| 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) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment