SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 9 |
| Total Lines | 49 |
| Duplicated Lines | 20.41 % |
| Changes | 0 | ||
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
| 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 SimulatedAnnealingOptimizer |
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| 8 | |||
| 9 | n_iter = 100 |
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| 11 | |||
| 12 | def get_score(pos_new): |
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| 13 | return -(pos_new[0] * pos_new[0]) |
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| 15 | |||
| 16 | space_dim = np.array([10]) |
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| 17 | init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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| 19 | |||
| 20 | View Code Duplication | def _base_test(opt, init_positions): |
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Duplication
introduced
by
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| 21 | for nth_init in range(len(init_positions)): |
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| 22 | pos_new = opt.init_pos(nth_init) |
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| 23 | score_new = get_score(pos_new) |
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| 24 | opt.evaluate(score_new) |
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| 25 | |||
| 26 | for nth_iter in range(len(init_positions), n_iter): |
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| 27 | pos_new = opt.iterate(nth_iter) |
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| 28 | score_new = get_score(pos_new) |
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| 29 | opt.evaluate(score_new) |
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| 31 | |||
| 32 | def _test_SimulatedAnnealingOptimizer( |
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| 33 | init_positions=init_positions, space_dim=space_dim, opt_para={} |
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| 34 | ): |
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| 35 | opt = SimulatedAnnealingOptimizer(init_positions, space_dim, opt_para) |
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| 36 | _base_test(opt, init_positions) |
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| 37 | |||
| 38 | |||
| 39 | def test_annealing_rate(): |
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| 40 | for annealing_rate in [1, 0.001]: |
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| 41 | opt_para = {"annealing_rate": annealing_rate} |
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| 42 | _test_SimulatedAnnealingOptimizer(opt_para=opt_para) |
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| 43 | |||
| 44 | |||
| 45 | def test_start_temp(): |
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| 46 | for start_temp in [0.001, 10000]: |
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| 47 | opt_para = {"start_temp": start_temp} |
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| 48 | _test_SimulatedAnnealingOptimizer(opt_para=opt_para) |
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| 49 |
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