def extract(self, instance): assert(isinstance(instance, Instance)) params = VAR(instance.eeg_data.T).fit(self.lags).params # hstack will collapse all entries into one big vector features = np.hstack(params.reshape( (np.prod(params.shape),1) )) self.assert_features(features) # features = a 1d ndarray return features
def extract(self, instance): assert (isinstance(instance, Instance)) params = VAR(instance.eeg_data.T).fit(self.lags).params # hstack will collapse all entries into one big vector features = np.hstack(params.reshape((np.prod(params.shape), 1))) self.assert_features(features) # features = a 1d ndarray return features
def extract(self, instance): # instance is an object of class Instance # Wittawat: Since VAR automatically does lags order selection, # other different instances may give a different lags values ? params = VAR(instance.eeg_data.T).fit(maxlags=2).params features = np.hstack(params.reshape( (np.prod(params.shape), 1) )) self.assert_features(features) # features = a 1d ndarray return features
def extract(self, instance): # instance is an object of class Instance # Wittawat: Since VAR automatically does lags order selection, # other different instances may give a different lags values ? params = VAR(instance.eeg_data.T).fit(maxlags=2).params features = np.hstack(params.reshape((np.prod(params.shape), 1))) self.assert_features(features) # features = a 1d ndarray return features
def varAR(self,x): params = VAR(x).fit(maxlags=2).params features = np.hstack(params.reshape((np.prod(params.shape), 1))) return features