/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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.apache.mahout.classifier.sequencelearning.hmm;
import com.google.common.io.Closeables;
import org.apache.commons.cli2.CommandLine;
import org.apache.commons.cli2.Group;
import org.apache.commons.cli2.Option;
import org.apache.commons.cli2.OptionException;
import org.apache.commons.cli2.builder.ArgumentBuilder;
import org.apache.commons.cli2.builder.DefaultOptionBuilder;
import org.apache.commons.cli2.builder.GroupBuilder;
import org.apache.commons.cli2.commandline.Parser;
import org.apache.mahout.common.CommandLineUtil;
import org.apache.mahout.common.commandline.DefaultOptionCreator;
import java.io.DataOutputStream;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Date;
import java.util.List;
import java.util.Scanner;
/**
* A class for EM training of HMM from console
*/
public final class BaumWelchTrainer {
private BaumWelchTrainer() {
}
public static void main(String[] args) throws IOException {
DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
ArgumentBuilder argumentBuilder = new ArgumentBuilder();
Option inputOption = DefaultOptionCreator.inputOption().create();
Option outputOption = DefaultOptionCreator.outputOption().create();
Option stateNumberOption = optionBuilder.withLongName("nrOfHiddenStates").
withDescription("Number of hidden states").
withShortName("nh").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
withName("number").create()).withRequired(true).create();
Option observedStateNumberOption = optionBuilder.withLongName("nrOfObservedStates").
withDescription("Number of observed states").
withShortName("no").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
withName("number").create()).withRequired(true).create();
Option epsilonOption = optionBuilder.withLongName("epsilon").
withDescription("Convergence threshold").
withShortName("e").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
withName("number").create()).withRequired(true).create();
Option iterationsOption = optionBuilder.withLongName("max-iterations").
withDescription("Maximum iterations number").
withShortName("m").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
withName("number").create()).withRequired(true).create();
Group optionGroup = new GroupBuilder().withOption(inputOption).
withOption(outputOption).withOption(stateNumberOption).withOption(observedStateNumberOption).
withOption(epsilonOption).withOption(iterationsOption).
withName("Options").create();
try {
Parser parser = new Parser();
parser.setGroup(optionGroup);
CommandLine commandLine = parser.parse(args);
String input = (String) commandLine.getValue(inputOption);
String output = (String) commandLine.getValue(outputOption);
int nrOfHiddenStates = Integer.parseInt((String) commandLine.getValue(stateNumberOption));
int nrOfObservedStates = Integer.parseInt((String) commandLine.getValue(observedStateNumberOption));
double epsilon = Double.parseDouble((String) commandLine.getValue(epsilonOption));
int maxIterations = Integer.parseInt((String) commandLine.getValue(iterationsOption));
//constructing random-generated HMM
HmmModel model = new HmmModel(nrOfHiddenStates, nrOfObservedStates, new Date().getTime());
List<Integer> observations = new ArrayList<Integer>();
//reading observations
Scanner scanner = new Scanner(new FileInputStream(input));
try {
while (scanner.hasNextInt()) {
observations.add(scanner.nextInt());
}
} finally {
scanner.close();
}
int[] observationsArray = new int[observations.size()];
for (int i = 0; i < observations.size(); ++i) {
observationsArray[i] = observations.get(i);
}
//training
HmmModel trainedModel = HmmTrainer.trainBaumWelch(model,
observationsArray, epsilon, maxIterations, true);
//serializing trained model
DataOutputStream stream = new DataOutputStream(new FileOutputStream(output));
try {
LossyHmmSerializer.serialize(trainedModel, stream);
} finally {
Closeables.closeQuietly(stream);
}
//printing tranied model
System.out.println("Initial probabilities: ");
for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) {
System.out.print(i + " ");
}
System.out.println();
for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) {
System.out.print(trainedModel.getInitialProbabilities().get(i) + " ");
}
System.out.println();
System.out.println("Transition matrix:");
System.out.print(" ");
for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) {
System.out.print(i + " ");
}
System.out.println();
for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) {
System.out.print(i + " ");
for (int j = 0; j < trainedModel.getNrOfHiddenStates(); ++j) {
System.out.print(trainedModel.getTransitionMatrix().get(i, j) + " ");
}
System.out.println();
}
System.out.println("Emission matrix: ");
System.out.print(" ");
for (int i = 0; i < trainedModel.getNrOfOutputStates(); ++i) {
System.out.print(i + " ");
}
System.out.println();
for (int i = 0; i < trainedModel.getNrOfHiddenStates(); ++i) {
System.out.print(i + " ");
for (int j = 0; j < trainedModel.getNrOfOutputStates(); ++j) {
System.out.print(trainedModel.getEmissionMatrix().get(i, j) + " ");
}
System.out.println();
}
} catch (OptionException e) {
CommandLineUtil.printHelp(optionGroup);
}
}
}