Initial commit

solvers.py

@@ -0,0 +1,48 @@
+import numpy as np
+
+from abc import ABC, abstractmethod
+from functions import Function
+from typing import Iterable, Tuple
+
+
+class Solver(ABC):
+    def __init__(self, function: Function):
+        self.function = function
+
+    @abstractmethod
+    def rank(self, samples: Iterable[Iterable[float]], elite_size: int) -> (Iterable[Tuple], Iterable[Tuple], float):
+        pass
+
+    @abstractmethod
+    def sample(self, n: int) -> Iterable[float]:
+        pass
+
+    @abstractmethod
+    def update(elite: Iterable[Tuple]):
+        pass
+
+
+class SimpleEvolutionStrategy(Solver):
+    def __init__(self, function: Function, mu: Iterable[float], sigma: Iterable[float] = None):
+        if sigma and len(mu) != len(sigma):
+            raise Exception('Length of mu and sigma must match')
+        super().__init__(function)
+        self.mu = mu
+        if sigma:
+            self.sigma = sigma
+        else:
+            self.sigma = [1] * len(mu)
+
+    def rank(self, samples: Iterable[Iterable[float]], elite_size: int) -> (Iterable[Tuple], Iterable[float]):
+        fitness = self.function.eval(*samples.transpose())
+        samples = samples[np.argsort(fitness)]
+        fitness = np.sort(fitness)
+        elite = samples[0:elite_size]
+        return elite, fitness[0]
+
+    def sample(self, n: int) -> Iterable[Iterable[float]]:
+        return np.array([np.random.multivariate_normal(self.mu, np.diag(self.sigma)) for _ in range(n)])
+
+    def update(self, elite: Iterable[Tuple]):
+        self.mu = elite[0]
+