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Author: assassint2017

Find Eventual Safe States/Find_Eventual_Safe_States.cpp

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+// 直接采用DFS来做
+// memo 记录每个节点的状态
+// 0 代表 not visited
+// 1 代表 unsafe
+// 2 代表 safe
+// 3 代表 访问过了，但是目前还不清楚安全与否
+
+// Runtime: 172 ms, faster than 62.12% of C++ online submissions for Find Eventual Safe States.
+// Memory Usage: 31.7 MB, less than 100.00% of C++ online submissions for Find Eventual Safe States.
+
+class Solution 
+{
+public:
+    vector<int> eventualSafeNodes(vector<vector<int>>& outadj) 
+    {
+        vector<int> res;
+        int numOfNode = outadj.size();
+        vector<int> memo(numOfNode, 0);
+        
+        
+        for (int i = 0; i < numOfNode; ++i)
+        {
+            if (dfs(outadj, i, memo))
+                res.push_back(i);
+        }
+        
+        return res;
+    }
+private:
+    bool dfs(vector<vector<int>>& outadj, int startNode, vector<int>& memo)
+    {
+        // 从备忘录中抽取结果
+        if (memo[startNode] != 0)
+        {
+            if (memo[startNode] == 1)
+                return false;
+            else if (memo[startNode] == 2)
+                return true;
+            else
+            {
+                memo[startNode] = 1;
+                return false;
+            }
+        }
+        
+        // dfs
+        memo[startNode] = 3;
+        for (int node : outadj[startNode])
+        {
+            if (dfs(outadj, node, memo) == false)
+            {
+                memo[startNode] = 1;
+                return false;
+            }
+        }
+        
+        memo[startNode] = 2;
+        return true;
+    }
+};

Find Eventual Safe States/Find_Eventual_Safe_States.py

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+# Runtime: 760 ms, faster than 81.43% of Python3 online submissions for Find Eventual Safe States.
+# Memory Usage: 20.5 MB, less than 7.69% of Python3 online submissions for Find Eventual Safe States.
+
+class Solution:
+    def eventualSafeNodes(self, outadj: List[List[int]]) -> List[int]:
+        
+        res = []
+        numOfNode = len(outadj)
+        memo = [0 for i in range(numOfNode)]
+
+        for i in range(numOfNode):
+            if self.dfs(outadj, i, memo) is True:
+                res.append(i)
+        
+        return res
+
+    def dfs(self, outadj, startNode, memo):
+
+        if memo[startNode] is not 0:
+            if memo[startNode] is 1:
+                return False
+            elif memo[startNode] is 2:
+                return True
+            else:
+                memo[startNode] = 1
+                return False
+        
+        memo[startNode] = 3
+        for node in outadj[startNode]:
+            if self.dfs(outadj, node, memo) is False:
+                memo[startNode] = 1
+                return False
+        
+        memo[startNode] = 2
+        return True