def load_pip_requirements(self, files_map=None, freeze=True):
        if files_map is None:
            files_map = self.default_pip_files
        for group, requirements_txt in files_map.items():
            path = Path(requirements_txt)
            if not path.exists() and group.lower() == "all" and freeze:
                cmd = envoy.run("pip freeze")
                self[group].loads(cmd.std_out)
            elif path.exists():
                self[group].load(path)
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)