def __init__(self, manager, task_id, sub_ids=None, dry_run=False, resubmit=False):
		self.__manager = manager
		self.__task	= self.__manager.load_task(task_id)
		self.__sub_ids = sub_ids
		self.__dry_run = dry_run
		self.__resubmit = resubmit

		self.__logger = logging.getLogger('JSUB')
		if self.__sub_ids==None:
			self.__sub_ids=range(len(self.__task.data['jobvar']))

		self.__initialize_manager()
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)