATGTGTTCATTGCGTGTTCATTGGTTTGTTCATTGGAGAAATGTTCATCTGTTCATATGTTCATTGTTCATTGTTCATTCGTGTTCATCCTTGTTCATCTTGTTCATGTTGTAGGATGTTCATTGTTCATTGTTCATAACTGTTCATAAATTGTTCATTGCTGTTCATGTTTGTTCATTGTTCATTTGTTCATGTGTTCATCATGTTCATAAGAGTGTTATATGTTCATTGTTCATTCTGTTCATTGTTCATGTTGTTCATGCTGTTCATCTGTTCATTGTTCATTGTGTTCATGTGTTCATAGTCTGTTCATCTGATGTTCATGATCAGCTTTGTTCATCATGCTAGTGTTCATTGTTCATAATGTTCATTATGTTGTTCATGCTGTTCATTGTTCATTGTTCATCATTATGTTCATAATGTTCATCATGTTCATATGTTCATACGTGTTCATGGTTGTTCATTTGTTCATAACTGTTCATTGTTCATGTGTTCATGATGTTCATTGTCGTGTCCCTGGTTGTTCATTGTTCATCGTTAATATGTTGTTCATATGTCGAGAGATGTTCATGCGGCGCTGTTCATTTGTTCATTGTTCATTCCTATGTTCATTGTTCATATGTTCATCTGTTCATATGTTCATGTGTTCATCACCTGTTCATTGTTCATTGTTCATTGTTCATATGAATGTTTGTTCATAGTATGTTCATAGGTGAATGATGTTCATATTGTTCATCTGTTCATTGTTCATGTGTTCATGGTGTTCATTGTTCATAGTGTTCATCGTGTTCATGGTGTTCATTTGTTCATAGTCCGTGTTCATTGTTCATCCTGTTCATGATGTTCATTATTTTATATGTTCATTTGTTCATTTAGTGTTCATTGTTCATAGTTGTTCATTGTTCATTGCGCTTGTTCATCACGTGTTCATGCTGTTCATAGGGCCTTTTATACTCGCTGTTCATGTCCCACTGTTCATGCCTGTTCATGCGTTGTTCATCGAGTGTTCATGAAGTGTTCATTGTTCATGTTGTTCATTATGTTCATTGTTCATGTTGTTCATATGTTCATGCAACTGTTCATGCACGTGTTCATTGTTCATCTGTTCATCTGTTCATTGTTCATGTGTTCATTGTTCATGTTGTTCATTTGTTCATTTGTTCATTTGTTCATATGTTCATCAGGGCCATGTTCATATGTTCATGTGTTCATGCCCTGTTCATTGTTCATGTGTTCATTGTTCATCTTGTTCATTGTTCATTGTTCATTCTGGTGTTCATGATGTTCATTAGTGTTCATTGTTCATGTGCAGGAATTGTTCATGTACCTGTTCATTCTGTTCATTATGTTCATGAGTCTCGTCTGGCCTCAAAACTTGTTCATATGTTCATTAAGTGTTCATATGTTCATTTATGTTCATTCGTTTGTTCATTGTTCATCGTGTTCATGGTATTGTTCATGGTTGTTCATTGTTCATATTGTTCATTTGTTCATCTCACCCACGCTGTTCATGCCTGTTCATGCATGTTCATATGTTCATCATGTCTGTTCATATGTTCATTGTGTTCATTGTTCATACTGTTCATTTGTTCATCTTGTTCATGCCCATTACGGGTGTTCATGCTCGTGTTCATTACTATGTTCATATGCGTGTTCATTGTTCATTGTTCATGGGATGTTCATTACTGTTCATTGTTCATTGTTCATAGCGAGTGTTCATGGGATGTTCATCTTGTTCATTGTTCATCACTGTTCATTTGTTCATGCCTGTTCATTCCTGTTCATGTGTTCATTGTTCATTCCTACAAAATCATACGCGATGATGTTCATGGTGTTCATAGTGTTCATACTGTTCATTCTTGTTCATCTGTTCATTGTTCATTGTTCATAGTGTTCATTGTTCATGAATGTTCATTGTTCATCTATGCTGTTTGTTCATCTAACCTGTTCATTGTTCATTTGTTCATCGTGGCGTGTTCATTAGCGCGGTGTTCATCCATGCGGTGTTCATTGTTCATATGTTCATAGCTTCCCTATGTTCATTGTTCATCTTAATATGTTGTTCATAATGAACTTGACCAGAGTATGTTCATAGTTTCCAGAATGTTCATTCCCGTGTTCATTGTTCATTTTCTGATGTTCATTGTTCATTGTTCATTGTTCATAGGTGTTCATTCTGTTCATTGTTCATGCCCATGTTCATCTCCGATTGTTCATTGTTCATTGTTCATAGTGTTCATTGTTCATCTGTTCATGTGTTCATGAGATGCTGTTCATTCAGACTCGTGTTCATACACATACGATGTTCATTGTTCATACAATGTTCATCTGTTCATTCGCTGTTCATTCTGTTCATAAGTCCTAAGCATGTTCATATAATGTTCATCCTGTTCATCCCCCGTGTTCATTTGTTGTTCATCCGCATAGCGCCGGGTATGTTCATTGTTCATGGCATTTGTTCATGTGTTCATCTTCTGTTCATCGTGTTCATTGTTCATGTCTGTGTTCATGTGTTCATATCTGTGTTCATTGTTCATTCAGCCTGTTCATACTGTTCATACAGATGGAATGTTCATGTGTTCATATTTGATTGCTCGGGGATGTTCATTTGACCTGTTCATTTGTTCATACGTGTTCATCATAGTATTTCTGTTCATTGTTCATTGTTCATGTGTTCATTCTTTACGCTGTTCATTCGGTACTGATATGTTCATGTTCGCACTAACCCTGTTCATAACGTGTTCATTGTTCATCTTATGTTCATGGCCGGGTCCGGCATTGTTCATCGGCATGTTCATATGTTCATCGATTGCGGGTGTGTTCATAAGGATGTTCATTTCCAAAGAATGTTCATCTCTGTTCATGTGTTCATTGTTCATTGCTGTTCATGAGTTGTTCATATTGTTCATTGTTCATCGGCATGTATGTTCATATGTTCATTGTTCATAATGTTCATTGTTCATGATGTTCATCAGGGAAATGTTCATCTGTTCATTGTTCATAACTGTTCATTGTTCATTGTTCATTGTTCATTGTTCATAGTTGTTCATAAGCGCTGTTCATCATGTTCATTGTTCATCGGGTGTTCATATCGACTCACTTGTTCATTGTTCATATGTTCATACTGTTCATGCTGTTCATATATGTTCATAATTGTTCATGGGTGTTCATTGTTCATCATGTTCATATGTTCATTGTTCATATGTTCATCTGTTCATTGTTCATCGGGCCGAGGTGAATGTTCATTGTTCATTCATTGTTCATAACCAGCGTTGTTCATTTGTTCATGTGTTCATTGTTCATATTGTTCATTGTTCATTGTTCATGCCAATGTTCATATGATTTTTGTTCATGTGTTCATTGTTCATTGTTCATTTACACTGTTCATCCCCCTGTTCATCTGTTCATTAAGGCCACTTCATGTTCATGATGTTCATTGTTCATCTTCAGCTGATGTTCATTGTTCATCCATTTGTTCATGATCATGTTCATATTGTTCATAGTGATGCTGTTCATACAACTGTTCATGGAGTGTTCATTGCTGTTCATGTGTTCATCACTGTTCATCGTGTTTGTTCATATGTTCATTGTTCATGGTTTGTGTTCATTGTTCATATGTTCATTGTTCATCCCCAGCATGTTCATAATGTTCATCCCTGTTCATTGTTCATTGTTCATTCACAAATTCTGTTCATAGATGTTCATTGTTCATAGTGTTCATTGTTCATTGTTCATAGGATGTTCATCATGTTCATAATCATGTTCATTGTTCATTACATTGTTCATTGTTCATCCGTTCAGCCATTGTTCATTGTTGTTCATTGTTCATCTGTTCATTTTGTTTCTGTTCATCTCCCTGTTCATACTGCTGTTCATTATGTTCATGTTCGTGTATTTGTTCATTGTTCATTGTTCATTTCTGTTCATCCAGGTGTTCATGTGTTCATTGTTCATGTGTTCATCTGTTCATATGTTCATATGTTCATCTCTGTTCATTGTTCATTGTTCATGGCTGTTCATTGTTCATACTTGTTCATTTGTTGTTCATTGTTCATGGCATCATGTTCATGTGTTCATTGTGTTCATCCTGTTCATCTGTTCATACAGAATTGTTCAT"
spacedPrint(findOccurrences(pattern_pm, text_pm))
print("-------------\n")
spacedPrint(kmp(text_pm, pattern_pm))
## EXERCISE BREAK
file = open("data/Vibrio_cholerae.txt", 'r')
text_vc = file.read()
pattern_vc = "CTTGATCAT"
spacedPrint(findOccurrences(pattern_vc, text_vc))
import time
alphabet = list(string.ascii_lowercase)
bm_meas = []
kmp_meas = []
k = 1000
x = [i for i in range(10 * k, 1 * k * k + 1, 10 * k)]
needle_len = k
for hs_len in x:
haystack = random.choices(alphabet, k=hs_len)
entry_index = hs_len // 2
needle = haystack[entry_index:entry_index + needle_len]
# Measure KMP
start = time.time()
kmp_search.kmp(needle, haystack)
kmp_meas.append(time.time() - start)
# Measure BM
start = time.time()
bm_search.search(haystack, needle)
bm_meas.append(time.time() - start)
plt.grid(True)
plt.ylabel("Время, сек.")
plt.xlabel("Длина текста для поиска паттерна")
plt.plot(x, kmp_meas, "o--")
plt.plot(x, bm_meas, "o--")
plt.legend(["Алгоритм Кнута-Морриса-Пратта", "Алгоритм Бойера-Мура"])
plt.show()
