def __init__(self):
CipDatabase.__init__(self)
self.num_scores_fitted = 0
self.X = []
self.y = []
# self.predictor = SGDRegressor()
self.predictor = KNeighborsRegressor()
def __init__(self):
CipDatabase.__init__(self)
self.num_scores_fitted = 0
self.X = []
self.y = []
#self.predictor = SGDRegressor()
self.predictor = KNeighborsRegressor()
def load_cip_data(self):
""" Load database and feature vectors from files."""
CipDatabase.load_cip_data(self)
self.X, self.y = load_svmlight_file('cip_rank_regression.data', n_features=self.vectorizer.feature_size,
zero_based=False)
def save_cip_data(self):
"""Save database and feature vectors to files."""
CipDatabase.save_cip_data(self)
dump_svmlight_file(self.X, self.y, 'cip_rank_regression.data', zero_based=False)
def load_cip_data(self):
""" Load database and feature vectors from files."""
CipDatabase.load_cip_data(self)
self.X, self.y = load_svmlight_file('cip_rank_regression.data', n_features=self.vectorizer.feature_size, zero_based=False)
def save_cip_data(self):
"""Save database and feature vectors to files."""
CipDatabase.save_cip_data(self)
dump_svmlight_file(self.X, self.y, 'cip_rank_regression.data', zero_based=False)
