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Java example source code file (NearestNeighborsResource.java)

This example Java source code file (NearestNeighborsResource.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

arraylist, exception, file, hashmap, inmemorylookuptable, list, map, nearestneighborsresource, net, network, override, path, post, response, util, vocabcache, wordvectors

The NearestNeighborsResource.java Java example source code

/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 *
 */

package org.deeplearning4j.ui.nearestneighbors;

import io.dropwizard.views.View;

import java.io.File;
import java.io.IOException;
import java.net.URI;
import java.net.URL;
import java.util.*;

import javax.ws.rs.GET;
import javax.ws.rs.POST;
import javax.ws.rs.Path;
import javax.ws.rs.Produces;
import javax.ws.rs.core.MediaType;
import javax.ws.rs.core.Response;

import org.apache.commons.io.FileUtils;
import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.clustering.sptree.DataPoint;
import org.deeplearning4j.clustering.vptree.VPTree;
import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
import org.deeplearning4j.models.word2vec.VocabWord;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.deeplearning4j.ui.api.UrlResource;
import org.deeplearning4j.ui.uploads.FileResource;
import org.deeplearning4j.util.SerializationUtils;

/**
 * Nearest neighbors
 *
 * @author Adam Gibson
 */
@Path("/nearestneighbors")
public class NearestNeighborsResource extends FileResource {
    private VPTree tree;
    private List<VocabWord> words;
    private Map<Integer,VocabWord> theVocab;
    private VocabCache vocab;
    private WordVectors wordVectors;
    private File localFile;

    /**
     * The file path for uploads
     *y
     * @param filePath the file path for uploads
     */
    public NearestNeighborsResource(String filePath) {
        super(filePath);
    }

    @GET
    public View get() {
        return new NearestNeighborsView();
    }

    @POST
    @Path("/update")
    @Produces(MediaType.APPLICATION_JSON)
    public Response updateFilePath(UrlResource resource) {
        if(!resource.getUrl().startsWith("http")) {
            this.localFile = new File(".",resource.getUrl());
            handleUpload(localFile);
        }
        else {
            File dl = new File(filePath,UUID.randomUUID().toString());
            try {
                FileUtils.copyURLToFile(new URL(resource.getUrl()), dl);
            } catch (Exception e) {
                e.printStackTrace();
            }

            handleUpload(dl);

        }

        return Response.ok(Collections.singletonMap("message","Uploaded file")).build();
    }

    @POST
    @Path("/vocab")
    @Produces(MediaType.APPLICATION_JSON)
    public Response getVocab() {
        List<String> words = new ArrayList<>();

        if(wordVectors != null) {
            words.addAll(wordVectors.vocab().words());
        }
        else {
            for(VocabWord word : this.words) {
                words.add(word.getWord());
            }
        }

        return Response.ok((new ArrayList<>(words))).build();
    }

    @POST
    @Produces(MediaType.APPLICATION_JSON)
    @Path("/words")
    public Response getWords(NearestNeighborsQuery query) {
        Map<String,Double> map = new HashMap<>();

        if(wordVectors != null) {
            Collection<String> words = wordVectors.wordsNearest(query.getWord(),query.getNumWords());
            for(String word : words) {
                map.put(word,wordVectors.similarity(query.getWord(),word));
            }
        }
        else {
            List<DataPoint> results = new ArrayList<>();
            List<Double> distances = new ArrayList<>();
            tree.search(tree.getItems().get(vocab.indexOf(query.getWord())),query.getNumWords(),results,distances);
            for(int i = 0; i < results.size(); i++) {
                map.put(theVocab.get(results.get(i).getIndex()).getWord(),distances.get(i));
            }
        }


        return Response.ok(map).build();
    }


    @Override
    public void handleUpload(File path) {
        try {
            if(path.getAbsolutePath().endsWith(".ser")) {
                WordVectors vectors = SerializationUtils.readObject(path);
                InMemoryLookupTable table = (InMemoryLookupTable) vectors.lookupTable();
                tree = new VPTree(table.getSyn0(),"dot",true);
                words = new ArrayList<>(vectors.vocab().vocabWords());
                theVocab = new HashMap<>();

                for(VocabWord word : words) {
                    theVocab.put(word.getIndex(),word);
                }
                this.vocab = vectors.vocab();


            }
            else if(path.getAbsolutePath().contains("Google")) {
                WordVectors vectors = WordVectorSerializer.loadGoogleModel(path, true);
                this.wordVectors = vectors;
            }

            else {
                Pair<InMemoryLookupTable, VocabCache> vocab = WordVectorSerializer.loadTxt(path);
                this.wordVectors = WordVectorSerializer.fromPair(vocab);

            }


        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

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