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import pytest |
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import numpy as np |
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3
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4
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from ._parametrize import optimizers |
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6
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7
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def objective_function(opt): |
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8
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score = -opt["x1"] * opt["x1"] |
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return score |
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10
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11
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12
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def objective_function_m5(opt): |
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score = -(opt["x1"] - 5) * (opt["x1"] - 5) |
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return score |
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15
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def objective_function_p5(opt): |
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score = -(opt["x1"] + 5) * (opt["x1"] + 5) |
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return score |
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search_space_0 = {"x1": np.arange(-100, 101, 1)} |
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search_space_1 = {"x1": np.arange(0, 101, 1)} |
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search_space_2 = {"x1": np.arange(-100, 1, 1)} |
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search_space_3 = {"x1": np.arange(-10, 11, 0.1)} |
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search_space_4 = {"x1": np.arange(0, 11, 0.1)} |
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search_space_5 = {"x1": np.arange(-10, 1, 0.1)} |
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search_space_6 = {"x1": np.arange(-0.0000000003, 0.0000000003, 0.0000000001)} |
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search_space_7 = {"x1": np.arange(0, 0.0000000003, 0.0000000001)} |
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search_space_8 = {"x1": np.arange(-0.0000000003, 0, 0.0000000001)} |
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objective_para = ( |
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"objective", |
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[ |
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(objective_function), |
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(objective_function_m5), |
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(objective_function_p5), |
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], |
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) |
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search_space_para = ( |
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"search_space", |
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[ |
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(search_space_0), |
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(search_space_1), |
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(search_space_2), |
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(search_space_3), |
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(search_space_4), |
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(search_space_5), |
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(search_space_6), |
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(search_space_7), |
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(search_space_8), |
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], |
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) |
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58
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59
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@pytest.mark.parametrize(*objective_para) |
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@pytest.mark.parametrize(*search_space_para) |
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@pytest.mark.parametrize(*optimizers) |
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def test_best_results_0(Optimizer, search_space, objective): |
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63
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search_space = search_space |
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64
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objective_function = objective |
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66
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initialize = {"vertices": 2} |
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opt = Optimizer() |
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opt.init(search_space, initialize) |
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opt.search(objective_function, n_iter=10) |
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assert opt.best_score == objective_function(opt.best_para) |
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SimonBlanke /
Hyperactive
