SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 3 |
| Total Lines | 30 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | # Author: Simon Blanke |
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| 2 | # Email: [email protected] |
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| 3 | # License: MIT License |
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| 4 | |||
| 5 | import pytest |
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| 6 | import numpy as np |
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| 7 | |||
| 8 | |||
| 9 | from gradient_free_optimizers import BayesianOptimizer |
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| 10 | |||
| 11 | |||
| 12 | def parabola_function(para): |
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| 13 | loss = para["x"] * para["x"] + para["y"] * para["y"] |
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| 14 | return -loss |
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| 15 | |||
| 16 | |||
| 17 | search_space = { |
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| 18 | "x": np.arange(-1, 1, 1), |
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| 19 | "y": np.arange(-1, 1, 1), |
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| 20 | } |
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| 21 | |||
| 22 | |||
| 23 | def test_replacement_0(): |
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| 24 | opt = BayesianOptimizer(search_space, replacement=True) |
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| 25 | opt.search(parabola_function, n_iter=15) |
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| 26 | |||
| 27 | with pytest.raises(ValueError): |
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| 28 | opt = BayesianOptimizer(search_space, replacement=False) |
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| 29 | opt.search(parabola_function, n_iter=15) |
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| 30 |
