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import numpy as np |
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from hyperactive.base import BaseExperiment |
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class AckleyFunction(BaseExperiment): |
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r"""Ackley function, common benchmark for optimization algorithms. |
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The Ackley function is a non-convex function used to test optimization algorithms. |
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It is defined as: |
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.. math:: |
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f(x, y) = -A \cdot \exp(-0.2 \sqrt{0.5 (x^2 + y^2)}) - \exp(0.5 (\cos(2 \pi x) + \cos(2 \pi y))) + \exp(1) + A |
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where A is a constant. |
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Parameters |
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---------- |
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A : float |
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Amplitude constant used in the calculation of the Ackley function. |
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22
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Example |
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------- |
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>>> from hyperactive.experiment.toy import AckleyFunction |
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>>> ackley = AckleyFunction(A=20) |
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>>> params = {"x0": 1, "x1": 2} |
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>>> score, add_info = ackley.score(params) |
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Quick call without metadata return or dictionary: |
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>>> score = ackley(x0=1, x1=2) |
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""" # noqa: E501 |
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_tags = { |
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"property:randomness": "deterministic", # random or deterministic |
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# if deterministic, two calls of score will result in the same value |
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# random = two calls may result in different values; same as "stochastic" |
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} |
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def __init__(self, A): |
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self.A = A |
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super().__init__() |
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def _paramnames(self): |
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return ["x0", "x1"] |
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View Code Duplication |
def _score(self, params): |
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x = params["x0"] |
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y = params["x1"] |
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loss1 = -self.A * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y))) |
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loss2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) |
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loss3 = np.exp(1) |
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loss4 = self.A |
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return -(loss1 + loss2 + loss3 + loss4), {} |
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@classmethod |
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def get_test_params(cls, parameter_set="default"): |
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"""Return testing parameter settings for the skbase object. |
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``get_test_params`` is a unified interface point to store |
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parameter settings for testing purposes. This function is also |
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used in ``create_test_instance`` and ``create_test_instances_and_names`` |
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to construct test instances. |
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``get_test_params`` should return a single ``dict``, or a ``list`` of ``dict``. |
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Each ``dict`` is a parameter configuration for testing, |
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and can be used to construct an "interesting" test instance. |
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A call to ``cls(**params)`` should |
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be valid for all dictionaries ``params`` in the return of ``get_test_params``. |
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The ``get_test_params`` need not return fixed lists of dictionaries, |
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it can also return dynamic or stochastic parameter settings. |
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Parameters |
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---------- |
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parameter_set : str, default="default" |
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Name of the set of test parameters to return, for use in tests. If no |
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special parameters are defined for a value, will return `"default"` set. |
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Returns |
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------- |
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params : dict or list of dict, default = {} |
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Parameters to create testing instances of the class |
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Each dict are parameters to construct an "interesting" test instance, i.e., |
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`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. |
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`create_test_instance` uses the first (or only) dictionary in `params` |
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""" |
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return [{"A": 0}, {"A": 20}, {"A": -42}] |
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@classmethod |
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def _get_score_params(self): |
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"""Return settings for the score function. |
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Returns a list, the i-th element corresponds to self.get_test_params()[i]. |
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It should be a valid call for self.score. |
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Returns |
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------- |
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list of dict |
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The parameters to be used for scoring. |
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""" |
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params0 = {"x0": 0, "x1": 0} |
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params1 = {"x0": 1, "x1": 1} |
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return [params0, params1] |
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SimonBlanke /
Hyperactive
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