def get_parameters(self):
        """
        This function creates some parameters to be used with K-Medoids.
        @return: A tuple with the generated parameters and an empty list corresponding to the clusterings.
        """
        run_parameters = []
        max_clusters = self.parameters["clustering"]["evaluation"]["maximum_clusters"]
        min_clusters = self.parameters["clustering"]["evaluation"]["minimum_clusters"]
        sizes = range(min_clusters,max_clusters+1,self.num_clusters_step)

        # Defaults
        if not "seeding_type" in self.parameters["clustering"]["algorithms"]["kmedoids"]:
            self.parameters["clustering"]["algorithms"]["kmedoids"]["seeding_type"] = "EQUIDISTANT"

        # This could be subclassed
        if self.parameters["clustering"]["algorithms"]["kmedoids"]["seeding_type"] == "RANDOM":
            tries = self.parameters["clustering"]["algorithms"]["kmedoids"]["tries"] if self.parameters["clustering"]["algorithms"]["kmedoids"] in "tries" else 10

            for i in range(tries):
                for one_size in sizes:
                    run_parameter = ParametersGenerator.get_base_parameters()
                    run_parameter["k"]  = one_size
                    run_parameter["seeding_type"] = "RANDOM"
                    run_parameter["rand_seed"] = int(hashlib.sha256(str({"dic":self.parameters,"iter":i})).hexdigest(),base=16)
                    run_parameters.append(run_parameter)
        else:
            for one_size in sizes:
                run_parameter = ParametersGenerator.get_base_parameters()
                run_parameter["k"]  = one_size
                run_parameter["seeding_type"] = "EQUIDISTANT"
                run_parameters.append(run_parameter)

        return run_parameters, []
dataset/ETHPy150Open victor-gil-sepulveda/pyProCT/pyproct/clustering/algorithms/kmedoids/parametersGeneration.py/ParametersGenerator.get_parameters/swapped operands of `in`