def test_iadd(self, evt):
        rval = event.ReturnValue(append_events=[evt])
        rval2 = event.ReturnValue(eat=True, append_events=[evt])
        rset = event.ResultSet()

        rset += rval
        rset += rval2
        rset += None
        assert rset.eat
        assert rset.append_events == [evt, evt]
def __calculate_initial_covariances(self, initial_clusters):
        covariances = []
        for initial_cluster in initial_clusters:
            if len(initial_cluster) > 1:
                cluster_sample = [self.__sample[index_point] for index_point in initial_cluster]
                covariances.append(numpy.cov(cluster_sample, rowvar=False))
            else:
                dimension = len(self.__sample[0])
                covariances.append(numpy.zeros((dimension, dimension)) + random.random() / 10.0)