14c7756802
added output for improvement of fitness added CMAES algorithm, that requires further testing
84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
import numpy as np
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from abc import ABC, abstractmethod
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from functions import Function
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from typing import Iterable, Tuple
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class Solver(ABC):
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def __init__(self, function: Function):
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self.function = function
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@abstractmethod
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def rank(self, samples: Iterable[Iterable[float]], elite_size: int) -> (Iterable[Iterable[float]], float):
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pass
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@abstractmethod
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def sample(self, n: int) -> Iterable[float]:
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pass
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@abstractmethod
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def update(elite: Iterable[Iterable[float]]):
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pass
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class SimpleEvolutionStrategy(Solver):
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def __init__(self, function: Function, mu: Iterable[float], sigma: Iterable[float] = None):
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if sigma and len(mu) != len(sigma):
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raise Exception('Length of mu and sigma must match')
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super().__init__(function)
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self.mu = mu
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if sigma:
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self.sigma = sigma
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else:
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self.sigma = [1] * len(mu)
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def rank(self, samples: Iterable[Iterable[float]], elite_size: int) -> (Iterable[Iterable[float]], Iterable[float]):
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fitness = self.function.eval(*samples.transpose())
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samples = samples[np.argsort(fitness)]
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fitness = np.sort(fitness)
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elite = samples[0:elite_size]
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return elite, fitness[0]
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def sample(self, n: int) -> Iterable[Iterable[float]]:
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return np.array([np.random.multivariate_normal(self.mu, np.diag(self.sigma)) for _ in range(n)])
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def update(self, elite: Iterable[Iterable[float]]):
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self.mu = elite[0]
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class CMAEvolutionStrategy(Solver):
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def __init__(self, function: Function, mu: Iterable[float], covariances: Iterable[float] = None):
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if covariances is not None and len(mu) != covariances.shape[0] and len(mu) != covariances.shape[1]:
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raise Exception('Length of mu and covariance matrix must match')
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super().__init__(function)
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self.mu = mu
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if covariances is not None:
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self.covariances = covariances
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else:
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self.covariances = np.array([[1.0, 0.0], [0.0, 1.0]])
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def rank(self, samples: Iterable[Iterable[float]], elite_size: int) -> (Iterable[Iterable[float]], Iterable[float]):
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fitness = self.function.eval(*samples.transpose())
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samples = samples[np.argsort(fitness)]
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fitness = np.sort(fitness)
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elite = samples[0:elite_size]
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return elite, fitness[0]
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def sample(self, n: int) -> Iterable[Iterable[float]]:
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return np.array([np.random.multivariate_normal(self.mu, self.covariances) for _ in range(n)])
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def update(self, elite: Iterable[Iterable[float]]):
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mu = self.mu
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x = elite.transpose()[0]
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y = elite.transpose()[1]
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self.mu[0] = np.average(x)
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self.mu[1] = np.average(y)
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# TODO fix covariance matrix calculation using the tutorial by Nikolaus Hansen
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self.covariances[0][0] = np.average((x - mu[0])**2)
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self.covariances[1][1] = np.average((y - mu[1])**2)
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self.covariances[0][1] = np.average((x - mu[0]) * (y - mu[1]))
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# self.covariances = np.cov(elite.transpose())
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