def classify(inputfile='slpdb.mat', epoch_length=30, make_plot=True, save=True, outputfile='classify.txt'):
# Load datas
dataset = loadData(inputfile)
ECG = groupByEpoch(dataset['ECG'], 250, epoch_length)
Resp_in, Resp_out = edr.main(dataset)
# Feature dictionary construction
d_test = dictionaryInitialization()
d_test = dico_construction(ECG, Resp_in, Resp_out, d_test)
Xarr_norm = createNormalizedDataframe(d_test)
# Predecition
model = joblib.load('model.pkl')
pred_res = list()
for j in range(0, len(ECG)):
l = str(model.predict(Xarr_norm[j])[0])
pred_res.append(l)
if make_plot:
# Plot result
plot(pred_res, dataset['labels'], epoch_length)
if save:
# Write prediction into text file
savePredictions(pred_res, outputfile)
def classify(inputfile='slpdb.mat',
epoch_length=30,
make_plot=True,
save=True,
outputfile='classify.txt'):
# Load datas
dataset = loadData(inputfile)
ECG = groupByEpoch(dataset['ECG'], 250, epoch_length)
Resp_in, Resp_out = edr.main(dataset)
# Feature dictionary construction
d_test = dictionaryInitialization()
d_test = dico_construction(ECG, Resp_in, Resp_out, d_test)
Xarr_norm = createNormalizedDataframe(d_test)
# Predecition
model = joblib.load('model.pkl')
pred_res = list()
for j in range(0, len(ECG)):
l = str(model.predict(Xarr_norm[j])[0])
pred_res.append(l)
if make_plot:
# Plot result
plot(pred_res, dataset['labels'], epoch_length)
if save:
# Write prediction into text file
savePredictions(pred_res, outputfile)
from data import loadData, groupByEpoch
from models import createNormalizedDataframe
from feature_construction import dictionaryInitialization, dico_construction
from sklearn.svm import SVC
from sklearn.externals import joblib
from edr import main
# Load datas
ECG = np.array([])
labels = np.array([])
binarylabels = np.array([])
for file in os.listdir("traindata"):
if file.endswith(".mat"):
dataset = loadData('traindata/' + file)
ECG = np.append(ECG, dataset['ECG'])
Resp_in, Resp_out = main(dataset)
labels = np.append(labels, dataset['labels'])
binarylabels = np.append(binarylabels, dataset['binarylabels'])
X = groupByEpoch(ECG)
# Train dictionary construction
d_train = dictionaryInitialization()
d_train = dico_construction(X, Resp_in, Resp_out, d_train)
y = np.array(labels)
Xarr_norm = createNormalizedDataframe(d_train)
Xarr_norm = np.nan_to_num(Xarr_norm).reshape((len(Xarr_norm), 1))
# Compute model and write to file
model = SVC(kernel='linear').fit(Xarr_norm, y)
joblib.dump(model, 'model.pkl')
from models import createNormalizedDataframe
from feature_construction import dictionaryInitialization, dico_construction
from sklearn.svm import SVC
from sklearn.externals import joblib
from edr import main
# Load datas
ECG = np.array([])
labels = np.array([])
binarylabels = np.array([])
for file in os.listdir("traindata"):
if file.endswith(".mat"):
dataset = loadData('traindata/' + file)
ECG = np.append(ECG, dataset['ECG'])
Resp_in, Resp_out = main(dataset)
labels = np.append(labels, dataset['labels'])
binarylabels = np.append(binarylabels, dataset['binarylabels'])
X = groupByEpoch(ECG)
# Train dictionary construction
d_train = dictionaryInitialization()
d_train = dico_construction(X, Resp_in, Resp_out, d_train)
y = np.array(labels)
Xarr_norm = createNormalizedDataframe(d_train)
Xarr_norm = np.nan_to_num(Xarr_norm).reshape((len(Xarr_norm), 1))
# Compute model and write to file
model = SVC(kernel='linear').fit(Xarr_norm, y)
joblib.dump(model, 'model.pkl')
