fix: RS running

flight_algorithms/algorithms/base_algorithm.py

@@ -2,30 +2,24 @@
 #from flight_algorithms.Flight import Flight
 import random
 import math
-
+import sys
 
 class FlightAlgorithm(metaclass=ABCMeta):
-    def __init__(self, domain, fitness_function, seed=random.randint(10, 100),
-                set_all_generators=False, init=[]):
+    def __init__(self, domain, fitness_function,seed=random.randint(10, 100),seed_init=True,init=[]):
         self.domain = domain
         self.fitness_function = fitness_function
         self.seed = seed
+        self.seed_init = seed_init
         self.init = init
-        self.init_seed(set_all_generators)
-
-        self.population = []
-        self.best_cost = 0.0  # returned
-        self.scores = 0.0  # returned
-        self.nfe = 0  # returned
-
+        if self.seed_init:
+            # Set the seed for initial population only
+            self.r_init = random.Random(seed)
+        else:
+            # Same seeds for both init and other random generators
+            self.r_init = random.Random(seed)
+            random.seed(seed)
 
-
-
-    def init_seed(self, set_all_generators=False):
-        self.r_init = random.Random(self.seed)
-
-        if set_all_generators:
-            random.seed(self.seed)
+        self.best_cost =0.0  # returned
 
     @abstractmethod
     def run(self, **kwargs) -> tuple:

flight_algorithms/algorithms/rs.py

@@ -0,0 +1,62 @@
+import sys
+import os 
+sys.path.append(os.getcwd())
+from abc import ABCMeta
+from utils.utils import plot_scores, print_schedule, read_file
+from flight_algorithms.algorithms.base_algorithm import FlightAlgorithm
+import heapq
+import math
+import random
+import sys
+from fitness import *
+
+
+class RandomSearch(FlightAlgorithm, metaclass=ABCMeta):
+    def __init__(self, domain, fitness_function, seed=random.randint(10, 100),
+                 seed_init=True, init=[], epochs=100):
+        super().__init__(domain, fitness_function, seed, seed_init, init)         
+        self.epochs = epochs
+        self.best_cost=sys.maxsize
+        self.best_solution=0.0
+
+
+    def run(self, **kwargs) -> tuple:
+        scores = []
+        nfe = 0
+        if len(self.init) > 0:
+            solution = self.init
+        else:
+            solution = [self.r_init.randint(self.domain[i][0], self.domain[i][1])
+                        for i in range(len(self.domain))]
+        for i in range(self.epochs):
+            if i != 0:
+                solution = [random.randint(self.domain[i][0], self.domain[i][1])
+                            for i in range(len(self.domain))]
+            if not self.fitness_function.__name__ == 'fitness_function':
+                cost = self.fitness_function(solution)
+            else:
+                cost = self.fitness_function(solution, 'FCO')
+            nfe += 1
+            if cost < self.best_cost:
+                self.best_cost = cost
+                self.best_solution = solution
+            scores.append(self.best_cost)
+        return self.best_solution, self.best_cost, scores, nfe, self.seed
+
+
+
+if __name__ == '__main__':
+    read_file('flights.txt')
+    rs=RandomSearch(domain=domain['domain'],fitness_function=fitness_function,seed=5,seed_init=False)
+    soln, cost, scores, nfe, seed=rs.run()
+    plot_scores(scores,rs.__class__.__name__,save_fig=False)
+    print_schedule(soln,'FCO')
+    """"
+    1. Change all algorithms
+    2. Docstrings 
+    3. Type hinting
+    4. Unit tests
+    5. Change all single_runs
+    6. Change all multiple runs
+    
+    """