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
