SimonBlanke /
Gradient-Free-Optimizers
| @@ 83-101 (lines=19) @@ | ||
| 80 | assert len(set(x1_results)) < len(x1_results) |
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| 81 | ||
| 82 | ||
| 83 | def test_attributes_results_7(): |
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| 84 | def objective_function(para): |
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| 85 | score = -para["x1"] * para["x1"] |
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| 86 | return score |
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| 87 | ||
| 88 | search_space = { |
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| 89 | "x1": np.arange(0, 10, 1), |
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| 90 | } |
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| 91 | ||
| 92 | opt = RandomSearchOptimizer(search_space) |
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| 93 | opt.search( |
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| 94 | objective_function, n_iter=20, initialize={"random": 1}, memory=True |
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| 95 | ) |
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| 96 | ||
| 97 | x1_results = list(opt.results["x1"].values) |
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| 98 | ||
| 99 | print("\n x1_results \n", x1_results) |
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| 100 | ||
| 101 | assert len(set(x1_results)) == len(x1_results) |
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| 102 | ||
| 103 | ||
| 104 | def test_attributes_results_8(): |
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| @@ 62-80 (lines=19) @@ | ||
| 59 | assert 10 in list(opt.results["x1"].values) |
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| 60 | ||
| 61 | ||
| 62 | def test_attributes_results_6(): |
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| 63 | def objective_function(para): |
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| 64 | score = -para["x1"] * para["x1"] |
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| 65 | return score |
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| 66 | ||
| 67 | search_space = { |
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| 68 | "x1": np.arange(0, 10, 1), |
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| 69 | } |
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| 70 | ||
| 71 | opt = RandomSearchOptimizer(search_space) |
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| 72 | opt.search( |
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| 73 | objective_function, n_iter=20, initialize={"random": 1}, memory=False |
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| 74 | ) |
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| 75 | ||
| 76 | x1_results = list(opt.results["x1"].values) |
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| 77 | ||
| 78 | print("\n x1_results \n", x1_results) |
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| 79 | ||
| 80 | assert len(set(x1_results)) < len(x1_results) |
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| 81 | ||
| 82 | ||
| 83 | def test_attributes_results_7(): |
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