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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import numpy as np |
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from hyperactive import Hyperactive |
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X, y = np.array([0]), np.array([0]) |
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memory = False |
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n_iter = 25 |
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def sphere_function(para, X_train, y_train): |
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loss = [] |
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for key in para.keys(): |
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if key == "iteration": |
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continue |
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loss.append(para[key] * para[key]) |
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return -np.array(loss).sum() |
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search_config = { |
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sphere_function: {"x1": np.arange(-3, 3, 0.1), "x2": np.arange(-3, 3, 0.1)} |
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} |
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def test_n_particles(): |
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for n_particles in [2, 100]: |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search( |
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search_config, |
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n_iter=n_iter, |
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optimizer={"ParticleSwarm": {"n_particles": n_particles}}, |
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) |
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def test_inertia(): |
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for inertia in [0.1, 0.9]: |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search( |
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search_config, |
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n_iter=n_iter, |
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optimizer={"ParticleSwarm": {"inertia": inertia}}, |
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) |
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def test_cognitive_weight(): |
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for cognitive_weight in [0.1, 0.9]: |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search( |
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search_config, |
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n_iter=n_iter, |
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optimizer={"ParticleSwarm": {"cognitive_weight": cognitive_weight}}, |
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) |
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def test_social_weight(): |
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for social_weight in [0.1, 0.9]: |
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opt = Hyperactive(X, y, memory=memory) |
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opt.search( |
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search_config, |
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n_iter=n_iter, |
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optimizer={"ParticleSwarm": {"social_weight": social_weight}}, |
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) |
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SimonBlanke /
Hyperactive
