(self, model=None)
return self._get_samples(self._questions_train, model=model)
Get a set of training samples. A tuple is returned where the first element is a list of
        graph sets and the second element is a list of indices. An index points to the correct graph parse
        from the corresponding graph set. Graph sets are all of size 30, negative graphs are subsampled or
        repeatedly sampled if there are more or less negative graphs respectively.
        Graph are stored in triples, where the first element is the graph.

        :param model: an optional qamodel that is used to select the negative samples, otherwise random
        :return: a set of training samples.
Get a set of training samples. A tuple is returned where the first element is a list of
        graph sets and the second element is a list of indices. An index points to the correct graph parse
        from the corresponding graph set. Graph sets are all of size 30, negative graphs are subsampled or
        repeatedly sampled if there are more or less negative graphs respectively.
        Graph are stored in triples, where the first element is the graph.
Get a set of training samples . A tuple is returned where the first element is a list of graph sets and the second element is a list of indices . An index points to the correct graph parse from the corresponding graph set . Graph sets are all of size 30 negative graphs are subsampled or repeatedly sampled if there are more or less negative graphs respectively . Graph are stored in triples where the first element is the graph .
def get_training_samples(self, model=None):
        """
        Get a set of training samples. A tuple is returned where the first element is a list of
        graph sets and the second element is a list of indices. An index points to the correct graph parse
        from the corresponding graph set. Graph sets are all of size 30, negative graphs are subsampled or
        repeatedly sampled if there are more or less negative graphs respectively.
        Graph are stored in triples, where the first element is the graph.

        :param model: an optional qamodel that is used to select the negative samples, otherwise random
        :return: a set of training samples.
        """
        return self._get_samples(self._questions_train, model=model)
def get_training_samples ( self , model = None ) : return self . _get_samples ( self . _questions_train , model = model )