本文整理汇总了Java中org.deeplearning4j.models.embeddings.wordvectors.WordVectors.wordsNearest方法的典型用法代码示例。如果您正苦于以下问题：Java WordVectors.wordsNearest方法的具体用法？Java WordVectors.wordsNearest怎么用？Java WordVectors.wordsNearest使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.deeplearning4j.models.embeddings.wordvectors.WordVectors的用法示例。
在下文中一共展示了WordVectors.wordsNearest方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: testGlove
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors; //导入方法依赖的package包/类
@Test
public void testGlove() throws Exception {
Glove glove = new Glove(true, 5, 100);
JavaRDD<String> corpus = sc.textFile(new ClassPathResource("raw_sentences.txt").getFile().getAbsolutePath())
.map(new Function<String, String>() {
@Override
public String call(String s) throws Exception {
return s.toLowerCase();
}
});
Pair<VocabCache<VocabWord>, GloveWeightLookupTable> table = glove.train(corpus);
WordVectors vectors = WordVectorSerializer
.fromPair(new Pair<>((InMemoryLookupTable) table.getSecond(), (VocabCache) table.getFirst()));
Collection<String> words = vectors.wordsNearest("day", 20);
assertTrue(words.contains("week"));
}
示例2: testLoadingWordVectors
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors; //导入方法依赖的package包/类
@Test
public void testLoadingWordVectors() throws Exception {
File modelFile = new File(pathToWriteto);
if (!modelFile.exists()) {
testRunWord2Vec();
}
WordVectors wordVectors = WordVectorSerializer.loadTxtVectors(modelFile);
Collection<String> lst = wordVectors.wordsNearest("day", 10);
System.out.println(Arrays.toString(lst.toArray()));
}
示例3: testLoaderTextSmall
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors; //导入方法依赖的package包/类
@Test
@Ignore
public void testLoaderTextSmall() throws Exception {
INDArray vec = Nd4j.create(new double[] {0.002001, 0.002210, -0.001915, -0.001639, 0.000683, 0.001511, 0.000470,
0.000106, -0.001802, 0.001109, -0.002178, 0.000625, -0.000376, -0.000479, -0.001658, -0.000941,
0.001290, 0.001513, 0.001485, 0.000799, 0.000772, -0.001901, -0.002048, 0.002485, 0.001901,
0.001545, -0.000302, 0.002008, -0.000247, 0.000367, -0.000075, -0.001492, 0.000656, -0.000669,
-0.001913, 0.002377, 0.002190, -0.000548, -0.000113, 0.000255, -0.001819, -0.002004, 0.002277,
0.000032, -0.001291, -0.001521, -0.001538, 0.000848, 0.000101, 0.000666, -0.002107, -0.001904,
-0.000065, 0.000572, 0.001275, -0.001585, 0.002040, 0.000463, 0.000560, -0.000304, 0.001493,
-0.001144, -0.001049, 0.001079, -0.000377, 0.000515, 0.000902, -0.002044, -0.000992, 0.001457,
0.002116, 0.001966, -0.001523, -0.001054, -0.000455, 0.001001, -0.001894, 0.001499, 0.001394,
-0.000799, -0.000776, -0.001119, 0.002114, 0.001956, -0.000590, 0.002107, 0.002410, 0.000908,
0.002491, -0.001556, -0.000766, -0.001054, -0.001454, 0.001407, 0.000790, 0.000212, -0.001097,
0.000762, 0.001530, 0.000097, 0.001140, -0.002476, 0.002157, 0.000240, -0.000916, -0.001042,
-0.000374, -0.001468, -0.002185, -0.001419, 0.002139, -0.000885, -0.001340, 0.001159, -0.000852,
0.002378, -0.000802, -0.002294, 0.001358, -0.000037, -0.001744, 0.000488, 0.000721, -0.000241,
0.000912, -0.001979, 0.000441, 0.000908, -0.001505, 0.000071, -0.000030, -0.001200, -0.001416,
-0.002347, 0.000011, 0.000076, 0.000005, -0.001967, -0.002481, -0.002373, -0.002163, -0.000274,
0.000696, 0.000592, -0.001591, 0.002499, -0.001006, -0.000637, -0.000702, 0.002366, -0.001882,
0.000581, -0.000668, 0.001594, 0.000020, 0.002135, -0.001410, -0.001303, -0.002096, -0.001833,
-0.001600, -0.001557, 0.001222, -0.000933, 0.001340, 0.001845, 0.000678, 0.001475, 0.001238,
0.001170, -0.001775, -0.001717, -0.001828, -0.000066, 0.002065, -0.001368, -0.001530, -0.002098,
0.001653, -0.002089, -0.000290, 0.001089, -0.002309, -0.002239, 0.000721, 0.001762, 0.002132,
0.001073, 0.001581, -0.001564, -0.001820, 0.001987, -0.001382, 0.000877, 0.000287, 0.000895,
-0.000591, 0.000099, -0.000843, -0.000563});
String w1 = "database";
String w2 = "DBMS";
WordVectors vecModel = WordVectorSerializer.loadGoogleModel(
new ClassPathResource("word2vec/googleload/sample_vec.txt").getFile(), false, true);
WordVectors vectorsBinary = WordVectorSerializer.loadGoogleModel(
new ClassPathResource("word2vec/googleload/sample_vec.bin").getFile(), true, true);
INDArray textWeights = vecModel.lookupTable().getWeights();
INDArray binaryWeights = vectorsBinary.lookupTable().getWeights();
Collection<String> nearest = vecModel.wordsNearest("database", 10);
Collection<String> nearestBinary = vectorsBinary.wordsNearest("database", 10);
System.out.println(nearestBinary);
assertEquals(vecModel.similarity("DBMS", "DBMS's"), vectorsBinary.similarity("DBMS", "DBMS's"), 1e-1);
}
示例4: testPortugeseW2V
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors; //导入方法依赖的package包/类
@Test
@Ignore
public void testPortugeseW2V() throws Exception {
WordVectors word2Vec = WordVectorSerializer.loadTxtVectors(new File("/ext/Temp/para.txt"));
word2Vec.setModelUtils(new FlatModelUtils());
Collection<String> portu = word2Vec.wordsNearest("carro", 10);
printWords("carro", portu, word2Vec);
portu = word2Vec.wordsNearest("davi", 10);
printWords("davi", portu, word2Vec);
}