model = HiddenMarkovModel.from_samples(NormalDistribution,
n_components=n_comp,
X=[normal_signal],
labels=[labels],
algorithm='labeled')
# model = HiddenMarkovModel.from_samples(GeneralMixtureModel, n_components = n_comp, X = [normal_signal],
# labels = [labels], algorithm='labeled')
# fig = plt.figure(num = 1000, figsize=(15,4))
# plt.plot(normal_signal,'b')
# plt.plot([x / 3 for x in sequence.path], 'r')
# plt.savefig('Graphs/path.png')
# plt.close('all')
pool = Pool(N_pool)
with open('model.txt', 'w') as file:
out = print_model_distribution(model)
file.write(out)
params = [
(model, normal_signal, N_train, N, alpha, norm_params, mean, variance,
an_params, anomal_mean, anomal_variance, n_comp, i + 1)
for i in range(count_launch)
]
start_time = time.time()
res = pool.starmap(f, params)
# print(res)
pool.close()
pool.join()
print('Time ', time.time() - start_time)
def run(arg, k):
np.random.seed(k)
exp_type = arg['type']
N = arg['N']
alpha = arg['alpha']
n_comp = arg['n_comp']
norm_params = arg['norm_params']
an_params = arg['an_params']
save_dir = arg['dir']
mean = arg['mean']
variance = arg['varience']
anomal_mean = arg['anomal_mean']
anomal_variance = arg['anomal_varience']
norm_gen = generator.Sequence(N,
alpha,
type=exp_type,
params=norm_params,
mean=mean,
variance=variance)
norm_signal = norm_gen.sequence
an_gen = generator.Sequence(N,
alpha,
type=exp_type,
params=an_params,
mean=anomal_mean,
variance=anomal_variance)
an_signal = an_gen.sequence
# an_signal[180:200] = np.random.normal(2,0.02,20)
an_labels = list(map(myutils.rename_state, an_gen.path))
labels = list(map(myutils.rename_state, norm_gen.path))
if exp_type == 'continue':
model = HiddenMarkovModel.from_samples(NormalDistribution,
n_components=n_comp,
X=[norm_signal],
labels=[labels],
algorithm='labeled')
an_model = HiddenMarkovModel.from_samples(NormalDistribution,
n_components=n_comp,
X=[an_signal],
labels=[an_labels],
algorithm='labeled')
else:
model = HiddenMarkovModel.from_samples(DiscreteDistribution,
n_components=n_comp,
X=[norm_signal],
labels=[labels],
algorithm='labeled')
an_model = HiddenMarkovModel.from_samples(DiscreteDistribution,
n_components=n_comp,
X=[an_signal])
# model = HiddenMarkovModel.from_samples(DiscreteDistribution, n_components = n_comp, X = [norm_signal])
# an_model = HiddenMarkovModel.from_samples(DiscreteDistribution, n_components = n_comp, X = [an_signal])
l1 = model.log_probability(norm_signal)
l2 = model.log_probability(an_signal)
cdir = os.getcwd()
path = cdir + '/' + arg['dir']
try:
os.mkdir(path)
except:
pass
with open(path + '/log_' + str(k) + '.txt', 'w') as file:
out = myutils.print_model_distribution(model)
file.write(out)
out = myutils.print_model_distribution(an_model)
file.write(out)
file.write('l_normal = {} l_anomal = {}'.format(l1, l2))
# out = myutils.print_model_distribution(model_2)
# file.write(out)
# file.write(str(an_model.to_json()))
# fig_sub = plt.figure(figsize = (18,5.9))
fig_sub = plt.figure(figsize=(16, 5.9))
ax2 = fig_sub.add_axes([0.12, 0.1, 0.07, 0.8])
ax2.plot([1] * len([l1]), l1, 'b.', markersize=12)
ax2.plot([1] * len([l2]), l2, 'r.', markersize=12)
# ax2.plot([1], normal_score, 'g*', markersize=12)
ax2.set_ylabel('log probability')
# ax2.set_xlim(0.9, 1.2)
ax2.set_xticks([0.95, 1, 1.05])
ax2.set_xticklabels(['', '', ''])
ax = fig_sub.add_axes([0.24, 0.1, 0.74, 0.8])
ax.plot(norm_signal, 'b', label='Normal') #Ошибка в цветах
ax.plot(an_signal, 'r', label='Abnormal')
ax.set_xlabel('Time', )
# ax.grid()
plt.legend(loc=1)
plt.tight_layout()
plt.savefig(path + '/plot' + str(k) + '.png', dpi=180)
plt.close()
# ax.set_y
print(' {}, {}'.format(l1, l2))
print('На аномальной')
l1 = an_model.log_probability(norm_signal)
l2 = an_model.log_probability(an_signal)
print(' {}, {}'.format(l1, l2))
print(' Норма\n {}'.format(model.predict_proba(an_signal)))
from pomegranate import *
import sequence_generator as generator
import matplotlib.pyplot as plt
import numpy as np
import myutils
test = generator.Sequence(10000, ['a', 'b'],
type='continue',
params={
'a': {
'len': [1000, 1010],
'depend_on': False
},
'b': {
'len': [25, 35],
'depend_on': False
}
},
mean=[0, 0.2],
variance=[0.03, 0.05])
fig = plt.figure(figsize=(20, 4))
plt.plot(test.sequence)
# plt.plot(test.path)
model = HiddenMarkovModel.from_samples(
NormalDistribution,
2, [test.sequence],
labels=[list(map(lambda x: myutils.rename_state(x), test.path))],
algorithm='labeled')
myutils.print_model_distribution(model)