Author: srowen
Date: Mon Jun 27 18:46:46 2011
New Revision: 1140264
URL: http://svn.apache.org/viewvc?rev=1140264&view=rev
Log:
MAHOUT-734 add HMM command lines
Added:
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/BaumWelchTrainer.java
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/LossyHmmSerializer.java
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/RandomSequenceGenerator.java
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/ViterbiEvaluator.java
Modified:
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmModel.java
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmTrainer.java
mahout/trunk/src/conf/driver.classes.props
Added:
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/BaumWelchTrainer.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/BaumWelchTrainer.java?rev=1140264&view=auto
==============================================================================
---
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/BaumWelchTrainer.java
(added)
+++
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/BaumWelchTrainer.java
Mon Jun 27 18:46:46 2011
@@ -0,0 +1,169 @@
+/**
+ * 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 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 = optionBuilder.withLongName("input").
+ withDescription("Text file with space-separated integers to train on").
+
withShortName("i").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
+ withName("path").create()).withRequired(true).create();
+
+ Option outputOption = optionBuilder.withLongName("output").
+ withDescription("Path trained HMM model should be serialized to").
+
withShortName("o").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
+ withName("path").create()).withRequired(true).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);
+ }
+ }
+}
Modified:
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmModel.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmModel.java?rev=1140264&r1=1140263&r2=1140264&view=diff
==============================================================================
---
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmModel.java
(original)
+++
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmModel.java
Mon Jun 27 18:46:46 2011
@@ -78,8 +78,7 @@ public class HmmModel implements Cloneab
* Get a copy of this model
*/
@Override
- public HmmModel clone() throws CloneNotSupportedException {
- super.clone();
+ public HmmModel clone() {
HmmModel model = new HmmModel(transitionMatrix.clone(),
emissionMatrix.clone(), initialProbabilities.clone());
if (hiddenStateNames != null) {
model.hiddenStateNames = HashBiMap.create(hiddenStateNames);
Modified:
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmTrainer.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmTrainer.java?rev=1140264&r1=1140263&r2=1140264&view=diff
==============================================================================
---
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmTrainer.java
(original)
+++
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmTrainer.java
Mon Jun 27 18:46:46 2011
@@ -220,14 +220,8 @@ public final class HmmTrainer {
pseudoCount = pseudoCount == 0 ? Double.MIN_VALUE : pseudoCount;
// allocate space for iteration models
- HmmModel lastIteration;
- HmmModel iteration;
- try {
- lastIteration = initialModel.clone();
- iteration = initialModel.clone();
- } catch (CloneNotSupportedException e) {
- throw new UnknownError("Cloning HmmModels broke. Check for programming
errors, changed APIs.");
- }
+ HmmModel lastIteration = initialModel.clone();
+ HmmModel iteration = initialModel.clone();
// allocate space for Viterbi path calculation
int[] viterbiPath = new int[observedSequence.length];
@@ -301,14 +295,9 @@ public final class HmmTrainer {
public static HmmModel trainBaumWelch(HmmModel initialModel,
int[] observedSequence, double
epsilon, int maxIterations, boolean scaled) {
// allocate space for the iterations
- HmmModel lastIteration;
- HmmModel iteration;
- try {
- lastIteration = initialModel.clone();
- iteration = initialModel.clone();
- } catch (CloneNotSupportedException e) {
- throw new UnknownError("Cloning HmmModels broke. Check for programming
errors, changed APIs etc.");
- }
+ HmmModel lastIteration = initialModel.clone();
+ HmmModel iteration = initialModel.clone();
+
// allocate space for baum-welch factors
int hiddenCount = initialModel.getNrOfHiddenStates();
int visibleCount = observedSequence.length;
Added:
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/LossyHmmSerializer.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/LossyHmmSerializer.java?rev=1140264&view=auto
==============================================================================
---
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/LossyHmmSerializer.java
(added)
+++
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/LossyHmmSerializer.java
Mon Jun 27 18:46:46 2011
@@ -0,0 +1,62 @@
+/**
+ * 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 org.apache.mahout.math.Matrix;
+import org.apache.mahout.math.MatrixWritable;
+import org.apache.mahout.math.Vector;
+import org.apache.mahout.math.VectorWritable;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+
+/**
+ * Utils for serializing Writable parts of HmmModel (that means without hidden
state names and so on)
+ */
+final class LossyHmmSerializer {
+
+ private LossyHmmSerializer() {
+ }
+
+ static void serialize(HmmModel model, DataOutput output) throws IOException {
+ MatrixWritable matrix = new MatrixWritable(model.getEmissionMatrix());
+ matrix.write(output);
+ matrix.set(model.getTransitionMatrix());
+ matrix.write(output);
+
+ VectorWritable vector = new
VectorWritable(model.getInitialProbabilities());
+ vector.write(output);
+ }
+
+ static HmmModel deserialize(DataInput input) throws IOException {
+ MatrixWritable matrix = new MatrixWritable();
+ matrix.readFields(input);
+ Matrix emissionMatrix = matrix.get();
+
+ matrix.readFields(input);
+ Matrix transitionMatrix = matrix.get();
+
+ VectorWritable vector = new VectorWritable();
+ vector.readFields(input);
+ Vector initialProbabilities = vector.get();
+
+ return new HmmModel(transitionMatrix, emissionMatrix,
initialProbabilities);
+ }
+
+}
Added:
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/RandomSequenceGenerator.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/RandomSequenceGenerator.java?rev=1140264&view=auto
==============================================================================
---
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/RandomSequenceGenerator.java
(added)
+++
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/RandomSequenceGenerator.java
Mon Jun 27 18:46:46 2011
@@ -0,0 +1,106 @@
+/**
+ * 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 java.io.DataInputStream;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.PrintWriter;
+
+/**
+ * Command-line tool for generating random sequences by given HMM
+ */
+public final class RandomSequenceGenerator {
+
+ private RandomSequenceGenerator() {
+ }
+
+ public static void main(String[] args) throws IOException {
+ DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
+ ArgumentBuilder argumentBuilder = new ArgumentBuilder();
+
+ Option outputOption = optionBuilder.withLongName("output").
+ withDescription("Output file with sequence of observed states").
+
withShortName("o").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
+ withName("path").create()).withRequired(false).create();
+
+ Option modelOption = optionBuilder.withLongName("model").
+ withDescription("Path to serialized HMM model").
+
withShortName("m").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
+ withName("path").create()).withRequired(true).create();
+
+ Option lengthOption = optionBuilder.withLongName("length").
+ withDescription("Length of generated sequence").
+
withShortName("l").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
+ withName("number").create()).withRequired(true).create();
+
+ Group optionGroup = new GroupBuilder().
+
withOption(outputOption).withOption(modelOption).withOption(lengthOption).
+ withName("Options").create();
+
+ try {
+ Parser parser = new Parser();
+ parser.setGroup(optionGroup);
+ CommandLine commandLine = parser.parse(args);
+
+ String output = (String) commandLine.getValue(outputOption);
+
+ String modelPath = (String) commandLine.getValue(modelOption);
+
+ int length = Integer.parseInt((String)
commandLine.getValue(lengthOption));
+
+ //reading serialized HMM
+ DataInputStream modelStream = new DataInputStream(new
FileInputStream(modelPath));
+ HmmModel model;
+ try {
+ model = LossyHmmSerializer.deserialize(modelStream);
+ } finally {
+ Closeables.closeQuietly(modelStream);
+ }
+
+ //generating observations
+ int[] observations = HmmEvaluator.predict(model, length,
System.currentTimeMillis());
+
+ //writing output
+ PrintWriter writer = new PrintWriter(new FileOutputStream(output));
+ try {
+ for (int observation : observations) {
+ writer.print(observation);
+ writer.print(' ');
+ }
+ } finally {
+ Closeables.closeQuietly(writer);
+ }
+ } catch (OptionException e) {
+ CommandLineUtil.printHelp(optionGroup);
+ }
+ }
+}
Added:
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/ViterbiEvaluator.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/ViterbiEvaluator.java?rev=1140264&view=auto
==============================================================================
---
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/ViterbiEvaluator.java
(added)
+++
mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/ViterbiEvaluator.java
Mon Jun 27 18:46:46 2011
@@ -0,0 +1,133 @@
+/**
+ * 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 java.io.DataInputStream;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.PrintWriter;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Scanner;
+
+/**
+ * Command-line tool for Viterbi evaluating
+ */
+public final class ViterbiEvaluator {
+
+ private ViterbiEvaluator() {
+ }
+
+ public static void main(String[] args) throws IOException {
+ DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
+ ArgumentBuilder argumentBuilder = new ArgumentBuilder();
+
+ Option inputOption = optionBuilder.withLongName("input").
+ withDescription("Text file with space-separated integers to segment").
+
withShortName("i").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
+ withName("path").create()).withRequired(true).create();
+
+ Option outputOption = optionBuilder.withLongName("output").
+ withDescription("Output file with decoded sequence of hidden states").
+
withShortName("o").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
+ withName("path").create()).withRequired(true).create();
+
+ Option modelOption = optionBuilder.withLongName("model").
+ withDescription("Path to serialized HMM model").
+
withShortName("m").withArgument(argumentBuilder.withMaximum(1).withMinimum(1).
+ withName("path").create()).withRequired(true).create();
+
+ Option likelihoodOption = optionBuilder.withLongName("likelihood").
+ withDescription("Compute likelihood of observed sequence").
+ withShortName("l").withRequired(false).create();
+
+ Group optionGroup = new GroupBuilder().withOption(inputOption).
+
withOption(outputOption).withOption(modelOption).withOption(likelihoodOption).
+ 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);
+
+ String modelPath = (String) commandLine.getValue(modelOption);
+
+ boolean computeLikelihood = commandLine.hasOption(likelihoodOption);
+
+ //reading serialized HMM
+ DataInputStream modelStream = new DataInputStream(new
FileInputStream(modelPath));
+ HmmModel model;
+ try {
+ model = LossyHmmSerializer.deserialize(modelStream);
+ } finally {
+ Closeables.closeQuietly(modelStream);
+ }
+
+ //reading observations
+ List<Integer> observations = new ArrayList<Integer>();
+ 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);
+ }
+
+ //decoding
+ int[] hiddenStates = HmmEvaluator.decode(model, observationsArray, true);
+
+ //writing output
+ PrintWriter writer = new PrintWriter(new FileOutputStream(output));
+ try {
+ for (int hiddenState : hiddenStates) {
+ writer.print(hiddenState);
+ writer.print(' ');
+ }
+ } finally {
+ Closeables.closeQuietly(writer);
+ }
+
+ if (computeLikelihood) {
+ System.out.println("Likelihood: " +
HmmEvaluator.modelLikelihood(model, observationsArray, true));
+ }
+ } catch (OptionException e) {
+ CommandLineUtil.printHelp(optionGroup);
+ }
+ }
+}
Modified: mahout/trunk/src/conf/driver.classes.props
URL:
http://svn.apache.org/viewvc/mahout/trunk/src/conf/driver.classes.props?rev=1140264&r1=1140263&r2=1140264&view=diff
==============================================================================
--- mahout/trunk/src/conf/driver.classes.props (original)
+++ mahout/trunk/src/conf/driver.classes.props Mon Jun 27 18:46:46 2011
@@ -37,4 +37,7 @@ org.apache.mahout.math.hadoop.stochastic
org.apache.mahout.clustering.spectral.eigencuts.EigencutsDriver = eigencuts :
Eigencuts spectral clustering
org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver =
spectralkmeans : Spectral k-means clustering
org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJob =
parallelALS : ALS-WR factorization of a rating matrix
-org.apache.mahout.cf.taste.hadoop.als.PredictionJob = predictFromFactorization
: predict preferences from a factorization of a rating matrix
\ No newline at end of file
+org.apache.mahout.cf.taste.hadoop.als.PredictionJob = predictFromFactorization
: predict preferences from a factorization of a rating matrix
+org.apache.mahout.classifier.sequencelearning.hmm.BaumWelchTrainer = baumwelch
: Baum-Welch algorithm for unsupervised HMM training
+org.apache.mahout.classifier.sequencelearning.hmm.ViterbiEvaluator = viterbi :
Viterbi decoding of hidden states from given output states sequence
+org.apache.mahout.classifier.sequencelearning.hmm.RandomSequenceGenerator =
hmmpredict : Generate random sequence of observations by given HMM
