rasa/core/policies/embedding_policy.py
def _create_X_slots_previous_actions(
        self,
        data_X: np.ndarray
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """Extract feature vectors

        for user input (X), slots and
        previously executed actions from training data.
        """

        featurizer = self.featurizer.state_featurizer
        slot_start = featurizer.user_feature_len
        previous_start = slot_start + featurizer.slot_feature_len

        X = data_X[:, :, :slot_start]
        slots = data_X[:, :, slot_start:previous_start]
        previous_actions = data_X[:, :, previous_start:]

        return X, slots, previous_actions
Extract feature vectors

        for user input (X), slots and
        previously executed actions from training data.
        
Extract feature vectors
for user input (X), slots and
previously executed actions from training data.
Extract feature vectors for user input ( X ) slots and previously executed actions from training data .
def _create_X_slots_previous_actions(
        self,
        data_X: np.ndarray
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        featurizer = self.featurizer.state_featurizer
        slot_start = featurizer.user_feature_len
        previous_start = slot_start + featurizer.slot_feature_len
        X = data_X[:, :, :slot_start]
        slots = data_X[:, :, slot_start:previous_start]
        previous_actions = data_X[:, :, previous_start:]
        return X, slots, previous_actions
def _create_X_slots_previous_actions ( self , data_X : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray , np . ndarray ] : featurizer = self . featurizer . state_featurizer slot_start = featurizer . user_feature_len previous_start = slot_start + featurizer . slot_feature_len X = data_X [ : , : , : slot_start ] slots = data_X [ : , : , slot_start : previous_start ] previous_actions = data_X [ : , : , previous_start : ] return X , slots , previous_actions
Extract feature vectors
for user input (X), slots and
previously executed actions from training data.
Extract feature vectors for user input ( X ) slots and previously executed actions from training data .
"""Extract feature vectors

        for user input (X), slots and
        previously executed actions from training data.
        """
self
data_X
np.ndarray