SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 1 |
| Total Lines | 30 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | import numpy as np |
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| 2 | from gradient_free_optimizers import HillClimbingOptimizer |
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| 5 | n_iter = 10 |
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| 8 | def get_score(pos_new): |
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| 9 | x1 = pos_new[0] |
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| 10 | |||
| 11 | return -x1 * x1 |
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| 14 | space_dim = np.array([100]) |
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| 15 | init_positions = [np.array([10])] |
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| 18 | opt = HillClimbingOptimizer(init_positions, space_dim, opt_para={}) |
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| 19 | |||
| 20 | for nth_init in range(len(init_positions)): |
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| 21 | pos_new = opt.init_pos(nth_init) |
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| 22 | score_new = get_score(pos_new) |
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| 23 | opt.evaluate(score_new) |
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| 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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| 30 |
