import org.xm.Similarity;
import org.xm.tendency.word.HownetWordTendency;

public class demo {
    public static void main(String[] args) {
        double result = Similarity.cilinSimilarity("电动车", "自行车");
        System.out.println(result);

        String word = "混蛋";
        HownetWordTendency hownetWordTendency = new HownetWordTendency();
        result = hownetWordTendency.getTendency(word);
        System.out.println(word + "  词语情感趋势值：" + result);
    }
}

package org.xm;

public class WordSimilarityDemo {

    public static void main(String[] args) {
        String word1 = "教师";
        String word2 = "教授";
        double cilinSimilarityResult = Similarity.cilinSimilarity(word1, word2);
        double pinyinSimilarityResult = Similarity.pinyinSimilarity(word1, word2);
        double conceptSimilarityResult = Similarity.conceptSimilarity(word1, word2);
        double charBasedSimilarityResult = Similarity.charBasedSimilarity(word1, word2);

        System.out.println(word1 + " vs " + word2 + " 词林相似度值：" + cilinSimilarityResult);
        System.out.println(word1 + " vs " + word2 + " 拼音相似度值：" + pinyinSimilarityResult);
        System.out.println(word1 + " vs " + word2 + " 概念相似度值：" + conceptSimilarityResult);
        System.out.println(word1 + " vs " + word2 + " 字面相似度值：" + charBasedSimilarityResult);
    }
}

public static void main(String[] args) {
    String phrase1 = "继续努力";
    String phrase2 = "持续发展";
    double result = Similarity.phraseSimilarity(phrase1, phrase2);

    System.out.println(phrase1 + " vs " + phrase2 + " 短语相似度值：" + result);
}

public static void main(String[] args) {
    String sentence1 = "中国人爱吃鱼";
    String sentence2 = "湖北佬最喜吃鱼";

    double morphoSimilarityResult = Similarity.morphoSimilarity(sentence1, sentence2);
    double editDistanceResult = Similarity.editDistanceSimilarity(sentence1, sentence2);
    double standEditDistanceResult = Similarity.standardEditDistanceSimilarity(sentence1,sentence2);
    double gregeorEditDistanceResult = Similarity.gregorEditDistanceSimilarity(sentence1,sentence2);

    System.out.println(sentence1 + " vs " + sentence2 + " 词形词序句子相似度值：" + morphoSimilarityResult);
    System.out.println(sentence1 + " vs " + sentence2 + " 优化的编辑距离句子相似度值：" + editDistanceResult);
    System.out.println(sentence1 + " vs " + sentence2 + " 标准编辑距离句子相似度值：" + standEditDistanceResult);
    System.out.println(sentence1 + " vs " + sentence2 + " gregeor编辑距离句子相似度值：" + gregeorEditDistanceResult);
}

@Test
public void getSimilarityScore() throws Exception {
        String text1 = "对于俄罗斯来说，最大的战果莫过于夺取乌克兰首都基辅，也就是现任总统泽连斯基和他政府的所在地。目前夺取基辅的战斗已经打响。";
        String text2 = "迄今为止，俄罗斯的入侵似乎没有完全按计划成功执行——英国国防部情报部门表示，在乌克兰军队激烈抵抗下，俄罗斯军队已经损失数以百计的士兵。尽管如此，俄军在继续推进。";
        TextSimilarity cosSimilarity = new CosineSimilarity();
        double score1 = cosSimilarity.getSimilarity(text1, text2);
        System.out.println("cos相似度分值：" + score1);

        TextSimilarity editSimilarity = new EditDistanceSimilarity();
        double score2 = editSimilarity.getSimilarity(text1, text2);
        System.out.println("edit相似度分值：" + score2);
        }

@Test
public void getTendency() throws Exception {
    HownetWordTendency hownet = new HownetWordTendency();
    String word = "美好";
    double sim = hownet.getTendency(word);
    System.out.println(word + ":" + sim);
    System.out.println("混蛋:" + hownet.getTendency("混蛋"));
}

@Test
public void testHomoionym() throws Exception {
    List<String> result = Word2vec.getHomoionym(RAW_CORPUS_SPLIT_MODEL, "武功", 10);
    System.out.println("武功 近似词：" + result);
}

@Test
public void testHomoionymName() throws Exception {
    String model = RAW_CORPUS_SPLIT_MODEL;
    List<String> result = Word2vec.getHomoionym(model, "乔帮主", 10);
    System.out.println("乔帮主 近似词：" + result);

    List<String> result2 = Word2vec.getHomoionym(model, "阿朱", 10);
    System.out.println("阿朱 近似词：" + result2);

    List<String> result3 = Word2vec.getHomoionym(model, "少林寺", 10);
    System.out.println("少林寺 近似词：" + result3);
}