写出用tfidf算法计算多篇中文文本相似度的Android代码

以下是基于TF-IDF算法计算多篇中文文本相似度的Android代码：

1. 首先，需要导入相关的依赖：

dependencies {
    ...
    implementation 'com.github.hankcs:hanlp:portable-1.7.8'
    implementation 'com.github.hankcs:HanLP-tokenizer:1.0.2'
    ...
}

2. 接着，定义一个方法来计算TF-IDF分数：

import com.hankcs.hanlp.HanLP;
import com.hankcs.hanlp.seg.common.Term;
import com.hankcs.hanlp.tokenizer.StandardTokenizer;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class TFIDF {
    public static Map<String, Float> getTFIDF(String text) {
        Map<String, Float> tfidfMap = new HashMap<>();
        
        // 分词
        List<Term> terms = StandardTokenizer.segment(text);
        
        // 计算TF
        Map<String, Integer> tfMap = new HashMap<>();
        for (Term term : terms) {
            String word = term.word;
            tfMap.put(word, tfMap.getOrDefault(word, 0) + 1);
        }
        for (String word : tfMap.keySet()) {
            float tf = (float) tfMap.get(word) / terms.size();
            
            // 计算IDF
            int df = 0;
            List<String> docList = getDocList();
            for (String doc : docList) {
                if (doc.contains(word)) {
                    df += 1;
                }
            }
            float idf = (float) Math.log((float) docList.size() / (df + 1));
            
            // 计算TF-IDF
            tfidfMap.put(word, tf * idf);
        }
        
        return tfidfMap;
    }
    
    // 模拟多篇文本
    private static List<String> getDocList() {
        List<String> docList = new ArrayList<>();
        docList.add("这是第一篇文本，用于测试。");
        docList.add("这是第二篇文本，用于测试。");
        docList.add("这是第三篇文本，用于测试。");
        return docList;
    }
}

3. 最后，在Activity中调用该方法来计算多篇文本之间的相似度：

public class MainActivity extends AppCompatActivity {
    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);
        
        String text1 = "这是第一篇文本，用于测试。";
        String text2 = "这是第二篇文本，用于测试，但与第一篇文本不同。";
        String text3 = "这是第三篇文本，用于测试，与前两篇文本都不同。";
        
        Map<String, Float> tfidf1 = TFIDF.getTFIDF(text1);
        Map<String, Float> tfidf2 = TFIDF.getTFIDF(text2);
        Map<String, Float> tfidf3 = TFIDF.getTFIDF(text3);
        
        float sim12 = getSimilarity(tfidf1, tfidf2);
        float sim13 = getSimilarity(tfidf1, tfidf3);
        float sim23 = getSimilarity(tfidf2, tfidf3);
        
        Log.d("MainActivity", "similarity between text1 and text2: " + sim12);
        Log.d("MainActivity", "similarity between text1 and text3: " + sim13);
        Log.d("MainActivity", "similarity between text2 and text3: " + sim23);
    }
    
    private float getSimilarity(Map<String, Float> tfidf1, Map<String, Float> tfidf2) {
        float numerator = 0;
        float denominator1 = 0;
        float denominator2 = 0;
        for (String word : tfidf1.keySet()) {
            float tfidfValue1 = tfidf1.get(word);
            float tfidfValue2 = tfidf2.getOrDefault(word, 0f);
            numerator += tfidfValue1 * tfidfValue2;
            denominator1 += tfidfValue1 * tfidfValue1;
        }
        for (String word : tfidf2.keySet()) {
            float tfidfValue2 = tfidf2.get(word);
            denominator2 += tfidfValue2 * tfidfValue2;
        }
        float denominator = (float) (Math.sqrt(denominator1) * Math.sqrt(denominator2));
        if (denominator == 0) {
            return 0;
        }
        return numerator / denominator;
    }
}