(self)
return X_train, Y_train, mask_train
get_training_data returns X_train, y_train, and mask_train
        where each row of each tensor is a variable/VID and
        y_train are weak labels for each variable i.e. they are
        set as the initial values.

        This assumes that we have a larger proportion of correct initial values
        and only a small amount of incorrect initial values which allow us
        to train to convergence.
get_training_data returns X_train, y_train, and mask_train
        where each row of each tensor is a variable/VID and
        y_train are weak labels for each variable i.e. they are
        set as the initial values.
get_training_data returns X_train y_train and mask_train where each row of each tensor is a variable / VID and y_train are weak labels for each variable i . e . they are set as the initial values .
def get_training_data(self):
        """
        get_training_data returns X_train, y_train, and mask_train
        where each row of each tensor is a variable/VID and
        y_train are weak labels for each variable i.e. they are
        set as the initial values.

        This assumes that we have a larger proportion of correct initial values
        and only a small amount of incorrect initial values which allow us
        to train to convergence.
        """
        train_idx = (self.weak_labels != -1).nonzero()[:,0]
        X_train = self.tensor.index_select(0, train_idx)
        Y_train = self.weak_labels.index_select(0, train_idx)
        mask_train = self.var_class_mask.index_select(0, train_idx)
        return X_train, Y_train, mask_train
def get_training_data ( self ) : train_idx = ( self . weak_labels != - 1 ) . nonzero ( ) [ : , 0 ] X_train = self . tensor . index_select ( 0 , train_idx ) Y_train = self . weak_labels . index_select ( 0 , train_idx ) mask_train = self . var_class_mask . index_select ( 0 , train_idx ) return X_train , Y_train , mask_train