tests/TabuSearch.py 1 location
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@@ 20-29 (lines=10) @@
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_TabuOptimizer( |
tests/EvolutionStrategy.py 1 location
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@@ 20-29 (lines=10) @@
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_EvolutionStrategyOptimizer( |
tests/HillClimbing.py 1 location
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_HillClimbingOptimizer( |
tests/RandomAnnealing.py 1 location
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@@ 20-29 (lines=10) @@
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_RandomAnnealingOptimizer( |
tests/TPE.py 1 location
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_TreeStructuredParzenEstimators( |
tests/Bayesian.py 1 location
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_BayesianOptimizer(opt_para): |
tests/RandomRestartHillClimbing.py 1 location
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@@ 20-29 (lines=10) @@
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_RandomRestartHillClimbingOptimizer( |
tests/StochasticHillClimbing.py 1 location
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_StochasticHillClimbingOptimizer( |
tests/ParticleSwarm.py 1 location
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_ParticleSwarmOptimizer( |
tests/ParallelTempering.py 1 location
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@@ 20-29 (lines=10) @@
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_ParallelTemperingOptimizer( |
tests/SimulatedAnnealing.py 1 location
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@@ 20-29 (lines=10) @@
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_SimulatedAnnealingOptimizer( |
tests/DecisionTree.py 1 location
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@@ 20-29 (lines=10) @@
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_DecisionTreeOptimizer(opt_para): |
tests/StochasticTunneling.py 1 location
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@@ 20-29 (lines=10) @@
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init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])] |
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def _base_test(opt, init_positions): |
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for nth_init in range(len(init_positions)): |
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pos_new = opt.init_pos(nth_init) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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for nth_iter in range(len(init_positions), n_iter): |
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pos_new = opt.iterate(nth_iter) |
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score_new = get_score(pos_new) |
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opt.evaluate(score_new) |
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def _test_StochasticTunnelingOptimizer( |
SimonBlanke /
Gradient-Free-Optimizers
