Add 2023 solutions

2023/d25.py

@@ -0,0 +1,87 @@
+from lib import *
+from random import choice
+from collections import deque
+
+
+# def plot(graph):
+#     import networkx as nx
+#     import matplotlib
+#     import matplotlib.pyplot as plt
+#     G = nx.Graph()
+#     for node, connected_nodes in graph.items():
+#         for connected_node in connected_nodes:
+#             G.add_edge(node, connected_node)
+#     # pos = nx.spring_layout(G, k=2.0, iterations=20)  # Adjust k as needed
+#     pos = nx.shell_layout(G)
+#     nx.draw(G, with_labels=True, node_color='lightblue', edge_color='gray', node_size=2000, font_size=15, font_weight='bold')
+#     matplotlib.use('qtagg')
+#     plt.show()
+
+
+def solve(input: Input):
+    graph = {}
+    edges = {}
+
+    for line in input.lines():
+        src, dsts = line.split(":")
+        dsts = dsts.strip().split(" ")
+
+        if not src in graph:
+            graph[src] = []
+
+        for dst in dsts:
+            graph[src].append(dst)
+            if not dst in graph:
+                graph[dst] = []
+            graph[dst].append(src)
+
+            edge = tuple(sorted([src, dst]))
+            edges[edge] = 0
+
+    for _ in range(100):
+        first_node = choice(list(graph.keys()))
+        seen = set([first_node])
+        visit = deque([first_node])
+        while visit:
+            node = visit.popleft()
+            for nb in graph[node]:
+                if not nb in seen:
+                    seen.add(nb)
+                    visit.append(nb)
+                    edge = tuple(sorted([node, nb]))
+                    edges[edge] += 1
+
+    # Orignally, I used `plot(graph)` to visually find the nodes that have to
+    # be removed. I then came up with this heuristic approach. The idea is that
+    # we have to cross one of the three nodes when we do a breadth first
+    # search. By repeatedly doing that we can identify the "bridges" as the
+    # three edges that are used the most often.
+    most_visited = sorted(edges.items(), key=lambda t: t[1], reverse=True)[:3]
+
+    # to_remove = (("plt", "mgb"), ("jxm", "qns"), ("dbt", "tjd"))  # found visually
+    # for node, count in most_visited:
+    #     print(node, count) # should print the same as `to_remove`
+
+    for (a, b), _ in most_visited:
+        graph[a].remove(b)
+        graph[b].remove(a)
+
+    to_visit = [choice(list(graph.keys()))]
+    seen = set(to_visit)
+    while to_visit:
+        node = to_visit.pop()
+        for nb in graph[node]:
+            if not nb in seen:
+                seen.add(nb)
+                to_visit.append(nb)
+
+    return len(seen) * (len(graph) - len(seen))
+
+
+def main():
+    DAY_INPUT = "d25.txt"
+    print("Solution 1:", solve(Input(DAY_INPUT)), "(hands-free)")
+
+
+if __name__ == "__main__":
+    main()