Interface changes

Author: firefly-cpp

README.md

@@ -31,8 +31,8 @@ from fireflyalgorithm import FireflyAlgorithm
 def sphere(x):
     return np.sum(x ** 2)
 
-FA = FireflyAlgorithm(function=sphere, dim=10, lb=-5, ub=5, max_evals=10000)
-best = FA.move()
+FA = FireflyAlgorithm()
+best = FA.run(function=sphere, dim=10, lb=-5, ub=5, max_evals=10000)
 
 print(best)
 ```

examples/run.py

@@ -6,7 +6,7 @@ def sphere(x):
     return np.sum(x ** 2)
 
 
-FA = FireflyAlgorithm(function=sphere, dim=10, lb=-5, ub=5, max_evals=10000)
-best = FA.move()
+FA = FireflyAlgorithm()
+best = FA.run(function=sphere, dim=10, lb=-5, ub=5, max_evals=10000)
 
 print(best)

fireflyalgorithm/fireflyalgorithm.py

@@ -2,40 +2,34 @@
 from numpy.random import default_rng
 
 
-class FireflyAlgorithm():
-    def __init__(self, function, dim, lb, ub, max_evals, pop_size=20, alpha=1.0, betamin=1.0, gamma=0.01, seed=None):
-        self.function = function
-        self.dim = dim
-        self.lb = lb
-        self.ub = ub
-        self.max_evals = max_evals
+class FireflyAlgorithm:
+    def __init__(self, pop_size=20, alpha=1.0, betamin=1.0, gamma=0.01, seed=None):
         self.pop_size = pop_size
         self.alpha = alpha
         self.betamin = betamin
         self.gamma = gamma
-        self.seed = seed
+        self.rng = default_rng(seed)
 
-    def move(self):
-        rng = default_rng(self.seed)
-        fireflies = rng.uniform(self.lb, self.ub, (self.pop_size, self.dim))
-        intensity = np.apply_along_axis(self.function, 1, fireflies)
+    def run(self, function, dim, lb, ub, max_evals):
+        fireflies = self.rng.uniform(lb, ub, (self.pop_size, dim))
+        intensity = np.apply_along_axis(function, 1, fireflies)
         best = np.min(intensity)
 
         evaluations = self.pop_size
         new_alpha = self.alpha
-        search_range = self.ub - self.lb
+        search_range = ub - lb
 
-        while evaluations <= self.max_evals:
+        while evaluations <= max_evals:
             new_alpha *= 0.97
             for i in range(self.pop_size):
                 for j in range(self.pop_size):
                     if intensity[i] >= intensity[j]:
                         r = np.sum(np.square(fireflies[i] - fireflies[j]), axis=-1)
                         beta = self.betamin * np.exp(-self.gamma * r)
-                        steps = new_alpha * (rng.random(self.dim) - 0.5) * search_range
+                        steps = new_alpha * (self.rng.random(dim) - 0.5) * search_range
                         fireflies[i] += beta * (fireflies[j] - fireflies[i]) + steps
-                        fireflies[i] = np.clip(fireflies[i], self.lb, self.ub)
-                        intensity[i] = self.function(fireflies[i])
+                        fireflies[i] = np.clip(fireflies[i], lb, ub)
+                        intensity[i] = function(fireflies[i])
                         evaluations += 1
                         best = min(intensity[i], best)
         return best