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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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from typing import List, Dict, Literal |
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from ..search import Search |
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from ..optimizers import PowellsMethod as _PowellsMethod |
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class PowellsMethod(_PowellsMethod, Search): |
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""" |
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A class implementing **pattern search** for the public API. |
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Inheriting from the `Search`-class to get the `search`-method and from |
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the `PowellsMethod`-backend to get the underlying algorithm. |
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Parameters |
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---------- |
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search_space : dict[str, list] |
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The search space to explore. A dictionary with parameter |
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names as keys and a numpy array as values. |
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initialize : dict[str, int] |
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The method to generate initial positions. A dictionary with |
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the following key literals and the corresponding value type: |
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{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]} |
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constraints : list[callable] |
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A list of constraints, where each constraint is a callable. |
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The callable returns `True` or `False` dependend on the input parameters. |
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random_state : None, int |
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If None, create a new random state. If int, create a new random state |
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seeded with the value. |
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rand_rest_p : float |
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The probability of a random iteration during the the search process. |
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epsilon : float |
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The step-size for the climbing. |
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distribution : str |
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The type of distribution to sample from. |
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n_neighbours : int |
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The number of neighbours to sample and evaluate before moving to the best |
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of those neighbours. |
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""" |
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def __init__( |
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self, |
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search_space: Dict[str, list], |
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initialize: Dict[ |
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Literal["grid", "vertices", "random", "warm_start"], int | List |
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] = {"grid": 4, "random": 2, "vertices": 4}, |
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constraints: List[callable] = [], |
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random_state: int = None, |
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rand_rest_p: float = 0, |
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nth_process: int = None, |
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iters_p_dim: int = 10, |
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): |
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super().__init__( |
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search_space=search_space, |
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initialize=initialize, |
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constraints=constraints, |
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random_state=random_state, |
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rand_rest_p=rand_rest_p, |
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nth_process=nth_process, |
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iters_p_dim=iters_p_dim, |
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) |
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SimonBlanke /
Gradient-Free-Optimizers
