36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
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from utils import phi
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import numpy as np
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def another_random_cube(objective, n_trials, n_dim, with_count, seed=None):
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prng = np.random.default_rng(seed)
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fopt = None
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xopt = None
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for i in range(n_trials):
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x = prng.uniform(size=n_dim)
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fx = objective(x)
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if fopt is None or xopt is None or fx < fopt:
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fopt = fx
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xopt = x
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return (fopt, xopt, n_trials) if with_count else (fopt, xopt)
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def quasirandom_cube(objective, n_trials, n_dim, with_count):
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# http://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/
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magic = phi(n_dim)
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alpha = np.zeros(n_dim)
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for i in range(n_dim):
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alpha[i] = pow(1 / magic, i + 1) % 1
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xs = np.zeros((n_trials, n_dim))
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xs[0, :] = 0.5 # first point is always dead center
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for i in range(1, n_trials):
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xs[i] = (xs[i - 1] + alpha) % 1
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best_so_far = None
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for i, x in zip(range(n_trials), xs):
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fx = objective(x)
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if best_so_far is None or fx < best_so_far[0]:
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best_so_far = (fx, x)
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fopt, xopt = best_so_far
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return (fopt, xopt, n_trials) if with_count else (fopt, xopt)
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