def __init__(self):
self.read_data = ReadData()
self.naive = Naive()
self.branch_and_bound = Branch_And_Bound()
self.tests = Tests()
self.data = []
self.choice = 0
self.starting_city = 0
def test_naive(filename, p):
start = time.time()
f = open(filename)
t = f.read()
alg1 = Naive(t)
results = alg1.match(p)
end = time.time()
col_time = end - start
return col_time, results
class Tests:
def __init__(self):
self.data = ReadData()
self.naive = Naive()
self.branch_and_bound = Branch_And_Bound()
# testy dla przeglądu zupełnego
def testing_naive(self):
for size in range(5, 12):
graphs = self.data.generate_random_data(
150, size) # generowanie losowych grafów
help_time = 0
total_time = 0
for j in range(50):
exec_time = time.time()
self.naive.naive_algorithm(graphs[j], 0)
exec_time = time.time() - exec_time
help_time += exec_time
if help_time >= 150.0:
break
for i in range(50, 150):
exec_time = time.time()
self.naive.naive_algorithm(graphs[j], 0)
exec_time = time.time() - exec_time # czas pojedynczego testu
total_time += exec_time # sumowanie czasu
if total_time >= 300.0:
print('Wykonano: ', i - 50, 'prób')
break
print('Rozmiar grafu: ', size)
print('Średni czas: ', total_time / (i - j))
# testy dla metody podziału i ograniczen
def testing_b_b(self):
# rozmiary instancji poszczególnych testów
test = [5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 18, 21]
for size in test:
graphs = self.data.generate_random_data(150, size)
help_time = 0
total_time = 0
for j in range(50):
exec_time = time.time()
self.branch_and_bound.find_solution(graphs[j], 0)
exec_time = time.time() - exec_time
help_time += exec_time
if help_time >= 150.0:
break
for i in range(50, 150):
exec_time = time.time()
self.branch_and_bound.find_solution(graphs[i], 0)
exec_time = time.time() - exec_time # czas pojedynczego testu
total_time += exec_time # sumowanie czasu
if total_time >= 300.0:
print('Wykonano: ', i - 50, 'prób')
break
print('Rozmiar grafu: ', size)
print('Średni czas: ', total_time / (i - j))
def crossValidationIris(k=5):
testData1 = np.load("iris_data/competitionData.npy")
testData2 = np.load("iris_data/evaluationData.npy")
trainData = np.load("iris_data/trainingData.npy")
wholeData = np.concatenate((trainData, testData1, testData2)).astype(float)
folds = k
attTypes = [1, 1, 1, 1]
trainingSets = np.split(wholeData,
[(i + 1) * round(len(wholeData) / folds)
for i in range(folds - 1)])
for set in trainingSets:
print('\n\n\n', set, '\n\n\n')
for idx, testData in enumerate(trainingSets):
print(np.r_[0:idx, idx + 1:len(trainingSets) + 1])
trainingData = np.concatenate(
[trainingSets[i] for i in np.r_[0:idx, idx + 1:len(trainingSets)]])
print('testData: ', len(testData))
print('trainingData: ', len(trainingData))
print('K FOLD #' + str(idx))
start = time.time()
model = Naive(trainingData, attTypes)
timer = time.time()
print('Time to train: ', timer - start)
start = time.time()
evalModel = Evaluation(model, testData, CLASS_AMM_IRIS)
evalModel.normalPrint()
print('Took ', time.time() - start, 's')
def BayesIris():
testData1 = np.load("iris_data/competitionData.npy")
testData2 = np.load("iris_data/evaluationData.npy")
trainData = np.load("iris_data/trainingData.npy").astype(float)
testData = np.concatenate((testData1, testData2)).astype(float)
model = Naive(trainData, [1, 1, 1, 1])
ev = Evaluation(model, testData, CLASS_AMM_IRIS)
ev.normalPrint()
def NBCovType(NBSizes=[5, 50, 500]):
attTypes = [1 for _ in range(10)] + [0 for _ in range(44)]
for size in NBSizes:
print('\n\nLoading data from path=' + "cov_data/" + str(size) +
"k/trainingData.npy")
trainingData = np.load("cov_data/" + str(size) + "k/trainingData.npy")
print('- Dataset of ' + str(size) + 'k')
start = time.time()
print('\n\n-- Train for NAIVE BAYES')
model = Naive(trainingData, attTypes)
timer = time.time()
print('Time to train: ', timer - start)
evalModel = Evaluation(model, competitionDataCov, CLASS_AMM_COV)
print('--- Evaluation for size=' + str(size))
evalModel.normalPrint()
print('Took ', time.time() - start, 's')
def crossValidationCovType(k=5, size=500):
attTypes = [1 for _ in range(10)] + [0 for _ in range(44)]
wholeData = np.load("cov_data/" + str(size) + "k/trainingData.npy")
folds = k
trainingSets = np.split(wholeData, [(i + 1) * (len(wholeData) // folds)
for i in range(folds - 1)])
print([len(i) for i in trainingSets])
for idx, testData in enumerate(trainingSets):
print(np.r_[0:idx, idx + 1:len(trainingSets) + 1])
trainingData = np.concatenate(
[trainingSets[i] for i in np.r_[0:idx, idx + 1:len(trainingSets)]])
print('K FOLD #' + str(idx))
start = time.time()
model = Naive(trainingData, attTypes)
timer = time.time()
print('Time to train: ', timer - start)
start = time.time()
evalModel = Evaluation(model, competitionDataCov, CLASS_AMM_COV)
evalModel.normalPrint()
print('Took ', time.time() - start, 's')
print(f"CASE {case}:")
start_time_n = time.time()
print(f"NAIVE: {' '.join(map(str, naive.find_pattern()))}")
end_time_n = count_time(start_time_n)
start_time_rk = time.time()
print(f"RABIN-KARP: {' '.join(map(str, rk.find_pattern()))}")
end_time_rk = count_time(start_time_rk)
start_time_kmp = time.time()
print(f"KNUTH-MORRIS-PRATT: {' '.join(map(str, kmp.find_pattern()))}")
end_time_kmp = count_time(start_time_kmp)
print("--------------------------------------------------")
print(
f"NAIVE TIME: {end_time_n}\nRABIN-KARP TIME: {end_time_rk}\nKNUTH-MORRIS-PRATT TIME: {end_time_kmp}"
)
print(naive.find_pattern() == rk.find_pattern() == kmp.find_pattern())
print("--------------------------------------------------")
if __name__ == "__main__":
files_name = ["pattern", "pattern1", "text", "text1"]
converted_files = setup_array(files_name)
n = Naive(converted_files[0], converted_files[2])
n1 = Naive(converted_files[1], converted_files[3])
r_k = RabinKarp(converted_files[0], converted_files[2], 128, 27077)
r_k1 = RabinKarp(converted_files[1], converted_files[3], 128, 27077)
k_m_p = KnuthMorrisPratt(converted_files[0], converted_files[2])
k_m_p1 = KnuthMorrisPratt(converted_files[1], converted_files[3])
test(1, n, r_k, k_m_p)
test(2, n1, r_k1, k_m_p1)
unittest.main()
class Menu:
def __init__(self):
self.read_data = ReadData()
self.naive = Naive()
self.branch_and_bound = Branch_And_Bound()
self.tests = Tests()
self.data = []
self.choice = 0
self.starting_city = 0
def main_menu(self):
while self.choice != 6:
print('_________MENU_________')
print('1. Wybierz dane')
print('2. Wyświetl dane')
print('3. Brute force')
print('4. Branch and bound')
print('5. Testy')
print('6. Wyjście')
self.choice = input('Wybór: ')
if self.choice == '1':
self.data = self.read_data.get_data()
elif self.choice == '2':
if self.data == [] or self.data == None:
print('Brak danych do wyświetlenie.')
else:
print('Dane: ')
for graph in self.data:
print('')
for row in graph:
print(row)
elif self.choice == '3':
for graph in self.data:
start = datetime.datetime.now()
solution, path = self.naive.naive_algorithm(
graph, self.starting_city)
duration = datetime.datetime.now() - start
print('Duration = ', duration)
print('Solution = ', solution)
print('Path = ', path)
elif self.choice == '4':
for graph in self.data:
start = datetime.datetime.now()
solution, path = self.branch_and_bound.find_solution(
graph, self.starting_city)
duration = datetime.datetime.now() - start
print('Duration = ', duration)
print('Solution = ', solution)
print('Path = ', path)
elif self.choice == '5':
print('1. Brute force: ')
print('2. Branch and bound: ')
choice = int(input('Wybór: '))
if choice == 1:
self.tests.testing_naive()
if choice == 2:
self.tests.testing_b_b()
elif self.choice == '6':
exit()
else:
print("Wprowadz poprawną liczbę")
from preprocessing import Preprocessing from naive import Naive if __name__ == "__main__": prepross = Preprocessing() instances = prepross.get_instances_of_file() preclassified_instances = prepross.pre_classfiy(instances) #prepross.bag_of_words(preclassified_instances) nv = Naive() nv.naive_bayes(preclassified_instances)
def test_naive(self):
naive_test = Naive(self.test_case[0], self.test_case[1])
naive_test1 = Naive(self.test_case1[0], self.test_case1[1])
self.assertEqual(naive_test.find_pattern(), [0, 9, 13])
self.assertEqual(naive_test1.find_pattern(), [])
from naive import Naive P = 'GCAGAGAG' T = 'GCATCGCAGAGAGTATACAGTACGGCAGAGAG' algorithm = Naive(T) print(algorithm.match(P))
def timeToClassify(count=360):
attTypes = [1 for _ in range(10)] + [0 for _ in range(44)]
print('===============================================================')
print('======= Time to classify augmenting counts on Train Set =======')
print('===============================================================')
trainingData = np.load("cov_data/500k/trainingData.npy").astype(float)
sizes = [i + 1 for i in range(count)]
knnTimes = []
id3TrainTimes = []
id3Times = []
nbTrainTimes = []
nbTimes = []
tupla = competitionDataCov[2, :-1]
for i in sizes:
start = time.time()
nb = Naive(trainingData[:i * 1000], attTypes)
ti = time.time() - start
nbTrainTimes.append(ti)
print('#', i, ' NB Train - ', ti)
minTime = takeMinTime(nb, tupla)
nbTimes.append(minTime)
print('#', i, ' NB Classify - ', minTime)
del nb
start = time.time()
id3 = ID3(trainingData[:i * 1000], 54, True, 2, attTypes, 1)
ti = time.time() - start
id3TrainTimes.append(ti)
print('#', i, ' ID3 Train - ', ti)
minTime = takeMinTime(id3, tupla)
id3Times.append(minTime)
print('#', i, ' ID3 Classify - ', minTime)
del id3
knn = Knn2(trainingData[:i * 1000], 7)
minTime = takeMinTime(knn, tupla)
knnTimes.append(minTime)
print('#', i, ' K-NN Classify - ', minTime)
del knn
plt.figure(1)
plt.subplot(211)
plt.ylabel('Time')
plt.title('Naive Bayes train time')
plt.plot(sizes, nbTrainTimes)
plt.subplot(212)
plt.ylabel('Time')
plt.title('ID3 train time')
plt.plot(sizes, id3TrainTimes)
plt.figure(2)
plt.subplot(311)
plt.ylabel('Time')
plt.title('Naive Bayes classify time')
plt.plot(sizes, nbTimes)
plt.subplot(312)
plt.ylabel('Time')
plt.title('ID3 classify time')
plt.plot(sizes, id3Times)
plt.subplot(313)
plt.ylabel('Time')
plt.title('K-NN classify time')
plt.plot(sizes, knnTimes)
plt.show()
from file_manager import File_manager
from naive import Naive
from datamuse import Datamuse
import asyncio
TRAIN_FILENAME = "train_data.tsv"
TEST_FILENAME = "data_estag_ds.tsv"
if __name__ == '__main__':
filemng = File_manager()
train_instances = filemng.get_instances_of_file(TRAIN_FILENAME)
test_instances = filemng.get_instances_of_file(TEST_FILENAME)
nv = Naive()
dic = filemng.read_dictionary()
dtmuse = Datamuse()
loop = asyncio.get_event_loop()
category_synonymous = loop.run_until_complete(
dtmuse.get_synonymous('smartphone'))
loop.close()
category_synonymous = eval(category_synonymous)
nv.naive_bayes(train_instances, dic, test_instances, category_synonymous)
image_size,
latent_size,
n_context,
test=True,
seed=seed)
test_data = DataGenerator("DAVIS_Challenge",
batch_size,
image_size,
latent_size,
n_context,
test=True,
seed=seed)
# model = VAE()
# model.load("model.h5")
model = Naive()
for name, data in zip(("train", "val", "test"),
(train_data, val_data, test_data)):
input_, (truth, ) = next(data)
(_, contexts) = input_
prevs, nexts = contexts[:, :, :, :3], contexts[:, :, :, 3:]
pred = model.model_test.predict_on_batch(input_)
for label, batch in zip(("1", "2", "truth", "pred"),
(prevs, nexts, truth, pred)):
result = np.round(np.clip(255 * batch, 0, 255)).astype("uint8")
for i in range(result.shape[0]):
imageio.imwrite("%s_%s_%d_%s.png" % (prefix, name, i, label),
result[i])
def __init__(self):
self.data = ReadData()
self.naive = Naive()
self.branch_and_bound = Branch_And_Bound()
def naive_time(self, index, iter):
NaiveAlgorithm = Naive(index, self.SymmetricalMatrix)
start = timer()
NaiveAlgorithm.travellingSalesmanProblem()
end = timer()
self.NaiveTime[iter] = end - start