SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 6 |
| 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 numpy as np |
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| 6 | |||
| 7 | from gradient_free_optimizers import TabuOptimizer |
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| 8 | from ._base_test import _base_test |
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| 9 | |||
| 10 | n_iter = 33 |
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| 11 | opt = TabuOptimizer |
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| 12 | |||
| 13 | |||
| 14 | def test_epsilon(): |
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| 15 | for epsilon in [0.00001, 100]: |
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| 16 | opt_para = {"epsilon": epsilon} |
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| 17 | _base_test(opt, n_iter, opt_para=opt_para) |
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| 18 | |||
| 19 | |||
| 20 | def test_n_neighbours(): |
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| 21 | for n_neighbours in [1, 100]: |
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| 22 | opt_para = {"n_neighbours": n_neighbours} |
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| 23 | _base_test(opt, n_iter, opt_para=opt_para) |
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| 24 | |||
| 25 | |||
| 26 | def test_tabu_factor(): |
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| 27 | for tabu_factor in [1, 3, 20, 50, 100]: |
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| 28 | opt_para = {"tabu_factor": tabu_factor} |
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| 29 | _base_test(opt, n_iter, opt_para=opt_para) |
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| 30 |
