优化上面的方法 public static String replaceWithStar(Set<String> strings, String input) { for (String str : strings) { input = input.replace(str, "***"); } return input; }
时间: 2023-05-21 15:04:05 浏览: 53
可以使用 StringBuilder 来优化这个方法,因为字符串是不可变的,每次调用 replace 方法都会创建一个新的字符串对象,而 StringBuilder 可以在原有的字符串基础上进行修改,避免了创建新对象的开销。以下是优化后的代码:
public static String replaceWithStar(Set<String> strings, String input) {
StringBuilder sb = new StringBuilder(input);
for (String str : strings) {
int index = sb.indexOf(str);
while (index != -1) {
sb.replace(index, index + str.length(), "***");
index = sb.indexOf(str, index + 3);
}
}
return sb.toString();
}
相关问题
优化这个方法 public static String replaceWithStar(Set<String> strings, String input) { StringBuilder sb = new StringBuilder(input); for (String str : strings) { int index = sb.indexOf(str); while (index != -1) { sb.replace(index, index + str.length(), "***"); index = sb.indexOf(str, index + 3); } } return sb.toString(); }
这个方法可以通过使用正则表达式来优化,可以使用replaceAll()方法来替换所有匹配的字符串,代码如下:
public static String replaceWithStar(Set<String> strings, String input) {
String regex = String.join("|", strings);
return input.replaceAll(regex, "***");
}
这个方法使用了String.join()方法来将Set中的字符串连接成一个正则表达式,然后使用replaceAll()方法来替换所有匹配的字符串。
Map<string, Set<string>> CluterAnagram (vector<string> words);
As an AI language model, I can provide you with a brief explanation of the ClusterAnagram function.
The ClusterAnagram function takes in a vector of strings and returns a map of strings to sets of strings. The input vector contains a list of words, and the function's goal is to group all anagrams of these words together into sets, where each set represents a cluster of anagrams.
The output map has string keys and set values. Each key represents a unique anagram group, and the corresponding set contains all the words that belong to that group. By the end of the function, all anagram clusters will be grouped together, and each word will be assigned to one and only one cluster.
Here is an example of how this function might work:
Input Vector: ["dog", "god", "cat", "act", "tac", "bat"]
Output Map:
{
"dgo": {"dog", "god"},
"act": {"cat", "act", "tac"},
"abt": {"bat"}
}
In this example, the function identified three anagram clusters: "dog" and "god", "cat", "act", and "tac", and "bat". The corresponding sets contain all the words that belong to each group.
Note that the function assumes that the input vector only contains lowercase letters and no punctuation. It also assumes that the words in the input vector are unique, meaning that no two words are identical.