SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 5 |
| Total Lines | 39 |
| 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 | |||
| 8 | def init_grid_search(space_dim, n_pos): |
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| 9 | positions = [] |
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| 10 | |||
| 11 | n_dim = len(space_dim) |
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| 12 | p_per_dim = int(np.power(n_pos, 1 / n_dim)) |
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| 13 | |||
| 14 | for dim in space_dim: |
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| 15 | dim_dist = int(dim / (p_per_dim + 1)) |
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| 16 | n_points = [n * dim_dist for n in range(1, p_per_dim + 1)] |
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| 17 | |||
| 18 | positions.append(n_points) |
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| 19 | |||
| 20 | pos_mesh = np.array(np.meshgrid(*positions)) |
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| 21 | positions = list(pos_mesh.T.reshape(-1, n_dim)) |
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| 22 | |||
| 23 | diff_pos = n_pos - len(positions) |
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| 24 | if diff_pos > 0: |
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| 25 | pos_rnd = init_random_search(space_dim, n_pos=diff_pos) |
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| 26 | |||
| 27 | return positions + pos_rnd |
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| 28 | |||
| 29 | return positions |
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| 30 | |||
| 31 | |||
| 32 | def init_random_search(space_dim, n_pos): |
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| 33 | positions = [] |
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| 34 | for nth_pos in range(n_pos): |
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| 35 | pos = np.random.randint(space_dim, size=space_dim.shape) |
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| 36 | positions.append(pos) |
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| 37 | |||
| 38 | return positions |
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| 39 |
