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Java example source code file (Word2VecParam.java)

This example Java source code file (Word2VecParam.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

atomiclong, broadcast, builder, deprecated, indarray, inmemorylookuptable, logger, serializable, word2vecparam

The Word2VecParam.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.spark.models.embeddings.word2vec;

import org.apache.spark.broadcast.Broadcast;
import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.Serializable;
import java.util.concurrent.atomic.AtomicLong;

/**
 * @author Adam Gibson
 */
@Deprecated
public class Word2VecParam implements Serializable {

    private boolean useAdaGrad = false;
    private double negative = 5;
    private int numWords = 1;
    private INDArray table;
    private int window = 5;
    private AtomicLong nextRandom = new AtomicLong(5);
    private double alpha = 0.025;
    private double minAlpha = 1e-2;
    private int totalWords = 1;
    private static transient final Logger log = LoggerFactory.getLogger(Word2VecPerformer.class);
    private int lastChecked = 0;
    private Broadcast<AtomicLong> wordCount;
    private InMemoryLookupTable weights;
    private int vectorLength;
    private Broadcast<double[]> expTable;
    private AtomicLong wordsSeen = new AtomicLong(0);
    private AtomicLong lastWords = new AtomicLong(0);

    public Word2VecParam(boolean useAdaGrad, double negative, int numWords, INDArray table, int window, AtomicLong nextRandom, double alpha, double minAlpha, int totalWords, int lastChecked, Broadcast<AtomicLong> wordCount, InMemoryLookupTable weights, int vectorLength,Broadcast expTable) {
        this.useAdaGrad = useAdaGrad;
        this.negative = negative;
        this.numWords = numWords;
        this.table = table;
        this.window = window;
        this.nextRandom = nextRandom;
        this.alpha = alpha;
        this.minAlpha = minAlpha;
        this.totalWords = totalWords;
        this.lastChecked = lastChecked;
        this.wordCount = wordCount;
        this.weights = weights;
        this.vectorLength = vectorLength;
        this.expTable = expTable;
    }

    public AtomicLong getLastWords() {
        return lastWords;
    }

    public void setLastWords(AtomicLong lastWords) {
        this.lastWords = lastWords;
    }

    public AtomicLong getWordsSeen() {
        return wordsSeen;
    }

    public void setWordsSeen(AtomicLong wordsSeen) {
        this.wordsSeen = wordsSeen;
    }

    public Broadcast<double[]> getExpTable() {
        return expTable;
    }

    public void setExpTable(Broadcast<double[]> expTable) {
        this.expTable = expTable;
    }

    public boolean isUseAdaGrad() {
        return useAdaGrad;
    }

    public void setUseAdaGrad(boolean useAdaGrad) {
        this.useAdaGrad = useAdaGrad;
    }

    public double getNegative() {
        return negative;
    }

    public void setNegative(double negative) {
        this.negative = negative;
    }

    public int getNumWords() {
        return numWords;
    }

    public void setNumWords(int numWords) {
        this.numWords = numWords;
    }

    public INDArray getTable() {
        return table;
    }

    public void setTable(INDArray table) {
        this.table = table;
    }

    public int getWindow() {
        return window;
    }

    public void setWindow(int window) {
        this.window = window;
    }

    public AtomicLong getNextRandom() {
        return nextRandom;
    }

    public void setNextRandom(AtomicLong nextRandom) {
        this.nextRandom = nextRandom;
    }

    public double getAlpha() {
        return alpha;
    }

    public void setAlpha(double alpha) {
        this.alpha = alpha;
    }

    public double getMinAlpha() {
        return minAlpha;
    }

    public void setMinAlpha(double minAlpha) {
        this.minAlpha = minAlpha;
    }

    public int getTotalWords() {
        return totalWords;
    }

    public void setTotalWords(int totalWords) {
        this.totalWords = totalWords;
    }

    public static Logger getLog() {
        return log;
    }

    public int getLastChecked() {
        return lastChecked;
    }

    public void setLastChecked(int lastChecked) {
        this.lastChecked = lastChecked;
    }

    public Broadcast<AtomicLong> getWordCount() {
        return wordCount;
    }

    public void setWordCount(Broadcast<AtomicLong> wordCount) {
        this.wordCount = wordCount;
    }

    public InMemoryLookupTable getWeights() {
        return weights;
    }

    public void setWeights(InMemoryLookupTable weights) {
        this.weights = weights;
    }



    public int getVectorLength() {
        return vectorLength;
    }

    public void setVectorLength(int vectorLength) {
        this.vectorLength = vectorLength;
    }

    public static class Builder {
        private boolean useAdaGrad = true;
        private double negative = 0;
        private int numWords = 1;
        private INDArray table;
        private int window = 5;
        private AtomicLong nextRandom;
        private double alpha = 0.025;
        private double minAlpha = 0.01;
        private int totalWords;
        private int lastChecked;
        private Broadcast<AtomicLong> wordCount;
        private InMemoryLookupTable weights;
        private int vectorLength = 300;
        private Broadcast<double[]> expTable;

        public Builder expTable(Broadcast<double[]> expTable) {
            this.expTable = expTable;
            return this;
        }


        public Builder useAdaGrad(boolean useAdaGrad) {
            this.useAdaGrad = useAdaGrad;
            return this;
        }

        public Builder negative(double negative) {
            this.negative = negative;
            return this;
        }

        public Builder numWords(int numWords) {
            this.numWords = numWords;
            return this;
        }

        public Builder table(INDArray table) {
            this.table = table;
            return this;
        }

        public Builder window(int window) {
            this.window = window;
            return this;
        }

        public Builder setNextRandom(AtomicLong nextRandom) {
            this.nextRandom = nextRandom;
            return this;
        }

        public Builder setAlpha(double alpha) {
            this.alpha = alpha;
            return this;
        }

        public Builder setMinAlpha(double minAlpha) {
            this.minAlpha = minAlpha;
            return this;
        }

        public Builder totalWords(int totalWords) {
            this.totalWords = totalWords;
            return this;
        }

        public Builder lastChecked(int lastChecked) {
            this.lastChecked = lastChecked;
            return this;
        }

        public Builder wordCount(Broadcast<AtomicLong> wordCount) {
            this.wordCount = wordCount;
            return this;
        }

        public Builder weights(InMemoryLookupTable weights) {
            this.weights = weights;
            return this;
        }

        public Builder setVectorLength(int vectorLength) {
            this.vectorLength = vectorLength;
            return this;
        }

        public Word2VecParam build() {
            return new Word2VecParam(useAdaGrad, negative, numWords, table, window, nextRandom, alpha, minAlpha, totalWords, lastChecked, wordCount, weights, vectorLength,expTable);
        }
    }
}

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