Java Code Examples for org.deeplearning4j.models.embeddings.wordvectors.WordVectors#wordsNearest()

The following examples show how to use org.deeplearning4j.models.embeddings.wordvectors.WordVectors#wordsNearest() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: Word2VecTests.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@Test
public void testLoadingWordVectors() throws Exception {
    String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
    if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
        skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
    }

    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()));
}
 
Example 2
Source File: WordVectorSerializerTest.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@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.readWord2VecModel(new ClassPathResource("word2vec/googleload/sample_vec.txt").getFile());
    WordVectors vectorsBinary = WordVectorSerializer.readWord2VecModel(new ClassPathResource("word2vec/googleload/sample_vec.bin").getFile());
    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);

}
 
Example 3
Source File: Word2VecTest.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@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);
}