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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import pytest |
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import numpy as np |
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from gradient_free_optimizers import EvolutionStrategyOptimizer |
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from ._base_para_test import _base_para_test_func |
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from gradient_free_optimizers import HillClimbingOptimizer |
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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return score |
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search_space = {"x1": np.arange(-100, 101, 1)} |
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parallel_tempering_para = [ |
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({"mutation_rate": 0.5}), |
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({"mutation_rate": 0.9}), |
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({"mutation_rate": 0.1}), |
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({"mutation_rate": 0}), |
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({"mutation_rate": 2}), |
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({"crossover_rate": 0.5}), |
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({"crossover_rate": 0.9}), |
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({"crossover_rate": 0.1}), |
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({"crossover_rate": 0}), |
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({"crossover_rate": 2}), |
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({"population": 1}), |
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({"population": 2}), |
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({"population": 100}), |
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( |
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{ |
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"population": [ |
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HillClimbingOptimizer(search_space), |
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HillClimbingOptimizer(search_space), |
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HillClimbingOptimizer(search_space), |
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HillClimbingOptimizer(search_space), |
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] |
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} |
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), |
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({"rand_rest_p": 0}), |
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({"rand_rest_p": 0.5}), |
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({"rand_rest_p": 1}), |
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] |
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pytest_wrapper = ("opt_para", parallel_tempering_para) |
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@pytest.mark.parametrize(*pytest_wrapper) |
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def test_hill_climbing_para(opt_para): |
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_base_para_test_func(opt_para, EvolutionStrategyOptimizer) |
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SimonBlanke /
Gradient-Free-Optimizers
