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add tests
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‎ml4co_kit/solver/lib/fem/mcl_fem.py‎

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import torch
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from torch import Tensor
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from typing import Callable
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from ml4co_kit.task.graph.mcl import MClTask
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from ml4co_kit.utils import to_tensor, to_numpy
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from ml4co_kit.task.graph.mcl import MCLTask
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def energy_mcl(adj: Tensor, p: Tensor, penalty: float = 10.0) -> Tensor:
@@ -54,7 +54,7 @@ def entropy_mcl(p: Tensor) -> Tensor:
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def mcl_fem(
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task_data: MCLTask,
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task_data: MClTask,
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num_trials: int,
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betas: Tensor,
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grad_opt_class: Callable,

‎tests/__init__.py‎

Whitespace-only changes.

‎tests/extension_test/__init__.py‎

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r"""
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Extension Test Module.
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"""
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# Copyright (c) 2024 Thinklab@SJTU
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# ML4CO-Kit is licensed under Mulan PSL v2.
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# You can use this software according to the terms and conditions of the Mulan PSL v2.
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# You may obtain a copy of Mulan PSL v2 at:
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# http://license.coscl.org.cn/MulanPSL2
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# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
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# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
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# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
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# See the Mulan PSL v2 for more details.
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from .mis_mcmc import MISMCMCTester

‎tests/extension_test/mis_mcmc.py‎

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r"""
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Test MIS MCMC Module.
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"""
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# Copyright (c) 2024 Thinklab@SJTU
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# ML4CO-Kit is licensed under Mulan PSL v2.
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# You can use this software according to the terms and conditions of the Mulan PSL v2.
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# You may obtain a copy of Mulan PSL v2 at:
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# http://license.coscl.org.cn/MulanPSL2
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# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
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# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
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# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
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# See the Mulan PSL v2 for more details.
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import numpy as np
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from ml4co_kit import MISTask
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from ml4co_kit.extension.mcmc import mis_mcmc
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class MISMCMCTester(object):
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"""Test cases for MIS MCMC."""
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def __init__(self) -> None:
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pass
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def test(self):
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# Get MIS Task
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mis_task = MISTask()
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mis_task.from_pickle("test_dataset/graph/mis/task/mis_er-700-800_no-weighted_task.pkl")
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# Test final solution and cost list (Basic)
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final_sol = mis_mcmc(
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task_data=mis_task,
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ref=True,
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penalty_coeff=1.001,
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tau=0.50,
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steps=1000,
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return_type="final_sol",
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return_cost_list=False
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)
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# Test final solution and cost list (Variable Temperature)
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tau_list_1 = np.array([3.0] * 20)
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tau_list_2 = np.linspace(0.10, 0.01, 980)
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tau_list = np.concatenate([tau_list_1, tau_list_2])
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# Test best solution
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best_sol = mis_mcmc(
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task_data=mis_task,
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ref=True,
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penalty_coeff=1.001,
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tau=tau_list,
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steps=1000,
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return_type="best_sol",
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return_cost_list=False
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)
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# Test mean solution
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mean_sol = mis_mcmc(
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task_data=mis_task,
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ref=True,
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penalty_coeff=1.001,
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tau=tau_list,
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steps=1000,
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return_type="mean_sol",
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return_cost_list=False
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)
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# Test mean solution
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better_sol_list, cost_list = mis_mcmc(
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task_data=mis_task,
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ref=True,
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penalty_coeff=1.001,
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tau=tau_list,
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steps=1000,
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return_type="better_sol_list",
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return_cost_list=True
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)
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# Test mean solution
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all_sol_list = mis_mcmc(
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task_data=mis_task,
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ref=True,
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penalty_coeff=1.001,
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tau=tau_list,
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steps=1000,
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return_type="all_sol_list",
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return_cost_list=False
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)

‎tests/generator_test/__init__.py‎

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r"""
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Generator Test Module.
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"""
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# Copyright (c) 2024 Thinklab@SJTU
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# ML4CO-Kit is licensed under Mulan PSL v2.
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# You can use this software according to the terms and conditions of the Mulan PSL v2.
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# You may obtain a copy of Mulan PSL v2 at:
9+
# http://license.coscl.org.cn/MulanPSL2
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# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
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# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
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# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
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# See the Mulan PSL v2 for more details.
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# Base Class
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from .base import GenTesterBase
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# Routing Problems
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from .routing.atsp import ATSPGenTester
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from .routing.cvrp import CVRPGenTester
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from .routing.op import OPGenTester
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from .routing.pctsp import PCTSPGenTester
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from .routing.spctsp import SPCTSPGenTester
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from .routing.tsp import TSPGenTester
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# Graph Problems
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from .graph.mcut import MCutGenTester
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from .graph.mcl import MClGenTester
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from .graph.mis import MISGenTester
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from .graph.mvc import MVCGenTester
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# Portfolio Problems
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from .portfolio.minvarpo import MinVarPOGenTester
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from .portfolio.maxretpo import MaxRetPOGenTester
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from .portfolio.mopo import MOPOGenTester
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# QAP Problems
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from .qap.gm import GMGenTester
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# SAT Problems
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from .sat.satp import SATPGenTester
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from .sat.sata import SATAGenTester
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from .sat.usatc import USATCGenTester

‎tests/generator_test/base.py‎

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r"""
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Base class for generator testers.
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"""
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# Copyright (c) 2024 Thinklab@SJTU
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# ML4CO-Kit is licensed under Mulan PSL v2.
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# You can use this software according to the terms and conditions of the Mulan PSL v2.
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# You may obtain a copy of Mulan PSL v2 at:
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# http://license.coscl.org.cn/MulanPSL2
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# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
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# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
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# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
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# See the Mulan PSL v2 for more details.
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from tqdm import tqdm
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from typing import Type, List
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from ml4co_kit import GeneratorBase
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class GenTesterBase(object):
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def __init__(
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self,
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test_gen_class: Type[GeneratorBase],
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test_args_list: List[dict]
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):
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self.test_gen_class = test_gen_class
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self.test_args_list = test_args_list
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def test(self):
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# Test for each distribution type
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for test_args in tqdm(
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self.test_args_list,
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desc=f"Testing {self.test_gen_class.__name__}"
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):
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try:
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generator = self.test_gen_class(**test_args)
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generator.generate()
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except:
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raise ValueError(
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f"Error occurred when testing {self.test_gen_class}\n"
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f"Test args: {test_args} "
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)

‎tests/generator_test/graph/mcl.py‎

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r"""
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Tester for MCl generator.
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"""
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# Copyright (c) 2024 Thinklab@SJTU
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# ML4CO-Kit is licensed under Mulan PSL v2.
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# You can use this software according to the terms and conditions of the Mulan PSL v2.
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# You may obtain a copy of Mulan PSL v2 at:
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# http://license.coscl.org.cn/MulanPSL2
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# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
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# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
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# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
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# See the Mulan PSL v2 for more details.
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from ml4co_kit import MClGenerator, GRAPH_TYPE
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from ml4co_kit.generator.graph.base import (
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GraphWeightGenerator, GRAPH_WEIGHT_TYPE
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)
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from tests.generator_test.base import GenTesterBase
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class MClGenTester(GenTesterBase):
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def __init__(self):
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super(MClGenTester, self).__init__(
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test_gen_class=MClGenerator,
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test_args_list=[
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# Uniform (w uniform weighted)
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{
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"distribution_type": GRAPH_TYPE.ER,
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"node_weighted": True,
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"node_weighted_gen": GraphWeightGenerator(
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weighted_type=GRAPH_WEIGHT_TYPE.UNIFORM),
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},
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# Uniform (w gaussian weighted)
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{
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"distribution_type": GRAPH_TYPE.ER,
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"node_weighted": True,
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"node_weighted_gen": GraphWeightGenerator(
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weighted_type=GRAPH_WEIGHT_TYPE.GAUSSIAN),
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},
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# Uniform (w poisson weighted)
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{
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"distribution_type": GRAPH_TYPE.ER,
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"node_weighted": True,
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"node_weighted_gen": GraphWeightGenerator(
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weighted_type=GRAPH_WEIGHT_TYPE.POISSON),
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},
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# Uniform (w exponential weighted)
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{
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"distribution_type": GRAPH_TYPE.ER,
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"node_weighted": True,
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"node_weighted_gen": GraphWeightGenerator(
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weighted_type=GRAPH_WEIGHT_TYPE.EXPONENTIAL),
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},
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# Uniform (w lognormal weighted)
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{
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"distribution_type": GRAPH_TYPE.ER,
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"node_weighted": True,
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"node_weighted_gen": GraphWeightGenerator(
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weighted_type=GRAPH_WEIGHT_TYPE.LOGNORMAL),
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},
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# Uniform (w powerlaw weighted)
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{
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"distribution_type": GRAPH_TYPE.ER,
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"node_weighted": True,
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"node_weighted_gen": GraphWeightGenerator(
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weighted_type=GRAPH_WEIGHT_TYPE.POWERLAW),
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},
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# Uniform (w binomial weighted)
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{
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"distribution_type": GRAPH_TYPE.ER,
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"node_weighted": True,
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"node_weighted_gen": GraphWeightGenerator(
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weighted_type=GRAPH_WEIGHT_TYPE.BINORMIAL),
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},
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# Watts-Strogatz (w/o weighted)
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{
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"distribution_type": GRAPH_TYPE.WS,
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"node_weighted": False,
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},
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# Barabasi-Albert (w/o weighted)
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{
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"distribution_type": GRAPH_TYPE.BA,
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"node_weighted": False,
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},
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# Holme-Kim (w/o weighted)
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{
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"distribution_type": GRAPH_TYPE.HK,
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"node_weighted": False,
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},
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# RB (w/o weighted)
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{
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"distribution_type": GRAPH_TYPE.RB,
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"node_weighted": False,
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},
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]
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)

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