def load(self, filename, create_if_missing=True):
        filename = Path(filename)
        if not filename.exists() and create_if_missing:
            self.load_pip_requirements()
            with filename.open("w") as f:
                f.write(yaml.dump(self.serialized, default_flow_style=False,
                                  encoding=None))
            self.filename = filename
            return self.save(filename)
        with filename.open() as f:
            for group, requirements in yaml.load(f.read()).items():
                for requirement in requirements:
                    self[group].add(Requirement.coerce(requirement))
        self.filename = filename
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)