SimonBlanke /
Gradient-Free-Optimizers
| @@ 46-59 (lines=14) @@ | ||
| 43 | assert opt.best_para == init |
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| 44 | ||
| 45 | ||
| 46 | def test_initialize_warm_start_2(): |
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| 47 | search_space = { |
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| 48 | "x1": np.arange(-10, 10, 1), |
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| 49 | } |
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| 50 | init = { |
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| 51 | "x1": -10, |
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| 52 | } |
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| 53 | ||
| 54 | initialize = {"warm_start": [init], "random": 0, "vertices": 0, "grid": 0} |
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| 55 | ||
| 56 | opt = RandomSearchOptimizer(search_space, initialize=initialize) |
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| 57 | opt.search(objective_function, n_iter=1) |
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| 58 | ||
| 59 | assert opt.best_para == init |
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| 60 | ||
| 61 | ||
| 62 | def test_initialize_vertices(): |
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| @@ 30-43 (lines=14) @@ | ||
| 27 | assert abs(opt.best_score) < 0.001 |
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| 28 | ||
| 29 | ||
| 30 | def test_initialize_warm_start_1(): |
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| 31 | search_space = { |
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| 32 | "x1": np.arange(-10, 10, 1), |
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| 33 | } |
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| 34 | init = { |
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| 35 | "x1": -10, |
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| 36 | } |
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| 37 | ||
| 38 | initialize = {"warm_start": [init]} |
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| 39 | ||
| 40 | opt = RandomSearchOptimizer(search_space, initialize=initialize) |
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| 41 | opt.search(objective_function, n_iter=1) |
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| 42 | ||
| 43 | assert opt.best_para == init |
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| 44 | ||
| 45 | ||
| 46 | def test_initialize_warm_start_2(): |
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