57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
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import numpy as np
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from alr_envs.stochastic_search.functions.f_base import BaseObjective
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class Rosenbrock(BaseObjective):
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def __init__(self, dim, int_opt=(-3., 3.)):
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super(Rosenbrock, self).__init__(dim, int_opt=int_opt)
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self.c = np.maximum(1, np.sqrt(self.dim) / 8)
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def evaluate_full(self, x):
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x = np.atleast_2d(x)
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assert x.shape[1] == self.dim
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z = self.c * (x - self.x_opt) + 1
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z_end = z[:, 1:]
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z_begin = z[:, :-1]
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a = z_begin ** 2 - z_end
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b = z_begin - 1
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return np.sum(100 * a ** 2 + b ** 2, axis=1) + self.f_opt
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class RosenbrockRotated(BaseObjective):
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def __init__(self, dim, int_opt=(-3., 3.)):
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super(RosenbrockRotated, self).__init__(dim, int_opt=int_opt)
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self.c = np.maximum(1, np.sqrt(self.dim) / 8)
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def evaluate_full(self, x):
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x = np.atleast_2d(x)
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assert x.shape[1] == self.dim
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z = (self.c * self.r @ x.T + 1 / 2).T
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a = z[:, :-1] ** 2 - z[:, 1:]
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b = z[:, :-1] - 1
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return np.sum(100 * a ** 2 + b ** 2, axis=1) + self.f_opt
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class RosenbrockRaw(BaseObjective):
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def __init__(self, dim, int_opt=(-3., 3.)):
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super(RosenbrockRaw, self).__init__(dim, int_opt=int_opt)
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self.x_opt = np.ones((1, dim))
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self.f_opt = 0
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def evaluate_full(self, x):
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x = np.atleast_2d(x)
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assert x.shape[1] == self.dim
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a = x[:, :-1] ** 2 - x[:, 1:]
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b = x[:, :-1] - 1
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out = np.sum(100 * a ** 2 + b ** 2, axis=1)
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return out
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