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Java example source code file (Viterbi.java)

This example Java source code file (Viterbi.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

indarray, pair, serializable, viterbi

The Viterbi.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.util;

import org.apache.commons.math3.util.FastMath;
import org.deeplearning4j.berkeley.Pair;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

import java.io.Serializable;

/**
 * Based on the impl from:
 * https://gist.github.com/rmcgibbo/3915977
 *
 */
public class Viterbi implements Serializable {

    private double metaStability = 0.9;
    private double pCorrect = 0.99;
    private INDArray possibleLabels;
    private  int states;

    private double logPCorrect;
    private double logPIncorrect;
    private double logMetaInstability = Math.log(metaStability);
    private  double logOfDiangnalTProb;
    private double logStates;

    /**
     * The possible outcomes for the chain.
     * This should be the labels in the form of the possible outcomes (1,2,3)
     * not the binarized label matrix
     * @param possibleLabels the possible labels of the markov chain
     */

    public Viterbi(INDArray possibleLabels) {
        this.possibleLabels = possibleLabels;
        this.states = possibleLabels.length();
        this.logPCorrect = FastMath.log(pCorrect);
        this.logPIncorrect = FastMath.log(1 - pCorrect / states - 1);
        logOfDiangnalTProb = FastMath.log(1 - metaStability / states - 1);
        this.logStates = FastMath.log(states);
    }

    /**
     * Decodes the given labels, assuming its a binary label matrix
     * @param labels the labels as a binary label matrix
     * @return the decoded labels and the most likely outcome of the sequence
     */
    public Pair<Double,INDArray> decode(INDArray labels) {
        return decode(labels,true);
    }

    /**
     * Decodes a series of labels
     * @param labels the labels to decode
     * @param binaryLabelMatrix whether the label  is a binary label matrix
     * @return the most likely sequence and the sequence labels
     */
    public Pair<Double,INDArray> decode(INDArray labels,boolean binaryLabelMatrix) {
        INDArray outcomeSequence = labels.isColumnVector() || labels.isRowVector() || binaryLabelMatrix ? toOutcomesFromBinaryLabelMatrix(labels) : labels;
        int frames = outcomeSequence.length();
        INDArray V = Nd4j.ones(frames, states);
        INDArray pointers = Nd4j.zeros(frames,states);
        INDArray assigned = V.getRow(0);
        assigned.assign(logPCorrect - logStates);
        V.putRow(0,assigned);
        V.put(0,  (int) outcomeSequence.getDouble(0), logPCorrect - logStates);
        for(int t = 1; t < frames; t++) {
            for(int k = 0; k < states; k++) {
                INDArray rowLogProduct = rowOfLogTransitionMatrix(k).add(V.getRow(t  - 1));
                int maxVal = Nd4j.getBlasWrapper().iamax(rowLogProduct);
                double argMax =  rowLogProduct.max(Integer.MAX_VALUE).getDouble(0);
                V.put(t,k,argMax);
                int element = (int) outcomeSequence.getDouble(t);
                if(k == element)
                    V.put(t,k,logPCorrect + maxVal);
                else
                    V.put(t,k,logPIncorrect + maxVal);

            }
        }

        INDArray rectified = Nd4j.zeros(frames);
        rectified.put(rectified.length() - 1,V.getRow(frames - 1).max(Integer.MAX_VALUE));
        for(int t = rectified.length() - 2; t > 0; t--) {
            rectified.putScalar(t,pointers.getDouble(t + 1,(int) rectified.getDouble(t + 1)));
        }


        return new Pair<>(V.getRow(frames - 1).max(Integer.MAX_VALUE).getDouble(0),rectified);
    }

    private INDArray rowOfLogTransitionMatrix(int k) {
        INDArray row = Nd4j.ones(1,states).muli(logOfDiangnalTProb);
        row.putScalar(k,logMetaInstability);
        return row;
    }


    private INDArray toOutcomesFromBinaryLabelMatrix(INDArray outcomes) {
        INDArray ret = Nd4j.create(outcomes.rows(),1);
        for(int i = 0; i < outcomes.rows(); i++)
            ret.put(i,0, Nd4j.getBlasWrapper().iamax(outcomes.getRow(i)));
        return ret;
    }

    public double getMetaStability() {
        return metaStability;
    }

    public void setMetaStability(double metaStability) {
        this.metaStability = metaStability;
    }

    public double getpCorrect() {
        return pCorrect;
    }

    public void setpCorrect(double pCorrect) {
        this.pCorrect = pCorrect;
    }

    public INDArray getPossibleLabels() {
        return possibleLabels;
    }

    public void setPossibleLabels(INDArray possibleLabels) {
        this.possibleLabels = possibleLabels;
    }

    public int getStates() {
        return states;
    }

    public void setStates(int states) {
        this.states = states;
    }

    public double getLogPCorrect() {
        return logPCorrect;
    }

    public void setLogPCorrect(double logPCorrect) {
        this.logPCorrect = logPCorrect;
    }

    public double getLogPIncorrect() {
        return logPIncorrect;
    }

    public void setLogPIncorrect(double logPIncorrect) {
        this.logPIncorrect = logPIncorrect;
    }

    public double getLogMetaInstability() {
        return logMetaInstability;
    }

    public void setLogMetaInstability(double logMetaInstability) {
        this.logMetaInstability = logMetaInstability;
    }

    public double getLogOfDiangnalTProb() {
        return logOfDiangnalTProb;
    }

    public void setLogOfDiangnalTProb(double logOfDiangnalTProb) {
        this.logOfDiangnalTProb = logOfDiangnalTProb;
    }

    public double getLogStates() {
        return logStates;
    }

    public void setLogStates(double logStates) {
        this.logStates = logStates;
    }
}

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