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SimonBlankeSimonBlanke
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add 1d test-functions
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6 files changed

+230
-4
lines changed

‎src/surfaces/test_functions/algebraic/__init__.py‎

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"""Algebraic test functions with closed-form analytical expressions."""
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from ._base_algebraic_function import AlgebraicFunction, MathematicalFunction
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from .test_functions_1d.damped_sine_function import DampedSineFunction
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from .test_functions_1d.forrester_function import ForresterFunction
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from .test_functions_1d.gramacy_and_lee_function import GramacyAndLeeFunction
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from .test_functions_1d.quadratic_exponential_function import QuadraticExponentialFunction
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from .test_functions_1d.sine_product_function import SineProductFunction
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from .test_functions_2d.ackley_function import AckleyFunction
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from .test_functions_2d.beale_function import BealeFunction
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from .test_functions_2d.booth_function import BoothFunction
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"BoothFunction",
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"BukinFunctionN6",
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"CrossInTrayFunction",
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"DampedSineFunction",
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"DropWaveFunction",
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"EasomFunction",
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"EggholderFunction",
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"ForresterFunction",
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"GoldsteinPriceFunction",
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"GramacyAndLeeFunction",
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"GriewankFunction",
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"LeviFunctionN13",
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"MatyasFunction",
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"McCormickFunction",
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"QuadraticExponentialFunction",
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"RastriginFunction",
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"RosenbrockFunction",
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"SchafferFunctionN2",
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"SimionescuFunction",
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"SineProductFunction",
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"SphereFunction",
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"StyblinskiTangFunction",
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"ThreeHumpCamelFunction",
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BoothFunction,
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BukinFunctionN6,
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CrossInTrayFunction,
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DampedSineFunction,
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DropWaveFunction,
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EasomFunction,
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EggholderFunction,
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ForresterFunction,
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GoldsteinPriceFunction,
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GramacyAndLeeFunction,
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GriewankFunction,
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LeviFunctionN13,
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MatyasFunction,
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McCormickFunction,
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QuadraticExponentialFunction,
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RastriginFunction,
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RosenbrockFunction,
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SchafferFunctionN2,
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SimionescuFunction,
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SineProductFunction,
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SphereFunction,
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StyblinskiTangFunction,
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ThreeHumpCamelFunction,
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mathematical_functions = algebraic_functions
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algebraic_functions_1d = [
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DampedSineFunction,
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ForresterFunction,
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GramacyAndLeeFunction,
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QuadraticExponentialFunction,
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SineProductFunction,
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]
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mathematical_functions_1d = algebraic_functions_1d
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‎src/surfaces/test_functions/algebraic/test_functions_1d/__init__.py‎

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# License: MIT License
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from .damped_sine_function import DampedSineFunction
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from .forrester_function import ForresterFunction
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from .gramacy_and_lee_function import GramacyAndLeeFunction
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from .quadratic_exponential_function import QuadraticExponentialFunction
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from .sine_product_function import SineProductFunction
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__all__ = [
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"DampedSineFunction",
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"ForresterFunction",
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"GramacyAndLeeFunction",
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"QuadraticExponentialFunction",
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"SineProductFunction",
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]

‎src/surfaces/test_functions/algebraic/test_functions_1d/damped_sine_function.py‎

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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 .._base_algebraic_function import AlgebraicFunction
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class DampedSineFunction(AlgebraicFunction):
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"""Damped Sine one-dimensional test function.
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A one-dimensional test function combining sine and linear terms
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with Gaussian damping. The exponential decay creates a localized
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oscillatory region near the origin.
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The function is defined as:
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.. math::
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f(x) = -(x + \\sin(x)) e^{-x^2}
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Parameters
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----------
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metric : str, default="score"
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Either "loss" (minimize) or "score" (maximize).
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sleep : float, default=0
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Artificial delay in seconds added to each evaluation.
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Attributes
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----------
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n_dim : int
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Number of dimensions (always 1).
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default_bounds : tuple
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Default parameter bounds (-10, 10).
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References
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----------
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.. [1] AMPGO (Adaptive Memory Programming for Global Optimization)
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benchmark suite, Problem06.
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http://infinity77.net/global_optimization/test_functions_1d.html
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.. [2] Gavana, A. (2013). "Global Optimization Benchmarks and AMPGO".
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Examples
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--------
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>>> from surfaces.test_functions import DampedSineFunction
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>>> func = DampedSineFunction()
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>>> func({"x0": 0.6796}) # Near global minimum
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-0.824...
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>>> search_space = func.search_space
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>>> len(search_space)
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1
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"""
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name = "Damped Sine Function"
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_name_ = "damped_sine_function"
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__name__ = "DampedSineFunction"
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_spec = {
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"convex": False,
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"unimodal": False,
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"separable": True,
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"scalable": False,
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}
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f_global = -0.8242393984760573
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x_global = np.array([0.6795787635255166])
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default_bounds = (-10.0, 10.0)
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n_dim = 1
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latex_formula = r"f(x) = -(x + \sin(x)) e^{-x^2}"
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pgfmath_formula = "-(#1 + sin(deg(#1))) * exp(-#1^2)"
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def __init__(
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self,
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objective="minimize",
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sleep=0,
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memory=False,
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collect_data=True,
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callbacks=None,
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catch_errors=None,
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noise=None,
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):
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super().__init__(objective, sleep, memory, collect_data, callbacks, catch_errors, noise)
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self.n_dim = 1
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def _create_objective_function(self):
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def damped_sine_function(params):
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x = params["x0"]
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return -(x + np.sin(x)) * np.exp(-(x**2))
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self.pure_objective_function = damped_sine_function
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def _search_space(self, min=-10.0, max=10.0, value_types="array", size=10000):
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return super()._create_n_dim_search_space(
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min=min, max=max, size=size, value_types=value_types
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)

‎src/surfaces/test_functions/algebraic/test_functions_1d/forrester_function.py‎

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@@ -66,8 +66,8 @@ class ForresterFunction(AlgebraicFunction):
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"scalable": False,
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}
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f_global = -6.020740095378691
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x_global = np.array([0.7572489953693498])
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f_global = -6.020740055766075
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x_global = np.array([0.7572487144081974])
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default_bounds = (0.0, 1.0)
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n_dim = 1

‎src/surfaces/test_functions/algebraic/test_functions_1d/quadratic_exponential_function.py‎

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"scalable": False,
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}
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f_global = -3.8504536747755516
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x_global = np.array([2.8680336666498003])
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f_global = -3.8504507087979953
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x_global = np.array([2.8680325095605212])
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default_bounds = (1.9, 3.9)
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n_dim = 1

‎src/surfaces/test_functions/algebraic/test_functions_1d/sine_product_function.py‎

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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 .._base_algebraic_function import AlgebraicFunction
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class SineProductFunction(AlgebraicFunction):
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"""Sine Product one-dimensional test function.
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A one-dimensional test function that multiplies a linear term
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with a sine function. The amplitude of oscillations grows with x,
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creating increasingly deep valleys at larger values.
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The function is defined as:
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.. math::
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f(x) = -x \\sin(x)
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Parameters
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----------
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metric : str, default="score"
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Either "loss" (minimize) or "score" (maximize).
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sleep : float, default=0
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Artificial delay in seconds added to each evaluation.
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Attributes
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----------
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n_dim : int
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Number of dimensions (always 1).
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default_bounds : tuple
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Default parameter bounds (0, 10).
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References
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----------
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.. [1] AMPGO (Adaptive Memory Programming for Global Optimization)
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benchmark suite, Problem10.
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http://infinity77.net/global_optimization/test_functions_1d.html
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.. [2] Gavana, A. (2013). "Global Optimization Benchmarks and AMPGO".
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Examples
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--------
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>>> from surfaces.test_functions import SineProductFunction
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>>> func = SineProductFunction()
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>>> func({"x0": 7.9787}) # Near global minimum
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-7.916...
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>>> search_space = func.search_space
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>>> len(search_space)
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1
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"""
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name = "Sine Product Function"
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_name_ = "sine_product_function"
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__name__ = "SineProductFunction"
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_spec = {
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"convex": False,
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"unimodal": False,
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"separable": True,
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"scalable": False,
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}
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f_global = -7.916727371587256
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x_global = np.array([7.9786653537049483])
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default_bounds = (0.0, 10.0)
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n_dim = 1
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latex_formula = r"f(x) = -x \sin(x)"
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pgfmath_formula = "-#1 * sin(deg(#1))"
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def __init__(
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self,
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objective="minimize",
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sleep=0,
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memory=False,
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collect_data=True,
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callbacks=None,
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catch_errors=None,
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noise=None,
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):
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super().__init__(objective, sleep, memory, collect_data, callbacks, catch_errors, noise)
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self.n_dim = 1
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def _create_objective_function(self):
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def sine_product_function(params):
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x = params["x0"]
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return -x * np.sin(x)
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self.pure_objective_function = sine_product_function
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def _search_space(self, min=0.0, max=10.0, value_types="array", size=10000):
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return super()._create_n_dim_search_space(
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min=min, max=max, size=size, value_types=value_types
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)

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