[英]Compare two strings without Apache StringUtils
Hi I am working with a voice command project. 嗨,我正在处理语音命令项目。 So I want to receive user's voice at first then I want to check the matches and then I want to do something according to the command.
因此,我想先接收用户的声音,然后再检查匹配项,然后根据命令执行一些操作。 For this, I found a way to match the strings using org.apache.commons.lang3.StringUtils but I find so many trouble with this.
为此,我找到了一种使用org.apache.commons.lang3.StringUtils匹配字符串的方法,但是我发现有很多麻烦。 For ex:- I face problem when I go to import the apache's external library to my android studio.
对于前:-当我将apache的外部库导入我的android studio时遇到问题。 So my question is that:- is there any other way to compare the user's voice data and my specific command without using Apache's StringUtils method?
所以我的问题是:-还有其他方法可以不使用Apache的StringUtils方法来比较用户的语音数据和我的特定命令吗? Please help if you can
如果可以的话请帮忙
There are many string functions you can use to compare strings, for example 您可以使用许多字符串函数来比较字符串,例如
if (result.equals("hello")) {
doSomething();
}
compares two strings 比较两个字符串
result.startsWith("search for") {
doSomething()
}
checks the beginning of the result 检查结果的开头
result.matches("yes|sure") {
doSomething()
}
checks result with regular expression. 用正则表达式检查结果。
You can find all that in a Java textbook. 您可以在Java教科书中找到所有这些内容。 See for example
例如看
https://docs.oracle.com/javase/tutorial/java/data/comparestrings.html https://docs.oracle.com/javase/tutorial/java/data/comparestrings.html
If you want to use Levenshtein distance you can insert the following function in your code: 如果要使用Levenshtein距离,可以在代码中插入以下函数:
public int LevenshteinDistance (String s0, String s1) {
int len0 = s0.length() + 1;
int len1 = s1.length() + 1;
// the array of distances
int[] cost = new int[len0];
int[] newcost = new int[len0];
// initial cost of skipping prefix in String s0
for (int i = 0; i < len0; i++) cost[i] = i;
// dynamically computing the array of distances
// transformation cost for each letter in s1
for (int j = 1; j < len1; j++) {
// initial cost of skipping prefix in String s1
newcost[0] = j;
// transformation cost for each letter in s0
for(int i = 1; i < len0; i++) {
// matching current letters in both strings
int match = (s0.charAt(i - 1) == s1.charAt(j - 1)) ? 0 : 1;
// computing cost for each transformation
int cost_replace = cost[i - 1] + match;
int cost_insert = cost[i] + 1;
int cost_delete = newcost[i - 1] + 1;
// keep minimum cost
newcost[i] = Math.min(Math.min(cost_insert, cost_delete), cost_replace);
}
// swap cost/newcost arrays
int[] swap = cost; cost = newcost; newcost = swap;
}
// the distance is the cost for transforming all letters in both strings
return cost[len0 - 1];
}
Take the source right from the library (Obviously follow the requirements of the Apache license) 直接从库中获取源代码(显然遵循Apache许可证的要求)
https://commons.apache.org/proper/commons-lang/apidocs/src-html/org/apache/commons/lang3/StringUtils.html https://commons.apache.org/proper/commons-lang/apidocs/src-html/org/apache/commons/lang3/StringUtils.html
Line 6865 6865行
/**
* <p>Find the Levenshtein distance between two Strings.</p>
*
* <p>This is the number of changes needed to change one String into
* another, where each change is a single character modification (deletion,
* insertion or substitution).</p>
*
* <p>The previous implementation of the Levenshtein distance algorithm
* was from <a href="http://www.merriampark.com/ld.htm">http://www.merriampark.com/ld.htm</a></p>
*
* <p>Chas Emerick has written an implementation in Java, which avoids an OutOfMemoryError
* which can occur when my Java implementation is used with very large strings.<br>
* This implementation of the Levenshtein distance algorithm
* is from <a href="http://www.merriampark.com/ldjava.htm">http://www.merriampark.com/ldjava.htm</a></p>
*
* <pre>
* StringUtils.getLevenshteinDistance(null, *) = IllegalArgumentException
* StringUtils.getLevenshteinDistance(*, null) = IllegalArgumentException
* StringUtils.getLevenshteinDistance("","") = 0
* StringUtils.getLevenshteinDistance("","a") = 1
* StringUtils.getLevenshteinDistance("aaapppp", "") = 7
* StringUtils.getLevenshteinDistance("frog", "fog") = 1
* StringUtils.getLevenshteinDistance("fly", "ant") = 3
* StringUtils.getLevenshteinDistance("elephant", "hippo") = 7
* StringUtils.getLevenshteinDistance("hippo", "elephant") = 7
* StringUtils.getLevenshteinDistance("hippo", "zzzzzzzz") = 8
* StringUtils.getLevenshteinDistance("hello", "hallo") = 1
* </pre>
*
* @param s the first String, must not be null
* @param t the second String, must not be null
* @return result distance
* @throws IllegalArgumentException if either String input {@code null}
* @since 3.0 Changed signature from getLevenshteinDistance(String, String) to
* getLevenshteinDistance(CharSequence, CharSequence)
*/
public static int getLevenshteinDistance(CharSequence s, CharSequence t) {
if (s == null || t == null) {
throw new IllegalArgumentException("Strings must not be null");
}
/*
The difference between this impl. and the previous is that, rather
than creating and retaining a matrix of size s.length() + 1 by t.length() + 1,
we maintain two single-dimensional arrays of length s.length() + 1. The first, d,
is the 'current working' distance array that maintains the newest distance cost
counts as we iterate through the characters of String s. Each time we increment
the index of String t we are comparing, d is copied to p, the second int[]. Doing so
allows us to retain the previous cost counts as required by the algorithm (taking
the minimum of the cost count to the left, up one, and diagonally up and to the left
of the current cost count being calculated). (Note that the arrays aren't really
copied anymore, just switched...this is clearly much better than cloning an array
or doing a System.arraycopy() each time through the outer loop.)
Effectively, the difference between the two implementations is this one does not
cause an out of memory condition when calculating the LD over two very large strings.
*/
int n = s.length(); // length of s
int m = t.length(); // length of t
if (n == 0) {
return m;
} else if (m == 0) {
return n;
}
if (n > m) {
// swap the input strings to consume less memory
final CharSequence tmp = s;
s = t;
t = tmp;
n = m;
m = t.length();
}
int p[] = new int[n + 1]; //'previous' cost array, horizontally
int d[] = new int[n + 1]; // cost array, horizontally
int _d[]; //placeholder to assist in swapping p and d
// indexes into strings s and t
int i; // iterates through s
int j; // iterates through t
char t_j; // jth character of t
int cost; // cost
for (i = 0; i <= n; i++) {
p[i] = i;
}
for (j = 1; j <= m; j++) {
t_j = t.charAt(j - 1);
d[0] = j;
for (i = 1; i <= n; i++) {
cost = s.charAt(i - 1) == t_j ? 0 : 1;
// minimum of cell to the left+1, to the top+1, diagonally left and up +cost
d[i] = Math.min(Math.min(d[i - 1] + 1, p[i] + 1), p[i - 1] + cost);
}
// copy current distance counts to 'previous row' distance counts
_d = p;
p = d;
d = _d;
}
// our last action in the above loop was to switch d and p, so p now
// actually has the most recent cost counts
return p[n];
}
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