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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 numpy as np |
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from gradient_free_optimizers import BayesianOptimizer |
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n_iter = 100 |
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def get_score(pos_new): |
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return -(pos_new[0] * pos_new[0]) |
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space_dim = np.array([10]) |
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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View Code Duplication |
def _base_test(opt): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_BayesianOptimizer(opt_para): |
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opt = BayesianOptimizer(init_positions, space_dim, opt_para=opt_para) |
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_base_test(opt) |
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def test_skip_retrain(): |
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for skip_retrain in ["many", "some", "few", "never"]: |
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opt_para = {"skip_retrain": skip_retrain} |
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_test_BayesianOptimizer(opt_para) |
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def test_start_up_evals(): |
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for start_up_evals in [0, 1, 100]: |
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opt_para = {"start_up_evals": start_up_evals} |
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_test_BayesianOptimizer(opt_para) |
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def test_warm_start_smbo(): |
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for warm_start_smbo in [True, False]: |
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opt_para = {"warm_start_smbo": warm_start_smbo} |
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_test_BayesianOptimizer(opt_para) |
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def test_max_sample_size(): |
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for max_sample_size in [10, 100, 10000, 10000000000]: |
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opt_para = {"max_sample_size": max_sample_size} |
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_test_BayesianOptimizer(opt_para) |
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SimonBlanke /
Gradient-Free-Optimizers
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