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from sklearn.model_selection import train_test_split
class StockPredictor(object):
def __init__(self, company, test_size=0.33,
n_latency_days=10, n_hidden_states=4):
self._init_logger()
self.company = company
self.n_latency_days = n_latency_days
self.hmm = GaussianHMM(n_components=n_hidden_states)
self._split_train_test_data(test_size)
def _split_train_test_data(self, test_size):
data = pd.read_csv(
'data/company_data/{company}.csv'.format(company=self.company))
_train_data, test_data = train_test_split(
data, test_size=test_size, shuffle=False)
self._train_data = _train_data
self._test_data = test_data
def fit(self):
self._logger.info('>>> Extracting Features')
feature_vector = StockPredictor._extract_features(self._train_data)
self._logger.info('Features extraction Completed <<<')
self.hmm.fit(feature_vector)
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