Implement best first local search algorithm

src/processing.py

@@ -1,7 +1,7 @@
 from preprocessing import parse_file
+from numpy.random import choice, randint, seed
 from pandas import DataFrame, Series
 from sys import argv
-from random import seed, randint
 from time import time
 
 
@@ -59,10 +59,10 @@ def greedy_algorithm(n, m, data):
     return solutions
 
 
-def get_pseudorandom_solution(n, data):
+def get_first_random_solution(m, data):
     seed(42)
-    solution = data.iloc[randint(a=0, b=n)]
+    random_indexes = choice(len(data.index), size=m)
-    return Series(data={"point": solution["destination"], "distance": 0})
+    return data.iloc[random_indexes]
 
 
 def local_search(n, m, data):
@@ -72,6 +72,17 @@ def local_search(n, m, data):
     for _ in range(m):
         pass
     return solutions
+def get_random_solution(previous, data):
+    solution = previous.copy()
+    worst_index = previous["distance"].astype(float).idxmin()
+    random_candidate = data.loc[randint(low=0, high=len(data.index))]
+    while (
+        solution.loc[worst_index, "distance"] <= previous.loc[worst_index, "distance"]
+    ):
+        solution.loc[worst_index] = random_candidate
+    return solution
+
+
 
 
 def execute_algorithm(choice, n, m, data):