day 8

day8/2.py

@@ -1,3 +1,42 @@
 with open(r'day8/input.txt', 'r') as input:
-    lines = input.read().split('\n')[:-1]
+    lines = list(map(lambda x: list(map(lambda y: int(y),x.split(','))),input.read().split('\n')[:-1]))
 
+def dist(a,b):
+    return (a[0]-b[0])**2 + (a[1]-b[1])**2 + (a[2]-b[2])**2
+
+excluded_neigbors = [set([i]) for i in range(len(lines))]
+
+def find_best(i):
+    best_distances[i] = [10**15,-1]
+    for j,node2 in enumerate(lines):
+        if j in excluded_neigbors[i]: continue
+        distance = dist(lines[i],node2)
+        best_distances[i] = min([distance,j], best_distances[i],key=lambda x : x[0])
+
+best_distances = []
+
+idx_circuit = list(range(len(lines)))
+
+for i in range(len(lines)):
+    best_distances.append(0)
+    find_best(i)
+
+size = 10**3-1
+
+min_idx = 0
+ass_idx = 0
+
+while size > 0:
+    min_idx = best_distances.index(min(best_distances, key = lambda x:x[0]))
+    if (old_idx := idx_circuit[min_idx]) != (new_idx := idx_circuit[(ass_idx := best_distances[min_idx][1])]): 
+        for i in range(len(lines)):
+            if idx_circuit[i] == old_idx:
+                idx_circuit[i] = new_idx
+        size -= 1
+    excluded_neigbors[min_idx].add(ass_idx)
+    excluded_neigbors[ass_idx].add(min_idx)
+    find_best(min_idx)
+    find_best(ass_idx)
+
+
+print("score:",lines[min_idx][0]*lines[ass_idx][0])