pyduino.optimization

 1from typing import Callable, List, Tuple
 2from abc import ABC, abstractmethod
 3import warnings
 4import numpy as np
 5
 6def linmap(domain: List[Tuple[float, float]], codomain: List[Tuple[float, float]]) -> Callable[[np.ndarray], np.ndarray]:
 7    """
 8    Linear mapping from domain to codomain.
 9    domain list of pairs:
10        Maxima and minima that each parameter can cover.
11        Example: [(0, 10), (0, 10)] for two parameters ranging from 0 to 10.
12    codomain list of pairs:
13        Maxima and minima that each parameter can cover.
14        Example: [(0, 10), (0, 10)] for two parameters ranging from 0 to 10.
15    """
16    domain = np.array(domain)
17    codomain = np.array(codomain)
18
19    M = (codomain[:, 1] - codomain[:, 0])/ (domain[:, 1] - domain[:, 0])
20
21    def f(x):
22        assert x.shape[1] == domain.shape[0], f"x must have the same number of features as the domain. Expected {domain.shape[0]}, got {x.shape[1]}."
23        return codomain[:, 0] + M * (x - domain[:, 0])
24
25    return f
26
27class Optimizer(ABC):
28    """
29    Abstract class for optimization algorithms
30    """
31    @abstractmethod
32    def __init__(self, growth_rate: float, population_size, ranges, iterations=None, rng_seed=0):
33        pass
34
35    @abstractmethod
36    def view(self, x, linmap):
37        pass
38
39    @abstractmethod
40    def view_g(self):
41        pass
42
43    @abstractmethod
44    def inverse_view(self, x, linmap=None):
45        pass
46
47    @abstractmethod
48    def ask_oracle(self) -> np.ndarray:
49        raise NotImplementedError("The oracle must be implemented.")
50
51    @abstractmethod
52    def init_oracle(self):
53        warnings.warn("The oracle initialization is not implemented.")
54        pass
55
56    @abstractmethod
57    def step(self):
58        pass