92 lines
2.1 KiB
Go
92 lines
2.1 KiB
Go
package bastion_init
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import "strings"
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// 假设我们的预定义字符串列表存储在这个map中，键为字符串索引，值为字符串本身
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var dictionary = map[string]struct{}{
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"apple": {},
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"apply": {},
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"apron": {},
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"docker": {},
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"docker-compose": {},
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"harbor": {},
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// ...其他词汇
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}
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// normalize 将字符串转换为小写并去除特殊符号
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func normalize(s string) string {
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return strings.ToLower(strings.ReplaceAll(s, "\\W", ""))
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}
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// findBestMatch 在字典中找到最合适的单词
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func findBestMatch(query string) string {
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normQuery := normalize(query)
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// 将查询字符串排序，以便二分查找
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sortedQuery := []rune(normQuery)
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var bestMatch string
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minDistance := len(dictionary) + 1 // 最初假设没有匹配项
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// 遍历字典中的所有单词
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for word, _ := range dictionary {
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normWord := normalize(word)
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distance := levenshteinDistance(sortedQuery, []rune(normWord)) // 计算编辑距离
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// 如果当前单词的编辑距离小于等于最佳匹配的距离，并且它是第一个匹配项（或者距离相同但字典序更低）
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if distance <= minDistance || (distance == minDistance && strings.ToLower(word) < normQuery) {
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minDistance = distance
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bestMatch = word
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}
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}
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return bestMatch
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}
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// levenshteinDistance 计算两个字符串之间的编辑距离（插入、删除或替换一个字符的次数）
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func levenshteinDistance(a, b []rune) int {
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lenA := len(a)
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lenB := len(b)
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dist := make([][]int, lenA+1)
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for i := range dist {
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dist[i] = make([]int, lenB+1)
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}
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for i := 0; i <= lenA; i++ {
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dist[i][0] = i
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}
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for j := 0; j <= lenB; j++ {
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dist[0][j] = j
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}
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for i := 1; i <= lenA; i++ {
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for j := 1; j <= lenB; j++ {
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cost := int(a[i-1] - b[j-1])
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dist[i][j] = minInt(dist[i-1][j]+1, dist[i][j-1]+1, dist[i-1][j-1]+cost)
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}
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}
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return dist[lenA][lenB]
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}
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// minInt 找到三个整数中的最小值
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func minInt(a, b, c int) int {
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if a < b {
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if a < c {
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return a
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}
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return c
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}
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if b < c {
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return b
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}
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return c
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}
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func main() {
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query := "app!"
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bestMatch := findBestMatch(query)
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println("The best match for", query, "is:", bestMatch)
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}
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