package org.deeplearning4j.models;
import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
import org.deeplearning4j.models.word2vec.Word2Vec;
import org.deeplearning4j.plot.BarnesHutTsne;
import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
import org.deeplearning4j.text.sentenceiterator.UimaSentenceIterator;
import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor;
import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
import org.deeplearning4j.ui.UiConnectionInfo;
import org.deeplearning4j.ui.api.UIServer;
import org.deeplearning4j.ui.standalone.ClassPathResource;
import org.junit.Ignore;
import org.junit.Test;
import java.io.File;
import java.util.ArrayList;
/**
* Created by Alex on 10/01/2017.
*/
@Ignore
public class UITest {
@Test
public void testPosting() throws Exception {
// File inputFile = new ClassPathResource("/big/raw_sentences.txt").getFile();
File inputFile = new ClassPathResource("/basic/word2vec_advance.txt").getFile();
SentenceIterator iter = UimaSentenceIterator.createWithPath(inputFile.getAbsolutePath());
// Split on white spaces in the line to get words
TokenizerFactory t = new DefaultTokenizerFactory();
t.setTokenPreProcessor(new CommonPreprocessor());
Word2Vec vec = new Word2Vec.Builder().minWordFrequency(1).iterations(1).epochs(1).layerSize(20)
.stopWords(new ArrayList<String>()).useAdaGrad(false).negativeSample(5).seed(42).windowSize(5)
.iterate(iter).tokenizerFactory(t).build();
vec.fit();
File tempFile = File.createTempFile("temp", "w2v");
tempFile.deleteOnExit();
WordVectorSerializer.writeWordVectors(vec, tempFile);
WordVectors vectors = WordVectorSerializer.loadTxtVectors(tempFile);
UIServer.getInstance(); //Initialize
UiConnectionInfo uiConnectionInfo =
new UiConnectionInfo.Builder().setAddress("localhost").setPort(9000).build();
BarnesHutTsne tsne = new BarnesHutTsne.Builder().normalize(false).setFinalMomentum(0.8f).numDimension(2)
.setMaxIter(10).build();
vectors.lookupTable().plotVocab(tsne, vectors.lookupTable().getVocabCache().numWords(), uiConnectionInfo);
Thread.sleep(100000);
}
}