def run(self):
if (self.RUN_READER):
read = Reader()
read.execute(self.BASE_PATH, self.READER_TYPES, self.BASE_SUBJECTS)
if (self.RUN_EXTRACTOR):
extract = Extractor()
extract.execute(self.BASE_PATH, self.BASE_WINDOW,
self.WINDOW_OVERLAP, self.BASE_SUBJECTS)
if (self.RUN_SELECTOR):
selection_results = {}
select = Selector()
for st in self.SELECTOR_SELECTION_TYPE:
for sig in self.SELECTOR_SIGNALS:
selection_results[sig] = []
selection_results[sig] = select.execute(
self.BASE_PATH, sig, self.BASE_SUBJECTS,
self.BASE_WINDOW, self.WINDOW_OVERLAP, st,
self.SELECTOR_ALL_SIGNS)
if (self.SELECTOR_ALL_SIGNS):
break
print('Results = ', st)
print('RATIO, STD')
print(selection_results)
if (self.RUN_CLASSIFIER):
# self.one_classifier_and_decision('lda', 8)
# self.one_classifier_and_decision('', 9)
self.individual('ecg', 'pca', 1)
# self.individual('ecg', 'lda', 2)
# self.individual('ecg', '', 3)
self.individual('eda', 'pca', 1)
# self.individual('eda', 'lda', 2)
# self.individual('eda', '', 3)
# self.individual('emg', 'pca', 1)
# self.individual('emg', 'lda', 2)
# self.individual('emg', '', 3)
self.individual('resp', 'pca', 1)
# self.individual('resp', 'lda', 2)
# self.individual('resp', '', 3)
self.todos('pca', 4)
# self.todos('lda', 5)
# self.todos('', 6)
self.one_classifier_and_decision('pca', 7)
i = 0
for i in range(len(variances[0])):
new_variances.insert(i, [])
for v in variances:
new_variances[i].extend([v[i]])
variance = []
i = 0
for i in range(len(new_variances)):
variance.insert(i, [])
variance[i].extend(
[np.mean(new_variances[i]),
np.std(new_variances[i])])
return variance
from selector import Selector
if __name__ == '__main__':
subjects = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17]
base_path = '/Volumes/My Passport/TCC/WESAD2/'
signal = 'ecg'
selection_type = 'lda'
window = 20
window_overlap = True
with_all_signals = False
select = Selector()
variance = select.execute(base_path, signal, subjects, window,
window_overlap, selection_type, with_all_signals)
print(variance)
