add simulated annealing

Author: SimonBlanke

src/hyperactive/opt/gfo/_simulated_annealing.py

@@ -2,6 +2,76 @@
 
 
 class SimulatedAnnealing(_BaseGFOadapter):
+    """Simulated annealing optimizer.
+
+    Parameters
+    ----------
+    search_space : dict[str, list]
+        The search space to explore. A dictionary with parameter
+        names as keys and a numpy array as values.
+    initialize : dict[str, int]
+        The method to generate initial positions. A dictionary with
+        the following key literals and the corresponding value type:
+        {"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
+    constraints : list[callable]
+        A list of constraints, where each constraint is a callable.
+        The callable returns `True` or `False` dependend on the input parameters.
+    random_state : None, int
+        If None, create a new random state. If int, create a new random state
+        seeded with the value.
+    rand_rest_p : float
+        The probability of a random iteration during the the search process.
+    epsilon : float
+        The step-size for the climbing.
+    distribution : str
+        The type of distribution to sample from.
+    n_neighbours : int
+        The number of neighbours to sample and evaluate before moving to the best
+        of those neighbours.
+    annealing_rate : float
+        The rate at which the temperature is annealed.
+    start_temp : float
+        The initial temperature.
+    n_iter : int, default=100
+        The number of iterations to run the optimizer.
+    verbose : bool, default=False
+        If True, print the progress of the optimization process.
+    experiment : BaseExperiment, optional
+        The experiment to optimize parameters for.
+        Optional, can be passed later via ``set_params``.
+    """
+
+    def __init__(
+        self,
+        search_space=None,
+        initialize=None,
+        constraints=None,
+        random_state=None,
+        rand_rest_p=0.1,
+        epsilon=0.01,
+        distribution="normal",
+        n_neighbours=10,
+        annealing_rate=0.97,
+        start_temp=1,
+        n_iter=100,
+        verbose=False,
+        experiment=None,
+    ):
+        self.random_state = random_state
+        self.rand_rest_p = rand_rest_p
+        self.epsilon = epsilon
+        self.distribution = distribution
+        self.n_neighbours = n_neighbours
+        self.annealing_rate = annealing_rate
+        self.start_temp = start_temp
+        self.search_space = search_space
+        self.initialize = initialize
+        self.constraints = constraints
+        self.n_iter = n_iter
+        self.experiment = experiment
+        self.verbose = verbose
+
+        super().__init__()
 
     def _get_gfo_class(self):
         """Get the GFO class to use.