SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 2 |
| Total Lines | 38 |
| 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 GridSearchOptimizer |
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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(-100, 100, 1), |
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| 19 | "y": np.arange(-100, 100, 1), |
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| 20 | } |
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| 21 | |||
| 22 | n_iter = 50 |
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| 23 | |||
| 24 | |||
| 25 | def test_direction_0(): |
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| 26 | n_initialize = 1 |
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| 27 | opt = GridSearchOptimizer( |
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| 28 | search_space, initialize={"vertices": n_initialize}, direction="orthogonal" |
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| 29 | ) |
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| 30 | opt.search(parabola_function, n_iter=n_iter) |
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| 31 | search_data = opt.search_data |
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| 32 | |||
| 33 | print("\n search_data \n", search_data, "\n") |
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| 34 | y_data_count = search_data["y"].value_counts().to_dict() |
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| 35 | print("\n y_data_count \n", y_data_count, "\n") |
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| 36 | |||
| 37 | assert y_data_count[-100] >= n_iter - n_initialize |
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| 38 |
