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import pytest |
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import numpy as np |
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from ._parametrize import optimizers_noSBOM, optimizers_SBOM |
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def objective_function(para): |
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return 1 |
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View Code Duplication |
@pytest.mark.parametrize(*optimizers_noSBOM) |
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def test_large_search_space_0(Optimizer): |
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search_space = { |
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"x1": np.arange(0, 100000), |
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"x2": np.arange(0, 100000), |
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"x3": np.arange(0, 100000), |
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} |
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opt = Optimizer(search_space, initialize={"random": 10}) |
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opt.search(objective_function, n_iter=150, verbosity=False) |
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View Code Duplication |
@pytest.mark.parametrize(*optimizers_noSBOM) |
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def test_large_search_space_1(Optimizer): |
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search_space = { |
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"x1": np.arange(0, 100, 0.001), |
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"x2": np.arange(0, 100, 0.001), |
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"x3": np.arange(0, 100, 0.001), |
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} |
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opt = Optimizer(search_space, initialize={"random": 10}) |
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opt.search(objective_function, n_iter=150, verbosity=False) |
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@pytest.mark.parametrize(*optimizers_noSBOM) |
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def test_large_search_space_2(Optimizer): |
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search_space = {} |
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for i in range(33): |
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key = "x" + str(i) |
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search_space[key] = np.arange(0, 100) |
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opt = Optimizer(search_space, initialize={"random": 34, "vertices": 34, "grid": 34}) |
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opt.search(objective_function, n_iter=1000, verbosity=False) |
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@pytest.mark.parametrize(*optimizers_SBOM) |
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def test_large_search_space_3(Optimizer): |
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search_space = {} |
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for i in range(10): |
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key = "x" + str(i) |
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search_space[key] = np.arange(0, 10) |
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opt = Optimizer(search_space, initialize={"random": 1}) |
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opt.search(objective_function, n_iter=2, verbosity=False) |
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SimonBlanke /
Gradient-Free-Optimizers
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