leecode更新

2022-12-08 13:26:33 +08:00 · 2022-12-08 13:26:33 +08:00 · 5f1ccb4541
parent 2d33b78f2c
commit 5f1ccb4541
1 changed files with 153 additions and 0 deletions
--- a/Leecode/src/main/java/com/markilue/leecode/dynamic/T17_WordBreak.java
+++ b/Leecode/src/main/java/com/markilue/leecode/dynamic/T17_WordBreak.java
@ -0,0 +1,153 @@
+package com.markilue.leecode.dynamic;
+
+import org.junit.Test;
+
+import java.lang.reflect.Array;
+import java.util.*;
+
+/**
+ * @BelongsProject: Leecode
+ * @BelongsPackage: com.markilue.leecode.dynamic
+ * @Author: markilue
+ * @CreateTime: 2022-12-08  10:38
+ * @Description:
+ * TODO  力扣139题  单词拆分：
+ *  给你一个字符串 s 和一个字符串列表 wordDict 作为字典。请你判断是否可以利用字典中出现的单词拼接出 s 。
+ *  注意：不要求字典中出现的单词全部都使用，并且字典中的单词可以重复使用。
+ * @Version: 1.0
+ */
+public class T17_WordBreak {
+
+    @Test
+    public void test(){
+        String s = "leetcode";
+
+        List<String> wordDict = new ArrayList<>(Arrays.asList("leet", "code"));
+        System.out.println(wordBreak(s,wordDict));
+    }
+
+    @Test
+    public void test1(){
+        String s = "applepenapple";
+
+        List<String> wordDict = new ArrayList<>(Arrays.asList("apple", "pen"));
+        System.out.println(wordBreak(s,wordDict));
+    }
+
+    /**
+     * 思路:实际上就是判断能重复用的wordDict能否构造出s
+     *      TODO  动态规划五部曲:
+     *              (1)dp定义:dp[i][j]使用[0-i]的wordDict能否构造出s[0-j]
+     *              (2)dp状态转移方程:dp[j]=dp[j]||(dp[j-wordDict[i].length]&&wordDict[i]==s.subString(j-wordDict[i].length))
+     *              (3)dp初始化:dp[0]=true
+     *              (4)dp遍历顺序:跟两个for的顺序有关系,因为word可以反复利用,只需要判断最后的word
+     *              (5)dp举例推导:
+     *       速度击败71.72%，内存击败60.22%
+     * @param s
+     * @param wordDict
+     * @return
+     */
+    public boolean wordBreak(String s, List<String> wordDict) {
+        boolean[] dp = new boolean[s.length()+1];
+        dp[0]=true;
+
+
+        for (int j = 0; j < dp.length; j++) {
+        for (int i = 0; i < wordDict.size(); i++) {
+            String word = wordDict.get(i);
+                if(j>=word.length()){
+                    String b = s.substring(j-word.length(),j);
+                    dp[j]=dp[j]||(dp[j-word.length()]&&word.equals(b));
+                }
+
+            }
+        }
+
+        return dp[s.length()];
+    }
+
+
+    /**
+     * 官方另一种背包，找到之后直接break是精髓，但是就看不出dp的状态转移方程了
+     * 速度击败92.41%，内存击败87.5%
+     * @param s
+     * @param wordDict
+     * @return
+     */
+    public boolean wordBreak2(String s, List<String> wordDict) {
+        boolean[] dp = new boolean[s.length() + 1];
+        dp[0] = true;
+
+        for (int i = 1; i <= s.length(); i++) {
+            for (String word : wordDict) {
+                int len = word.length();
+                if (i >= len && dp[i - len] && word.equals(s.substring(i - len, i))) {
+                    dp[i] = true;
+                    break;
+                }
+            }
+        }
+
+        return dp[s.length()];
+    }
+
+
+    /**
+     * 官方及代码随想录的的动态规划解法，利用hashset加快速度
+     * 速度击败33.98%，内存击败7.9%
+     * @param s
+     * @param wordDict
+     * @return
+     */
+    public boolean wordBreak1(String s, List<String> wordDict) {
+        HashSet<String> set = new HashSet<>(wordDict);
+        boolean[] valid = new boolean[s.length() + 1];
+        valid[0] = true;
+
+        for (int i = 1; i <= s.length(); i++) {
+            for (int j = 0; j < i && !valid[i]; j++) {
+                if (set.contains(s.substring(j, i)) && valid[j]) {
+                    valid[i] = true;
+                }
+            }
+        }
+
+        return valid[s.length()];
+    }
+
+
+    /**
+     * 回溯+记忆化搜索：本质上就是把已经用过的东西存起来，便于剪枝
+     * 速度击败79.1%，内存击败24.38%
+     */
+    private Set<String> set;
+    private int[] memo;
+    public boolean wordBreak3(String s, List<String> wordDict) {
+        memo = new int[s.length()];
+        set = new HashSet<>(wordDict);
+        return backtracking(s, 0);
+    }
+
+    public boolean backtracking(String s, int startIndex) {
+        // System.out.println(startIndex);
+        if (startIndex == s.length()) {
+            return true;
+        }
+        if (memo[startIndex] == -1) {
+            return false;
+        }
+
+        for (int i = startIndex; i < s.length(); i++) {
+            String sub = s.substring(startIndex, i + 1);
+            // 拆分出来的单词无法匹配
+            if (!set.contains(sub)) {
+                continue;
+            }
+            boolean res = backtracking(s, i + 1);
+            if (res) return true;
+        }
+        // 这里是关键，找遍了startIndex~s.length()也没能完全匹配，标记从startIndex开始不能找到
+        memo[startIndex] = -1;//关键在于这里，添加了记忆，即从startIndex开始是不能找到，以后到这就不用继续往下回溯了
+        return false;
+    }
+}